In the engineering of complex technical systems, Systems Engineering (SE) is a key approach that is becoming increasingly relevant in more and more industries due to the ever-increasing complexity of systems. Over the decades of practical application and research, various specializations and forms of the Systems Engineering approach have developed, but there has so far been a lack of an overarching context and positioning in meaningful stages for the introduction of Systems Engineering in companies. For this reason, this research will systematize common Systems Engineering approaches and bring them together in a stage model for Systems Engineering. Based on a systematic literature review, use cases are identified for each approach and stage, which support companies in selecting an approach suitable for their own organization.
ABSTRACT Artificial intelligence and machine learning (AI/ML) rapidly transform systems by providing autonomous capabilities. This new class of systems can become a constituent system in a system of systems (SoS) to evolve it into a system of autonomous systems (SoAS). SoAS is fraught with new systems engineering (SE) challenges for architecture development, integration, testing, and evaluation that originate from the level of autonomy (LoA). The LoA refers to the level of autonomous capabilities of a system depending on its AI/ML technology. This paper examines SoAS architecture and integration challenges such as interface compatibility, safety, and security. We propose a model‐based systems engineering (MBSE) method for architecture development that falls under the system engineering for AI (SE4AI) umbrella, where SE principles are tailored to accommodate challenges posed by the integration of autonomy. The proposed method builds upon the object‐oriented systems engineering method (OOSEM) and modifies it to facilitate autonomy integration by leveraging the MBSE SWOT (i.e., Strength , Weakness , Opportunity , Threat ) analysis and SoAS taxonomy. It also tailors the unified architecture framework (UAF) to develop SoAS architectures with varying LoAs. This study leads to generating necessary evaluation data for a trade study and selecting an architecture with the most suitable LoA. We also present a conceptual example of a search‐and‐rescue SoS to demonstrate the implementation and effectiveness of the proposed method in investigating the evolution of LoA in constituent systems.
The automatic identification of P-wave arrival times in microseismic signals is crucial for the intelligent monitoring and early warning of dynamic hazards in coal mines. Traditional methods suffer from low accuracy and poor stability due to complex underground geological conditions and substantial noise interference. This paper proposes a microseismic P-wave arrival time automatic picking model that integrates physical constraints with a deep learning architecture. This study trained and optimized the model using a high-quality, manually labeled dataset. A systematic comparison with the AR picker algorithm and the short-term–long-term average ratio method revealed that this model achieved a precision of 96.60%, a recall of 90.59%, and an F1 score of 93.50% on the test set, with a P-wave arrival time-picking error of less than 20 ms. The average arrival time error was only 5.49 ms, significantly outperforming traditional methods. In cross-mining area generalization tests, the model performed excellently in two mining areas with consistent sampling frequencies (1000 Hz) and high signal-to-noise ratios, demonstrating good engineering transferability. However, its performance decreased in a mining area with a higher sampling rate and stronger noise, indicating its sensitivity to data acquisition parameters. This study developed a high-precision, robust, and potentially cross-domain adaptive model for automatically picking microseismic P-wave arrival times. This model provides support for the automation, precision, and intelligence of coal mine microseismic monitoring systems and has significant practical value in promoting real-time early warning and risk prevention for mine dynamic hazards.
A multi-target robust observation method for satellite constellations based on hypergraph algebraic connectivity and observation precision theory is proposed to address the challenges posed by the surge in space targets and system failures. First, a precision metric framework is constructed based on nonlinear batch least squares estimation theory, deriving the theoretical precision covariance through cumulative observation matrices to provide a theoretical foundation for tracking accuracy evaluation. Second, multi-satellite collaborative observation is modeled as an edge-dependent vertex-weighted hypergraph, enhancing system robustness by maximizing algebraic connectivity. A constrained simulated annealing (CSA) algorithm is designed, employing a precision-guided perturbation strategy to efficiently solve the optimization problem. Simulation experiments are conducted using 24 Walker constellation satellites tracking 50 targets, comparing the proposed method with greedy algorithm, CBBA, and CSA-bipartite Graph methods across three scenarios: baseline, maneuvering, and failure. Results demonstrate that the CSA-hypergraph method achieves 0.089 km steady-state precision in the baseline scenario, representing a 41.4% improvement over traditional methods; in maneuvering scenarios, detection delay is reduced by 34.3% and re-achievement time is decreased by 47.4%; with a 30% satellite failure rate, performance degradation is only 9.8%, significantly outperforming other methods.
Programming education has become increasingly vital within global K–12 curricula, and large language models (LLMs) offer promising solutions to systemic challenges such as limited teacher expertise and insufficient personalized support. Adopting a human-centric and systems-oriented perspective, this study employed a six-week quasi-experimental design involving 103 Grade 7 students in China to investigate the effects of instruction supported by the iFLYTEK Spark model. Results showed that the experimental group significantly outperformed the control group in programming performance, cognitive interest, and programming self-efficacy. Beyond these quantitative outcomes, qualitative interviews revealed that LLM-assisted instruction enhanced students’ self-directed learning, a sense of real-time human–machine interaction, and exploratory learning behaviors, forming an intelligent human–AI learning system. These findings underscore the integrative potential of LLMs to support competence, autonomy, and engagement within digital learning systems. This study concludes by discussing the implications for intelligent educational system design and directions for future socio-technical research.
Abstract Robust and trusted digital human representations are necessary to successfully account for human considerations in model‐based systems engineering (MBSE). Multiple domains and modeling frameworks leverage verification, validation, and accreditation (VV&A) processes to characterize when and under what conditions a model is valid to establish credibility. A literature review was completed on mathematical, physics‐based, software development, discrete event simulation, agent‐based, system dynamics, and MBSE models with the goal of proposing a process for performing VV&A on digital engineering (DE) and MBSE models for sociotechnical systems. However, this research also revealed the need for a broader framework to characterize the risk associated with using these models for making high‐consequence decisions. While accomplishing the literature review, another approach to building credibility was identified that is used heavily in the financial industry, namely model risk management (MRM). This process is extended by leveraging MRM approaches from within the financial community to propose a framework for sociotechnical model users to characterize the risk of using MBSE models to make programmatic decisions. The primary contribution of this work is to document a meta‐analysis of model VV&A while proposing an alternative approach to characterizing and communicating credibility that was discovered during this analysis. This approach could be a viable option for ensuring the credibility of human systems integration in MBSE models.
Abstract Background Pulsatilla chinensis (PC) is a traditional Chinese medicine (TCM) known for its beneficial activities. It has been historically used to treat dysentery, vaginal trichomoniasis, bacterial infections, and malignant tumors. The therapeutic potential of PC in the management of hypercholesterolemia remains largely unexplored. Methods A high-throughput screening based on high-throughput sequencing was conducted in HepG2 cells to construct gene expression profiles for several hundred TCMs. In vivo evaluation of the efficacy of PC was performed using rats with hypercholesterolemia. Transcriptome analysis was carried out on PC-treated rat livers and HepG2 cells to investigate the mechanism of action of PC in vitro. The findings were further validated using RT-qPCR and western blot techniques. Results PC was identified as similar to Rhizoma Coptidis based on signature genes related to metabolism. Administration of PC via gavage in rats with hypercholesterolemia for 11 weeks resulted in substantially reduced serum total cholesterol and low-density lipoprotein (LDL) cholesterol and ameliorated fatty liver. Transcriptome analysis revealed that PC regulated various pathways associated with lipid metabolism. The LDL receptor (LDLR), a key player in cholesterol metabolism, was upregulated by PC both in vivo and in vitro. It was discovered that PC achieved this upregulation by activating extracellular regulated protein kinase (ERK) signaling in HepG2 cells. To uncover the major bioactive components responsible for the anti- hypercholesterolemia effect of PC, two major saponins, named Pulsatilla saponin D (PCD) and PC anemoside B4 (PCB4), were assessed. PCD, but not PCB4, was identified as the active ingredient responsible for the upregulation of LDLR by PC. Conclusion These findings demonstrated that PC acts as an antihypercholesterolemic agent by upregulating LDLR in an ERK-dependent manner and holds potential in the treatment of hypercholesterolemia.
Significant advances in reverse engineering and additive manufacturing have the potential to provide a faster, accurate, and cost-effective process chain for preserving, analyzing, and presenting forensic impression evidence in both 3D digital and physical forms. The objective of the present research was to evaluate the capabilities and limitations of five 3D scanning technologies, including laser scanning (LS), structured-light (SL) scanning, smartphone (SP) photogrammetry, Microsoft Kinect v2 RGB-D camera, and iPhone’s LiDAR (iLiDAR) Sensor, for 3D reconstruction of 3D impression evidence. Furthermore, methodologies for 3D reconstruction of latent impression and visible 2D impression based on a single 2D photo were proposed. Additionally, the FDM additive manufacturing process was employed to build impression evidence models created by each procedure. The results showed that the SL scanning system generated the highest reconstruction accuracy. Consequently, the SL system was employed as a benchmark to assess the reconstruction quality of other systems. In comparison to the SL data, LS showed the smallest absolute geometrical deviations (0.37 mm), followed by SP photogrammetry (0.78 mm). In contrast, the iLiDAR exhibited the largest absolute deviations (2.481 mm), followed by Kinect v2 (2.382 mm). Additionally, 3D printed impression replicas demonstrated superior detail compared to Plaster of Paris (POP) casts. The feasibility of reconstructing 2D impressions into 3D models is progressively increasing. Finally, this article explores potential future research directions in this field.
Anderson Silva, Guilherme Lopes, Marcos Corazza
et al.
This study aimed to investigate the liquid–liquid equilibrium (LLE) behavior of sesame fatty acid ethyl ester (FAEE) and methyl ester (FAME) in combination with glycerol and the co-solvents ethanol and methanol. FAEE and FAME were produced through the transesterification of mechanically extracted and purified sesame oil, using potassium hydroxide (KOH) as a homogeneous base catalyst. The reactions were conducted in ethanol and methanol to produce FAEE and FAME, respectively. Post-reaction, the products were separated and purified, followed by an analysis of the LLE behavior at 313.15 K and 323.15 K under atmospheric pressure (101.3 kPa). The experimental process for the miscibility analysis utilized a jacketed glass cell adapted for this study. Miscibility limits or binodal curves were determined using the turbidity-point method. Tie lines were constructed by preparing mixtures of known concentrations within the two-phase region, which allowed the phases to separate after agitation. Samples from both phases were analyzed to determine their composition. This study revealed that higher temperatures promoted greater phase separation and enhanced the biodiesel purification process. The NRTL model effectively correlated the activity coefficients with the experimental data, showing good agreement, with a root-mean-square deviation of 3.5%. Additionally, the data quality was validated using Marcilla’s method, which yielded an R<sup>2</sup> value close to 1. Attraction factors and distribution coefficients were also calculated to evaluate the efficiency of the co-solvents as extraction agents. The findings indicated higher selectivity for methanol than for ethanol, with varying degrees of distribution among the co-solvents. These results offer significant insights into enhancing biodiesel production processes by considering the effects of co-solvents on the LLE properties of mixtures, ultimately contributing to more efficient and cost-effective biodiesel production.
Mohamed Kamal Cherier, Maamar Hamdani, Ehsan Kamel
et al.
Opaque surfaces, such as walls, are well-known for their significant contributions to heat loss and energy demands in buildings. However, transparent surfaces, such as windows, are equally critical to a building's energy performance. The design of these transparent elements requires a careful balance of various factors, including window size, glazing type, and orientation, each of which plays a pivotal role in enhancing energy efficiency. This study explores the optimization of these factors during the design process, emphasizing their impact on the overall building performance.This research evaluates the potential energy savings in a building archetype representative of the Algerian building stock. Utilizing the EnergyPlus simulation tool, the study conducted 1152 simulations on a baseline model to generate a comprehensive dataset detailing the building's energy demands for heating and cooling across various climatic conditions. The findings reveal that annual energy savings for this type of housing essentially depend on its climatic zone and can range from 6.92 % for a hot semi-arid climate (Bsh) to reach a maximum of 9.75 % in a cold semi-arid climate (Bsk), a window-to-wall ratio (WWR) of 60 % typically maximizes energy efficiency, low-E glazing proved most effective in most cases, although regions needing significant solar protection favored alternative glazing types. Optimal window orientation generally trends Eastward, except in regions where southern exposure better supports solar management, highlighting the complex relationship between architectural design choices and energy efficiency.
Srinadh Kodali, Chris Flores-Lopez, Isabelle Lobdell
et al.
Global herbicide-resistant weed populations continue rising due to selection pressures exerted by herbicides. Despite this, herbicides continue to be farmers’ preferred weed-control method due to cost and efficiency relative to physical or biological methods. However, weeds developing resistance to herbicides not only challenges crop production but also threatens ecosystem services by disrupting biodiversity, reducing soil health, and impacting water quality. Our objective was to develop a simulation model that captures the feedback between weed population dynamics, agricultural management, profitability, and farmer decision-making processes that interact in unique ways to reinforce herbicide resistance in weeds. After calibration to observed data and evaluation by subject matter experts, we tested alternative agronomic, mechanical, or intensive management strategies to evaluate their impact on weed population dynamics. Results indicated that standalone practices enhanced farm profitability in the short term but lead to substantial adverse ecological outcomes in the long term, indicated by elevated herbicide resistance (e.g., harm to non-target species, disrupting natural ecosystem functions). The most management-intensive test yielded the greatest weed control and farm profit, albeit with elevated residual resistant seed bank levels. We discuss these findings in both developed and developing-nation contexts. Future work requires greater connectivity of farm management and genetic-resistance models that currently remain disconnected mechanistically.
Uncertainty of target motion, limited perception ability of onboard cameras, and constrained control have brought new challenges to unmanned aerial vehicle (UAV) dynamic target tracking control. In virtue of the powerful fitting ability and learning ability of the neural network, this paper proposes a new deep reinforcement learning (DRL)-based end-to-end control method for UAV dynamic target tracking. Firstly, a DRL-based framework using onboard camera image is established, which simplifies the traditional modularization paradigm. Secondly, neural network architecture, reward functions, and soft actor-critic (SAC)-based speed command perception algorithm are designed to train the policy network. The output of the policy network is denormalized and directly used as speed control command, which realizes the UAV dynamic target tracking. Finally, the feasibility of the proposed end-to-end control method is demonstrated by numerical simulation. The results show that the proposed DRL-based framework is feasible to simplify the traditional modularization paradigm. The UAV can track the dynamic target with rapidly changing of speed and direction.
Josiah Cherian Chekotu, Russell Goodall, David Kinahan
et al.
In this work, nitinol samples were produced via Laser Powder Bed Fusion (L-PBF) in the horizontal and vertical orientations with systematic variations in laser power, scan speed and hatch spacing parameters. Increased density was positively correlated with increased laser power, scan speed and hatch spacing for the horizontally built samples but not for the vertically built samples. A smaller difference in the average temperature within a printed layer, associated with the vertically built samples, was linked with reduced porosity and reduced porosity variability between samples. Control of the L-PBF parameters was found to allow control of the resulting part chemical composition which also directly affected phase transformation temperatures, and related phase structures. The laser process parameters were found to have a significant effect (p < 0.01) on the martensite start/finish temperature, austenite start/finish temperatures, and the total temperature span. The volumetric energy density was also found to have a direct correlation with both the cooling (r = 0.52) and heating (r = 0.53) enthalpies, which was found to be due to increased nickel evaporation. Such control of phase change properties afforded from L-PBF is important for many of the end applications for nitinol components including within the energy and precision actuation sectors.
Materials of engineering and construction. Mechanics of materials
With the development of knowledge as economy, knowledge become the asset for the organizations. In this context, it is very essential organizational strategy to cop up with environmental changes. order to survive and compete effectively in the global environment. Research purpose of the study is to examine the relationship between knowledge management and job satisfaction among the university librarians of the Punjab, Pakistan. For data collection process survey research method was used. On the basis of literature review, a questionnaire was designed for data collection. The analyzed data showed a good relationship of the research main constructs between satisfaction of librarians’ jobs and different aspects of knowledge management. It was evaluated that there was a good relation of knowledge acquisition and knowledge sharing with job satisfaction. There is positive impact of knowledge management process on an organization and help improve efficiency and effectiveness. Beside this, job satisfaction is a important aspect for organizational success. It plays a significant role in achieving the organizational goals. The study concluded that both job satisfaction and KM draw a significant task in increasing the services availability, efficiency, effectiveness, productivity and performance of the professionals. Academic libraries and other organizations can use the findings of this study to improve their practices. This might help to increase innovation, productivity, opportunity and competitive advantages.
Information theory, Management information systems
Sonia F. Roberts, Elana Pyfrom, Jacob A. Hoffman
et al.
This study reviews literature on racially equitable admissions practices relevant to graduate programs in STEM. Graduate Record Exam (GRE) scores correlate more strongly with race, gender, and socioeconomic status than performance metrics for research during or after graduate school. Structural changes to admissions processes that can improve equity of admissions decisions and reduce correlations between admissions decisions and demographic data include using holistic review or composite scores that quantize more components of an application, removing hard limits on course requirements, admitting students as a cohort instead of to individual faculty sponsors, and diversifying admissions committees. Some alternative scoring methods attempt to measure personality traits, but performing these measurements during admissions may present difficulties. Bridge programs—whether they are implemented as collaborations with a minority-serving institution, a personalized educational program for each student admitted to a program, or a stand-alone program before the doctoral degree program—may be able to improve both recruitment and retention of students with underrepresented racial and ethnic identities in their field of study. Finally, financial barriers to applications can disproportionately affect underrepresented applicants due to systemic racism. We end with recommendations for graduate programs to improve equity in admissions processes.
Heavy-duty hybrid electric vehicles and marine vessels need a sizeable electric energy storage system (ESS). The size and energy management strategy (EMS) of the ESS affects the system performance, cost, emissions, and safety. Traditional power-demand-based and fuel-economy-driven ESS sizing and energy management has often led to shortened battery cycle life and higher replacement costs. To consider minimizing the total lifecycle cost (LCC) of hybrid electric propulsion systems, the battery performance degradation and the life prediction model is a critical element in the optimal design process. In this work, a new Li-ion battery (LIB) performance degradation model is introduced based on a large set of cycling experiment data on LiFePO<sub>4</sub> (LFP) batteries to predict their capacity decay, resistance increase and the remaining cycle life under various use patterns. Critical parameters of the semi-empirical, amended equivalent circuit model were identified using least-square fitting. The model is used to calculate the investment, operation, replacement and recycling costs of the battery ESS over its lifetime. Validation of the model is made using battery cycling experimental data. The new LFP battery performance degradation model is used in optimizing the sizes of the key hybrid electric powertrain component of an electrified ferry ship with the minimum overall LCC. The optimization result presents a 12 percent improvement over the traditional power demand-driven hybrid powertrain design method. The research supports optimal sizing and EMS development of hybrid electric vehicles and vessels to achieve minimum lifecycle costs.
S. Venkataramana, B.V.D.S. Sekhar, Bh. Raju
et al.
In modern secure Wireless Sensor Networks (WSN), the sensor-nodes need extra energy owing to secure transmission ofperceived information. So the energy-utilization of sensor-node should calculate while transfer the sensed-attributes securelyto network. In this experimentation, we are proposing a revised Low Energy Adaptive Clustering Hierarchy (LEACH)protocol as LEATCH along secure information transmission (privacy and node authentication) in various levels using Qualityof Protection Modeling Language (QoPML), which balance the Security-Energy trade-offs. This research experimentallyanalyzes the impact of data privacy, authentication operations on energy-utilization at sensor-node level while applying aLEACH & LEATCH. The obtained outcomes indicate the optimized LEATCH is outperforming correlated to the basic Leachwith respect to minimal energy-utilization, time efficiency and expands life-time of modern-secure-WSNs.
Microbial fuel cells (MFCs) are eco-friendly bio-electrochemical reactors that use exoelectrogens as biocatalyst for electricity harvest from organic biomass, which could also be used as biosensors for long-term environmental monitoring. Glucose and xylose, as the primary ingredients from cellulose hydrolyzates, is an appealing substrate for MFC. Nevertheless, neither xylose nor glucose can be utilized as carbon source by well-studied exoelectrogens such as Shewanella oneidensis. In this study, to harvest the electricity by rapidly harnessing xylose and glucose from corn stalk hydrolysate, we herein firstly designed glucose and xylose co-fed engineered Klebsiella pneumoniae-S. oneidensis microbial consortium, in which K. pneumoniae as the fermenter converted glucose and xylose into lactate to feed the exoelectrogens (S. oneidensis). To produce more lactate in K. pneumoniae, we eliminated the ethanol and acetate pathway via deleting pta (phosphotransacetylase gene) and adhE (alcohol dehydrogenase gene) and further constructed a synthesis and delivery system through expressing ldhD (lactate dehydrogenase gene) and lldP (lactate transporter gene). To facilitate extracellular electron transfer (EET) of S. oneidensis, a biosynthetic flavins pathway from Bacillus subtilis was expressed in a highly hydrophobic S. oneidensis CP-S1, which not only improved direct-contacted EET via enhancing S. oneidensis adhesion to the carbon electrode but also accelerated the flavins-mediated EET via increasing flavins synthesis. Furthermore, we optimized the ratio of glucose and xylose concentration to provide a stable carbon source supply in MFCs for higher power density. The glucose and xylose co-fed MFC inoculated with the recombinant consortium generated a maximum power density of 104.7 ± 10.0 mW/m2, which was 7.2-folds higher than that of the wild-type consortium (12.7 ± 8.0 mW/m2). Lastly, we used this synthetic microbial consortium in the corn straw hydrolyzates-fed MFC, obtaining a power density 23.5 ± 6.0 mW/m2.
Ross M. McKenzie, Jamie O. Roberts, Mohammed E. Sayed
et al.
Peristaltic conveyance can be used for the sorting and transport of delicate and nonrigid objects such as meat or soft fruit. The non‐linearity and stochastic behavior of peristaltic systems make them difficult to control. Optimizing controllers using machine learning represents a promising path to effective peristaltic control but currently, there is no suitable simulated model of a peristaltic table in which to run these optimizations. A simple, simulated model of a peristaltic conveyor that can be used for optimizing peristaltic control on a variety of peristaltic tables is presented. This simulator is demonstrated through a limited control problem evaluated on our real‐world system that is built for peristaltic conveyance. This simulator is available as the python package PeriSim so that it can be used by the robotics community for peristaltic control development.
Computer engineering. Computer hardware, Control engineering systems. Automatic machinery (General)