Design Of Systems Research Articles

A Programmable 2D/3D Anti‐Counterfeiting System via Shape Memory Polymer with Surface Wrinkle and Fluorescence

AbstractThe issue of counterfeiting has always been a major challenge in today's society, putting forward high requirements for anti‐counterfeiting technology. Here, a promising 2D/3D anti‐counterfeiting security system is developed based on anthracene‐functionalized poly(styrene‐butadiene‐styrene) (SBS‐CAN), which possessed the physical unclonable self‐wrinkling patterns, fluorescence, and shape memory behavior. By controlling the dimerization of anthracene groups with ultraviolet light and external pre‐stretched operation, 2D information with surface wrinkle and fluorescence is achieved, allowing for the design of security systems based on actual needs. Furthermore, 3D structures constructed based on the shape memory behavior can further be utilized to encrypt these 2D information, enhancing the security level and weakening the cloneable risk of surface wrinkle and fluorescence for further. This programmable 2D/3D anti‐counterfeiting system has multiple advantages over the commonly used fluorescent wrinkled tag or fluorescent shape memory label, providing an intelligent choice for high‐security information carriers.

Backcrossing Failure between Sikitita Olive and Its Male Parent Arbequina: Implications for the Self-Incompatibility System and Pollination Designs of Olive Orchards.

Backcrossing between Sikitita and its male parent Arbequina, offers the possibility to check the suitability of different self-incompatibility models proposed for olive. To determine Sikitita's response to self- and cross-pollination treatments, including pollination with its father Arbequina, we compared the parameters following pollen-pistil interaction, the resulting initial and final fruit set, and the paternity of the seeds produced under different crosses. The results showed that Sikitita behaves as a self-incompatible cultivar due to the inhibition of pollen tube growth in the pistil of self-pollinated flowers. This incompatibility reaction led to a significant reduction of self-fertilization and fruit set. Seed paternity analyses confirmed the self-incompatibility response of Sikitita. A similar incompatibility response was observed in Sikitita flowers when hand-pollinated with pollen of Arbequina and Koroneiki. On the contrary, cross-pollination with Arbosana gave excellent results, with analyses showing that pollen of Arbosana is largely preferred by Sikitita to father its seeds more than the pollen of other cultivars presented in the orchard. The backcross failure of Sikitita with Arbequina pollen suggests that the self-incompatibility system in olives is not of the gametophytic type. In contrast, pollination tests fit features of previously reported sporophytic self-incompatibility systems. However, some amendments are proposed, among them the incompatibility groups for Sikitita and Koroneiki.

AI Ethics in Journalism (Studies): An Evolving Field Between Research and Practice

The integration of artificial intelligence (AI) in journalism has sparked complex ethical debates, particularly with the rise of generative AI systems. By now, AI permeates the entire news cycle, from information gathering to news dissemination, raising questions revolving around issues such as transparency, accountability, responsibility, bias, and diversity. Previous research showed that news organizations have slowly approached and adapted to ethical concerns regarding the use of AI, developing critical stances mainly due to rising AI power, growing audience skepticism, and mounting tensions within the industry between news publishers’ strategies and journalists’ anxieties. Consequently, ethical guidelines have started to emerge in news organizations, but their practical application remains challenging and under-studied, not only due to the opacity of AI algorithms, but also due to the difficulties of “embedding” journalistic values into AI systems. In the light of an intensifying discourse about ethical concerns in the news industry and growing efforts by governments and institutions such as the European Union to strengthen AI governance, journalism studies have started to explore the issue as well. However, research on AI ethics is still in its infancy, with significant gaps in understanding the practical enforcement of ethical guidelines within newsrooms, in particular when it comes to the design of AI systems. This essay critically discusses the way journalism (studies) approach ethical issues related to the use and the design of AI systems, given that the responsible use and design of AI systems in journalism is crucial given its integral role for democracy and society.

Method of Detecting Liquid Meniscus in Capillary Using Ultraviolet Sensor

The accuracy of determining the physical and chemical properties of a liquid, in particular, assessing the viscosity of the liquid used, is considered an important technical problem in medicine, food, oil, chemical industries, as well as in other areas that determine the quality and safety of human life and activity. For example, assessing the viscosity of the liquid used is a key stage in the design of hydraulic systems. Today, the improvement of methods for determining the viscosity of a liquid is on the path to automation (digitalization) of measurement instruments and methods. In this area, the activities of metrologists and engineers are consistent with the national program «Digital Economy of the Russian Federation» adopted in 2017. The article discusses the capillary method for determining the kinematic viscosity of a liquid as the most accurate of all possible methods at present. The method is based on determining the time of liquid flow between two marks, which are applied to the walls of a glass capillary viscometer forming a measuring tank. One of the main problems of this method is that all stages of measurement are performed by a person, even recording the moment when the liquid meniscus crosses the mark, which is a violation of one of the key principles of process automation – independence of execution, which only implies monitoring the operation of the system. Since ready-made solutions do not fit the laboratory’s measurement tasks, the management decided to develop and conduct a study of an information and measuring system that will carry out the process of detecting the liquid meniscus at the mark level, as well as recalculate the measured time interval into the kinematic viscosity value. The key decision in this development is the choice of a sensor for detecting the liquid meniscus. Based on the literature review, it was decided to use a photoelectric sensor. A study of the transmission spectra of liquids used in this method was conducted to select the wavelength at which the sensor will operate. Thus, a type of photoelectric sensor designed to operate in the UV range has been selected to automate the method of detecting the liquid meniscus when it crosses a mark applied to a capillary tube. The article is addressed to metrologists who perform verification and calibration of glass capillary viscometers, specialists in the field of experimental and theoretical viscometry. The method proposed by the authors can become the basis for further improvement of the method for measuring the kinematic viscosity of a liquid.

Advancing Economical and Environmentally Conscious Electrification: A Comprehensive Framework for Microgrid Design in Off‐Grid Regions

AbstractThe design of renewable energy systems traditionally emphasizes life cycle costs, often focusing primarily on emissions rather than a comprehensive life cycle impact assessment. This research proposes a four‐tier methodology to balance cost‐effectiveness and sustainability in the electrification of remote areas. Tier 1 focuses on understanding the community context by analyzing electrical load profiles, meteorological data, and component specifications for microgrid design. Tier 2 evaluates the feasibility of various systems, optimizing them through cost analysis and Multi‐Criteria Decision‐Making (MCDM) to rank alternatives. Tier 3 assesses environmental impacts using life cycle assessment, ranking alternatives based on environmental criteria. Tier 4 integrates cost and environmental rankings to determine the most suitable energy configurations, followed by sensitivity analysis to ensure robust decision‐making. The methodology is validated through a case study of an unelectrified remote community, demonstrating that the PV‐Wind Turbine‐Biomass Generator‐Converter configuration is the most robust alternative, proving to be the optimal choice in 50% of the analyzed scenarios, achieving a Cost of Energy of 0.213 USD/kWh while minimizing environmental impact across all 18 criteria considered over a 25‐year life cycle. This novel framework offers a scalable approach to designing renewable energy systems, enhancing sustainable electrification efforts in developing regions.

Modeling and design for metal hydride-based hydrogen storage systems in underwater PEMFC applications

Utilizing metal hydride materials presents a promising avenue for establishing a highly concentrated hydrogen medium suitable for onboard vehicle applications, including underwater contexts. This paper introduces a design solution devised to estimate the characteristics of a metal hydride-based hydrogen storage system tailored for underwater vehicle deployment. The solution facilitates sensitivity analysis across various parameters by offering system mass and volume estimations. The findings highlight the significance of thermodynamic properties and operational conditions in determining the suitability of metal hydrides for compact and efficient hydrogen storage. The study underscores the importance of refueling time, L/D ratio, cooling fluid temperature and velocity, and hydrogen storage capacity in influencing system mass, volume, and thermal management. The model provides insights into the design and optimization of metal hydride-based hydrogen storage systems, offering a comprehensive approach to enhancing performance and reliability for underwater applications.

Application of Barycentric Coordinates and the Jacobian Matrix to the Analysis of a Closed Structure Robot

A new approach is presented to study the kinematic properties of stationary robots with a closed structure. It combines the application of conventional methods from kinematics with geometric parameters represented in a barycentric coordinate system. This allows examining the influence of the proportions of the robot’s links on its basic mechanical characteristics. Each point from the newly introduced barycentric space corresponds to a set of robots with the same link proportions. The proposed approach is used to study three aspects: the link proportions for which the robot can exist; the shape of the robot’s workspace; and the possible singular configurations. This is valuable when evaluating the qualities of existing robots and could be applied to the design of new mechanical systems. An example of a 5-link robot with a closed structure is considered. The conditions for the existence of the mechanism and the conditions under which certain types of singular configurations can occur are defined. The example reveals the great potential of combining barycentric coordinates and Jacobian properties. The barycentric coordinates of 10 robots with a 5-link closed structure known from the literature are determined, and their properties are analyzed. The results are presented graphically. An extension of the application area of the approach is discussed.

Методика перетворення кодових послідовностей у відповідності до системи координат модуляційного диска

An analysis of current approaches used in the design of optical recording systems for modulation disks has been conducted. By adapting mathematical models, software algo-rithms were developed to transform code sequences represented in polar, homogeneous, and parametric coordinate systems, and the unique characteristics of each approach in de-veloping a universal method for ensuring accurate and efficient modulation pattern trans-formation were established. It was noted that the polar coordinate system is the most suit-able for practical applications in code sequence transformations. Its ability to effectively represent circular and symmetrical structures, as well as the convenient representation of objects with radial symmetry, provides advantages in the creation of modulation disks. As a result of the study, effective software solutions for automating data processing in the formation of modulation patterns were identified. The implementation of the developed software algorithms for the presented approach involves adapting the code sequence to the current metric within the design of an optical system based on modulation disks. The pre-sented methodology ensures the ability to transform code sequences regardless of the cho-sen coordinate system, which significantly enhances its flexibility and versatility for prac-tical applications.

Should voice navigation prompts be earlier and more complicated? A study of drivers’ visual attention, physiology and operating performance

Voice navigation systems can help drivers access information about road conditions and make correct decisions, but inappropriate information level and information prompt timing may lead to untimely processing of information, distraction, or even misjudgment of drivers, which in turn may cause safety hazards. The purpose of this study is to investigate the effects of voice information level and information prompt timing on drivers’ driving behavior and physiological characteristics. Twenty-seven participants were recruited to conduct driving simulator experiments. The results showed that the driver’s visual attention is more focused and the driving performance is better when the voice navigation prompts contain four information units (driving directions, traffic facility information, distance information, and road condition information at the same time). Moreover, when the voice navigation prompts with four information units were played 300 meters from the intersection, the driver’s overall driving performance was the highest. The results offer additional theoretical insights for the design of voice navigation systems, aiding drivers in accurately understanding driving conditions and providing them with ample reaction time to enhance their driving decisions.

Artificial photosynthetic system for diluted CO2 reduction in gas-solid phase

Rational design of robust photocatalytic systems to direct capture and in-situ convert diluted CO2 from flue gas is a promising but challenging way to achieve carbon neutrality. Here, we report a new type of host-guest photocatalysts by integrating CO2-enriching ionic liquids and photoactive metal-organic frameworks PCN-250-Fe2M (M = Fe, Co, Ni, Zn, Mn) for artificial photosynthetic diluted CO2 reduction in gas-solid phase. As a result, [Emim]BF4(39.3 wt%)@PCN-250-Fe2Co exhibits a record high CO2-to-CO reduction rate of 313.34 μmol g−1 h−1 under pure CO2 atmosphere and 153.42 μmol g−1 h−1 under diluted CO2 (15%) with about 100% selectivity. In scaled-up experiments with 1.0 g catalyst and natural sunlight irradiation, the concentration of pure and diluted CO2 (15%) could be significantly decreased to below 85% and 10%, respectively, indicating its industrial application potential. Further experiments and theoretical calculations reveal that ionic liquids not only benefit CO2 enrichment, but also form synergistic effect with Co2+ sites in PCN-250-Fe2Co, resulting in a significant reduction in Gibbs energy barrier during the rate-determining step of CO2-to-CO conversion.

Numerical Study on the Influence of the Slope Composition of the Asymmetric V-Shaped Tunnel on Smoke Spread in Tunnel Fire

Asymmetrical V-shaped tunnels often appear in tunnels crossing the river or urban underground road tunnels. The smoke flow inside is affected by a lot of factors. A full understanding of the smoke flow in this kind of tunnel is the basis of the smoke control. In this study, the effects of slope composition and fire heat release rate (HRR) on the longitudinal induced airflow velocity, the smoke back-layering length at the small slope side, and the maximum ceiling temperature were studied by the numerical method. The results show that when the fire occurs at the slope change point of the V-shaped tunnel, the maximum ceiling temperature decreases with the increase in the slope of the large-slope side tunnel. The longitudinally induced velocity is primarily related to the slope of the large-slope side tunnel and the fire HRR. When the slope difference between the side tunnels or the slope of the large-slope side tunnel is large, the smoke in the small-slope side tunnel flows back toward the fire source after reaching its maximum dispersion distance and then reaches a quasi-steady state. The smoke back-layering length is mainly affected by the slope and length of the large-slope side tunnel. When the slope of the large-slope side tunnel is 9%, the induced airflow velocity from the small-slope side can prevent the spread of smoke. The empirical models of the smoke back-layering length and the longitudinal induced airflow velocity in the small-slope side tunnel are drawn, respectively, by the theoretical analysis and the numerical results. This study can provide technical support for the design and operation of smoke control systems in V-shaped tunnels.

Employing machine learning for advanced gap imputation in solar power generation databases

This research evaluates the application of advanced machine learning algorithms, specifically Random Forest and Gradient Boosting, for the imputation of missing data in solar energy generation databases and their impact on the size of green hydrogen production systems. The study demonstrates that the Random Forest model notably excels in harnessing solar data to optimize hydrogen production, achieving superior prediction accuracy with mean absolute error (MAE) of 0.0364, mean squared error (MSE) of 0.0097, root mean squared error (RMSE) of 0.0985, and a coefficient of determination (R2) of 0.9779. These metrics surpass those obtained from baseline models including linear regression and recurrent neural networks, highlighting the potential of accurate imputation to significantly enhance the efficiency and output of renewable energy systems. The findings advocate for the integration of robust data imputation methods in the design and operation of photovoltaic systems, contributing to the reliability and sustainability of energy resource management. Furthermore, this research makes significant contributions by showcasing the comparative performance of traditional machine learning models in handling data gaps, emphasizing the practical implications of data imputation on optimizing hydrogen production systems. By providing a detailed analysis and validation of the imputation models, this work offers valuable insights for future advancements in renewable energy technology.

Refining and expanding accountability policy for English learners to support Lau implementation: A critical policy analysis

ABSTRACT Title I accountability requirements under the Every Student Succeeds Act (ESSA) represent the present-day instantiation of Lau, which requires schools to provide a program for English learners (ELs) that supports their meaningful engagement and to provide transparent information about EL program quality. This study uses critical policy analysis to explore whether ESSA accomplishes what it intends. Employing a parallel mixed methods design, we first analyze longitudinal data from two states to examine whether their approaches to assessing EL students’ academic performance and progress support valid claims about EL achievement. Then, we analyze focus group and interview data from educators, advocates, and families in states to understand how they assess the quality of a school’s EL program. Taken together, our findings highlight the need for the incorporation of multiple perspectives into the design of state accountability systems and for the development of robust data systems that facilitate the sharing of accurate and nuanced information related to EL student outcomes and opportunities.

Robotic Button Mushroom Harvesting Systems: A Review of Design, Mechanism, and Future Directions

The global demand for button mushrooms has surged in recent years, driven by their health benefits, creating a significant challenge for the mushroom industry in meeting this increasing demand. The increasing reliance on human labor, which is becoming unsustainable due to labor shortages and rising wage costs, highlights the urgent need for automated harvesting solutions. This review examines the integration of automated systems in button mushroom harvesting, delving into the key components such as robots, mechanisms, machine elements, programming, and algorithms. It offers a thorough analysis of the performance, design, operational mechanisms, and advantages and limitations of robotic systems, comparing the different methods employed in automated harvesting. This paper compares the performance of all the mushroom harvesters, including the commercially available ones with manual harvesting, and identifies their potential and limitations. The commercial harvesters are shown to pick 2000 mushrooms per hour on average, which is similar to how much a skilled worker picks at the same time. However, commercial automation harvesting has a relatively low success rate, high initial cost, high operating cost, and energy consumption, identifying areas for future research and challenges. This paper serves as a valuable resource for researchers and industry professionals striving to advance automated harvesting technology and improve its efficiency in meeting the rising demand for button mushrooms.

Palette‐based colour normalization for histopathology images

AbstractWith the emergence of computer‐aided diagnostic (CAD) systems, the speed of histopathological image analysis and the accuracy of cancer detection have considerably improved. However, the appearance of histopathological slides can vary depending on the consistency of tissue thickness, stain concentrations, and equipment, thus affecting the judgment of CAD systems. This study proposes a palette‐based colour normalization method for histopathology images is proposed to solve the problem of colour differences between histopathology images. The method builds a graphical user interface based on an improved palette generation algorithm and colour transfer definitions, through which the user can complete the corresponding colour modification to achieve colour normalization. The evaluation of the metrics on histopathological images shows that our method is able to achieve higher image quality and better preservation of structural information of the source image compared with four other colour normalization algorithms. The peak signal‐to‐noise ratio values obtained by the proposed method on two publicly available datasets were 24.1914 and 21.3666, and the structural similarity index matrix values were 0.9871 and 0.9760. The proposed method provides new ideas for the development and design of CAD systems.

Design and Simulation of eVTOL Aircraft Thermal Management System

Abstract Vertical Take-off and Landing (VTOL) aircraft with hybrid or fully-electric propulsion systems are becoming popular for Urban Air Mobility (UAM) applications. However, they face challenges such as limited power and energy density, stringent operating temperature constraints, and the generation of low-grade waste heat. These issues emphasize the need for effective Thermal Management Systems (TMS) design. In light of the above, this paper aims to design the TMS for a parallel hybrid electric XV-15 aircraft, a widely known civil tiltrotor concept. The TMS is integrated into the aircraft system to assess its impact on energy efficiency and emissions at both aircraft and mission levels. This study considers current state of the art technology and expected advancements over the next two decades to identify the benefits of electrification. In the designed TMS, liquid cooling cold plates serve as the main heat acquisition and cooling system for each electric component. An air-coolant heat exchanger is also incorporated to dissipate the accumulated heat load. The study conducts a comparative analysis to determine the optimal TMS design point. It involves a comparison between two design scenarios: one focused on the cruise condition, which constitutes a significant portion of the flight mission, and another designed specifically for peak heat load conditions. This ensures a holistic evaluation of the TMS's performance across various flight phases, balancing efficiency and resilience to peak demands. Finally, the energy efficiency and emission penalty associated with the TMS integration are quantified and discussed in the study.

Path Tracing-Inspired Modeling of Non-Line-of-Sight SPAD Data.

Non-Line of Sight (NLOS) imaging has gained attention for its ability to detect and reconstruct objects beyond the direct line of sight, using scattered light, with applications in surveillance and autonomous navigation. This paper presents a versatile framework for modeling the temporal distribution of photon detections in direct Time of Flight (dToF) Lidar NLOS systems. Our approach accurately accounts for key factors such as material reflectivity, object distance, and occlusion by utilizing a proof-of-principle simulation realized with the Unreal Engine. By generating likelihood distributions for photon detections over time, we propose a mechanism for the simulation of NLOS imaging data, facilitating the optimization of NLOS systems and the development of novel reconstruction algorithms. The framework allows for the analysis of individual components of photon return distributions, yielding results consistent with prior experimental data and providing insights into the effects of extended surfaces and multi-path scattering. We introduce an optimized secondary scattering approach that captures critical multi-path information with reduced computational cost. This work provides a robust tool for the design and improvement of dToF SPAD Lidar-based NLOS imaging systems.

Recent Advances in Bonding Regulation of Metalloporphyrin-Modified Carbon-Based Catalysts for Accelerating Energy Electrocatalytic Applications.

Metalloporphyrins modified carbon-based materials, owing to the excellent acid-base resistance, optimal electron transfer rates, and superior catalytic performance, have shown great potential in energy electrocatalysis. Recently, numerous efforts have concentrated on employing carbon-based substrates as platforms to anchor metalloporphyrins, thereby fabricating a diverse array of composite catalysts tailored for assorted electrocatalytic processes. However, the interplay through bonding regulation of metalloporphyrins with carbon materials and the resultant enhancement in catalyst performance remains inadequately elucidated. Gaining an in-depth comprehension of the synergistic interactions between metalloporphyrins and carbon-based materials within the realm of electrocatalysis is imperative for advancing the development of innovative composite catalysts. Herein, the review systematically classifies the binding modes (i.e., covalent grafting and non-covalent interactions) between carbon-based materials and metalloporphyrins, followed by a discussion on the structural characteristics and applications of metalloporphyrins supported on various carbon-based substrates, categorized according to their binding modes. Additionally, this review underscores the principal challenges and emerging opportunities for carbon-supported metalloporphyrin composite catalysts, offering both inspiration and methodological insights for researchers involved in the design and application of these advanced catalytic systems.

PULPO: A framework for efficient integration of life cycle inventory models into life cycle product optimization

AbstractThis work presents the PULPO (Python‐based user‐defined lifecycle product optimization) framework, developed to efficiently integrate life cycle inventory (LCI) models into life cycle product optimization. Life cycle optimization (LCO), which has found interest in both the process systems engineering and life cycle assessment (LCA) communities, leverages LCA data to go beyond simple assessments of a limited number of alternatives and identify the best possible product systems configuration subject to a manifold of choices, constraints, and objectives. However, typically, aggregated inventories are used to build the optimization problems. Contrary to existing frameworks, PULPO integrates whole LCI databases and user inventories as a backbone for the optimization problem, considering economy‐wide feedback loops between fore‐ and background systems that would otherwise be omitted. The open‐source implementation combines functions from Brightway2 for the manipulation of inventory data and pyomo for the formulation and solution of the optimization problem. The advantages of this approach are demonstrated in a case study focusing on the design of optimal future global green methanol production systems from captured CO2 and electrolytic H2. It is shown that the approach can be used to assess sector‐coupling with multi‐functional processes and prospective background databases that would otherwise be impractical to approach from a standalone LCA perspective. The use of PULPO is particularly appealing when evaluating large‐scale decisions that have a strong impact on socioeconomic systems, resulting in changes in the technosphere on which the background system is based and which is often assumed constant in standard LCO approaches regardless of the decisions taken. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.

Emergency evacuation risk assessment for toxic gas attacks in airport terminals: Model, algorithm, and application

Transportation infrastructure has often been the target of terrorist attacks, and mitigation of the risk of toxic gas attacks is a challenging task in the design of indoor emergency evacuation systems. Considering multiple emergency response modes, we propose an agent-based risk assessment model and its algorithm to integrate gas diffusion and pedestrian movement data for emergency response, quickly assessing average individual exposure risk. We assessed the exposure status of individuals with respect to their emergency response actions following a toxic gas attack in an airport terminal. The results indicate that in the event of a general gas attack on an airport terminal, ventilation must be immediately ceased along with early evacuation. In areas with a shelter-in-place environment, the ventilation mode and shelter-in-place time should be determined based on the concentration of indoor and outdoor gases. In areas with nerve gas exposure and high population density, a new exit must be established at evacuation bottlenecks, and pedestrians must be guided to evacuate while promptly closing ventilation. These results offer suggestions and strategies for emergency response and decision-making in airport terminals during such incidents.