Experimental Case Study Research Articles

Experimental Case Study for Optimizing Heat Sink Performance using ZnO Nanocomposites integrated with Phase Change Materials for Environmental Sustainability

Experimental Case Study for Optimizing Heat Sink Performance using ZnO Nanocomposites integrated with Phase Change Materials for Environmental Sustainability

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.

Artificial Intelligence: Communication, Technology, and Society (a Systematic Literature Review)

The development of artificial intelligence (AI) technology has transformed the way various social sectors operate, including healthcare, communications, and decision-making. The integration of AI into these domains not only opens up new opportunities for efficiency and innovation, but also presents significant challenges. Among these challenges, the emergence of algorithmic bias, inadequate data protection, technical limitations, and complex ethical dilemmas are of major concern. These issues affect the reliability and fairness of the use of AI technologies, potentially harming individuals and society as a whole. This study aims to investigate the barriers associated with the implementation of AI in society and provide theoretical and methodological solutions to address these challenges. This study uses a systematic literature review methodology to review 40 journals, with the aim of identifying key issues and suggesting solutions for the ethical and successful integration of AI. The findings show that the potential of AI is often hampered by biased algorithms, inadequate privacy laws, and a lack of transparency in decision-making processes. The study recommends the use of qualitative and quantitative methodologies, including experimental research and case studies, to investigate practical applications of AI. In addition, a multidisciplinary approach is suggested to address the technical and social dimensions of AI applications.

The Influence of Graph Theory Approach in Preventing Cheating at Higher Learning Institution: A case of Institute of Accountancy Arusha

Academic dishonesty has been more common in recent years, both through self-reported and detected cases, which has brought significant attention to the issue of cheating in educational settings. Students often cheat for a variety of reasons, including lack of study time, unclear learning objectives, fear of failing, peer pressure, and parental pressure for academic success. Students who rely on plagiarizing answers miss the opportunity to build their skills and may face challenges in job requirements. The problem of cheating / copying the neighbor's works during the examination is particularly well known. With graph theory especially graph coloring, it is proposed to develop the students' sitting arrangement that can be the solution of cheating. An experimental case study focuses on three master's programs, three bachelor's degree programs, three diploma programs, and three certificate programs at the Institute of Accountancy Arusha. This is done when one room or a venue is assigned to two or more programs with different backgrounds depending on the size of the room. The result shows that using graph coloring has reduced the tendency of students to copy or ask questions from their neighbors in the examination room. Based on the assessment of graph theory's effectiveness in preventing cheating at the Institute of Accountancy Arusha, several recommendations can be made. First, the institution should invest in developing a comprehensive data collection system that captures relevant information such as seating arrangements, student interactions, and exam performance patterns. Collaboration with other institutions in implementing similar technologies could also create a shared knowledge base and lead to best practices in academic integrity. Finally, continuous monitoring and evaluation of this system should be conducted to refine its processes and adapt to new cheating technique.

AI-driven transcriptome profile-guided hit molecule generation

Denovo generation of bioactive and drug-like hit molecules is a pivotal goal in computer-aided drug discovery. While artificial intelligence (AI) has proven adept at generating molecules with desired chemical properties, previous studies often overlook the influence of disease-specific cellular environments. This study introduces GxVAEs, a novel AI-driven deep generative model designed to produce hit molecules from transcriptome profiles using dual variational autoencoders (VAEs). The first VAE, ProfileVAE, extracts latent features from transcriptome profiles to guide the second VAE, MolVAE, in generating hit molecules. GxVAEs aim to bridge the gap between molecule generation and the biological context of disease, producing molecules that are biologically relevant within specific cellular environments or pathological conditions. Experimental results and case studies focused on hit molecule generation demonstrate that GxVAEs surpass current state-of-the-art methods, in terms of reproducibility of known ligands. This approach is expected to effectively find potential molecular structures with bioactivities across diverse disease contexts.

Unsupervised deep denoising for four-dimensional scanning transmission electron microscopy

By simultaneously achieving high spatial and angular sampling resolution, four dimensional scanning transmission electron microscopy (4D STEM) is enabling analysis techniques that provide great insight into the atomic structure of materials. Applying these techniques to scientifically and technologically significant beam-sensitive materials remains challenging because the low doses needed to minimise beam damage lead to noisy data. We demonstrate an unsupervised deep learning model that leverages the continuity and coupling between the probe position and the electron scattering distribution to denoise 4D STEM data. By restricting the network complexity it can learn the geometric flow present but not the noise. Through experimental and simulated case studies, we demonstrate that denoising as a preprocessing step enables 4D STEM analysis techniques to succeed at lower doses, broadening the range of materials that can be studied using these powerful structure characterization techniques.

Analysis and mitigation of uncertainties in damage identification by modal-curvature based methods

The objective of this paper is to address and reduce the uncertainties associated with measurement noise and discretization in damage identification methods based on modal curvature analysis. The experimental case study considers the local reduction in the bending stiffness of a slender beam under free-free and simply supported boundary conditions. First, the analysis of error sources and their propagation is theoretically set up. Second, the mitigation of uncertainties in damage localization is pursued using a two-stage approach based on multiple hypothesis testing relative to the normalized indices and the definition of a combined macro-index. Finally, the Monte Carlo method is exploited to obtain the statistical error distribution of the experimental damage position and severity predictions by randomizing the numerical displacement mode shapes with the identified noise. The present analysis allows us to find the optimal number of sensors that minimizes the combination of bias and truncation errors, to highlight how sensor spacing and data noise affect damage localization, and to determine the uncertainty bounds of the predicted damage severity. The two-stage approach, enhanced by selecting thresholds related to real noise levels and tuned on SHM objectives, appears to improve identification accuracy compared to the separate use of damage indices based on absolute confidence levels.

Operational Modal Analysis of Self-excited Vibrations in Milling Considering Periodic Dynamics

Abstract A new method is presented to identify the dynamics of regenerative chatter from measured process vibrations in milling. This method combines the synchronous once-per-revolution sampling of process vibrations with Operational Modal Analysis to estimate the Floquet multipliers of the delayed linear time-periodic dynamics in milling, all from the natural process vibrations without external excitation. The identified multipliers quantify vibration stability, enabling chatter prediction before it occurs. In addition to this, they can be used to calibrate physics-based chatter models based on vibration measurements solely within the stable region. The method's accuracy in identifying Floquet multipliers is validated through extensive numerical simulations and two experimental case studies. The results show that chatter due to both Hopf and period-doubling bifurcations can be predicted from the process vibrations during stable cuts. Moreover, the experimental case studies demonstrate a vibration measurement system for implementing the presented method in standard milling operations and confirm its effectiveness in practice.

A deep drug prediction framework for viral infectious diseases using an optimizer-based ensemble of convolutional neural network: COVID-19 as a case study.

The SARS-CoV-2 outbreak highlights the persistent vulnerability of humanity to epidemics and emerging microbial threats, emphasizing the lack of time to develop disease-specific treatments. Therefore, it appears beneficial to utilize existing resources and therapies. Computational drug repositioning is an effective strategy that redirects authorized drugs to new therapeutic purposes. This strategy holds significant promise for newly emerging diseases, as drug discovery is a lengthy and expensive process. Through this study, we present an ensemble method based on the convolutional neural network integrated with genetic algorithm and deep forest classifier for virus-drug association prediction (CGDVDA). We generated feature vectors by combining drug chemical structure and virus genomic sequence-based similarities, and extracted prominent deep features by applying the convolutional neural network. The convoluted features are optimized using the genetic algorithm and classified using the ensemble deep forest classifier to predict novel virus-drug associations. The proposed method predicts drugs for COVID-19 and other viral diseases in the dataset. The model could achieve ROC-AUC scores of 0.9159 on fivefold cross-validation. We compared the performance of the model with state-of-the-art approaches and classifiers. The experimental results and case studies illustrate the efficacy of CGDVDA in predicting drugs against viral infectious diseases.

Physics-supported Bayesian machine learning for chatter prediction with process damping in milling

Chatter stability of milling operations is a complicated phenomenon causing serious productivity issues in the manufacturing industry, yet a shop-floor implementable solution is lacking. This paper follows a physics-supported Bayesian machine learning approach and incorporates the potential effect of process damping on the stability of the process. Using a likelihood function based on the Nyquist stability criterion, the learning system monitors the actual stability state of the process during arbitrary cuts and refines the underlying model parameter uncertainties in the structural dynamics, cutting force coefficients, as well as the process damping. The framework can operate with limited training data and display the remaining uncertainties in stability predictions to the machine operator. Experimental case studies show the effectiveness of the proposed method and highlight the importance of considering process damping for certain endmills.