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Leveraging Network Science for Customer Segmentation and Product Recommendation
Get PDFThe rapid growth in e-commerce has forced the development and implementation of enhanced customer segmentation and recommendation systems, improving business results and improving customer experience. Traditional approaches, such as RFM analysis and clustering algorithms like K-means, are very helpful in many situations but usually fail to catch complex interdependencies among customers and products. This paper proposes a new approach using network science methodologies, a bipartite graph model, toward the advancement of customer segmentation and product recommendation. It implements a bipartite graph of customers and products using the Online Retail II dataset and proceeds with community detection, segmenting customers into unique groups. This current study converts the bipartite graph into a customer-to-customer network, where complex interrelations are revealed that may allow the development of highly targeted and precise marketing strategies. Centrality measures like degree, closeness, and betweenness centrality are applied to quantify the power of customers and their interconnectedness in the network. These metrics provide very important information to focused marketing, on customer roles such as the key influencer and bridging nodes. Indeed, network analysis approaches help explore customers\u27 behaviors much deeper than traditional methodologies, such as in community detection, centrality analysis, and collaborative filtering for much more personalized recommendations. This research by the authors has been done to emphasize the need for network science methodologies within customer segmentation and product recommendations for insights on targeted marketing strategies, customer loyalty initiatives, and cross-selling possibilities in competitive e-commerce contexts
Jewish Emigration and Satire in the Soviet Union (ca. 1970)
Get PDFThe Dymshits-Kuznetsov affair provides an important case study of the use of satire in Soviet media, primarily through easily understood cartoons like the ones seen in Krokodil. These satirical cartoons exploit Jews for alleged scapegoatism, anti-Sovietism, and other characteristics relating to internal problems. In this paper, the author explores the idea that when disinformation disguises itself as satire, it subverts the revolutionary and critical components that are traditionally attributed to the genre of satire
The Future Arrives On Flight 11: 9/11, the Crisis of Meaning, and Disinformation in the 21st Century
Get PDFPeter Pomerantsev has argued that, because the Soviet Union lost the Cold War, Russia had to adapt to a new epistemological world – a new way of knowing – more quickly than the “West;” He posits the fall of the Soviet Union as a defining moment in the development of disinformation as it is today. In this paper, the author builds on Pomerantsev’s argument to explore the idea that 9/11 triggered a similar epistemic shift in American society and that the ensuing traumas created a crisis of meaning that helps to explain the prevalence of disinformation in contemporary American discourse. Furthermore, I posit two responses to 9/11 that are symptomatic of the crisis of meaning: the “war on terror” narrative, and the 9/11 Truth conspiracy movement. I explain how these are epistemically harmful, and open the door for epistemic wrongs, e.g., disinformation
Rebuttal to Winn et al. (2024)\u27s Redefining searching in non-medical sciences systematic reviews
No full textLetter to the Editor and Rebuttal to Winn et al. (2024)
We are writing to express concerns regarding the article, Redefining searching in non-medical sciences systematic reviews: The ascendance of Google Scholar as the primary database (Winn et al., 2024a). Our concerns are numerous, and include issues involving the research questions, data availability and validity, representations of evidence synthesis methods, and the disregard for established scholarship which refutes the reliability, transparency and reproducibility of Google Scholar searches for evidence synthesis reviews
A Novel Preprocessing Model for Multi Modal Brain MRI image Classification for Stroke Prognosis
Get PDFMagnetic Resonance Imaging (MRI) is an imaging technique used for the diagnosis and observing the progression in various neurological disorders. Stroke is one of the prominent neurological disorders that creates significant impacts in the patients. It occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissues from getting oxygen and nutrients. Multimodal data from various modalities help clinicians in proper prognosis of stroke. Ischemic Stroke Lesion Segmentation Challenge (ISLES22) provides data of stroke data for various stroke patients, the dataset consists of three modalities of data – Fluid Attenuated Inversion Recovery (FLAIR), Apparent Diffusion Coefficient (ADC) and Diffusion-Weighted Imaging (DWI). Multimodal data gives a comprehensive understanding of the brain and the stroke lesions. Complex algorithms and processing steps are required to ensure that the data is prepared for further processing. The objective of this experimental research is to create a novel multimodal preprocessing model that can be used for the preprocessing of the multimodal data from various MRI modalities (FLAIR, DWI and ADC). The proposed model supports the automatic removal of artefacts from the multimodal data, by identifying and applying the best preprocessing techniques for Image Registration (Affine or non-rigid transformations), Normalization (Z Score or min-max normalizations), Denoising Techniques (Gaussian, Median, Non-Local Means, or Anisotropic Diffusion filters) and Bias Filed correction. The best technique is been identified using the evaluation techniques of Dice Coefficient, Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Root Mean Squared Error (RMSE). Preprocessing and preparing of the data are critical for better outcome from the subsequent analysis including segmentation. Here, we propose an Enhanced Image Registration and Artefact Correction (EIRAC) model with Best Image Registration Technique (BIRT) and Multiple Orientation Normalization Denoising and Bias filed correction Parallelly (MONDBP) algorithms for the preprocessing of multimodal MRI images to provides better results for the segmentation of stroke lesions through Machine Learning models
Zine Workshop #1
Get PDFThis zine was created from a presentation shared at Autumn Jacobs\u27 first Zine Workshop as a Library Research Scholar. It included the slides as well as contact information that participants could refer to if they had questions in the future about zine submission.https://orb.binghamton.edu/zines/1000/thumbnail.jp
The effect of IL-1β inhibitor canakinumab (Ilaris®) on IL-6 production in human skeletal muscle cells
No full textMuscle inflammation is one of the hallmarks of Duchenne muscular dystrophy (DMD). Dystrophin-deficient skeletal muscle cells produce higher levels of pro-inflammatory cytokines such as interleukin 1β (IL-1β) in response to toll-like receptor stimulation compared to normal muscle skeletal cells. IL- 1β induces the human skeletal muscle secretion of the myokine Interleukin-6 (IL-6). Here, we evaluated the effect of a human IgG1κ monoclonal antibody (canakinumab (Ilaris®)) that specifically blocks the IL-1β effect on IL-6 secretion by human skeletal muscle cells. Canakinumab is an excellent candidate for therapeutic repositioning to treat DMD because it is an FDA-approved drug to treat periodic fever syndromes and systemic juvenile idiopathic arthritis. Unlike previous generations of IL-1 inhibitors, canakinumab is highly specific for the IL-1β ligand, has a longer half-life, and does not interfere with other IL-1-activated inflammatory pathways. Following cell culture optimization and viability assays to assess toxicity, skeletal muscle cells were stimulated with IL-1β (10 ng/mL) for 48 hours in the presence of nine concentrations of canakinumab ranging from 0.001 nM to 1000 nM, and IL-6 production was measured with an enzyme-linked immunosorbent assay. Pre-incubation of myoblasts with canakinumab before IL-1β-stimulation, significantly reduced IL-6 production at concentrations of 1, 10, 100, 250, and 1000 nM relative to controls, yielding an IC50 of 0.264 nM. On the other hand, co-incubation of canakinumab with IL-1β before addition to myoblasts resulted in a significant inhibition with the IC50 reducing to 0.126 nM, less than half of the previous method. Canakinumab also did not affect myotube viability at 10 nM and was also able to significantly reduce the production of IL-6, when the cells were stimulated with IL-1β (10 ng/ml). Taken together, our results show that canakinumab is a potent inhibitor of IL-1β signaling in muscle cells. These results align with previously published pre-clinical work with other IL-1 inhibitors in the mdx mouse model and support further investigation into the clinical utility of repositioning canakinumab to treat DMD
Real-World Applications of Imipenem-Cilastatin-Relebactam: Insights From a Multicenter Observational Cohort Study
Get PDFBackground: Multidrug-resistant (MDR) gram-negative infections are a substantial threat to patients and public health. Imipenem-cilastatin-relebactam (IMI/REL) is a β-lactam/β-lactamase inhibitor with expanded activity against MDR Pseudomonas aeruginosa and carbapenem-resistant Enterobacterales. This study aims to describe the patient characteristics, prescribing patterns, and clinical outcomes associated with IMI/REL. Methods: This was a retrospective, multicenter, observational study of patients ≥18 years old who received IMI/REL for ≥48 hours for a suspected or confirmed gram-negative infection. The primary outcome was clinical success, defined as improvement or resolution of infection-related signs or symptoms while receiving IMI/REL and the absence of 30-day microbiologic failure. Multivariable logistic regression analysis was performed to identify independent predictors of clinical success. Results: The study included 151 patients from 24 US medical centers. IMI/REL was predominantly prescribed for lower respiratory tract infections, accounting for 52.3% of cases. Most patients were infected with a carbapenem-nonsusceptible pathogen (85.4%); P aeruginosa was frequently targeted (72.2%). Clinical success was achieved in 70.2% of patients. Heart failure, receipt of antibiotics within the past 90 days, intensive care unit admission at time of index culture collection, and isolation of difficult-to-treat resistant P aeruginosa were independently associated with a reduced odds of clinical success. Adverse events were reported in 6.0% of patients, leading to discontinuation of IMI/REL in 3 instances. Conclusions: This study provides a comprehensive analysis of the real-world effectiveness and safety of IMI/REL. Comparative studies and investigations of specific subgroups will further enhance our understanding of IMI/REL in treating MDR infections
Interstellar Inspiration: A Collaborative Keyword Brainstorming Activity for Your Students
No full textTeaching students to brainstorm keywords and helping them understand the precise language they will need to find research in library databases can be tricky. Help your students gather the rocket fuel they will need to blast off into the world of research by guiding them in a collaborative activity to generate keywords for database searching. In this activity students use padlet to anonymously enter information about a research question or topic and then spend time commenting on each other\u27s topics with potential keywords. Students then can use their peers\u27 ideas for keywords while searching in databases. Over time, I have discovered that creating good examples for students to follow and explaining this activity clearly can help them soar into using keywords in database searching. While I have used this lesson mostly in first year classes, it could be adapted to upper level information literacy sessions or used asynchronously. In this short video, participants will learn how to use or adapt this same lesson for their own teaching and how to effectively teach their students to help classmates develop a list of keywords for database searching
Open Repository @ Binghamton Monthly Additions and Top Downloads - April 2025
Get PDFNewly added work in the Open Repository @ Binghamton (ORB) from 4/1/2025-4/30/2025. Top downloads from same time frame