Design Of Systems Research Articles

"Not my Priority:" Ethics and the Boundaries of Computer Science Identities in Undergraduate CS Education

Researchers in the CSCW community have long problematized the separation of social and ethical considerations from design work. Despite increasing attention to tech ethics and ethics education, however, computer scientists' sense of ethical responsibility remains of concern. This paper offers insights on how this boundary between tech and ethics is maintained and reinforced for students as they develop their identities as computer scientists. Drawing on interviews with eight undergraduate computer science (CS) students at McGill University, we explore the role that ethics play in the legitimate peripheral participation of students inside and outside their formal education. We found that while individual opinions on the importance of ethics varied, students agreed that ethics are not valued or rewarded in their education, extracurriculars, or future work prospects. We describe how placing ethics outside the boundary of computing acts as a form of occupational closure, excluding both important multidisciplinary work and marginalized bodies. We argue that in order to promote ethical practice in the design of CSCW systems, we must make it in the interest of future designers to learn socially grounded ethics. This requires that designers, researchers, and future employers actively reshape the boundaries of computing by asserting social and ethical considerations as values of computing and design.

Existence conditions for functional ODE observer design of descriptor systems revisited

This paper is devoted to the problem of designing functional observers for linear time-invariant (LTI) descriptor systems. The observers are realized by using state–space systems governed by ordinary differential equations (ODEs). Available existence results for functional ODE observers in the literature are extended by introducing new and milder sufficient conditions. These conditions are purely algebraic and provided directly in terms of the system coefficient matrices. The proposed observer has an order less than or equal to the dimension of the functional vector to be estimated. The observer parameter matrices are obtained by using simple matrix theory, and the design algorithm is illustrated by numerical examples.

Understanding Farmers' Data Collection Practices on Small-to-Medium Farms for the Design of Future Farm Management Information Systems

Farm Management Information Systems (FMIS) integrate data from a variety of sources, including sensors, for the purpose of enabling farmers to interpret past activity and predict future performance. FMIS is traditionally designed for and used by large farms, given their capital and need for automation and scale-up. This paper examines the current data collection practices on small and medium farms so that FMIS systems can be better designed to their needs. Our empirical research comprises interviews conducted during 10 farm visits. Our semi-structured interviews incorporated questions about daily activities, points of decision-making, data sharing, and incentives for data collection. We analyzed the interviews by focusing on possible obstacles to adopting expanding digital data collection practices and how expanded data collection might help fulfill farmers' goals and motivations. We found that farmers use their own bespoke data collection techniques instead of or in parallel to more formalized methods and often hold key observations and hypotheses in their heads rather than committing them to any data collection system at all. Key barriers to FMIS adoption include technology skepticism, technical hurdles, lack of support, and self-doubt in technical skills. Based on this empirical work and analysis, we recommend that FMIS systems can best address the needs of small and medium farms by 1) accounting for the farmers' different approaches to memorizing vs. storing data, 2) integrating rather than trying to replace existing practices, and 3) considering the economic and political motivations driving farm decision-making and practices.

"That comes with a huge career cost:" Understanding Collaborative Ideation Experiences of Disabled Professionals

Collaborative ideation plays a vital role in driving creativity and innovation across various professional and educational contexts. This study investigates the experiences of disabled individuals within the collaborative ideation process, specifically examining their utilization of digital whiteboarding tools. Through interviews with 19 professionals and academics with disabilities, alongside a thematic analysis of online forum posts for two popular digital whiteboarding platforms (Miro and Figma), we delve into the access barriers encountered by disabled individuals and the strategies they employ to create access in collaborative ideation. Our findings illuminate the multifaceted nature of access barriers, encompassing issues such as inaccessible visual features, technology-induced discomfort, unstructured nature of freeform content, and complex communication setups. Furthermore, we uncover the intricate dynamics involved in negotiating diverse access needs and conflicts within teams involving people with different disabilities. Through this analysis, we highlight tensions around proficiency with inaccessible technologies stemming from ableist standards of professional success and discuss the implications of our findings for the design of accessible collaborative ideation systems.

Pectin-based emulsion gels prepared by acidic and ionotropic methods for intestinal targeted delivery in vitro

Considering the different potential roles of the small intestine and colon in the functions of bioactive components, this study was performed to design a pectin-based controlled delivery system targeting different intestinal sections according to emulsification gelation methods (i.e., pectin gelated with acidified ethanol, iron, and calcium: PE-H, PE-Fe, and PE-Ca, respectively). The properties and targeted delivery of pectin-based emulsion gels were compared. All emulsion gels exhibited similar diameter and acceptable roundness with naringin encapsulation efficiency of approximately 90%. Cross-sections of PE-H, PE-Fe, and PE-Ca exhibited loose networks, large voids, and dense structures, resulting in diverse time dependence during digestion. Furthermore, PE-Ca showed the fastest completion of gelation and exhibited excellent hardness properties, suggesting that it would have the strongest anti-digestive effect. PE-Fe showed time- and pH-dependent swelling behavior in simulated intestinal fluid. Simulated digestion experiments indicated burst release and slow release of naringin from PE-H and PE-Fe, respectively, due to their differences in pH responsiveness and time dependence; a similar amount of naringin was released from PE-Ca after enzyme-triggered digestion in simulated colon fluid. Overall, distinct emulsion gels achieved targeted disintegration in intestinal sites. PE-H and PE-Fe would contribute to the rapid and sustained release of naringin in the small intestine, respectively, whereas PE-Ca would contribute to naringin release in the colon. These findings provide new insights for the future design of specific delivery systems that can be used in the functional food industry; they indicate the robust potential for including pectin in emulsion-based foods with targeted functions.

Main approaches and features of the design of aircraft hydro-mechanical control systems

Main approaches and features of the design of aircraft hydro-mechanical control systems

Effect of light intensity on the survival of European spiny lobster (Palinurus elephas) larvae reared in aquaculture system

Light plays a crucial role in the growth, development, and survival of aquatic organisms. To evaluate the survival of European spiny lobster (Palinurus elephas) larvae in relation to different light intensities (26, 359, and 910 lux, equivalent to 0.35, 4.74, and 12.29 μmol·m−2·s−1, respectively), this study monitored the survival rates of reared spiny lobster from the early instar phyllosoma stages to their metamorphosis into the puerulus stage (111 days after hatching) at the three light intensities. The newly hatched larvae were cultured in 65-L Kreisel tanks with 200 individuals each (three replicates per treatment, i.e., light intensity). Phyllosoma stages I to III larvae showed no significant differences in survival rates under different light intensities. However, light intensity did have a significant impact on the survival rates of phyllosoma stages IV and V as well as the metamorphosis of phyllosoma VI into puerulus. The highest survival rates were observed under high and medium light intensity conditions. At the end of the experiment, the highest mean survival rate for stage VI, the metamorphosis into the puerulus stage, was 42.0 ± 2.52 % under high light intensity. These survival rates for reared pueruli were 84 times higher than those achieved in previous studies. The present study results show promise for the successful implementation of commercial scale rearing protocols for European spiny lobster and have implications for the design of crustacean larvae culture systems.

Optimization of the OEE indicator through meta-models’ simulation in the buffer allocation problem

Purpose: The buffer allocation problem (BAP) arises in the design of production systems; it involves analyzing and defining the optimal distribution of buffers within a production line. This paper presents a BAP formulation in a parallel series line from a cup sublimation process with unreliable operating conditions. The main objective of this study is to develop a new BAP solution proposal, considering the optimization of the OEE indicator used in Lean Manufacturing.Design/methodology/approach: The BAP was analyzed under an optimization approach from two different criteria: firstly, the maximization of the OEE indicator (Overall Equipment Effectiveness) utilized in Lean Manufacturing, as well as the maximization of the average production rate (Throughput). The case study involves unreliable operating conditions. Process times, and timeframes between failures and repairs, consider normal distribution functions. The evaluation method employed in the study includes the use of simulation meta-models built from experiment designs and production line simulations; on the other hand, the nonlinear GRG algorithm is used to solve the mathematical models.Findings: In the study carried out, it is shown that the OEE indicator can be affected when more buffers are allocated than necessary, hence it is important to calculate and establish the best configuration for them through an analysis such as the one proposed in this document.Research limitations/implications: The research is limited to a case study of an unreliable production line from a cup sublimation process.Practical implications: The proposed solution established in this study can be used in other production lines with configurations different from the one analyzed, considering the optimization criterion of the OEE indicator. Originality/value: Seeking that the allocation of buffers within the production line improves the OEE indicator is something new in the literature, therefore, the results achieved in this research become even more relevant.

THE ROLE OF BIG DATA IN SYSTEMS ENGINEERING: A REVIEW

This research review explores the transformative intersection of Big Data and Systems Engineering (SE), a convergence that leverages the vast capabilities of Big Data analytics to enhance the design, analysis, and management of complex systems. As the digital era ushers in unprecedented volumes of data, traditional systems engineering approaches are challenged to adapt, necessitating a paradigm shift towards integrating Big Data technologies. This review draws on recent scholarly contributions to highlight the implications, challenges, and innovative solutions emerging at this intersection. The integration of Big Data into Systems Engineering introduces significant opportunities for innovation. By processing and analyzing vast datasets, systems engineers can uncover hidden patterns and insights, leading to more efficient, reliable, and adaptable systems. However, this integration is not without its challenges. Information security emerges as a paramount concern, with the risk of insider attacks necessitating the development of new architectural solutions. Furthermore, the benchmarking and evaluation of Big Data systems present unique challenges due to the diversity of data and workloads involved.  To address these challenges, this review examines several key contributions to the field. It discusses a formal framework for designing information systems that handle heterogeneous data, emphasizing the role of ontological models in separating system architecture from its implementation. The review also highlights the importance of security in Big Data systems, proposing a novel architecture for detecting insider attacks through data replication. Additionally, it explores the development of BigDataBench, a benchmark suite that facilitates the comprehensive evaluation of Big Data systems and architectures. Moreover, the review delves into the design of Big Data analytics architectures, focusing on goal-oriented modeling and the resolution of obstacles to quality goal achievement. It also introduces datar, a unified framework for Big Data Management Systems, showcasing a solution that manages Big Data in a pluggable, automatic, and intelligent manner. In conclusion, the intersection of Big Data and Systems Engineering heralds a new era of system design and management. By addressing the inherent challenges and leveraging innovative solutions, this convergence holds the potential to significantly enhance the capabilities of Systems Engineering, driving forward the development of complex, data-driven systems.
 Keywords: Big Data, Systems Engineering, Information Security, Data Management Systems, Benchmarking Big Data Systems.

Artificial Intelligence for Thermal Energy Storage Enhancement: A Comprehensive Review

Abstract Thermal energy storage (TES) plays a pivotal role in a wide array of energy systems, offering a highly effective means to harness renewable energy sources, trim energy consumption and costs, reduce environmental impact, and bolster the adaptability and dependability of power grids. Concurrently, artificial intelligence (AI) has risen in prominence for optimizing and fine-tuning TES systems. Various AI techniques, such as particle swarm optimization, artificial neural networks, support vector machines, and adaptive neurofuzzy inference systems, have been extensively explored in the realm of energy storage. This study provides a comprehensive overview of how AI, across diverse applications, categorizes, and optimizes energy systems. The study critically evaluates the effectiveness of these AI technologies, highlighting their impressive accuracy in achieving a range of objectives. Through a thorough analysis, the paper also offers valuable recommendations and outlines future research directions, aiming to inspire innovative concepts and advancements in leveraging AI for TESS. By bridging the gap between TES and AI techniques, this study contributes significantly to the progress of energy systems, enhancing their efficiency, reliability, and sustainability. The insights gleaned from this research will be invaluable for researchers, engineers, and policymakers, aiding them in making well-informed decisions regarding the design, operation, and management of energy systems integrated with TES.

Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials

This paper presents a study investigating the performance of functionally graded material (FGM) annular fins in heat transfer applications. An annular fin is a circular or annular structure used to improve heat transfer in various systems such as heat exchangers, electronic cooling systems, and power generation equipment. The main objective of this study is to analyze the efficiency of the ring fin in terms of heat transfer and temperature distribution. The fin surfaces are exposed to convection and radiation to dissipate heat. A supervised machine learning method was used to study the heat transfer characteristics and temperature distribution in the annular fin. In particular, a feedback architecture with the BFGS Quasi-Newton training algorithm (trainbfg) was used to analyze the solutions of the mathematical model governing the problem. This approach allows an in-depth study of the performance of fins, taking into account various physical parameters that affect its performance. To ensure the accuracy of the obtained solutions, a comparative analysis was performed using guided machine learning. The results were compared with those obtained by conventional methods such as the homotopy perturbation method, the finite difference method, and the Runge–Kutta method. In addition, a thorough statistical analysis was performed to confirm the reliability of the solutions. The results of this study provide valuable information on the behavior and performance of annular fins made from functionally graded materials. These findings contribute to the design and optimization of heat transfer systems, enabling better heat management and efficient use of available space.

Model-free compensation learning control of asymmetric hysteretic systems with initial state learning

In this article, a model-free compensation learning control scheme is presented for asymmetric hysteretic systems to achieve high-precision output tracking, where the effects of asymmetric input hysteresis nonlinearities are denoted by the asymmetric Prandtl–Ishlinskii model (APIM). In the presented control scheme, the feedforward iterative learning technique is generalized to the control design of asymmetric hysteretic systems. To improve the robustness and dynamic performance of the existing feedforward learning control for hysteretic systems, both the feedback action and the differential term are utilized to design a discrete-time PD-P open-closed-loop learning control law for simultaneously compensating the asymmetric input hysteresis nonlinearities and the linear dynamics effects without constructing compensators based on their models. The initial state error of such a system is also considered, a modified initial state learning strategy is proposed to ensure the initial state error tends to a prescribed level with the increasing of iterations. By fully analyzing the properties of the APIM, the convergence conditions with respect to the input error, the state error, and the tracking error along the iteration domain are given. The simulation and experimental results are provided to demonstrate strong robustness and excellent tracking accuracy with the proposed model-free compensation learning control scheme.

A time-domain spectral finite element method for acoustoelasticity: Modeling the effect of mechanical loading on guided wave propagation

Ultrasonic testing techniques such as guided wave-based structural health monitoring aim to evaluate the integrity of a material with sensors and actuators that operate in situ, i.e. while the material is in use. Since ultrasonic wave propagation is sensitive to environmental conditions such as pre-deformation of the structure, the design and performance evaluation of monitoring systems in this context is a complicated task that requires quantitative data and the associated modeling effort. In our work, we propose a set of numerical tools to solve the problem of mechanical wave propagation in materials subjected to pre-deformation. This type of configuration is usually treated in the domain of acoustoelasticity. A relevant modeling approach is to consider two different problems: a quasi-static nonlinear problem for the large displacement field of the structure and a linearized time-domain wave propagation problem. After carefully reviewing the modeling ingredients to represent the configurations of interest, we propose an original combination of numerical tools that leads to a computationally efficient algorithm. More specifically, we use 3D shell elements for the quasi-static nonlinear problem and the time-domain spectral finite element method to numerically solve the wave propagation problem. Our approach can represent any type of material constitutive law, geometry or mechanical solicitation. We present realistic numerical results on 3D cases related to the monitoring of both isotropic and anisotropic materials, illustrating the genericity and efficiency of our method. We also validate our approach by comparing it to experimental data from the literature.

MRI compatible electromagnetic actuator: magneto-thermal design and optimization

Purpose The purpose of this study is to develop a magnetic resonance imaging (MRI)-safe iron-free electrical actuator for MR-guided surgical interventions. Design/methodology/approach The paper deals with the design of an MRI compatible electrical actuator. Three-dimensional electromagnetic and thermal analytical models have been developed to design the actuator. These models have been validated through 3D finite element (FE) computations. The analytical models have been inserted in an optimization procedure that uses genetic algorithms to find the optimal parameters of the actuator. Findings The analytical models are very fast and precise compared to the FE models. The computation time is 0.1 s for the electromagnetic analytical model and 3 min for the FE one. The optimized actuator does not perturb imaging sequence even if supplied with a current 10 times higher than its rated one. Indeed, the actuator’s magnetic field generated in the imaging area does not exceed 1 ppm of the B0 field generated by the MRI scanner. The actuator can perform up to 25 biopsy cycles without any risk to the actuator or the patient since he maximum temperature rise of the actuator is about 20°C. The actuator is compact and lightweight compared to its pneumatic counterpart. Originality/value The MRI compatible actuator uses the B0 field generated by scanner as inductor. The design procedure uses magneto-thermal coupled models that can be adapted to the design of a variety actuation systems working in MRI environment.

Reversible Assembly of Proteins and Phenolic Polymers for Intracellular Protein Delivery with Serum Stability.

The design of intracellular delivery systems for protein drugs remains a challenge due to limited delivery efficacy and serum stability. Herein, we propose a reversible assembly strategy to assemble cargo proteins and phenolic polymers into stable nanoparticles for this purpose using a heterobifunctional adaptor (2-formylbenzeneboronic acid). The adaptor is easily decorated on cargo proteins via iminoboronate chemistry and further conjugates with catechol-bearing polymers to form nanoparticles via boronate diester linkages. The nanoparticles exhibit excellent serum stability in culture media but rapidly release the cargo proteins triggered by lysosomal acidity and GSH after endocytosis. In a proof-of-concept animal model, the strategy successfully transports superoxide dismutase to retina via intravitreal injection and efficiently ameliorates the oxidative stress and cellular damage in the retina induced by ischemia-reperfusion (I/R) with minimal adverse effects. The reversible assembly strategy represents a robust and efficient method to develop serum-stable systems for the intracellular delivery of biomacromolecules.

Pro-Con Debate: Universal Versus Selective Continuous Monitoring of Postoperative Patients.

In this Pro-Con commentary article, we discuss use of continuous physiologic monitoring for clinical deterioration, specifically respiratory depression in the postoperative population. The Pro position advocates for 24/7 continuous surveillance monitoring of all patients starting in the postanesthesia care unit until discharge from the hospital. The strongest arguments for universal monitoring relate to inadequate assessment and algorithms for patient risk. We argue that the need for hospitalization in and of itself is a sufficient predictor of an individual's risk for unexpected respiratory deterioration. In addition, general care units carry the added risk that even the most severe respiratory events will not be recognized in a timely fashion, largely due to higher patient to nurse staffing ratios and limited intermittent vital signs assessments (e.g., every 4 hours). Continuous monitoring configured properly using a "surveillance model" can adequately detect patients' respiratory deterioration while minimizing alarm fatigue and the costs of the surveillance systems. The Con position advocates for a mixed approach of time-limited continuous pulse oximetry monitoring for all patients receiving opioids, with additional remote pulse oximetry monitoring for patients identified as having a high risk of respiratory depression. Alarm fatigue, clinical resource limitations, and cost are the strongest arguments for selective monitoring, which is a more targeted approach. The proponents of the con position acknowledge that postoperative respiratory monitoring is certainly indicated for all patients, but not all patients need the same level of monitoring. The analysis and discussion of each point of view describes who, when, where, and how continuous monitoring should be implemented. Consideration of various system-level factors are addressed, including clinical resource availability, alarm design, system costs, patient and staff acceptance, risk-assessment algorithms, and respiratory event detection. Literature is reviewed, findings are described, and recommendations for design of monitoring systems and implementation of monitoring are described for the pro and con positions.

Quantum metaphotonics: Recent advances and perspective

Quantum metaphotonics has emerged as a cutting-edge subfield of meta-optics employing subwavelength resonators and their planar structures, such as metasurfaces, to generate, manipulate, and detect quantum states of light. It holds a great potential for the miniaturization of current bulky quantum optical elements by developing a design of on-chip quantum systems for various applications of quantum technologies. Over the past few years, this field has witnessed a surge of intriguing theoretical ideas, groundbreaking experiments, and novel application proposals. This Perspective aims to summarize the most recent advancements and also provides a perspective on the further progress in this rapidly developing field of research.

The hydraulic characteristics of a subsurface flow stone biofilter for treating polluted runoff from an informal settlement

Abstract Polluted runoff from informal settlements in developing countries poses a growing challenge due to the elevated and variable nature of contaminants, particularly nutrients and pathogens, introduced to the environment. Cost-effective and scalable treatment systems with the ability to reduce nutrient and other pollutant concentrations in contaminated runoff are desirable. Biofilters are passive water treatment systems that have the potential to achieve this. The Franschhoek Water Hub, a research site for nature-based solutions, features six large biofiltration cells designed to remediate runoff from an informal settlement. Due to their large size, understanding hydraulic behaviour and validating the design proves challenging. To address this, a scaled-down version of the Water Hub's biofilters was constructed to inform design criteria for purpose-built filters. The pilot-scale subsurface flow biofilter, filled with 8–11 mm aggregate stone, had an available volume of 225 L. Pulse tracer studies conducted at various flow rates demonstrated that the system approximated plug flow behaviour. Lower flow rates resulted in deeper tracer infiltration, which is crucial for maximising the distribution of nutrients within the filter bed. This research contributes to the effective design and operation of biofiltration systems, which hold promise for addressing surface water contamination issues in resource-constrained regions.

Control and mathematical design of PV-based renewable energy systems for LCL-SR filter-based power quality improvement

Power quality challenges arise when photovoltaic (PV) systems are integrated into power networks. This research work examines the control and mathematical design of an LCL-SR (series resistance) filter for power quality improvement in PV-based sustainable energy systems to meet this difficulty. Power quality may be improved via an LCL filter in grid-connected systems. A non-linear load-connected PV system incorporates a variety of power electronics controllers, including an MPPT controller, battery controller, and inverter current controller, which introduce harmonics into the grid. A passive filter, LCL-SR, is suggested for PV applications that interact with voltage source inverters and the utility grid or electric load to reduce harmonics. A detailed mathematical modeling is presented to design an LCL-SR filter, and its performance is evaluated under different load conditions in terms of THD (Total Harmonics Distortion) and efficiency. LCL-SR filter has 4.32% THD but in the case of LCL filter with parallel R is less THD, which is 3.61%. In terms of efficiency and THD LCL-SR is best among all compared filters which have THD is 4.32% along with an efficiency is 75.95%. The results are compared without a filter, and different configurations of LCL-SR are simulated to show the effectiveness of the proposed filter for PV & residential applications.

Simulink Model for LMV and Conceptualization of 2 Speed E-Axle

Heavy-duty trucks are the primary contributor to the global emissions of greenhouse gases. Because of this, electric haulage has seen significant growth in popularity. Electric heavy-duty trucks aren't widely adopted because of their limited range, payload capacity, and high prices. Electric axle systems, sometimes known as "E-Axles," are capable of electrifying heavy-duty vehicles effectively. The motor, power electronics, and gearbox are all combined into one unit in an E-axle. E-axles that are devoid of motors and gearboxes have the potential to boost performance while also reducing weight and fuel consumption. Heavy-duty automobiles are similar to them. E-axles have the potential to increase the fuel efficiency, flexibility, and emissions of heavy-load trucks. The e-axles are being inspected. E-axles for heavy-duty trucks bring both issues and opportunities concerning finances, infrastructure, and costs. Examine the performance standards as well as the technology behind batteries. This paper provides an introduction to MATLAB Simulink, which focuses on engineering and science. Simulink facilitates the design, simulation, and evaluation of systems. This article demonstrates the fundamental principles, features, and benefits of Simulink through the application of system modeling, control design, and dynamic simulation. It presents Simulink methodologies, libraries, and blocks, as well as interactive simulations.
 Keywords: MATLAB, Simulink, EV Control systems, EV simulation, EV powertrain modeling, EV test and validation Simulink, WLTP drive cycle