ISME Research Publications

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    Effect of consistency of the review set on causal attribution: the moderating roles of repeating purchase cues and product knowledge
    (Emerald Publishing, 2024-01) Peng, Xiao; Vali, Hessam; Peng, Xixian; Xu, David Jingjun; Yildirim, Mehmet Bayram
    Purpose: The study examines the potential moderating effects of repeating purchase cues and product knowledge on the relationship between the varying consistency of the review set and causal attribution. This study also investigates how causal attribution correlates with the perceived misleadingness of the review set. Design/methodology/approach: A scenario-based experiment was conducted with 170 participants to explore the relationship between the consistency of the review set and causal attribution and how repeating purchase cues and product knowledge moderates this relationship. Findings: Findings suggest that inconsistent review sets lead to more product (vs reviewer) attribution than consistent review sets. The repeating purchase cues mitigate the negative relationship between the consistency of the review set and product attribution, whereas product knowledge mitigates the positive relationship between the consistency of the review set and reviewer attribution. Furthermore, the results indicate that high product attribution and low reviewer attribution are associated with low perceived misleadingness. Originality/value: This study is novel because it examines the moderating effects of repeating purchase cues and product knowledge on the relationship between the consistency of the review set and causal attribution. It adds to the literature by shedding light on the causal attribution process underlying the formation of perceived misleadingness of online reviews. The findings of this study provide valuable insights for managers on how to enhance the positive effects of consistent review sets and mitigate the negative effects of inconsistent review sets.
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    Decision support system for appointment scheduling and overbooking under patient no-show behavior
    (Springer, 2024-01) Topuz, Kazim; Urban, Timothy L.; Russell, Robert A.; Yildirim, Mehmet Bayram
    Data availability enables clinics to use predictive analytics to improve appointment scheduling and overbooking decisions based on the predicted likelihood of patients missing their appointment (no-shows). Analyzing data using machine learning can uncover hidden patterns and provide valuable business insights to devise new business models to better meet consumers' needs and seek a competitive advantage in healthcare. The innovative application of machine learning and analytics can significantly increase the operational efficiency of online scheduling. This study offers an intelligent, yet explainable, analytics framework in scheduling systems for primary-care clinics considering individual patients' no-show rates that may vary for each appointment day and time while generating appointment and overbooking decisions. We use the predicted individual no-show rates in two ways: (1) a probability-based greedy approach to schedule patients in time slots with the lowest no-show likelihood, and (2) marginal analysis to identify the number of overbookings based on the no-show probabilities of the regularly-scheduled patients. We find that the summary measures of profit and cost are considerably improved with the proposed scheduling approach as well as an increase in the number of patients served due to a substantial decrease in the no-show rate. Sensitivity analysis confirms the effectiveness of the proposed dynamic scheduling framework even further.
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    A novel nonnegative matrix factorization-based model for attributed graph clustering by incorporating complementary information
    (Elsevier Ltd, 2024-05) Jannesari, Vahid; Keshvari, Maryam; Berahmand, Kamal
    Attributed graph clustering is a prominent research area, catering to the increasing need for understanding real-world systems by uncovering exhaustive meaningful latent knowledge from heterogeneous spaces. Therefore, the critical challenge of this problem is the strategy used to extract and integrate meaningful heterogeneous information from structure and attribute sources. To this end, in this paper, we propose a novel Nonnegative Matrix Factorization (NMF)-based model for attributed graph clustering. In this method, firstly, we filter structure and attribute spaces from noise and irrelevant information for clustering by applying Symmetric NMF and NMF during the clustering task, respectively. Then, to overcome the heterogeneity of discovered partitions from spaces, we suggest a new regularization term to inject the complementary information from the attribute partition into the structure by transforming them into their pairwise similarity spaces, which are homogeneous. Simultaneously, by setting orthogonality constraints on the discovered communities, we encourage the representation of distinct and non-overlapping communities within the attributed graph. Finally, we collect all these terms in a unified framework to learn a meaningful partition containing consensus and complementary information from structure and attributes. Then a new iterative multiplicative updating strategy is proposed to solve the proposed model, and its convergence is proven theoretically. Our experiments on the nine popular real-world networks illustrate the supremacy of our methods among eleven widely recognized and stat-of-the-arts attributed graph clustering methods in terms of accurately matching the ground truth and quality-based metrics.
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    Hydrophilic and Antibacterial Electrospun Nanofibers from Monofilament Fishing Lines
    (Korean Fiber Society, 2023-12) Ijaola, Ahmed O.; Mohammed, Qamar Saberi; Obi, Mmasi; Akamo, Damilola O.; Ajiboye, Emmanuel Gboyega; Twomey, Janet M.; Yang, Shang-You; Asmatulu, Eylem
    Microplastics such as monofilament fishing lines (MFLs) are major pollutants in the marine environment and affect marine life and water quality. To solve this global menace, many researchers have been working on several ways to recycle these wastes and convert them into value-added products such as nanofibers. In this study, we produced novel nanofibers through the electrospinning of a polymeric solution consisting of MFL, hydroxyapatite (HAP), and silver nanoparticles (AgNPs). These fabricated nanofibers were further characterized to study their wettability, surface morphology, surface chemistry, thermal degradation, and antibacterial capability. Results from the incorporation of HAP and AgNPs showed increased fiber diameter and scattered fiber orientation. The addition of 0.4 and 1.5 wt% AgNPs to the nanofibers improved their thermal stabilities for temperatures above 350°C. The MFL + HAP + 1.5 wt% AgNPs nanofibers showed the best antibacterial performance against Escherichia coli (gram-negative) and Staphylococcus aureus (gram-positive), with bactericidal efficiencies of 70.5% and 68.6%, respectively. Also, increasing the size of the nanofiber aids cell proliferation. These fabricated nanofibers could be used for biomedical and water purification applications.
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    Detection of Small Screws Using Machine Learning
    (Institute of Electrical and Electronics Engineers Inc., 2023-10) Ambaye, Getachew; Krishnan, Krishna K.; Boldsaikhan, Enkhsaikhan
    Small screws are commonly employed in assenrbling similar or dissimilar components of various industrial or consumer products. In automated assembly lines, detecting and sorting small targets like screws require accurate sensing and machine learning. Identifying the size of the screw is crucial for assembling and disassembling. This study focuses on the detection of small screws captured in images using a recent machine learning platform named You Only Look Once (YOLO) Version 8. This algorithmic platform offers powerful supervised machine-learning engines with highly competitive accuracy and speed. The data set from this study consists of images of small black carbon steel flat head screws. The dataset is divided into training, development, and testing subsets. There are 24 classes of screws with extremely small difference in attributes in their sizes. The dataset is annotated using the Make Sense open-source annotating tool. The results indicate that the machine learning method demonstrates 99.3% mean average precision (mAP). This detection performance is promising in comparison to the results documented by previous studies.