Hasil untuk "Systems engineering"

F. Verstraete, M. Wolf, J. Cirac

Priscilla E. M. Purnick, Ron Weiss

P. Malafaya, G. A. Silva, R. Reis

The present paper intends to overview a wide range of natural-origin polymers with special focus on proteins and polysaccharides (the systems more inspired on the extracellular matrix) that are being used in research, or might be potentially useful as carriers systems for active biomolecules or as cell carriers with application in the tissue engineering field targeting several biological tissues. The combination of both applications into a single material has proven to be very challenging though. The paper presents also some examples of commercially available natural-origin polymers with applications in research or in clinical use in several applications. As it is recognized, this class of polymers is being widely used due to their similarities with the extracellular matrix, high chemical versatility, typically good biological performance and inherent cellular interaction and, also very significant, the cell or enzyme-controlled degradability. These biocharacteristics classify the natural-origin polymers as one of the most attractive options to be used in the tissue engineering field and drug delivery applications.

P. Jurcevic, B. Lanyon, P. Hauke et al.

The key to explaining and controlling a range of quantum phenomena is to study how information propagates around many-body systems. Quantum dynamics can be described by particle-like carriers of information that emerge in the collective behaviour of the underlying system, the so-called quasiparticles. These elementary excitations are predicted to distribute quantum information in a fashion determined by the system’s interactions. Here we report quasiparticle dynamics observed in a quantum many-body system of trapped atomic ions. First, we observe the entanglement distributed by quasiparticles as they trace out light-cone-like wavefronts. Second, using the ability to tune the interaction range in our system, we observe information propagation in an experimental regime where the effective-light-cone picture does not apply. Our results will enable experimental studies of a range of quantum phenomena, including transport, thermalization, localization and entanglement growth, and represent a first step towards a new quantum-optic regime of engineered quasiparticles with tunable nonlinear interactions.

D. V. Steward

Robert L. Wears

T. Hu, Zongli Lin

N. Jennings

C. U. Smith

Ross J. Anderson

Daniele Pirone, Giusy Giugliano, Michela Schiavo et al.

Virtual staining is the current state-of-the-art computational technique to cleverly enhance intracellular specificity in unstained biological samples by using convolutional neural networks (CNNs) trained on co-registered pairs of unstained/stained images. While effective, this approach suffers from unpredictable biases inherent to fluorescence microscopy and encounters challenges when applied to flow cytometry data as it would require accurate co-registration on a huge number of images. Here, we present a novel method that exploits for the first time a Holotomography-driven learning to completely eliminate the need for co-registration. We demonstrate that training a CNN on a stain-free dataset of 3D refractive index tomograms of flowing cells unlocks stain-free intracellular specificity for the first time in quantitative phase imaging flow cytometry. This self-supervised solution, by circumventing the critical obstacle of fluorescence co-registration, opens unprecedented perspectives for label-free, high-throughput imaging flow cytometry, offering a powerful new paradigm for advanced 2D and 3D single-cell analysis.

Zaid Allal, Hassan N. Noura, Flavien Vernier et al.

Accurate prediction of the Remaining Useful Life (RUL) of fuel cell (FC) systems is essential to ensure operational reliability, optimize maintenance strategies, and extend system lifetime in safety-critical hydrogen applications. As FC degradation is governed by complex, nonlinear, and stochastic mechanisms, machine learning (ML) has emerged as a powerful paradigm for data-driven prognostics. This paper presents a structured and comprehensive review of recent ML-based approaches for FC RUL estimation, encompassing supervised, unsupervised, and hybrid methodologies, including regression techniques, support vector machines, ensemble models, neural networks, and advanced deep learning architectures. Despite notable progress, our analysis reveals persistent limitations in the current literature, particularly the widespread neglect of underlying electrochemical and physical degradation laws, as well as the scarcity and ambiguity of explicit RUL and End-of-Life (EoL) labels in publicly available datasets. These challenges significantly constrain model generalization, interpretability, and real-world applicability. To address these gaps, we conduct a comparative analysis of more than 20 recent state-of-the-art studies and propose a unified and generalizable RUL estimation pipeline. This framework integrates data acquisition, preprocessing, feature engineering, model design, and validation, while explicitly accounting for physical consistency and operational constraints. In addition, the paper formulates practical, multi-level recommendations, including first-order guidelines for data modeling and learning strategies, second-order recommendations targeting validation protocols and real-world deployment, and the systematic integration of uncertainty quantification (UQ) techniques to enhance robustness, interpretability, and trustworthiness. By consolidating methodological insights, emerging paradigms, and deployment-oriented considerations, this review provides a comprehensive reference and a forward-looking roadmap for the development of reliable, physics-consistent, and scalable RUL prognostic frameworks for fuel cell systems.

Matteo Vaccargiu, Azmat Ullah, Pierluigi Gallo

Carbon credit systems have emerged as a policy tool to incentivize emission reductions and support the transition to clean energy. Reliable carbon-credit certification depends on mechanisms that connect actual, measured renewable-energy production to verifiable emission-reduction records. Although blockchain and IoT technologies have been applied to emission monitoring and trading, existing work offers limited support for certification processes, particularly for small and medium-scale renewable installations. This paper introduces a blockchain-based carbon-credit certification architecture, demonstrated through a 100 kWp photovoltaic case study, that integrates real-time IoT data collection, edge-level aggregation, and secure on-chain storage on a permissioned blockchain with smart contracts. Unlike approaches focused on trading mechanisms, the proposed system aligns with European legislation and voluntary carbon-market standards, clarifying the practical requirements and constraints that apply to photovoltaic operators. The resulting architecture provides a structured pathway for generating verifiable carbon-credit records and supporting third-party verification.

Oleksandr Kosenkov, Ehsan Zabardast, Jannik Fischbach et al.

Implementing privacy by design (PbD) according to the General Data Protection Regulation (GDPR) is met with a growing number of requirements engineering (RE) approaches. However, the question of which RE method for PbD fits best the goals of organisations remains a challenge. We report our endeavor to close this gap by synthesizing a goal-centric approach for PbD methods assessment. We used literature review, interviews, and validation with practitioners to achieve the goal of our study. As practitioners do not approach PbD systematically, we suggest that RE methods for PbD should be assessed against organisational goals, rather than process characteristics only. We hope that, when further developed, the goal-centric approach could support the development, selection, and tailoring of RE practices for PbD.

Ziheng Shangguan

The global acceleration of population urbanization has transformed cities into primary spatial hubs of human activity. As urban populations continue to expand, identifying the socioeconomic drivers of urbanization and elucidating their underlying mechanisms are essential for achieving Sustainable Development Goal 11, established by the United Nations. This study leverages machine learning and big data to investigate the determinants of population urbanization in China over the period 1991–2023. Utilizing the XGBoost algorithm combined with SHAP (Shapley Additive Explanations), the analysis reveals a tripartite structure of key drivers encompassing industrial support, employment orientation, and infrastructure accessibility. Regional assessments indicate distinct urbanization patterns: Eastern coastal areas are predominantly driven by finance and service industries; central inland regions follow an investment-led trajectory anchored in infrastructure development and real estate expansion, while the western interior relies mainly on employment-centered strategies. Partial Dependence Plots (PDPs) highlighted spatial variations in the effects of sensitive factors, with interaction analyses revealing synergistic effects between tertiary sector shares and the working-age share in eastern coastlands, structural amplification by real estate investment with appropriate working-age population shares in the central inlands, and balancing interactions between GDP growth rates and tertiary sector shares in the western interior. These findings contribute to a more nuanced understanding of the socioeconomic forces shaping urbanization and offer evidence-based recommendations for policymakers in other developing countries seeking to foster sustainable urban growth.

Salim Hussain, Adeniyi Oyebade, Md Riyad Hossain et al.

The demand for effective, economical, and sustainable anode materials for metal-ion batteries (MIBs) has increased significantly due to the rapid growth of energy storage technologies. Among various candidates, carbon-based materials have emerged as highly promising due to their abundance, structural versatility, and favorable electrochemical properties. This review highlights the current status and future directions of carbon-based anode materials in MIBs, with a particular focus on graphite, hard carbon, carbon nanotubes, heteroatom-doped carbons, carbon-based composites, and other related structures. Various synthesis strategies for these materials are presented, along with discussions on their physicochemical characteristics, including structural features that influence electrochemical performance. Furthermore, we provided an overview on the performance of newly developed carbon-based anode materials in lithium-, sodium-, potassium-, and other emerging metal-ion battery systems to assess the impact of different synthesis approaches. Special attention is given to surface engineering, heteroatom doping, and composite design that can address intrinsic challenges such as limited ion diffusion, low reversible capacity, and poor cycling stability in MIBs. This review does not cover any carbon materials which have been used as an additive. In addition, the review explores emerging opportunities enabled by advanced characterization techniques, computational modeling, and artificial intelligence for optimizing the design of next-generation carbon anode. Finally, this article provides future perspectives and insights into the design principles of novel carbon-based anode materials that can accelerate the development of high-performance, durable, and sustainable MIB technologies.

Xiaoling Zhang, Yanan Li, Kang Wang et al.

Riboflavin, an important vitamin utilized in pharmaceutical products and as a feed additive, is mainly produced by metabolically engineered bacterial fermentation. However, the reliance on antibiotics in the production process leads to increased costs and safety risks. To address these challenges, an antibiotic-free Escherichia coli riboflavin producer was constructed using metabolic engineering approaches coupled with a novel plasmid stabilization system. Initially, competitive pathways and feedback inhibition were attenuated to enhance the metabolic flux towards riboflavin. Key genes in the purine pathway were overexpressed to boost the availability of riboflavin precursors. Subsequently, a plasmid stabilization system based on toxin was screened and characterized, achieving a plasmid retention rate of 84.9% after 10 days of passaging. Finally, transcriptomic analysis at the genome-wide level revealed several rate-limiting genes, including pgl, gnd, and yigB, which were subsequently upregulated, leading to a 26% improvement in riboflavin production. With optimization of the culture medium, the final strain allowed the production of 11.5 g/L of riboflavin with a yield of 90.4 mg/g glucose in 5 L bioreactors without antibiotics. These strategies can be extended to other plasmid-based riboflavin derivative production systems.

Aqsa Razzaq, T. Hayat, Sohail A. Khan et al.

In the modern era concept of artificial neural networks is an innovative phenomenon in industrial, mechanical, pharmaceutical and automotive applications. Furthermore, the advanced computational approach based on artificial neural networks (ANNs) employing Levenberg-Marquardt algorithm (LMA) provides exceptional capabilities in accurately obtaining solutions for the complex features of thermal and solutal transport rates in nonlinear flow problems. The current analysis scrutinizes the chemically reactive MHD flow of Reiner-Rivlin nanoliquid by curved stretched surface. Artificial neural networks (ANNs) based on (LMA) is differentiated by its remarkable stability and used to compute the flow characteristic of Reiner-Rivlin nanoliquid employing validation check, regression plots (RP), mean square error (MSE), fitness curve, error histograms and comparative solution analyses. Variable fluid characteristics are under consideration. Energy expression consists of heat generation, Joule heating and thermal radiation. Buongiorno's model is utilized to discuss nanofluid features by thermophoresis and Brownian motion. First order reaction is accounted. New concept of entropy generation in reactive flow with subject to heat generation, magnetohydrodynamics and radiation is considered. Related nonlinear equations are altered into dimensionless ordinary equations by using suitable transformations. Numerical solutions of nonlinear ordinary systems are obtained using Bvp4c scheme via MATLAB. Subsequently the advanced computational technique (ANNs) based on Levenberg-Marquardt algorithm (LMA) is integrated to train the resulting datasets and facilitate predictions of advanced solutions. Analysis for liquid flow, concentration, entropy rate and temperature via pertinent variables are graphically explored. Comparative results of Bvp4c scheme and artificial neural networks (ANNs) model are also discussed.

Zolboo Byambadorj, Koji Asami, Akio Higo et al.

The emergence of millimeter-wave-based 5G/Beyond 5G and 6G communication technologies has led to advancements in high data rates and low latency. However, there is a massive challenge in implementing the hardware platform including the millimeter-wave antenna. Over-the-air (OTA) testing is crucial for evaluating these antennas, often requiring large facilities to obtain far-field (FF) characteristics. In addition, the measurement accuracy is often limited mainly due to the path loss of the millimeter waves in the far field. Therefore, it is necessary to test the antenna in the near-field (NF) region. In this paper, we present a practical method for obtaining an FF pattern of the antenna-under-test by using an NF-to-FF transformation method with a probe correction technique. The feasibility of the process is validated by the OTA measurement results with a dedicated NF scanning system, which accurately reconstructs the FF patterns from the NF measurement results. A key contribution of this paper is the successful demonstration of a planar multi-patch antenna as an effective multi-probe for NF measurements, enabling a significant reduction in measurement time while maintaining high reconstruction accuracy. Its compact and planar form factor facilitates easy integration into space-constrained measurement setups, making it particularly suitable for automatic test equipment (ATE) and scalable testing systems.

Shalini Chakraborty, Sebastian Baltes

The IT industry provides supportive pathways such as returnship programs, coding boot camps, and buddy systems for women re-entering their job after a career break. Academia, however, offers limited opportunities to motivate women to return. We propose a diverse multicultural research project investigating the challenges faced by women with software engineering (SE) backgrounds re-entering academia or related research roles after a career break. Career disruptions due to pregnancy, immigration status, or lack of flexible work options can significantly impact women's career progress, creating barriers for returning as lecturers, professors, or senior researchers. Although many companies promote gender diversity policies, such measures are less prominent and often under-recognized within academic institutions. Our goal is to explore the specific challenges women encounter when re-entering academic roles compared to industry roles; to understand the institutional perspective, including a comparative analysis of existing policies and opportunities in different countries for women to return to the field; and finally, to provide recommendations that support transparent hiring practices. The research project will be carried out in multiple universities and in multiple countries to capture the diverse challenges and policies that vary by location.