Hasil untuk "Engineering design"

Wei Gao, Yunbo Zhang, Devarajan Ramanujan et al.

Jihong Zhu, Weihong Zhang, L. Xia

S. Mirjalili, Pradeep Jangir, Shahrzad Saremi

N. Cross

Noor S. Sadeq, Siti Norain Harun, Gan Yian Chee et al.

Metal–Organic Frameworks (MOFs) have rapidly emerged as versatile nanoparticles system for their use in biomedical field, especially in drug delivery. Of the materials in this class, zeolitic imidazolate framework ZIF-8 has recently captured significant interest due to its tunable structure and significant potential for surface modification. It is these same features which allow ZIF-8 to act as a suitable carrier in both single agent and combination therapy. In particular, surface chemistry will be reiterated throughout the present article as significant in improving the physicochemical stability, biocompatibility and targeted drug delivery of the materials. In addition, through well-designed modification strategies ZIF-8 herein we propose to show controlled release profiles, improved colloidal stability, and improved therapeutic efficacy. This review will discuss recent advances made in the surface engineering of ZIF-8 and present salient design principles and functionalisation techniques and their usefulness in the area of drug-delivery applications.

Dinesh Panday, Parinaz Heydar, Casey Lapham et al.

Industrial hemp (Cannabis sativa L.) is an emerging crop for renewable fiber materials. For farmers, finding a balance between agronomic performance and economic return is crucial, especially when targeting specific markets like the textile industry, which values not just fiber quantity, but overall quality. This field study, conducted at the Rodale Institute in Kutztown, Pennsylvania, assessed the effects of tillage (till vs. no till), cover crop (with cover vs. no cover), and nitrogen (N) rate (0, 50, 100, 150 kg ha⁻¹) on hemp fiber yield, N concentrations in leaf and stalk, and mechanical performance under regenerative organic conditions. Fiber mechanical properties, including maximum load, tenacity, work of rupture, and modulus of elasticity were analyzed at Thomas Jefferson University. Results showed that biomass yield increased with N input, peaking at 9.2 Mg ha⁻¹ under till systems with cover crop at 150 kg N ha⁻¹ . However, fiber quality declined at higher N rates. The highest fiber quality metrics, including tenacity (610.5 MPa), modulus of elasticity (3.5 GPa), and work of rupture (31.4 newton mm⁻²) was achieved in no till system with cover crops and no N addition. A clear trade-off emerged: high N increased biomass yield but compromised fiber quality, while moderate input levels (e.g., till system with cover crop at 50 kg N ha⁻¹) offered a balanced outcome. This suggests that regenerative practices not only support soil health but also improve fiber strength and flexibility. Farmers can tailor input strategies to match end-use goals: low-input systems for premium textile fibers and moderate inputs for bio-composite applications, supporting both ecologically sound and market demands.

Ning Zhang, Yongcheng Wang, Gang Li et al.

In current super-resolution (SR) research, blind SR models capable of handling multiple degradations have attracted significant attention. Inspired by variational autoencoders (VAEs) that model data distributions through latent representations, this paper proposes a VAE framework for unsupervised remote sensing image (RSI) SR. VAEs excel at learning rich latent representations, modeling probabilistic distributions of input data and unsupervised learning, making them inherently well-suited to real-world blind SR scenarios. The proposed framework consists of an encoder that maps low-resolution (LR) images into a latent space and a decoder that reconstructs super-resolved images from the latent representations. To enhance latent modeling, an alternating optimization strategy is implemented for training the encoder and decoder. Furthermore, a comprehensive loss function and a latent coding regularization strategy are designed to constrain latent representations while maintaining image domain consistency. Experimental results demonstrate that on synthetic data, our method achieves favorable performance in both visual quality and quantitative metrics. It also demonstrates competitively performance compared to supervised methods, particularly in 4× and 8× SR tasks. Additionally, evaluations on Jilin-1 satellite RSIs further validate the effectiveness of our approach.

Roham Koohestani, Philippe de Bekker, Begüm Koç et al.

Benchmarks are essential for unified evaluation and reproducibility. The rapid rise of Artificial Intelligence for Software Engineering (AI4SE) has produced numerous benchmarks for tasks such as code generation and bug repair. However, this proliferation has led to major challenges: (1) fragmented knowledge across tasks, (2) difficulty in selecting contextually relevant benchmarks, (3) lack of standardization in benchmark creation, and (4) flaws that limit utility. Addressing these requires a dual approach: systematically mapping existing benchmarks for informed selection and defining unified guidelines for robust, adaptable benchmark development. We conduct a review of 247 studies, identifying 273 AI4SE benchmarks since 2014. We categorize them, analyze limitations, and expose gaps in current practices. Building on these insights, we introduce BenchScout, an extensible semantic search tool for locating suitable benchmarks. BenchScout employs automated clustering with contextual embeddings of benchmark-related studies, followed by dimensionality reduction. In a user study with 22 participants, BenchScout achieved usability, effectiveness, and intuitiveness scores of 4.5, 4.0, and 4.1 out of 5. To improve benchmarking standards, we propose BenchFrame, a unified framework for enhancing benchmark quality. Applying BenchFrame to HumanEval yielded HumanEvalNext, featuring corrected errors, improved language conversion, higher test coverage, and greater difficulty. Evaluating 10 state-of-the-art code models on HumanEval, HumanEvalPlus, and HumanEvalNext revealed average pass-at-1 drops of 31.22% and 19.94%, respectively, underscoring the need for continuous benchmark refinement. We further examine BenchFrame's scalability through an agentic pipeline and confirm its generalizability on the MBPP dataset. All review data, user study materials, and enhanced benchmarks are publicly released.

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.

Matt Landreman, Humberto Torreblanca, Antoine Cerfon

In fusion reactor design, steels under consideration for the blanket are ferromagnetic, so the steel's effect on the plasma physics must be examined. For efficient calculation of these fields, we can exploit the fact that the magnetic material gives a small perturbation relative to the fields from the electromagnetic coils and plasma. Moreover the magnetization is saturated due to the strong fields in typical fusion systems. These approximations significantly reduce the nonlinearity of the problem, so the magnetic materials can be described by an array of point dipoles of known magnitude, oriented in the direction of the coil and plasma field. The approach is verified by comparison to finite-element calculations with commercial software and shown to be accurate. As no linear or nonlinear solve is required, only evaluation of Biot-Savart-type integrals, the method here is significantly simpler to implement than other methods, and extremely fast. The method is compatible with arbitrary CAD geometry, and also allows rapid computation of the magnetic forces. We demonstrate adding the ferromagnetic effects to free-boundary MHD equilibrium calculations, assessing the effect on plasma properties such as confinement and stability. Moreover, it is straightforward to differentiate through the model to get the derivative of the field with respect to the electromagnet parameters. We thereby demonstrate gradient-based coil optimization for a quasi-isodynamic stellarator in which the field contribution from a ferromagnetic blanket is included. Even a significant steel volume is found to have little impact on the plasma physics properties, with the main effects being a slight destabilization of ballooning modes and a radial shift of the edge islands due to decrease in rotational transform. Both issues are corrected by minor reoptimization of the coil shapes to account for the field from the steel.

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.

Binrui Xu, Yong Liu, Bo Zhao et al.

Electrolyte additive engineering is a crucial method for enhancing the performance of aqueous zinc—ion batteries (AZIBs). Recently, most research predominantly focuses on the role of functional groups in regulating electrolytes, often overlooking the impact of molecule stereoscopic configuration. Herein, two isomeric sugar alcohols, mannitol and sorbitol, are employed as electrolyte additives to investigate the impact of the stereoscopic configuration of additives on the ZnSO₄ electrolyte. Experimental analysis and theoretical calculations reveal that the primary factor for improving Zn anode performance is the regulation of the solvation sheath by these additives. Among the isomers, mannitol exhibits stronger binding energies with Zn²⁺ ions and water molecules due to its more suitable stereoscopic configuration. These enhanced bindings allow mannitol to coordinate with Zn²⁺, contributing to solvation structure formation and reducing the active H₂O molecules in the bulk electrolyte, resulting in suppressed parasitic reactions and inhibited dendritic growth. As a result, the zinc electrodes in mannitol—modified electrolyte exhibit excellent cycling stability of 1600 h at 1 mA cm⁻² and 900 h at 10 mA cm⁻², respectively. Hence, this study provides novel insights into the importance of suitable stereoscopic molecule configurations in the design of electrolyte additives for highly reversible and high—rate Zn anodes.

Yang Feng, Nazhafati Muhanmaitijiang, Jianqing Ye et al.

Marvin Wyrich, Marvin Muñoz Barón, Justus Bogner

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.

Sourojit Ghosh, Sarah Coppola

Traditional and currently-prevalent pedagogies of design perpetuate ableist and exclusionary notions of what it means to be a designer. In this paper, we trace such historically exclusionary norms of design education, and highlight modern-day instances from our own experiences as design educators in such epistemologies. Towards imagining a more inclusive and sustainable future of design education, we present three case studies from our own experience as design educators in redesigning course experiences for blind and low-vision (BLV), deaf and hard-of-hearing (DHH) students, and students with other disabilities. In documenting successful and unsuccessful practices, we imagine what a pedagogy of care in design education would look like.

G. E. Dieter