Hasil untuk "Systems engineering"

G. Knott, J. Doudna

The diversity, modularity, and efficacy of CRISPR-Cas systems are driving a biotechnological revolution. RNA-guided Cas enzymes have been adopted as tools to manipulate the genomes of cultured cells, animals, and plants, accelerating the pace of fundamental research and enabling clinical and agricultural breakthroughs. We describe the basic mechanisms that set the CRISPR-Cas toolkit apart from other programmable gene-editing technologies, highlighting the diverse and naturally evolved systems now functionalized as biotechnologies. We discuss the rapidly evolving landscape of CRISPR-Cas applications, from gene editing to transcriptional regulation, imaging, and diagnostics. Continuing functional dissection and an expanding landscape of applications position CRISPR-Cas tools at the cutting edge of nucleic acid manipulation that is rewriting biology.

P. Runeson, Martin Höst

Case study is a suitable research methodology for software engineering research since it studies contemporary phenomena in its natural context. However, the understanding of what constitutes a case study varies, and hence the quality of the resulting studies. This paper aims at providing an introduction to case study methodology and guidelines for researchers conducting case studies and readers studying reports of such studies. The content is based on the authors’ own experience from conducting and reading case studies. The terminology and guidelines are compiled from different methodology handbooks in other research domains, in particular social science and information systems, and adapted to the needs in software engineering. We present recommended practices for software engineering case studies as well as empirically derived and evaluated checklists for researchers and readers of case study research.

Michael Pinedo

M. Phadke

J. Duffie, W. Beckman, J. Mcgowan

A. Chopra

W. Dally, J. Poulton

J. Castro, Manuel Kolp, J. Mylopoulos

J. Estefan

W. Pedrycz, F. Gomide

M. Schumacher, E. Fernández, D. Hybertson et al.

Nei Pereira Junior

Lignocellulosic biomass is one of the most abundant renewable carbon resources available, currently used predominantly for energy generation through direct combustion, yet still underutilized as a feedstock for higher-value biochemical conversion. Its structural complexity and intrinsic recalcitrance continue to challenge efficient biological processing. Overcoming these barriers requires an integrated understanding of plant cell-wall architecture, pretreatment chemistry, enzymatic mechanisms, and process engineering. This review provides a clear and conceptually grounded synthesis of these elements, illustrating how they converge to enable the development of second-generation (2G) lignocellulosic biorefineries. This review examines the hierarchical organization of cellulose, hemicelluloses, and lignin; the principles and performance of modern pretreatment technologies; the synergistic action of cellulolytic systems, including lytic polysaccharide monooxygenases (LPMOs) and non-hydrolytic proteins such as swollenins; advances in C5/C6 sugar fermentation; and emerging strategies for lignin upgrading. In addition to a comprehensive analysis of the literature, representative industrial and experimental case studies reported in the literature are discussed to illustrate practical process behavior and design considerations. By integrating mechanistic insight with industrially relevant examples, this review highlights the technical feasibility, current maturity, and remaining challenges of lignocellulosic biorefineries, underscoring their strategic role in enabling a competitive, low-carbon bioeconomy.

B.S. Prashanth, Manoj Kumar, Ariful Hoque et al.

The development of online banking has brought about an increase in fraudulent operations, which is a major problem for banks. This study delves into the urgent requirement for interpretable, scalable, and top-notch fraud detection systems by using TabNet, an adaptable deep learning framework, on a Kaggle dataset consisting of actual bank transactions in India. Maximizing operational risk management by improving the accuracy of transaction anomaly detection and ensuring regulatory compliance through transparent models is the goal.We utilize a supervised learning pipeline that incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to ensure that classes are balanced. Subsequently, we conduct thorough exploratory data analysis (EDA) to identify patterns of fraud, both during specific times and across behaviors. On this dataset, five different deep learning architectures are tested: DNN, GRU, LSTM, CNN1D, and TabNet. Assessment of predictive performance was carried out using a 3-fold cross-validation framework. With a ROC-AUC of 0.9739 and an accuracy of 97.39 %, TabNet considerably outperformed the competition. The method of sparse feature selection used improved interpretability, generalized better on tabular data, and produced fewer false positives and negatives.Critical insights for operational fraud detection systems and a contribution to the broader literature on explainable AI (XAI) in financial decision-making are offered by the findings. Goals 8 and 16 of the Sustainable Development Agenda are supported by this study, which promotes inclusive economic growth and institutional transparency. Supporting strong, policy-compliant, and interpretable decision-support systems, it also offers practical use for real-time implementation in banking infrastructure.

Kecheng Liu

S. Vijayan, Chiranji Lal Chowdhary

Abstract The agricultural industry significantly relies on autonomous systems for detecting and analyzing rice diseases to minimize financial and resource losses, reduce yield reductions, improve processing efficiency, and ensure healthy crop production. Advances in deep learning have greatly enhanced disease diagnostic techniques in agriculture. Accurate identification of rice plant diseases is crucial to preventing the severe consequences these diseases can have on crop yield. Current methods often struggle with reliably diagnosing conditions and detecting issues in leaf images. Previously, leaf segmentation posed challenges, and while analyzing complex disease stages can be effective, it is computationally intensive. Therefore, segmentation methods need to be more accurate, cost-effective, and reliable. To address these challenges, we propose a hybrid bio-inspired algorithm, named the Hybrid WOA_APSO algorithm, which merges Adaptive Particle Swarm Optimization (APSO) with the Whale Optimization Algorithm (WOA). For disease classification in rice crops, we utilize a Convolutional Neural Network (CNN). Multiple experiments are conducted to evaluate the performance of the proposed model using benchmark datasets (Plantvillage), with a focus on feature extraction, segmentation, and preprocessing. Optimizing feature selection is a critical factor in enhancing the classification algorithm’s accuracy. We compare the accuracy, sensitivity, and specificity of our model against industry-standard techniques such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and conventional CNN models. The experimental results indicate that the proposed hybrid approach achieves an impressive accuracy of 97.5% (Refer Table 8), which could inspire further research in this field.

GAO Xin, FENG Shijie, ZHANG Lianqing

［Objective］ By establishing a numerical seepage analysis model that aligns with real drainage systems and introducing the concept of a ′virtual permeability coefficient′ for secondary lining, the objective is to delve into the correlation between numerical methods and theoretical formulas, with expectation to leverage the efficiency and practicality of theoretical formulas in predicting external water pressure. ［Method］ Based on the principle of equivalent stable drainage volume in underwater tunnels, the concept of a ′virtual permeability coefficient′ for the secondary lining is introduced. On this basis, key factors, including the spacing of circumferential drainage blind pipes, the thickness of geotextiles, and their permeability coefficients, are selected as primary research factors. By adjusting these factors, multiple numerical seepage analysis models consistent with real drainage systems are established. ［Result & Conclusion］ The actual external water pressure acting on the secondary lining exhibits significant spatial distribution characteristics. Longitudinally, the variation in external water pressure displays periodic fluctuations corresponding to the spacing of circumferential drainage blind pipes. Circumferentially, the closer the position is to the longitudinal drainage blind pipe, the lower the external water pressure, with maximum circumferential water pressure occurring at the arch vault, followed by the inverted arch, and the smallest pressure on sidewalls. The reduction coefficients of external water pressure calculated with theoretical formulas are generally smaller than those derived from numerical methods. The stronger the drainage capacity of the design parameters, the smaller the difference between the two calculation results. The reduction coefficient consistently follows a decreasing trend from the vault to the invert to the sidewalls. When applying theoretical formulas directly in quantitative engineering design, it is necessary to introduce a comprehensive correction factor greater than 1.0 to ensure engineering safety. The value of comprehensive correction factor should be determined based on the specific structural location, with zones divided by the sidewalls. For the upper structure, a range of 1.48-1.97 is recommended, while a proper range of 1.21-1.39 for the lower structure

Felipe Baesler, Oscar Cornejo, Carlos Obreque et al.

This study presents a novel approach that integrates Discrete Event Simulation (DES) with Design of Experiments (DOE) techniques, framed within a stochastic optimization context and guided by a multi-objective goal programming methodology. The focus is on enhancing the operational efficiency of an emergency department (ED), illustrated through a real-world case study conducted in a Chilean hospital. The methodology employs Response Surface Methodology (RSM) to explore and optimize the impact of four critical resources: physicians, nurses, rooms, and radiologists. The response variable, formulated as a goal programming function, captures the aggregated patient flow time across four representative care tracks. The optimization process proceeded iteratively: early stages relied on linear approximations to identify promising improvement directions, while later phases applied a central composite design to model nonlinear interactions through a quadratic response surface. This progression revealed complex interdependencies among resources, ultimately leading to a local optimum. The proposed approach achieved a 50% reduction in the aggregated objective function and improved individual patient flow times by 7% to 26%. Compared to traditional metaheuristic methods, this simulation–optimization framework offers a computationally efficient alternative, particularly valuable when the simulation model is complex and resource-intensive. These findings underscore the value of combining simulation, RSM, and multi-objective optimization to support data-driven decision-making in complex healthcare settings. The methodology not only improves ED performance but also offers a flexible and scalable framework adaptable to other clinical environments seeking resource optimization and operational improvement.

Siyuan Liu, Yingchao Fan, Qi Hu et al.

Hyperspectral image (HSI) has more spectral information than conventional images, which helps to distinguish targets in a complex scene more accurately. However, HSI typically has a low spatial resolution, which limits their application scenarios. To achieve high-resolution HSI, we propose a spectral and spatial multiscale coupling fusion model (SSMSFuse) for hyperspectral and multispectral image (MSI). SSMSFuse couples the spatial information of MSI and the spectral information of HSI at multiscales by means of a two-branch network structure, thus obtaining the fused images with high spatial and spectral resolution. SSMSFuse consists of two branches, namely the spatial embedding network (Spa-Net) and the spectral embedding network (Spe-Net). Spa-Net is constructed using a multiscale convolutional neural network to better mine multilevel spatial features from MSI. Spe-Net is constructed using self-attention, which can model the long-distance spectral dependencies of HSI to better extract spectral information from HSI. Finally, to achieve interactive coupling of dual-branch information, we designed a spatial–spectral guidance fusion block to fuse features at different scales to avoid loss of spatial and spectral details. Experiments are carried out on four public datasets, and the results show that the proposed method can effectively improve the objective indicators of the fusion results, such as the peak signal to noise ratio, which is increased by 1.36%, and the root mean square error, which is increased by 9.72% on the CAVE dataset, and satisfactory subjective results are also obtained.

Jürgen Soom, Isabel Boavida, Renan Leite et al.

The need for efficient approaches to track and assess fish behavior in rivers impacted by hydropeaking is increasing. Nonetheless, employing an automated camera system for underwater monitoring requires that the algorithms function under highly variable environmental conditions, which affect the ability to detect and assess fish size. Additionally, there is a lack of openly accessible freshwater fish classification and size estimation datasets. To address these limitations, we propose a binocular underwater fish monitoring system capable of real-time fish detection and size estimation. The system was deployed and tested over one week in two Portuguese rivers affected by hydropeaking. The week-long analysis also provided new insights regarding wild fish behavior in rivers affected by hydropeaking. Results indicate that hydropeaking strongly influences how fish may use instream flow refuges during hydropeaking. Fish were less frequently detected in the flow refuge during peak flow events, suggesting that the flow conditions created habitat instability and difficulty accessing the flow refuge. In contrast, fish in the non-hydropeaking river consistently used refuge areas, reinforcing their importance as shelter during natural flow variations. This study demonstrates the potential of a computer vision-based pipeline for real-time, fully automated fish monitoring of hydropeaking’s impacts on riverine fish. Additionally, we provide PTFish, an open dataset with 18,523 manually annotated frames featuring infrared and color video frames. These findings emphasize that automated, camera-based solutions for hydropeaking monitoring can be used to develop evidence-based mitigation strategies to sustain fish populations in rivers impacted by hydropeaking.