Silicone rotor is the main dehumidification technology to realize humidity control of ambient air； optimizing the rotor size and reducing the operating energy consumption are inevitable trends in rotor dehumidification technology development. In this study， based on the theoretical research method of computational fluid dynamics （CFD）， we first established and verified a three-dimensional dynamic simulation model of the dehumidification and regeneration process of the rotor， and we conducted a dynamic simulation for the silica gel dehumidification rotor used for the protection of bridge cables， and we then analyzed and discussed the influence of rotor thickness， wind speed， and regeneration wind volume and temperature on the moisture removal capacity （MRC） and specific energy consumption （SEC） of the rotor to optimize the design of the rotor. The purpose was to optimize the rotor design. The results showed that the optimal thickness of the rotor existed under different wind speeds， and the optimal thickness increased with the increase in the wind speed. Compared with the original design， the optimized wheel improved the average MRC by approximately 10% while reducing the SEC by approximately 15%， demonstrating the effectiveness of dimensional optimization. Notably， the SEC decreased with increasing regeneration temperature. Therefore， in practical design， the regeneration temperature should be selected in accordance with the dehumidification requirements and maintained as low as feasible to enhance the overall economic performance.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Nidhal Selmi, Jean-michel Bruel, Sébastien Mosser
et al.
Decision-making is a core engineering design activity that conveys the engineer's knowledge and translates it into courses of action. Capturing this form of knowledge can reap potential benefits for the engineering teams and enhance development efficiency. Despite its clear value, traditional decision capture often requires a significant amount of effort and still falls short of capturing the necessary context for reuse. Model-based systems engineering (MBSE) can be a promising solution to address these challenges by embedding decisions directly within system models, which can reduce the capture workload while maintaining explicit links to requirements, behaviors, and architectural elements. This article discusses a lightweight framework for integrating decision capture into MBSE workflows by representing decision alternatives as system model slices. Using a simplified industry example from aircraft architecture, we discuss the main challenges associated with decision capture and propose preliminary solutions to address these challenges.
Chaos engineering reveals resilience risks but is expensive and operationally risky to run broadly and often. Model-based analyses can estimate dependability, yet in practice they are tricky to build and keep current because models are typically handcrafted. We claim that a simple connectivity-only topological model - just the service-dependency graph plus replica counts - can provide fast, low-risk availability estimates under fail-stop faults. To make this claim practical without hand-built models, we introduce model discovery: an automated step that can run in CI/CD or as an observability-platform capability, synthesizing an explicit, analyzable model from artifacts teams already have (e.g., distributed traces, service-mesh telemetry, configs/manifests) - providing an accessible gateway for teams to begin resilience testing. As a proof by instance on the DeathStarBench Social Network, we extract the dependency graph from Jaeger and estimate availability across two deployment modes and five failure rates. The discovered model closely tracks live fault-injection results; with replication, median error at mid-range failure rates is near zero, while no-replication shows signed biases consistent with excluded mechanisms. These results create two opportunities: first, to triage and reduce the scope of expensive chaos experiments in advance, and second, to generate real-time signals on the system's resilience posture as its topology evolves, preserving live validation for the most critical or ambiguous scenarios.
Paloma Guenes, Rafael Tomaz, Bianca Trinkenreich
et al.
Research shows that more than half of software professionals experience the Impostor Phenomenon (IP), with a notably higher prevalence among women compared to men. IP can lead to mental health consequences, such as depression and burnout, which can significantly impact personal well-being and software professionals' productivity. This study investigates how IP manifests among software professionals across intersections of gender with race/ethnicity, marital status, number of children, age, and professional experience. Additionally, it examines the well-being of software professionals experiencing IP, providing insights into the interplay between these factors. We analyzed data collected through a theory-driven survey (n = 624) that used validated psychometric instruments to measure IP and well-being in software engineering professionals. We explored the prevalence of IP in the intersections of interest. Additionally, we applied bootstrapping to characterize well-being within our field and statistically tested whether professionals of different genders suffering from IP have lower well-being. The results show that IP occurs more frequently in women and that the prevalence is particularly high among black women as well as among single and childless women. Furthermore, regardless of gender, software engineering professionals suffering from IP have significantly lower well-being. Our findings indicate that effective IP mitigation strategies are needed to improve the well-being of software professionals. Mitigating IP would have particularly positive effects on the well-being of women, who are more frequently affected by IP.
Over the past ten years, the application of artificial intelligence (AI) and machine learning (ML) in engineering domains has gained significant popularity, showcasing their potential in data-driven contexts. However, the complexity and diversity of engineering problems often require the development of domain-specific AI approaches, which are frequently hindered by a lack of systematic methodologies, scalability, and robustness during the development process. To address this gap, this paper introduces the "ABCDE" as the key elements of Engineering AI and proposes a unified, systematic engineering AI ecosystem framework, including eight essential layers, along with attributes, goals, and applications, to guide the development and deployment of AI solutions for specific engineering needs. Additionally, key challenges are examined, and eight future research directions are highlighted. By providing a comprehensive perspective, this paper aims to advance the strategic implementation of AI, fostering the development of next-generation engineering AI solutions.
A unified Aspen HYSYS v 12.1 model was developed to benchmark two competing low temperature routes for associated petroleum gas (APG) valorization: conventional two stage low temperature separation (LTS) and full cryogenic rectification (CR) equipped with a turbo expander. Both schemes were simulated for a “rich” Western Siberian APG (9.0 MPa, 12.6 mol % C₃, Σ C₄+ ≈ 8 mol %). Switching from LTS (–30 °C) to CR (–88 °C) increases C₂+ recovery from 41 % to 64.8 % while raising total refrigeration duty by 24 %. The additional cold demand is partly offset by 93 kW of turbo expander power, improving the net power balance from –248 kW to –155 kW. Relative CAPEX and OPEX indices for CR amount to 1.2 and 1.5, respectively; at spot prices of 140 USD t⁻¹ ethane and 480 USD t⁻¹ propane the incremental investment is repaid in < 2.5 years. A hybrid “LTS→CR” configuration lowers the specific OPEX to 57 kWh t⁻¹, requires only +15 % CAPEX and pays back in ~26 months while preserving 92 % of full scale CR recovery. Deep rectification also cuts the carbon footprint of sales gas by ~18 kg CO₂ eq t⁻¹ – enough to offset ≈ 7 % of annual operating costs under the forthcoming EU CBAM tariff. The study provides a decision matrix linking reservoir pressure and dew point specification to the most cost effective processing route.
Stirling cryocoolers, serving as critical components for maintaining low-temperature environments in infrared detectors, ensure optimal detector performance by providing necessary cooling for infrared chip materials. Their compact structure, broad temperature range cooling capacity, and closed-cycle operation have established them as core elements for cryogenic environment maintenance. However, operational challenges including heat generation in the compression chamber, resistive losses in driving components, and copper/iron losses in motor stators lead to surface temperature rise, directly impairing detector performance (typically requiring the surface temperature not to exceed ambient by more than 10°C). With expanding applications of Stirling cryocoolers in harsh environments (e.g., elevated external temperatures and solar radiation), thermal management design faces intensified challenges. This study investigates a combined thermal management solution integrating heat sinks and phase change materials (PCMs) through simulation and experimental validation. By optimizing temperature control performance under extreme operating conditions, the research provides theoretical support for engineering applications, enabling long-term stable operation of integrated systems in complex and demanding environments.
The comparative analysis of the results of low-temperature mechanical tests of the samples of amorphous and amorphous-crystalline polyimide of the Kapton H type was carried out. In the experiments [East Eur. J. Phys., No. 4, 144 (2020)], tensile diagrams σ(ɛ;T,ε˙) of such samples were recorded, namely, the dependences of the deforming stress on the strain ɛ = ε˙t at constant values of the strain rate ε˙ = 7⋅10–5, 7⋅10–4, 6⋅10–3 s–1, and temperature T = 293, 77, and 4.2 K. The initial aim of these experiments was to study the effect of moderate (77 K) and deep (4.2 K) cooling on the structure and some mechanical characteristics of polyimide, important for its use in cryogenic and aerospace engineering. Later (Low Temp. Phys. 49, 521 (2023) [Fiz. Nyzk. Temp. 49, 569 (2023)]), there was a need and opportunity to supplement the experimental results with additional analysis in order to use them to test new aspects of the theory of low-temperature elastic-viscous deformation of polymers, in particular, the description of the effects of forced elasticity and their competition with brittle fracture processes. A detailed comparison of the tensile diagrams of the polyimide samples with amorphous and amorphous-crystalline molecular structures performed in this study showed that at T = 293 K both structures have clearly pronounced properties of the elastomers, namely, the rubber-like materials with high elasticity and the ability to reversible deformation. It has been established that amorphous samples retain these properties up to deep cooling at T = 4.2 K, and amorphous-crystalline ones only to a state of moderate cooling: at T < 77 K they acquire the properties of glassy materials with brittle fracture at the initial stage of elastic deformation. It is also shown that the kinetics of highly elastic deformation of polyimide with molecular structures of both types is due to the thermomechanical activation of soliton-like elaston excitations on molecular chains in the amorphous component of the material and is described by a nonlinear rheological equation derived earlier for the molecular model of an amorphous polymer: Low Temp. Phys. 48, 253 (2022) [Fiz. Nyzk. Temp. 48, 281 (2022)], Low Temp. Phys. 49, 228 (2023) [Fiz. Nyzk. Temp. 49, 246 (2023)]. By comparing the results of experiments and theory, an analytical description of the tension diagrams σ(ɛ;T,ε˙) of polyimide samples with molecular structures of both types was obtained, as well as empirical estimates of their rheological characteristics and microscopic parameters of elaston excitations. During low-temperature deformation of a polymer with a mixed structure, rigid crystalline fibrils immersed in the softer amorphous medium undergo only minor elastic deformations, but significantly increase the intensity of elaston activation and fracture processes in the amorphous component. Upon cooling, this leads to the convergence of critical stresses of highly elastic relaxation and fracture and to the transformation of an elastomer with such a structure into a glassy brittle material.
Absorption refrigeration technology was developed as a response to major challenges, including the energy crisis, rising fuel costs, and the environmental drawbacks of conventional vapor-compression refrigeration systems. Extensive research has focused on developing strategies to enhance the COP of absorption systems, with the aim of making absorption refrigeration technology more competitive compared to conventional compression systems This study investigates a hybrid absorption heat pump integrated with heat recovery combined heating and cooling production to improve the COP of absorption refrigeration systems. The system is based on low- and high-pressure absorber/evaporator pairs, operating with H₂O/LiBr as the working fluid, and driven by a low-temperature heat source. In order to drive the heat pump’s heat generator, natural gas (prepared in a boiler) or/and solar energy (prepared in solar panels) was used. The experimental reasearch were conducted on an experimental stand located in the Department of Heat Engineering and Thermal Equipment Laboratory from the Technical University of Civil Engineering, Bucharest. It can simultaneously supply both cooling and heating for preparation of domestic hot water. A performance analysis is carried out through experimental measuring data to calculate the total COP vs. COP for conventional single-effect absorption chiller under the same conditions. In recovery mode, the maximum total COP increases to 5.446 because of heat recovery from condenser and absorber. Nevertheless, the system achieves a temperature of hot water between 33.58–43.92 °C. Notably, the recovery heat mode performance is strongly influenced by the solar radiation during the month, which therefore represents a key design parameter for the proposed system.
Saadoon Abdul Hafedh, Layth Abed, Hasnawi Al-Rubaye
et al.
This study proposes a novel liquefied-natural-gas-assisted combined cooling, heating, and power (CCHP) plant in which the cryogenic energy released during regasification is cascaded through a closed nitrogen Brayton cold-box, medium-temperature heat is coupled to a toluene organic Rankine cycle (ORC), and a lithium-bromide/water absorption refrigeration cycle (ARC) provides cooling while domestic hot water at 60 °C is recovered. A comprehensive steady-state model was developed in Engineering Equation Solver (EES) and benchmarked against three published data sets; the largest deviations were 2.8% for the gas-turbine block and 0.3% for the chiller, confirming the model’s reliability. Energy and exergy balances were solved simultaneously for every component, enabling consistent comparison of power-only, CHP, and CCHP modes for a 160-MW-class plant. Under nominal conditions the system delivers 168.8 MW net power, 11.7 MW cooling, and 143.7 kg/s hot water, with energy and exergy efficiencies of 93.2% and 51%, respectively. Parametric sweeps of combustor outlet temperature, compressor pressure ratio, LNG regasification pressure, and heat exchanger pinch points expose design trade-offs, pinpoint a practical 6–7 MPa regasification-pressure window, and versus a recent benchmark lift exergy efficiency from 41% to 51% (~24% gain) while cutting specific fuel consumption by 18% at equal net power. These improvements are achieved without exotic working fluids or deep cryogenic stages, making the concept deployable with current hardware. The integrated methodology and data set form a transferable benchmark for future LNG-based energy hubs and highlight clear upgrade paths most notably staged combustion and advanced heat-exchanger networks to push performance even further.
Construction of infrastructures in cold regions and the Arctic has grown rapidly since the 2000s, including railways, platforms, bridges, roads, and pipelines. However, the harsh low temperatures significantly influence the mechanical behaviors of construction materials, and bring safety and durability challenges to these engineering structures. This study made a state‐of‐the‐art review on materials and structures exposed to low temperatures. This review started from constructional‐material mechanical properties, including concrete, steel reinforcement, mild/high‐strength steel plate, and steel strand at low temperatures. It reflected that low temperatures improved the strength of construction materials. However, the freeze–thaw cycles (FTCs) had a detrimental effect on the modulus and strength of concrete. Furthermore, it was revealed that low temperatures increased the interfacial bonding strength between the steel reinforcements (or shear connectors) and concrete. Moreover, low temperatures improved the bending, shear, and compression resistances of reinforced concrete (RC) or prestressed concrete structures, but reduced the ductility of RC columns under lateral cyclic loads. Finally, reviews also found that low temperatures improved the compression resistance of concrete‐filled steel tubes using mild, high‐strength, and stainless steels, whereas FTCs and erosion reduced their compression capacity. In addition, low temperatures increased the bending resistance of steel–concrete composite structures, but the FTCs reduced it. The low temperatures bring challenges to the safety and resilience of engineering constructions, which requires careful further studies. Continuous further studies may focus on the durability of materials and the resilience of structures under diverse hazards, including earthquakes, impacts, and even blasts.
The paper discusses the role of social marketing in preventing health-related harmful habits such as tobacco consumption and smoking. These habits are the causes of deadly diseases such as lung cancer, tuberculosis, and other chronic infections which are detrimental to life of the people. Children fall prey to the wrong habits in the wrong company and become tobacco addicts. So many cases of teen drug addicts are reported in a large number. They have a lack of conscience at a tender age and negligence of their counselling and awareness increases the number of smokers, drunkards, and drug addicts. Once they are afflicted with the diseases they must run for medicines and treatment. Therefore, it should be prevented before suffering as the saying goes, “Prevention is better than cure “. They are unaware that they are prevented not only by clinical treatment and medicines but also by social awareness and education. Social mobilization of the people through awareness programs, education, camps, campaigns, etc. is known as social marketing. The significance of social marketing is its effects on the prevention of physically detrimental habits in the youth which contributed a lot to the reduction of cases of diseases. The role of government programs, educational and medical institutions, social workers, and NGOs is worth applauding in India which undertake and complete projects, organize awareness camps, and educate parents and youths to save themselves from the consumption of harmful substances. It has also produced good output in India that the cases of smoking and drug addiction have reduced to support the country’s development as India is advancing towards becoming the third largest economy and a developed country by 2030 and 2047 respectively.
To solve the problems of poor thermal conductivity and low thermal energy storage efficiency of paraffin wax, carbon-containing materials with high thermal conductivity are used to improve its thermal energy storage performance. Three carbon nanotube powders with different diameters, 8–12 nm, 10–20 nm, and 20–40 nm, are added to paraffin wax at a mass fraction of 5% to evaluate the influence of the carbon nanotube diameter on the thermal energy storage properties and the anisotropic thermal conductivity of carbon nanotube/paraffin wax composite phase change material (CPCM). The experimental results show that the specific heat capacity and latent heat value of CPCM decreases as the diameter of the carbon nanotubes decreases. The phase change temperature decreases by approximately 1 ℃; the latent heat value decreases by 0.5%–3.8%, and the specific heat capacity decreases by 16.2%–16.5% compared with those of pure paraffin wax. The improved thermal conductivity and thermal diffusivity shorten the paraffin wax melting time, and the melting time of 8–12 nm composites is reduced by 18.2%–46.7%. Temperature uniformity in the vertical direction was also effectively improved. At the external measurement points, the temperature fluctuation of 8–12 nm composites was 47.7% compared with pure paraffin, whereas at the internal measurement points, the temperature fluctuation of 10–20 nm was 32.9% of pure paraffin. The diameter of the carbon nanotubes significantly improved the thermal energy storage properties of the CPCM.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Software engineering (SE) is full of abstract concepts that are crucial for both researchers and practitioners, such as programming experience, team productivity, code comprehension, and system security. Secondary studies aimed at summarizing research on the influences and consequences of such concepts would therefore be of great value. However, the inability to measure abstract concepts directly poses a challenge for secondary studies: primary studies in SE can operationalize such concepts in many ways. Standardized measurement instruments are rarely available, and even if they are, many researchers do not use them or do not even provide a definition for the studied concept. SE researchers conducting secondary studies therefore have to decide a) which primary studies intended to measure the same construct, and b) how to compare and aggregate vastly different measurements for the same construct. In this experience report, we discuss the challenge of study selection in SE secondary research on latent variables. We report on two instances where we found it particularly challenging to decide which primary studies should be included for comparison and synthesis, so as not to end up comparing apples with oranges. Our report aims to spark a conversation about developing strategies to address this issue systematically and pave the way for more efficient and rigorous secondary studies in software engineering.
Aline de Campos, Jorge Melegati, Nicolas Nascimento
et al.
Generative Artificial Intelligence (GenAI) has become an emerging technology with the availability of several tools that could impact Software Engineering (SE) activities. As any other disruptive technology, GenAI led to the speculation that its full potential can deeply change SE. However, an overfocus on improving activities for which GenAI is more suitable could negligent other relevant areas of the process. In this paper, we aim to explore which SE activities are not expected to be profoundly changed by GenAI. To achieve this goal, we performed a survey with SE practitioners to identify their expectations regarding GenAI in SE, including impacts, challenges, ethical issues, and aspects they do not expect to change. We compared our results with previous roadmaps proposed in SE literature. Our results show that although practitioners expect an increase in productivity, coding, and process quality, they envision that some aspects will not change, such as the need for human expertise, creativity, and project management. Our results point to SE areas for which GenAI is probably not so useful, and future research could tackle them to improve SE practice.
Elicitation interviews are the most common requirements elicitation technique, and proficiency in conducting these interviews is crucial for requirements elicitation. Traditional training methods, typically limited to textbook learning, may not sufficiently address the practical complexities of interviewing techniques. Practical training with various interview scenarios is important for understanding how to apply theoretical knowledge in real-world contexts. However, there is a shortage of educational interview material, as creating interview scripts requires both technical expertise and creativity. To address this issue, we develop a specialized GPT agent for auto-generating interview scripts. The GPT agent is equipped with a dedicated knowledge base tailored to the guidelines and best practices of requirements elicitation interview procedures. We employ a prompt chaining approach to mitigate the output length constraint of GPT to be able to generate thorough and detailed interview scripts. This involves dividing the interview into sections and crafting distinct prompts for each, allowing for the generation of complete content for each section. The generated scripts are assessed through standard natural language generation evaluation metrics and an expert judgment study, confirming their applicability in requirements engineering training.
The cold thermal energy storage characteristics of CO2 hydrates were studied in different mass concentrations of SDS surfactant (0.4, 0.5, and 0.6 g/L), SDBS surfactant (0.2, 0.3, and 0.4 g/L), and compound surfactants (SDS+SDBS) using a vapor compression refrigeration system. Compared with a pure-water system, SDS, SDBS, and compound (SDS+SDBS) surfactants improve the CO2 hydrate cold thermal energy storage performance, and the best concentrations of the surfactants were 0.5 g/L, 0.3 g/L, and 0.5 g/L(SDS)+0.3 g/L(SDBS), respectively. Comparing the cold thermal energy storage performance of SDS, SDBS, and compound (SDS+SDBS) surfactants, the compound surfactant 0.5 g/L(SDS)+0.3 g/L(SDBS) had the best cold thermal energy storage performance: the precooling time (21.51 min) and cold thermal energy storage time (27.56 min) were the shortest. The latent heat storage capacity (1 308.27 kJ), total storage capacity (2 967.35 kJ), average charging rate (1.79 kW), and hydrate formation mass (2.55 kg) were the largest. The findings indicate that the compound surfactant had the most significant effect on the CO2 hydrate cold thermal energy storage characteristics of this system.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration