Experimental Case Study Research Articles

Smart Tags as Enablers for Digital Product Passports in Circular Electronics Value Chains

Abstract The electronics industry is expected to adopt more sustainable and circular product concepts and operations. Since the electronics value chains are complex, digital product passports (DPPs) that provide value chain transparency and traceability can be seen as one key enabler for shifting towards circular economy. Data carriers that are physical identifiers attached to products provide access to product data stored in the cloud and databases. Smart tags that combine item-level identification with condition monitoring are proposed to enable access also to dynamic lifecycle data of products to improve decision-making at end-of-life based on conditions that the product has been exposed to during its lifecycle. This dynamic information could be effectively used together with product data to decide on which circular economy strategy to adopt: reuse, remanufacture, repair, recycle etc. This paper analyses the data requirements of electronics value chain for DPPs, specifically focusing on which conditions to monitor with the help of smart tags. The data for this analysis was collected from ten developmental value chains aiming for sustainability and circularity with a questionnaire related to data needs, data access, data gaps, and data availability. The responses highlighted the need for data exchange and tools to monitor performance of components during storage and use. A printed visual humidity sensor is developed and analyzed as an experimental case study to help the value chains to dynamically monitor lifecycle conditions of products. This smart tag principal was functional with a visible colour change over time at different humidities between 33–72%RH, while not reacting at 0%RH. The relevance of different smart tag concepts is discussed and other important aspects, such as sustainability and durability of the smart tags, are included in the discussion.

Dendrite neural network scheme for estimating output power and efficiency for a class of solar free-piston Stirling engine

ABSTRACT This paper presents the dendrite neural network (DNN) scheme for predicting two challenging parameters, namely the output power and efficiency of the solar free-piston Stirling engine (FPSE). This paper utilizes vital characteristics to predict the output power and efficiency accurately. MSE and regression analysis are employed to investigate the reliability of the proposed DNN structure. As a result, not only was the MSE close to zero, but the regression outcomes were also very close to 1, which confirmed the reliability of the DNN. In the next step, several experimental case studies are used to study the effectiveness of the proposed DNN more thoroughly. Therefore, a maximum error of 5% was observed, confirming good agreement between the empirical data and the predictions made by the DNN. Lastly, through the simulation and experimental investigations, it can be concluded that the method is reliable and can be used for other types of FPSEs.

Experimental case study for optimizing heat sink performance using ZnO nanocarriers integrated with phase change materials for environmental sustainability

Experimental case study for optimizing heat sink performance using ZnO nanocarriers integrated with phase change materials for environmental sustainability

Future Prospects of Biodegradable Natural Fiber Composites: Innovations and Enhanced Performance in Roofing and Packaging Applications

This paper explores advancements in sustainable composite materials, focusing on biodegradable fibers, recycling innovations, and enhanced performance for packaging and roofing applications. With growing environmental concerns industries are shifting toward materials that reduce ecological footprints without compromising performance. Biodegradable fibers derived from natural sources such as hemp, jute, and flax have gained attention for their potential to replace synthetic fibers in composite manufacturing. These natural fibers offer environmental benefits including reduced carbon emissions, energy savings, and easier disposal. However, challenges remain in optimizing their mechanical properties to match conventional materials. Recycling innovations also play a critical role in achieving sustainability goals in composite manufacturing. Recent advancements in chemical and mechanical recycling processes allow the reuse of composite waste, reducing landfill dependency and resource depletion. Enhanced recycling methods improve material recovery and retain quality extending product life cycles. This research examines how these processes can be effectively applied in packaging and roofing, two sectors with high material demand and waste output. The study assesses the performance of sustainable composites in terms of durability, thermal stability, and resistance to environmental factors, critical for both packaging and roofing. Through a combination of experimental testing and case studies, this thesis demonstrates that sustainable composites can offer viable alternatives to traditional materials supporting a transition to greener construction and packaging solutions. This paper contributes valuable insights into the practical applications of biodegradable and recyclable composites paving the way for more sustainable material choices in industrial sectors

Various Types of Research for Early Childhood Education

This research aims to understand and analyze various types of early childhood education (ECE) research based on objectives, approaches, methods, and data sources. The main issue raised is the lack of deep understanding among students and practitioners regarding the types of research relevant to improving the quality of education. The method used is a literature study, with data collection through analyzing literature from books, scientific journals, and related articles. Data is analyzed descriptively to identify each type of research’s characteristics, advantages, and disadvantages. The findings of this study show that there are four categories based on purpose, approach, method and data source. Based on purpose, there are descriptive, explanatory, exploratory, predictive, and evaluative types of research. Based on the approach, research is divided into quantitative, qualitative, and mixed methods. Based on method, there are experimental research, action research, case studies, longitudinal research, and cross-sectional research. Finally, based on data sources, research can be conducted through field, library, or laboratory research.. The results of this study contribute as a reference for researchers in the field of Early Childhood Education to choose the right type of research based on four research categories, namely based on objectives, approaches, methods, and data sources.

+SSLIP: Automated Radon‐Assisted and Rotation‐Corrected Identification of Complex HCP Slip System Activity Fields from DIC Data

ABSTRACTIdentification of crystallographic slip in metals and alloys is crucial to understand and improve their mechanical behaviour. Recently, a novel slip system identification framework, termed SSLIP (for slip system–based local identification of plasticity), was introduced to leap from conventional trace‐based identification to automated, point‐by‐point identification, exploiting the full deformation kinematics. Using sub‐micron‐scale digital image correlation (DIC) deformation fields aligned to electron backscatter diffraction (EBSD) data, SSLIP matches the measured in‐plane displacement gradient tensor to the kinematics of the optimal combination of multiple slip system activities, at each DIC datapoint. SSLIP was demonstrated to be successful on virtual and experimental case studies of FCC and BCC metals. However, for more challenging HCP crystal structures, the complete identification of all slip systems was found to be more challenging, posing limitations on automation and flexibility. To extend the capabilities of SSLIP, we propose an extended framework, hereinafter referred to as the +SSLIP method, which includes (i) a preselection of slip systems using a Radon transform, (ii) robustness to measured rigid body rotation by simultaneous identification of the local rotation field, (iii) identification of the two best matching slip systems for each data point and (iv) a procedure to determine groups of slip systems with in‐plane displacement gradient tensors that cannot be discriminated. This procedure yields the full (HCP) slip system activity maps for every slip system in each grain. The resulting objective identification method does not rely on the Schmid factor to select which slip system is active at each point. We show how slip systems from multiple slip families are successfully identified on virtual and real experiments on a Zn polycrystalline coating.

Human–wildlife coexistence needs more evidence‐based interventions to reduce the losses of crops, livestock and fishery catches

Abstract Evidence‐based interventions designed to reduce wildlife‐caused losses are essential for human–wildlife coexistence. The lack of systematic summarization of research effort and evidence makes it challenging for researchers, managers and policymakers to prioritize interventions for evaluation and implementation. Here, we compiled experimental case studies of nonlethal technical interventions designed to reduce the losses of crops, livestock and fishery catches caused by terrestrial carnivores, elephants, farmland birds and marine fauna worldwide. Then, we summarized the research effort and the performance of interventions by their sensory stimuli and target animals. We found that: (i) 54 of 88 interventions included in this study had statistically effective evidence, where only 39% (21/54) were evaluated with more than three experiments; (ii) physical‐, sound‐, chemical‐ and light (or visual) ‐based interventions were the most in numbers and their performance varied greatly; (iii) farmland birds, seabirds and cetaceans were the most studied animal groups while there are only a few experiments for elephants; and (iv) the interventions for marine fauna generally had no impact on the target catch of fisheries. Syntheses and applications: Our results indicated that collective effort is needed to further evaluate interventions using various sensory stimuli and launch incentive programs to motivate the implementation of interventions, particularly related to marine fauna conservation. Our synthesis could be helpful for stakeholders to tackle the negative human‐wildlife interactions outlined as Target 4 of the Kunming–Montreal Global Biodiversity Framework.

Advanced Security Measures for Virtualized Data Storage: A Comprehensive Study

With the widespread application of virtualization technology in data centers, the security issues of data storage have become increasingly prominent. This paper aims to explore the application of information security in virtualized data storage to address the increasingly complex security challenges. The article first reviews the development of virtualization technology and the key technologies of virtualized data storage, then analyzes the current state and threats faced by information security in virtualized environments. Based on this, the paper discusses security requirements such as data integrity, access control, and privacy protection in detail, and delves into the application of information security technologies such as data encryption, intrusion detection and defense systems, security information and event management, and virtualization security policy management. Furthermore, this paper proposes a comprehensive security framework for virtualized data storage and describes its design principles, component architecture, and implementation deployment strategies in detail. Through experimental design and case studies, the paper validates the effectiveness of the proposed framework and evaluates its performance. Finally, the paper summarizes the research findings and proposes prospects for future research directions. This study not only provides theoretical support and practical guidance for information security in virtualized data storage but also offers valuable references for researchers and practitioners in related fields.

Research on Advanced Material Applications for Enhancing the Performance of New Energy Vehicle Components

This study aims to explore the application of advanced materials in new energy vehicle (NEV) components and assess their contribution to enhancing the overall vehicle performance. With the growing global demand for sustainable transportation solutions, NEVs have become a significant direction for the automotive industry. Against this backdrop, the performance enhancement of components is key to achieving higher energy efficiency, longer driving range, and a better driving experience. This paper first reviews the development history of new energy vehicle technology and the classification of advanced materials, then analyzes in detail the application of advanced materials in battery systems, electric motors and electronic control systems, lightweight structures, and thermal management systems. Through experimental research and case studies, this paper evaluates the specific impact of these materials on component performance and discusses the challenges faced in practical applications and future development directions. The research results show that the application of advanced materials can significantly enhance the performance of new energy vehicles, providing strong technical support for the further development of new energy vehicles. Finally, this paper proposes solutions to the challenges and limitations of performance enhancement in the application of advanced materials and suggests future research directions.

Bayesian stability and force modeling for uncertain machining processes

Accurately simulating machining operations requires knowledge of the cutting force model and system frequency response. However, this data is collected using specialized instruments in an ex-situ manner. Bayesian statistical methods instead learn the system parameters using cutting test data, but to date, these approaches have only considered milling stability. This paper presents a physics-based Bayesian framework which incorporates both spindle power and milling stability. Initial probabilistic descriptions of the system parameters are propagated through a set of physics functions to form probabilistic predictions about the milling process. The system parameters are then updated using automatically selected cutting tests to reduce parameter uncertainty and identify more productive cutting conditions, where spindle power measurements are used to learn the cutting force model. The framework is demonstrated through both numerical and experimental case studies. Results show that the approach accurately identifies both the system natural frequency and cutting force model.

Formulation And Stability Of Gel Toothpaste With Sungkai Leaf (Peronema Canescens Jack) Extract And Na-Cmc Gelling Agent

The Sungkai leaf (Peronema canescens Jack), which is rich in alkaloids, flavonoids, tannins, and phenolics, has long been used as a natural immune booster, particularly during the pandemic. With increasing concerns over the health risks of fluoride, such as dental fluorosis and systemic toxicity, the demand for natural alternatives in oral care has risen. This study aimed to formulate and evaluate the physical stability of a gel toothpaste containing Sungkai leaf extract by examining the effects of varying concentrations of Na-CMC (Sodium Carboxymethyl Cellulose) as a gelling agent. Using an experimental one-shot case study design, the physical stability of different concentrations of NaCMC (3%, 4%, 5%, and 6%) was assessed. Stability testing was performed on 5 sample formulations per concentration. The tests included organoleptic properties, homogeneity, pH, foam height, and viscosity over four weeks. The results showed that all formulations met the Indonesian National Standard (SNI No.12-3524-1995), with semi-solid consistency, distinctive Sungkai odor, and slightly bitter taste. The average pH ranged from 6.42 to 6.84, the foam heights were stable, and the viscosity increased with higher Na-CMC concentrations, confirming the stability of the formulation. These findings suggest that natural Sungkai leaf-based toothpaste could provide a promising alternative to traditional fluoride-based products, particularly in markets concerned about chemical ingredients in oral care products.

Evaluation of the Effectiveness of the Security Protection System Based on the Improved AHP-FE Method

With the rapid development of information technology, security protection systems (SPS) have become vital for safeguarding data and resources. A significant decision-making challenge is presented to design an optimal security strategy amidst complex factors. This paper applies a combined approach of Fuzzy Evaluation (FE) and the Analytic Hierarchy Process (AHP) to enhance the design and evaluation of SPS. The methodology integrates FE for constructing an evaluation model to determine the appropriate weight of each index and AHP, paired with the Paired Majority Rule (PMR) and the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), to hierarchically analyze security factors. This approach creates a decision model based on consistency checks to derive the optimal security strategy. The results show that the proposed methods can effectively quantify complex security factors and generate optimized solutions for security protection. Experimental case studies validate the method's accuracy and reliability, demonstrating the wide application potential of integrating big data technology into complex decision-making processes for security systems.

Model predictive control based on single-phase shift modulation for triple active bridge DC-DC converter

The triple-active bridge (TAB) converter is widely used in various applications due to its high efficiency and power density. However, the high-frequency (HF) transformer coupling between the ports presents challenges for controller design. This article presents a model predictive control (MPC) approach based on single-phase shift modulation for the TAB converter. The developed MPC offers improved transient performance, control flexibility, and precision, ensuring compliance with DC voltage regulations and achieving optimal solutions for port decoupling. The MPC utilizes a cost function to provide robust voltage regulation, and an algorithm based on Karush-Kuhn-Tucker (KKT) conditions is developed to derive closed-form solutions for optimal control parameters. To validate the performance of the TAB converter with the proposed MPC control, Typhoon 602 hardware-in-loop (HIL) experimental case study is conducted. Additionally, a comparison with previous works is carried out to confirm the effectiveness of the proposed method. The results of the HIL experimental setup and the comparative analysis demonstrate that the developed method is effective, providing faster dynamic characteristics and port power decoupling operation capability.

Development of a novel multifunctional superconducting device for performance enhancement of DC microgrids

DC microgrids have become an important infrastructure for increasing the integration of renewable energy sources in power grids. However, these DC microgrids suffer from inherent fluctuations from renewable energy sources as well as high fault currents during grid disturbances. Addressing these challenges simultaneously with a single device is a significant technical issue. This paper proposes a novel Multifunctional Superconducting Device (MSCD) with dual functionality to overcome these challenges. The MSCD can operate as a Superconducting Magnetic Energy Storage (SMES) system during normal operation, providing mitigation of renewable energy fluctuations through its shunt connection. During grid faults, the MSCD transits to operate as a Superconducting Fault Current Limiter (SFCL) in series with the grid, limiting the fault current. The proposed MSCD has only an additional controlled switch to the conventional power electronic converter of SMES. The dual functionality of the proposed MSCD has been evaluated using the PSCAD/EMTCD computer package under various operating conditions, validating its ability to seamlessly transition between SMES and SFCL modes. The proposed MSCD could effectively smooth DC bus voltage fluctuations caused by variations in wind speed and solar radiation and could significantly limit the fault current contribution from the DC microgrid under fault conditions. Furthermore, a superconducting coil with a pancake structure has been built and integrated with a control circuit to experimentally validate the functionality of the MSCD. Two experimental case studies were conducted - the first testing the charging and discharging behavior, and the second evaluating the current limiting capability of the MSCD prototype.

Estimating farmers’ willingness to pay for crop insurance with application of choice experiment: case study in South Gondar Zone, Ethiopia

PurposeThis study aims to estimate farmers’ willingness to pay for crop insurance, utilizing a choice experiment case study in the South Gondar Zone, Ethiopia.Design/methodology/approachPrimary cross-sectional data were collected in 2023 from 240 farm households. The choice experiment method was employed to elicit farmers’ willingness to pay for crop insurance. Five attributes, including monetary cost, were identified for the choice experiment, with two improved scenarios and a status quo presented to respondents. The mixed logit model and extended mixed logit model were used for analysis.FindingsThe econometric model indicated that, with the exception of one attribute, all were positive and statistically significant. Farmers showed a preference for improved scenarios over the status quo, demonstrating a willingness to pay for crop insurance. The extended mixed logit model revealed that factors such as livestock ownership, household head’s sex, family size, income, farming experience, crop risk exposure, and additional occupations significantly influenced farmers’ preferences for crop insurance.Research limitations/implicationsThe study’s sample size was limited to 240 farm households, which is relatively small. More reliable results could be obtained with a larger sample size. Another significant limitation is the study’s failure to account for institutional setups when assessing farmers’ willingness to pay for crop insurance.Practical implicationsAgricultural risk, particularly crop risk, is severe in the study area. The results suggest that farmers have a genuine need for risk mitigation mechanisms, such as crop insurance. The findings reflect farmers’ interest in crop insurance, indicating that responsible entities, whether governmental or private insurance companies, can readily implement crop insurance schemes.Social implicationsThe study has significant social implications, as the society in the study area is highly vulnerable to crop risk, which adversely affects their livelihood. Introducing a crop insurance scheme could enhance the welfare and livelihood of the local population.Originality/valueTo the best of our knowledge, this study is novel in both concept and methodology. Unlike previous studies, which focused on specific crop risks, this study considers multiple crop risks. The findings offer valuable insights for policymakers and other stakeholders involved in crop insurance. Understanding farmers’ preferences for crop insurance is crucial for designing effective crop insurance policies.

Degradation Mechanism of Phosphate-Based Li-Nasicon Conductors in Alkaline Environment

Li-NASICON solid-state superionic conductors have traditionally been regarded as promising candidates for Li-air applications because of their stability in ambient air and water [1], [2]. However, the presence of water in the cathode can change the discharge product from Li2O2 to LiOH [3], [4], inducing a highly alkaline environment which may degrade cathode and separator materials. Here, we investigate the impact of octahedral substitution on the alkaline stability of common Li-NASICON chemistries through a systematic experimental case study of LiTixGe2-x(PO4)3 (LTGP) with varying x = 0 - 2.0. Density functional theory calculations are combined to gain mechanistic understandings of the alkaline instability. We demonstrated that the alkaline instability of LTGP is mainly driven by the dissolution of PO4 polyanion groups, which subsequently precipitate as Li3PO4. The introduction of Ti facilitates the formation of a Ti-rich compound on the surface that eventually passivates the material, but only after significant bulk degradation. Consequently, phosphate-based Li-NASICON materials exhibit limited alkaline stability, raising concerns of their viability in humid Li-air batteries.[1] N. Imanishi, S. Hasegawa, T. Zhang, A. Hirano, Y. Takeda, and O. Yamamoto, “Lithium anode for lithium-air secondary batteries,” Journal of Power Sources, vol. 185, no. 2, pp. 1392–1397, Dec. 2008, doi: 10.1016/j.jpowsour.2008.07.080.[2] R. Chen, Q. Li, X. Yu, L. Chen, and H. Li, “Approaching Practically Accessible Solid-State Batteries: Stability Issues Related to Solid Electrolytes and Interfaces,” Chem. Rev., vol. 120, no. 14, pp. 6820–6877, Jul. 2020, doi: 10.1021/acs.chemrev.9b00268.[3] S. Ma, J. Wang, J. Huang, Z. Zhou, and Z. Peng, “Unveiling the Complex Effects of H2O on Discharge–Recharge Behaviors of Aprotic Lithium–O2 Batteries,” J. Phys. Chem. Lett., vol. 9, no. 12, pp. 3333–3339, Jun. 2018, doi: 10.1021/acs.jpclett.8b01333.[4] T. Liu et al., “Cycling Li-O2 batteries via LiOH formation and decomposition,” Science, vol. 350, no. 6260, pp. 530–533, Oct. 2015, doi: 10.1126/science.aac7730.

High-Order Local Clustering on Hypergraphs

Graphs are a commonly used model in data mining to represent complex relationships, with nodes representing entities and edges representing relationships. However, graphs have limitations in modeling high-order relationships. In contrast, hypergraphs offer a more versatile representation, allowing edges to join any number of nodes. This capability empowers hypergraphs to model multiple relationships and capture high-order information present in real-world applications. We focus on the problem of local clustering in hypergraphs, which computes a cluster near a given seed node. Although extensively explored in the context of graphs, this problem has received less attention for hypergraphs. Current methods often directly extend graph-based local clustering to hypergraphs, overlooking their inherent high-order features and resulting in low-quality local clusters. To address this, we propose an effective hypergraph local clustering model. This model introduces a novel conductance measurement that leverages the high-order properties of hypergraphs to assess cluster quality. Based on this new definition of hypergraph conductance, we propose a greedy algorithm to find local clusters in real time. Experimental evaluations and case studies on real-world datasets demonstrate the effectiveness of the proposed methods.

Ship Cybersecurity Risk Assessment for Safe Operation with Human Involvement: An Experimental Case Study

Ship Cybersecurity Risk Assessment for Safe Operation with Human Involvement: An Experimental Case Study

Digital-twin-based multi-scale simulation supports urban emergency management: a case study of urban epidemic transmission

ABSTRACT Improving emergency management capabilities is vital for sustainable cities. However, urban emergency management is a complex, giant system that interacts between urban virtual and real spaces, requiring simulation in the virtual space and feedback to the real space. The digital twin model can potentially play an important role in this process, but still faces significant challenges in terms of modeling, simulation, and visualization. In this study, we propose a digital-twin-based multi-scale simulation method and take epidemic transmission as a case study. First, an entity-oriented unified urban database integrating multi-source urban data was constructed as the digital twins of urban human settlements for emergency modeling. Then, a multi-scale urban emergency simulation method in 3D geographic environment was proposed to support emergency simulation for multi-level urban management units. Moreover, an adaptive fusion rendering of emergency information and 3D scene was proposed to support the refinement of emergency visualization. An experimental case study on epidemic risk estimation was conducted through thousands of outbreak scenarios. The results demonstrate that the proposed method can establish the digital twins of urban human settlements, where modeling, simulation, and visualization of emergency at multi-scales can be carried out, thus enhancing the efficacy and efficiency of emergency management.

A novel approach developed to enhance spatiotemporal accuracy for static chamber observations of carbon fluxes in paddy: A case study of double-site experiments in Southern China

More precise measurements of carbon fluxes (CH4 and CO2) in paddy fields have been crucial for estimating the global carbon budget, yet they were largely constrained by varying observational methods. Our study employed a carbon emission monitoring experiment at a dual-site rice paddy in southern China, aiming to refine the widely used static chamber method (STCM) through the integration of limited eddy covariance system (ECS) observations and environmental factor regressions. This approach sought to provide more convenient methods and accurate assessments for paddy field management and monitoring practices. The results revealed that STCM tended to overestimate carbon fluxes particularly CH4, compared to ECS across different temporal scales. Stepwise regression models using environmental factors demonstrated both simplicity and accuracy in simulating CH4 and CO2, effectively enhancing the temporal resolution of STCM. Our proposed method achieved an index of agreement above 79.54 % for any carbon flux simulation under various treatments, significantly improving the spatiotemporal precision of paddy field CH4 and CO2 monitoring and estimations. By reassessing the greenhouse effect in the study area, we found that the corrected emission reduction potential of the basin is 30.3 %. Our novel approach for STCM can exhibit superior scalability, providing robust support for achieving agricultural carbon neutrality goals.