Experimental Case Study Research Articles

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.

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.

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.

Pair-EGRET: Enhancing the prediction of protein-protein interaction sites through graph attention networks and protein language models.

Proteins are responsible for most biological functions, many of which require the interaction of more than one protein molecule. However, accurately predicting protein-protein interaction (PPI) sites (the interfacial residues of a protein that interact with other protein molecules) remains a challenge. The growing demand and cost associated with the reliable identification of PPI sites using conventional experimental methods call for computational tools for automated prediction and understanding of PPIs. We present Pair-EGRET, an edge-aggregated graph attention network that leverages the features extracted from pre-trained transformer-like models to accurately predict PPI sites. Pair-EGRET works on a k-nearest neighbor graph, representing the three-dimensional structure of a protein, and utilizes the cross-attention mechanism for accurate identification of interfacial residues of a pair of proteins. Through an extensive evaluation study using a diverse array of experimental data, evaluation metrics, and case studies on representative protein sequences, we demonstrate that Pair-EGRET can achieve remarkable performance in predicting PPI sites. Moreover, Pair-EGRET can provide interpretable insights from the learned cross-attention matrix. Pair-EGRET is freely available in open source form at the GitHub Repository https://github.com/1705004/Pair-EGRET. Supplementary data are available at Bioinformatics online.

Experimental Case Study on Mixed Ignition According to the Law of Safety Management of Hazardous Materials

Experimental Case Study on Mixed Ignition According to the Law of Safety Management of Hazardous Materials

Screening of Key Transcripts from Expression Data Using Applied Artificial Intelligence for Cancer Prediction

Research on the effects of artificial intelligence (AI)-driven solutions in the field of oncology is still being conducted all around the world. Applications of AI to identify the transcripts that cause cancer are being investigated, particularly when employing ensemble learning techniques. Ensemble feature fusion is the process of distributing the feature selection process and combining the local features that were chosen to create a smaller global feature set. This article addresses the use of ensemble feature fusion in the field of differential transcript expression analysis to screen for important transcripts linked to a disease. Owing to the necessity and significance of research in cancer diagnosis, an ensemble feature fusion approach experimental case study has been carried out using 109 liver cancer samples obtained from Mitranscriptome dataset. It was found that the expression data were used to filter the most pertinent transcripts, which allowed for the best possible differentiation between sample type. Accordingly, a set of 26 significant liver cancer causing transcripts has been screened using unanimous voting-scheme, giving an accuracy of percentage of 96. During generalization testing, cancer prediction classifiers constructed with this essential transcript collection shown excellent discriminating power and performed well in differentiating between normal and malignant cells. By resolving the “high dimension-low sample (High p Low n)” issue that is typically present in the expression data, this improves the predicting ability of cancer diagnostic systems. In the field of oncology, artificial intelligence-powered solutions will facilitate the development of applications that prioritize Sustainable Development Goal 3: Good Health and Well-Being.

Optimizing capital allocation in microfinance projects: an experimental case study in Barranquilla, Colombia

Optimizing capital allocation in microfinance projects: an experimental case study in Barranquilla, Colombia

Decision support system based on bipolar complex fuzzy Hamy mean operators

The important feature of the multi-attribute decision-making (MADM) technique is to identify an ideal solution and aggregate collective cognitive fuzzy information of human opinion. To serve this purpose, we explore the concepts of the bipolar complex fuzzy set with positive and negative support terms. A few applications of the Hamy mean (HM) and Dual Hamy mean (DHM) models are also discussed to find out the relationship among input arguments or different preferences. For this, we derive a family of mathematical approaches by incorporating the theory of bipolar complex fuzzy information such as bipolar complex fuzzy Hamy mean (BCFHM), bipolar complex fuzzy weighted Hamy mean (BCFWHM), bipolar complex fuzzy Dual Hamy mean (BCFDHM), and bipolar complex fuzzy weighted Dual Hamy mean (BCFWDHM) operators. Derived mathematical approaches are more applicable and can express the influence of uncertain information due to the involvement of additional parameter values. Based on diagnosed research work and mathematical methodologies, we establish a decision algorithm for the MADM problem to resolve real-life dilemmas. An experimental case study demonstrates the compatibility of derived approaches and evaluates the investment policy's sustainability based on certain parameters. The advantages and consistency of the proposed research work are verified in the comparative study with various existing aggregation operators.

Flashlight model: Integrating attention distribution and attention resources for pilots’ visual behaviour analysis and performance prediction

Pilot performance is almost the last line of defense in aircraft safety. Recent years have seen a surge in research aimed at utilizing eye-tracking technology to predict pilot performance, enhancing aviation safety margins. A decline in pilot performance is often attributed to either misdirected attention towards irrelevant tasks or inefficient information processing owing to limited attention resources. Previous research has shown that eye-tracking data can effectively capture these issues and provide accurate performance predictions. Nevertheless, the existing studies either focus on attention distribution or attention resources separately, neglecting the complex interactions between them. To address this gap, our study proposes a synthesized Flashlight model-based eye-tracking analysis for pilot performance prediction, integrating the two perspectives. Accordingly, the combined AOI-gaze metrics are proposed to offer a more nuanced analysis of information processing across specific Areas of Interest (AOIs), thereby enhancing the analysis of gaze metrics. We examined the efficacy of the combined AOI-gaze metrics in the Gradient-boosted decision trees(GBDT) model for pilot performance prediction and compared them with other widely used eye-tracking metrics in a simulated flight experiment case study. Moreover, we employed the SHapley Additive exPlanations (SHAP) method to identify the most influential eye-tracking measurements for pilots’ performance prediction. The result demonstrated that the selected eye-tracking measurements obtained the highest accuracy in performance prediction.

Development and validation of a fully coupled thermo-mechanical model for in-situ micro-rolling in laser-directed energy deposition: Single-track multi-layer case

Directed Energy Deposition (DED) has recently been hybridised with in-situ rolling, providing elevated temperature deformations and enhancing build quality. Analysing the thermo-mechanical aspects of deformations with sufficient fidelity can not be achieved experimentally. So, a fully coupled thermo-mechanical model for a multi-layer case using the finite element method has been developed to capture the thermal and mechanical facets of the deposition and rolling processes. Modelling features a hybrid active and quiet element activation strategy that mitigates convergence issues from activating a newly deposited layer over previously rolled ones. The model's predictions have been validated with reasonable accuracy against temperature history and rolling load measurements in an experimental case study using IN718. These predictions provide insights into the in-situ rolling effects with their quantification, including the impact on the thermal and stress-strain fields and histories. For instance, the temperature at the rolling location and the local temperature drop increase with the number of layers. The thermal stress induced by deposition overshadows the influence of in-situ rolling. The rolling deformation penetrates till the third preceding layer, and deposition leads to above the recrystallisation temperature reheating till the fourth layer. These predictions can be used for parametric optimisation with metallurgical criteria.

Explaining Genetic Programming-Evolved Routing Policies for Uncertain Capacitated Arc Routing Problems

Genetic programming has been successfully used to evolve routing policies that can make real-time routing decisions for uncertain arc routing problems. Although the evolved routing policies are highly effective, they are typically very large and complex, and hard to be understood and trusted by real users. Existing studies have attempted to improve the interpretability by developing new genetic programming approaches to evolve both effective and interpretable (e.g., with smaller program size) routing policies. However, they still have limitations due to the trade-off between effectiveness and interpretability. To address this issue, we propose a new post-hoc explanation approach to explaining the effective but complex routing policies evolved by genetic programming. The new approach includes a local ranking explanation and a global explanation module. The local ranking explanation uses particle swarm optimisation to learn an interpretable linear model that accurately explains the local behaviour of the routing policy for each decision situation. Then, the global explanation module uses a clustering technique to summarise the local explanations into a global explanation. The experimental results and case studies on the benchmark datasets show that the proposed method can obtain accurate and understandable explanations of the routing policies evolved for uncertain arc routing problems. Our explanation approach is not restricted to uncertain arc routing, but has a great potential to be generalised to other optimisation and machine learning problems such as learning classifier systems and reinforcement learning.

Attachment-Based Family Therapy for Transgender Adolescent with Suicidal Thoughts: A Case Study

Aim: The aim of present study was the application of the Attachment-Based Family Therapy (ABFT) for a transgender adolescent with suicidal thoughts. Methods: Multiple baseline experimental single case study was used as the method of the present study. The population sample is a transgender adolescent (female to male) and his/her parent chosen according to the purposive sampling method. The adolescent completed The Suicidal Ideation Scale (SIS) over the course of the sessions. ABFT was implemented in twenty-seven 90 minute-sessions. Furthermore, visual inspection is applied to analyze and interpret data. Results: The results of this qualitative and step-by-step investigation of the attachment-based family therapy showed that this therapy was effective in significantly reducing the suicidal thoughts in the transgender adolescent. Conclusion: Effective intervention with transgender adolescents and their families can foster a secure attachment relationship. Family therapists may employ attachment-based family therapy to address suicidal tendencies among transgender individuals. This approach also supports families in navigating the transformative journey of their transgender child.

Proposal of a scissor-based model for the non-linear analysis of RC beam-column joints strengthened by FRP

The paper presents a new simple modeling approach for studying the monotonic response of RC beam-column joints externally strengthened by Fiber Reinforced Polymer (FRP) materials. The approach assumes a parallel combination of the behavior of the joint in the unstrengthened configuration and the contribution of strengthening system. To achieve this, the common scissor model, where the behavior of joint shear panel is modeled through a rotational spring, is here modified by introducing an additional spring, arranged parallel to the concrete spring, to account for the contribution of strengthening system. Regarding the behavior of the additional spring, the authors proposed a multilinear simplified constitutive law by appropriately combining analytical models available from the literature with specific guidelines provided by Italian technical standards. The proposed model, implemented in the computer code OpenSees and validated against experimental case studies from the literature, demonstrates its strong capability to capture the contribution of the strengthening system on both the strength and ductility of joints.

Teaching compassionate-based behaviors across communication partners to students of applied behavior analysis: a preliminary investigation of comparative effectiveness

Applied behavior analysis (ABA) focuses on providing socially significant treatments and practices. Recent advancements in ethically-minded research have encouraged practitioners to think beyond the technical skills required for implementing behavior-analytic practices and consider how these practices are delivered. Compassionate care in ABA has recently been conceptually defined and subsequently researched to explore ways to explicitly and systematically teach compassionate-care-based skills within the scope of ABA. Researchers have noted barriers specific to the time required to teach compassionate-care-based skills using behavior skills training (BST) especially when working to train groups in compassionate care skills. Further, these studies have focused primarily on using compassionate care towards interactions with caregivers as communication partners. This study operationalized the compassionate care-based skill of receiving feedback within a behavioral analytic lens and taught it to eight master's students enrolled in an ABA course. An experimental case study approach was used to evaluate the relative benefit of modified behavioral skills training (BST) versions, including conditions with and without rehearsal opportunities and individualized performance feedback. Results indicated that all participants demonstrated significant skill increases across multiple communication partners (i.e., client, caregiver, colleague) compared to baseline sessions, with conditions including opportunities for rehearsal and performance feedback resulting in higher skill acquisition for seven of the eight participants. Conditions that required the participants to generalize to novel scenarios and novel communication partners resulted in lower competencies; however, all participant skills across all communication partners remained higher than baseline measures. Limitations included practicing skills within role-play scenarios only, the application of compassionate care-based skills in a variety of cultures, and limits related to using an experimental case study as a methodological approach. Implications for future research and practitioner-based applications are discussed.

Review on Advanced Storage Control Applied to Optimized Operation of Energy Systems for Buildings and Districts: Insights and Perspectives

In the context of increasing energy demands and the integration of renewable energy sources, this review focuses on recent advancements in energy storage control strategies from 2016 to the present, evaluating both experimental and simulation studies at component, system, building, and district scales. Out of 426 papers screened, 147 were assessed for eligibility, with 56 included in the final review. As a first outcome, this work proposes a novel classification and taxonomy update for advanced storage control systems, aiming to bridge the gap between theoretical research and practical implementation. Furthermore, the study emphasizes experimental case studies, moving beyond numerical analyses to provide practical insights. It investigates how the literature on energy storage is enhancing building flexibility and resilience, highlighting the application of advanced algorithms and artificial intelligence methods and their impact on energy and financial savings. By exploring the correlation between control algorithms and the resulting benefits, this review provides a comprehensive analysis of the current state and future perspectives of energy storage control in smart grids and buildings.

A preliminary investigation of the inverse Finite Element Method applied to bridge structures

Deflection monitoring plays a key role in bridge Structural Health Monitoring, as deflection data may offer valuable insights into potential structural issues, such as cracks and corrosion. However, monitoring bridge deflections under operative loads presents challenges, particularly in scenarios where establishing a fixed reference point for displacement sensors is impractical, such as when a bridge spans a waterway or the sensors may be vulnerable to vandalism. In recent years, the inverse Finite Element Method (iFEM) has emerged as a possible solution. This method provides a load- and material-independent means of computing structural displacements from strain measurements, in real time at a minimal computational cost. Additionally, the iFEM can serve as a virtual sensing approach to monitor strains at locations lacking sensors, enabling the assessment of the fatigue life consumption at virtually any point in the structure. Despite its potential, applications of the iFEM in the civil engineering field are very limited, with no validation on full scale bridges has been performed to date. This study addresses this gap by investingating the application of the iFEM for bridge deflection monitoring through distributed strain measurements. The proposed approach is tested on experimental case studies, involving cross-validatiion of reference-free deflections inferred by the iFEM with those obtained from displacement sensors. This work contributes to the advancements of structural health monitoring methodologies in civil engineering, demonstrating the practicality and reliability of iFEM in bridge applications.