Abstract Products made of titanium and its alloys are widely used in modern areas like the mechanical engineering, instrument making, aerospace and medical sector. High strength and low thermal conductivity are the causes of difficulties with the machinability of these alloys. It is important to find ways to increase machinability by cutting titanium alloys. One way to implement this is to apply various methods of cooling on workpieces of titanium alloys and on cutting tools during machining. In this review article, an extensive analysis of the literature on such cooling techniques as dry, conventional cooling system, minimum quantity of lubricant (MQL), minimum quantity cooling lubrication (MQCL), cryogenic lubrication, and high-pressure cooling (HPC) is performed. The following groups of Ti alloys are considered: high-strength structural and high-temperature Ti alloys, intermetallic compounds, pure titanium, as well as composites CFRPs/Ti alloys. For the processes of turning, milling, drilling, and grinding, etc. it is shown how the type of cooling affects the surface integrity include surface roughness, tool wear, tool life, temperature, cutting forces, environmental aspects, etc. The main advantages, disadvantages and prospects of different cooling methods are also shown. The problems and future trends of these methods for the machining of Ti and its alloys are indicated.
The proliferation of AI-powered search engines has shifted information discovery from traditional link-based retrieval to direct answer generation with selective source citation, creating new challenges for content visibility. While existing Generative Engine Optimization (GEO) approaches focus primarily on semantic content modification, the role of structural features in influencing citation behavior remains underexplored. In this paper, we propose GEO-SFE, a systematic framework for structural feature engineering in generative engine optimization. Our approach decomposes content structure into three hierarchical levels: macro-structure (document architecture), meso-structure (information chunking), and micro-structure (visual emphasis), and models their impact on citation probability across different generative engine architectures. We develop architecture-aware optimization strategies and predictive models that preserve semantic integrity while improving structural effectiveness. Experimental evaluation across six mainstream generative engines demonstrates consistent improvements in citation rate (17.3 percent) and subjective quality (18.5 percent), validating the effectiveness and generalizability of the proposed framework. This work establishes structural optimization as a foundational component of GEO, providing a data-driven methodology for enhancing content visibility in LLM-powered information ecosystems.
The behavior of a disk made of silicon nitride (Si₃N₄) under thermal loads was investigated using numerical analysis. Due to its superior properties such as high thermal resistance, low coefficient of thermal expansion, and high fracture toughness, Si₃N₄ stands out among advanced ceramic materials. Owing to these characteristics, it is widely used in gas turbine engine components, bearing systems, cutting tools, and engineering elements operating at high temperatures. In this study, it was assumed that the elastic modulus does not vary with temperature, and a formulation describing this relationship was derived. Thermal analyses were conducted at temperature levels of 80 °C, 100 °C, 120 °C, 140 °C, and 160 °C. The results revealed that with increasing temperature, both radial and tangential stresses rise in magnitude. Furthermore, it was determined that tangential stresses are significantly higher compared to radial stresses.
Abstract This study investigates the challenges posed by rising average outdoor temperatures due to global warming on the efficiency of refrigeration cycles, specifically for low-temperature (LT) applications in industrial refrigeration systems (temperatures ranging from −50 °C to −100 °C). As global temperatures increase, condenser temperatures in refrigeration systems are forced to rise, leading to higher electrical energy consumption and greater environmental impact. The importance of addressing these issues is critical in ensuring sustainable energy use and mitigating the environmental footprint of industrial refrigeration systems. The novelty of this work lies in its comprehensive evaluation of innovative strategies to enhance refrigeration cycle performance. Specifically, the study explores the utilization of ejectors to improve thermodynamic performance, the addition of nanoparticles to the refrigerant at optimized rates to enhance heat transfer and reduce energy consumption, and new cycle designs that can increase the coefficient of performance (COP). These advancements offer significant improvements over previous efforts in literature, providing practical solutions to reduce the impact of rising condenser temperatures on refrigeration system performance. The study’s findings suggest that incorporating these innovations can significantly improve the COP of refrigeration cycles, leading to reduced energy consumption and enhanced system performance. Furthermore, the paper discusses the potential of new refrigerants and cycle configurations to address the challenges posed by global warming. The scope of the work extends to both theoretical and practical applications in industrial refrigeration, offering a roadmap for future research and design improvements in refrigeration cycle technology. In conclusion, by carefully selecting an appropriate refrigeration cycle, incorporating an ejector, and adding nanoparticles to a suitably chosen refrigerant, a minimum 25% improvement in the COP can be achieved compared to conventional cycles used for low-temperature applications.
Hashini Gunatilake, John Grundy, Rashina Hoda
et al.
Empathy plays a crucial role in software engineering (SE), influencing collaboration, communication, and decision-making. While prior research has highlighted the importance of empathy in SE, there is limited understanding of how empathy manifests in SE practice, what motivates SE practitioners to demonstrate empathy, and the factors that influence empathy in SE work. Our study explores these aspects through 22 interviews and a large scale survey with 116 software practitioners. Our findings provide insights into the expression of empathy in SE, the drivers behind empathetic practices, SE activities where empathy is perceived as useful or not, and the other factors that influence empathy. In addition, we offer practical implications for SE practitioners and researchers, offering a deeper understanding of how to effectively integrate empathy into SE processes.
From its early foundations in the 1970s, empirical software engineering (ESE) has evolved into a mature research discipline that embraces a plethora of different topics, methodologies, and industrial practices. Despite its remarkable progress, the ESE research field still needs to keep evolving, as new impediments, shortcoming, and technologies emerge. Research reproducibility, limited external validity, subjectivity of reviews, and porting research results to industrial practices are just some examples of the drivers for improvements to ESE research. Additionally, several facets of ESE research are not documented very explicitly, which makes it difficult for newcomers to pick them up. With this new regular ACM SIGSOFT SEN column (SEN-ESE), we introduce a venue for discussing meta-aspects of ESE research, ranging from general topics such as the nature and best practices for replication packages, to more nuanced themes such as statistical methods, interview transcription tools, and publishing interdisciplinary research. Our aim for the column is to be a place where we can regularly spark conversations on ESE topics that might not often be touched upon or are left implicit. Contributions to this column will be grounded in expert interviews, focus groups, surveys, and position pieces, with the goal of encouraging reflection and improvement in how we conduct, communicate, teach, and ultimately improve ESE research. Finally, we invite feedback from the ESE community on challenging, controversial, or underexplored topics, as well as suggestions for voices you would like to hear from. While we cannot promise to act on every idea, we aim to shape this column around the community interests and are grateful for all contributions.
Hashini Gunatilake, John Grundy, Rashina Hoda
et al.
Empathy plays a critical role in software engineering (SE), influencing collaboration, communication, and user-centred design. Although SE research has increasingly recognised empathy as a key human aspect, there remains no validated instrument specifically designed to measure it within the unique socio-technical contexts of SE. Existing generic empathy scales, while well-established in psychology and healthcare, often rely on language, scenarios, and assumptions that are not meaningful or interpretable for software practitioners. These scales fail to account for the diverse, role-specific, and domain-bound expressions of empathy in SE, such as understanding a non-technical user's frustrations or another practitioner's technical constraints, which differ substantially from empathy in clinical or everyday contexts. To address this gap, we developed and validated two domain-specific empathy scales: EmpathiSEr-P, assessing empathy among practitioners, and EmpathiSEr-U, capturing practitioner empathy towards users. Grounded in a practitioner-informed conceptual framework, the scales encompass three dimensions of empathy: cognitive empathy, affective empathy, and empathic responses. We followed a rigorous, multi-phase methodology, including expert evaluation, cognitive interviews, and two practitioner surveys. The resulting instruments represent the first psychometrically validated empathy scales tailored to SE, offering researchers and practitioners a tool for assessing empathy and designing empathy-enhancing interventions in software teams and user interactions.
The automotive industry generates vast amounts of data from sensors, telemetry, diagnostics, and real-time operations. Efficient data engineering is critical to handle challenges of latency, scalability, and consistency. Modern data lakehouse formats Delta Parquet, Apache Iceberg, and Apache Hudi offer features such as ACID transactions, schema enforcement, and real-time ingestion, combining the strengths of data lakes and warehouses to support complex use cases. This study presents a comparative analysis of Delta Parquet, Iceberg, and Hudi using real-world time-series automotive telemetry data with fields such as vehicle ID, timestamp, location, and event metrics. The evaluation considers modeling strategies, partitioning, CDC support, query performance, scalability, data consistency, and ecosystem maturity. Key findings show Delta Parquet provides strong ML readiness and governance, Iceberg delivers high performance for batch analytics and cloud-native workloads, while Hudi is optimized for real-time ingestion and incremental processing. Each format exhibits tradeoffs in query efficiency, time-travel, and update semantics. The study offers insights for selecting or combining formats to support fleet management, predictive maintenance, and route optimization. Using structured datasets and realistic queries, the results provide practical guidance for scaling data pipelines and integrating machine learning models in automotive applications.
The world faces escalating crises: record-breaking temperatures, widespread fires, severe flooding, increased oceanic microplastics, and unequal resource distribution. Academia introduces courses around sustainability to meet the new demand, but software engineering education lags behind. While software systems contribute to environmental issues through high energy consumption, they also hold the potential for solutions, such as more efficient and equitable resource management. Yet, sustainability remains a low priority for many businesses, including those in the digital sector. Business as usual is no longer viable. A transformational change in software engineering education is urgently needed. We must move beyond traditional curriculum models and fully integrate sustainability into every aspect of software development. By embedding sustainability as a core competency, we can equip future engineers not only to minimise harm but also to innovate solutions that drive positive, sustainable change. Only with such a shift can software engineering education meet the demands of a world in crisis and prepare students to lead the next generation of sustainable technology. This article discusses a set of challenges and proposes a customisable education roadmap for integrating sustainability into the software engineering curricula. These challenges reflect our perspective on key considerations, stemming from regular, intensive discussions in regular workshops among the authors and the community, as well as our extensive research and teaching experience in the field.
In order to meet the requirements of weight, vibration, and size in the field of infrared imaging, Lihan Cryogenics has developed a lightweight and compact free-piston Stirling cooler. The cooler weighs 265g and includes a built-in electronic controller and active balancer. The system comprises a gas-bearing moving magnet compressor and a low-temperature rod-less displacer, are arranged in-line to have axial symmetry (SymCoolTM). This is also designed to be IDCA (integrated dewar cooler assembly) optional to meet industry-standard. Furthermore, an active vibration controller is developed to reduce vibration, utilizing an active motor on the same axis as the compressor piston to offset vibrations. The refrigeration performance is experimentally tested under different heat dissipation and cold head working temperatures. The test results show that the cooler acquires 0.5W cooling capacity at 77K with 12.6W power consumption, and the power consumption of the active vibration controller is less than 3W.
Data center computer room air-conditioning equipment operates for prolonged periods, and its performance must be tested and evaluated annually to ensure safe and efficient operation. This study conducts field measurements on the server layout, blind plate structure, and working condition adaptability in the rack. The thermal environment of a data center using the closed cold aisle underfloor air distribution system is studied, and the thermal environment safety and energy efficiency throughout the year are evaluated through thermal performance indicators and energy efficiency indicators. The results show that it is recommended to install a rack with a power of more than 2 kW in the middle area of the cold aisle. Installing a blind plate in the gap between the racks can promote the circulation of cold air inside the server, reduce the backflow interference of hot air, and reduce the maximum outlet temperature by 3.32 °C. The average supply air speed was reduced by approximately 24%. Under summer operating conditions, the PUE, WUE, and CUE of the data center were approximately 1.2, 3.5, and 0.84, respectively, and the WUE exhibited strong seasonality. Under winter operating conditions, free cooling can effectively reduce the energy consumption of data centers. In addition, the overheating problem of the racks at the end of the cold aisle of the data center is significant. The cabinet cooling index of the 16 racks was less than 90%, and the heat loss was high.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Jinqi Luo, Tianjiao Ding, Kwan Ho Ryan Chan
et al.
Large Language Models (LLMs) are being used for a wide variety of tasks. While they are capable of generating human-like responses, they can also produce undesirable output including potentially harmful information, racist or sexist language, and hallucinations. Alignment methods are designed to reduce such undesirable outputs via techniques such as fine-tuning, prompt engineering, and representation engineering. However, existing methods face several challenges: some require costly fine-tuning for every alignment task; some do not adequately remove undesirable concepts, failing alignment; some remove benign concepts, lowering the linguistic capabilities of LLMs. To address these issues, we propose Parsimonious Concept Engineering (PaCE), a novel activation engineering framework for alignment. First, to sufficiently model the concepts, we construct a large-scale concept dictionary in the activation space, in which each atom corresponds to a semantic concept. Given any alignment task, we instruct a concept partitioner to efficiently annotate the concepts as benign or undesirable. Then, at inference time, we decompose the LLM activations along the concept dictionary via sparse coding, to accurately represent the activations as linear combinations of benign and undesirable components. By removing the latter ones from the activations, we reorient the behavior of the LLM towards the alignment goal. We conduct experiments on tasks such as response detoxification, faithfulness enhancement, and sentiment revising, and show that PaCE achieves state-of-the-art alignment performance while maintaining linguistic capabilities.
Building up competencies in working with data and tools of Artificial Intelligence (AI) is becoming more relevant across disciplinary engineering fields. While the adoption of tools for teaching and learning, such as ChatGPT, is garnering significant attention, integration of AI knowledge, competencies, and skills within engineering education is lacking. Building upon existing curriculum change research, this practice paper introduces a systems perspective on integrating AI education within engineering through the lens of a change model. In particular, it identifies core aspects that shape AI adoption on a program level as well as internal and external influences using existing literature and a practical case study. Overall, the paper provides an analysis frame to enhance the understanding of change initiatives and builds the basis for generalizing insights from different initiatives in the adoption of AI in engineering education.
Pulsating heat pipes (PHP) are novel heat transfer devices which have wide prospects of application in energy, refrigeration, cryogenic systems and electronics cooling because of simple design and high efficiency of heat transfer. However, heat transfer characteristics of PHP depend on many parameters. This paper is dedicated to experimental investigation of the impact of heating zone length and angle of inclination on the start-up and operational characteristics of PHP. The investigation involved a systematic variation of the heating zone length and inclination angle with measuring of key performance indicators, including start-up heat flux density and temperature, transient heat flux density, and thermal resistance, for water, methanol and pentane as a heat carrier. 5-turn PHP made of copper capillary tube with inner diameter 1,1 mm was used as experimental sample. Main operational modes of PHP were defined and described in the paper based on obtained experimental data. Results indicate that increasing the heating zone length from 10 to 50 mm at all tested inclination angles significantly enhances the thermal performance of PHP by reducing start-up, transient heat flux densities and thermal resistance. Specifically, thermal resistance decreased by up to 51,8% and start-up, transient heat flux densities up to 68-71% with longer heating zone lengths. Also, it was shown that maximum transferred heat flux increased up to 59% with increasing in heating zone length. These findings suggest that optimizing heat zone length can improve start-up efficiency and overall heat transfer performance. The impact of inclination angle varies with the coolant used; generally, water outperforms methanol and pentane across all orientations. Methanol ranks next, with pentane showing the least performance. However, methanol and pentane are viable choices for low heat flux applications in vertical bottom heating mode. The study provides valuable insights for the design of PHP in energy, refrigeration and electronics cooling systems, highlighting the importance of heating zone (HZ) length configuration in achieving optimal performance
Additive manufacturing holds promise for the fabrication of three-dimensional scaffolds with precise geometry, to serve as substrates for the guided regeneration of natural tissue. In this work, a bioinspired approach is adopted for the synthesis of hybrid hydroxyapatite hydrogels, which were subsequently printed to form 3D scaffolds for bone tissue engineering applications. These hydrogels consist of hydroxyapatite nanocrystals, biomimetically synthesized in the presence of both chitosan and l-arginine. To improve their mechanical properties, chemical crosslinking was performed using a natural crosslinking agent (genipin), and their rheology was modified by employing an acetic acid/gelatin solution. Regarding the 3D printing process, several parameters (flow, infill and perimeter speed) were studied in order to accurately produce scaffolds with predesigned geometry and micro-architecture, while also applying low printing temperature (15 °C). Following the printing procedure, the 3D scaffolds were freeze dried in order to remove the entrapped solvents and therefore, obtain a porous interconnected network. Evaluation of porosity was performed using micro-computed tomography and nanomechanical properties were assessed through nanoindentation. Results of both characterization techniques, showed that the scaffolds' porosity as well as their modulus values, fall within the corresponding range of the respective values of cancellous bone. The biocompatibility of the 3D printed scaffolds was assessed using MG63 human osteosarcoma cells for 7 days of culturing. Cell viability was evaluated by MTT assay as well as double staining and visualized under fluorescence microscopy, while cell morphology was analyzed through scanning electron microscopy. Biocompatibility tests, revealed that the scaffolds constitute a cell-friendly environment, allowed them to adhere on the scaffolds' surface, increase their population and maintain high levels of viability.
According to the Environmental Protection Agency, the agriculture industry in the United States accounts for 11% of total greenhouse gas emissions. Some of these emissions are due to the refrigeration and transportation needs of agricultural products; therefore, it would be beneficial to utilize renewable energy for these functions. This study focuses on the design and implementation of a solar-powered mobile cooling unit to power refrigerated storage and transportation units, specifically for small-scale farming. To inform the design process, small-scale farmers were engaged as potential stakeholders to learn about their current practices and opportunities to create value for them with a mobile cooling unit. A series of experiments were conducted to understand the performance capabilities of the Internal Cooling System and the Solar Battery System. Temperature studies were run to ensure that desirable temperatures could be achieved and maintained within the unit. These were conducted in different weather conditions to quantify external impacts. It was shown that desired temperatures could be achieved and maintained, and that a solar-powered system would be able to serve as the power source for a mobile cooling unit.
Rudrajit Choudhuri, Dylan Liu, Igor Steinmacher
et al.
Conversational Generative AI (convo-genAI) is revolutionizing Software Engineering (SE) as engineers and academics embrace this technology in their work. However, there is a gap in understanding the current potential and pitfalls of this technology, specifically in supporting students in SE tasks. In this work, we evaluate through a between-subjects study (N=22) the effectiveness of ChatGPT, a convo-genAI platform, in assisting students in SE tasks. Our study did not find statistical differences in participants' productivity or self-efficacy when using ChatGPT as compared to traditional resources, but we found significantly increased frustration levels. Our study also revealed 5 distinct faults arising from violations of Human-AI interaction guidelines, which led to 7 different (negative) consequences on participants.