Trine University

Solid‐oxide fuel cells based on doped ceria electrolytes and operating at 500°C are shown to be feasible. The operating regime of doped ceria electrolytes is discussed. It is shown that the ionic conductivity of ceria‐based fuel cells is sufficiently high for operation with hydrogen fuel at low temperatures. The major challenges of fabricating a thin electrolyte by a conventional method and the development of high‐performance cathodes capable of operating at 500–600°C are addressed. Cells based on thin‐film ceria electrolytes also exhibited good open‐circuit voltages between 0.97 and 1 V. Cathode materials with high performance have been developed from pyrochlores, perovskites, and cermets of silver and doped bismuth oxide. The advantages and disadvantages of different cathode materials are discussed. The maximum power density obtained at 500°C was . © 1999 The Electrochemical Society. All rights reserved.

In today’s global market, a brand’s marketing strategy must go head‐to‐head, not only with regional or national brands, but also with international competitors’ marketing strategies. This adds an entirely new dimension to a company’s marketing strategy when it comes to identifying, attracting, and retaining a market. This paper examines the concept of brand loyalty, discusses the various issues connected with brand loyalty, discusses cross‐cultural views on brand loyalty throughout the world, and illustrates the proliferation of brand loyalty across international frontiers.

A numerical investigation on the self-induced unsteadiness in tip leakage flow is presented for a transonic fan rotor. NASA Rotor 67 is chosen as the computational model. It is found that under certain conditions the self-induced unsteadiness can be originated from the interaction of two important driving “forces:” the incoming main flow and the tip leakage flow. Among all the simulated cases, the self-induced unsteadiness exists when the size of the tip clearance is equal to or larger than the design tip clearance. The originating mechanism of the unsteadiness is clarified through time-dependent internal flow patterns in the rotor tip region. It is demonstrated that when strong enough, the tip leakage flow impinges the pressure side of neighboring blade and alters the blade loading significantly. The blade loading in turn changes the strength of the tip leakage flow and results in a flow oscillation with a typical signature frequency. This periodic process is further illustrated by the time-space relation between the driving forces. A correlation based on the momentum ratio of tip leakage flow over the incoming main flow at the tip region is used as an indicator for the onset of the self-induced unsteadiness in tip leakage flow. It is discussed that the interaction between shock wave and tip leakage vortex does not initiate the self-induced unsteadiness, but might be the cause of other types of unsteadiness, such as broad-banded turbulence unsteadiness.

BACKGROUND: Chronic spinal pain is the most prevalent chronic disease with employment of multiple modes of interventional techniques including epidural interventions. Multiple randomized controlled trials (RCTs), observational studies, systematic reviews, and guidelines have been published. The recent review of the utilization patterns and expenditures show that there has been a decline in utilization of epidural injections with decrease in inflation adjusted costs from 2009 to 2018. The American Society of Interventional Pain Physicians (ASIPP) published guidelines for interventional techniques in 2013, and guidelines for facet joint interventions in 2020. Consequently, these guidelines have been prepared to update previously existing guidelines. OBJECTIVE: To provide evidence-based guidance in performing therapeutic epidural procedures, including caudal, interlaminar in lumbar, cervical, and thoracic spinal regions, transforaminal in lumbar spine, and percutaneous adhesiolysis in the lumbar spine. METHODS: The methodology utilized included the development of objective and key questions with utilization of trustworthy standards. The literature pertaining to all aspects of epidural interventions was viewed with best evidence synthesis of available literature and recommendations were provided. RESULTS: In preparation of the guidelines, extensive literature review was performed. In addition to review of multiple manuscripts in reference to utilization, expenditures, anatomical and pathophysiological considerations, pharmacological and harmful effects of drugs and procedures, for evidence synthesis we have included 47 systematic reviews and 43 RCTs covering all epidural interventions to meet the objectives.The evidence recommendations are as follows: Disc herniation: Based on relevant, high-quality fluoroscopically guided epidural injections, with or without steroids, and results of previous systematic reviews, the evidence is Level I for caudal epidural injections, lumbar interlaminar epidural injections, lumbar transforaminal epidural injections, and cervical interlaminar epidural injections with strong recommendation for long-term effectiveness.The evidence for percutaneous adhesiolysis in managing disc herniation based on one high-quality, placebo-controlled RCT is Level II with moderate to strong recommendation for long-term improvement in patients nonresponsive to conservative management and fluoroscopically guided epidural injections. For thoracic disc herniation, based on one relevant, high-quality RCT of thoracic epidural with fluoroscopic guidance, with or without steroids, the evidence is Level II with moderate to strong recommendation for long-term effectiveness.Spinal stenosis: The evidence based on one high-quality RCT in each category the evidence is Level III to II for fluoroscopically guided caudal epidural injections with moderate to strong recommendation and Level II for fluoroscopically guided lumbar and cervical interlaminar epidural injections with moderate to strong recommendation for long-term effectiveness.The evidence for lumbar transforaminal epidural injections is Level IV to III with moderate recommendation with fluoroscopically guided lumbar transforaminal epidural injections for long-term improvement. The evidence for percutaneous adhesiolysis in lumbar stenosis based on relevant, moderate to high quality RCTs, observational studies, and systematic reviews is Level II with moderate to strong recommendation for long-term improvement after failure of conservative management and fluoroscopically guided epidural injections. Axial discogenic pain: The evidence for axial discogenic pain without facet joint pain or sacroiliac joint pain in the lumbar and cervical spine with fluoroscopically guided caudal, lumbar and cervical interlaminar epidural injections, based on one relevant high quality RCT in each category is Level II with moderate to strong recommendation for long-term improvement, with or without steroids. Post-surgery syndrome: The evidence for lumbar and cervical post-surgery syndrome based on one relevant, high-quality RCT with fluoroscopic guidance for caudal and cervical interlaminar epidural injections, with or without steroids, is Level II with moderate to strong recommendation for long-term improvement. For percutaneous adhesiolysis, based on multiple moderate to high-quality RCTs and systematic reviews, the evidence is Level I with strong recommendation for long-term improvement after failure of conservative management and fluoroscopically guided epidural injections. LIMITATIONS: The limitations of these guidelines include a continued paucity of high-quality studies for some techniques and various conditions including spinal stenosis, post-surgery syndrome, and discogenic pain. CONCLUSIONS: These epidural intervention guidelines including percutaneous adhesiolysis were prepared with a comprehensive review of the literature with methodologic quality assessment and determination of level of evidence with strength of recommendations.

Breast cancer is a relatively common cancer among gynecological cancers. Its diagnosis often relies on the pathology of cells in the lesion. The pathological diagnosis of breast cancer not only requires professionals and time, but also sometimes involves subjective judgment. To address the challenges of dependence on pathologists expertise and the time-consuming nature of achieving accurate breast pathological image classification, this paper introduces an approach utilizing convolutional neural networks (CNNs) for the rapid categorization of pathological images, aiming to enhance the efficiency of breast pathological image detection. And the approach enables the rapid and automatic classification of pathological images into benign and malignant groups. The methodology involves utilizing a convolutional neural network (CNN) model leveraging the Inceptionv3 architecture and transfer learning algorithm for extracting features from pathological images. Utilizing a neural network with fully connected layers and employing the SoftMax function for image classification. Additionally, the concept of image partitioning is introduced to handle high-resolution images. To achieve the ultimate classification outcome, the classification probabilities of each image block are aggregated using three algorithms: summation, product, and maximum. Experimental validation was conducted on the BreaKHis public dataset, resulting in accuracy rates surpassing 0.92 across all four magnification coefficients (40X, 100X, 200X, and 400X). It demonstrates that the proposed method effectively enhances the accuracy in classifying pathological images of breast cancer.

Gastric cancer and Colon adenocarcinoma represent widespread and challenging malignancies with high mortality rates and complex treatment landscapes. In response to the critical need for accurate prognosis in cancer patients, the medical community has embraced the 5-year survival rate as a vital metric for estimating patient outcomes. This study introduces a pioneering approach to enhance survival prediction models for gastric and Colon adenocarcinoma patients. Leveraging advanced image analysis techniques, we sliced whole slide images (WSI) of these cancers, extracting comprehensive features to capture nuanced tumor characteristics. Subsequently, we constructed patient-level graphs, encapsulating intricate spatial relationships within tumor tissues. These graphs served as inputs for a sophisticated 4-layer graph convolutional neural network (GCN), designed to exploit the inherent connectivity of the data for comprehensive analysis and prediction. By integrating patients’ total survival time and survival status, we computed C-index values for gastric cancer and Colon adenocarcinoma, yielding 0.57 and 0.64, respectively. Significantly surpassing previous convolutional neural network models, these results underscore the efficacy of our approach in accurately predicting patient survival outcomes. This research holds profound implications for both the medical and AI communities, offering insights into cancer biology and progression while advancing personalized treatment strategies. Ultimately, our study represents a significant stride in leveraging AI-driven methodologies to revolutionize cancer prognosis and improve patient outcomes on a global scale.

The extracellular matrix (ECM) composition greatly influences cancer progression, leading to differential invasion, migration, and metastatic potential. In breast cancer, ECM components, such as fibroblasts and ECM proteins, have the potential to alter cancer cell migration. However, the lack of in vitro migration models that can vary ECM composition limits our knowledge of how specific ECM components contribute to cancer progression. Here, a microfluidic model was used to study the effect of 3D heterogeneous ECMs (i.e., fibroblasts and different ECM protein compositions) on the migration distance of a highly invasive human breast cancer cell line, MDA-MB-231. Specifically, we show that in the presence of normal breast fibroblasts, a fibronectin-rich matrix induces more cancer cell migration. Analysis of the ECM revealed the presence of ECM tunnels. Likewise, cancer-stromal crosstalk induced an increase in the secretion of metalloproteinases (MMPs) in co-cultures. When MMPs were inhibited, migration distance decreased in all conditions except for the fibronectin-rich matrix in the co-culture with human mammary fibroblasts (HMFs). This model mimics the in vivo invasion microenvironment, allowing the examination of cancer cell migration in a relevant context. In general, this data demonstrates the capability of the model to pinpoint the contribution of different components of the tumor microenvironment (TME).

BACKGROUND: Chronic axial spinal pain is one of the major causes of significant disability and health care costs, with facet joints as one of the proven causes of pain. OBJECTIVE: To provide evidence-based guidance in performing diagnostic and therapeutic facet joint interventions. METHODS: The methodology utilized included the development of objectives and key questions with utilization of trustworthy standards. The literature pertaining to all aspects of facet joint interventions, was reviewed, with a best evidence synthesis of available literature and utilizing grading for recommendations.Summary of Evidence and Recommendations:Non-interventional diagnosis: • The level of evidence is II in selecting patients for facet joint nerve blocks at least 3 months after onset and failure of conservative management, with strong strength of recommendation for physical examination and clinical assessment. • The level of evidence is IV for accurate diagnosis of facet joint pain with physical examination based on symptoms and signs, with weak strength of recommendation. Imaging: • The level of evidence is I with strong strength of recommendation, for mandatory fluoroscopic or computed tomography (CT) guidance for all facet joint interventions. • The level of evidence is III with weak strength of recommendation for single photon emission computed tomography (SPECT) . • The level of evidence is V with weak strength of recommendation for scintography, magnetic resonance imaging (MRI), and computed tomography (CT) .Interventional Diagnosis:Lumbar Spine: • The level of evidence is I to II with moderate to strong strength of recommendation for lumbar diagnostic facet joint nerve blocks. • Ten relevant diagnostic accuracy studies with 4 of 10 studies utilizing controlled comparative local anesthetics with concordant pain relief criterion standard of ≥80% were included. • The prevalence rates ranged from 27% to 40% with false-positive rates of 27% to 47%, with ≥80% pain relief.Cervical Spine: • The level of evidence is II with moderate strength of recommendation. • Ten relevant diagnostic accuracy studies, 9 of the 10 studies with either controlled comparative local anesthetic blocks or placebo controls with concordant pain relief with a criterion standard of ≥80% were included. • The prevalence and false-positive rates ranged from 29% to 60% and of 27% to 63%, with high variability. Thoracic Spine: • The level of evidence is II with moderate strength of recommendation. • Three relevant diagnostic accuracy studies, with controlled comparative local anesthetic blocks, with concordant pain relief, with a criterion standard of ≥80% were included. • The prevalence varied from 34% to 48%, whereas false-positive rates varied from 42% to 58%.Therapeutic Facet Joint Interventions: Lumbar Spine: • The level of evidence is II with moderate strength of recommendation for lumbar radiofrequency ablation with inclusion of 11 relevant randomized controlled trials (RCTs) with 2 negative studies and 4 studies with long-term improvement. • The level of evidence is II with moderate strength of recommendation for therapeutic lumbar facet joint nerve blocks with inclusion of 3 relevant randomized controlled trials, with long-term improvement. • The level of evidence is IV with weak strength of recommendation for lumbar facet joint intraarticular injections with inclusion of 9 relevant randomized controlled trials, with majority of them showing lack of effectiveness without the use of local anesthetic. Cervical Spine: • The level of evidence is II with moderate strength of recommendation for cervical radiofrequency ablation with inclusion of one randomized controlled trial with positive results and 2 observational studies with long-term improvement. • The level of evidence is II with moderate strength of recommendation for therapeutic cervical facet joint nerve blocks with inclusion of one relevant randomized controlled trial and 3 observational studies, with long-term improvement. • The level of evidence is V with weak strength of recommendation for cervical intraarticular facet joint injections with inclusion of 3 relevant randomized controlled trials, with 2 observational studies, the majority showing lack of effectiveness, whereas one study with 6-month follow-up, showed lack of long-term improvement. Thoracic Spine: • The level of evidence is III with weak to moderate strength of recommendation with emerging evidence for thoracic radiofrequency ablation with inclusion of one relevant randomized controlled trial and 3 observational studies. • The level of evidence is II with moderate strength of recommendation for thoracic therapeutic facet joint nerve blocks with inclusion of 2 randomized controlled trials and one observational study with long-term improvement. • The level of evidence is III with weak to moderate strength of recommendation for thoracic intraarticular facet joint injections with inclusion of one randomized controlled trial with 6 month follow-up, with emerging evidence. Antithrombotic Therapy: • Facet joint interventions are considered as moderate to low risk procedures; consequently, antithrombotic therapy may be continued based on overall general status. Sedation: • The level of evidence is II with moderate strength of recommendation to avoid opioid analgesics during the diagnosis with interventional techniques. • The level of evidence is II with moderate strength of recommendation that moderate sedation may be utilized for patient comfort and to control anxiety for therapeutic facet joint interventions. LIMITATIONS: The limitations of these guidelines include a paucity of high-quality studies in the majority of aspects of diagnosis and therapy. CONCLUSIONS: These facet joint intervention guidelines were prepared with a comprehensive review of the literature with methodologic quality assessment with determination of level of evidence and strength of recommendations. KEY WORDS: Chronic spinal pain, interventional techniques, diagnostic blocks, therapeutic interventions, facet joint nerve blocks, intraarticular injections, radiofrequency neurolysis.

Effects of spectral energy distributions of sources and colors of backgrounds on the pleasantness of object colors were determined by having 5 men and 5 women rate 125 object colors on 25 colored backgrounds in 5 sources of illumination. In addition, foods and complexions were rated in the same sources. All main effects were found to be highly significant statistically. While lightness and chromatic contrasts of object and background were more important than quality of illuminants, the latter were very important in the case of some object and background color combinations. Differences between the sexes were highly significant in that men tended to prefer cool source, object, and background colors, women the warm colors. The best colors for backgrounds had either low chroma and high reflectance (the pastel colors), or low chroma and low reflectance. The most important single factor determining the pleasantness of color combinations was lightness contrast. Hue and chroma contrasts, while of some importance, were not as decisive as lightness contrast. Some closely related color families may be substituted for each other, e.g., 5 and 10R or 5 and 10G, while others may not be, e.g, 5 and 10GY. The complex interactions of quality of sources with hue, value, and chroma of object and background colors on aesthetic responses to colors help to account for the conflicting statements often found in the literature regarding color harmony. In spite of the complexities of the problem, some generalizations regarding color harmony were found valid and others were shown to be in need of further investigation. This study was based on 156,250 individual ratings of object colors.

Diabetic retinopathy (DR) is a severe diabetes complication that impacts a substantial proportion of individuals living with diabetes. It is estimated that around 40-45% of Americans with diabetes experience various stages of this condition. Preventing blindness relies heavily on timely detection and treatment. We use an enhanced vision transformer-based model in the detection of DR, named Twins-PCPVT. The model incorporates a twin architecture that captures both global and local features of fundus images, attaining impressive accuracy and AUC values of 87.43% and 0.952, respectively, on the Kaggle Diabetic Retinopathy Detection dataset. Our proposed method demonstrates great potential in aiding early diagnosis and treatment of DR. Our proposed deep learning-based approach offers a notable advantage over the current laborious and time-consuming manual detection process. It is not only faster but also more efficient. By implementing our method, early detection and treatment of DR can be significantly improved, playing a crucial role in preventing vision loss.

The Purpose of this paper is to present some recent laboratory model test results on the breakout resistance of shallow horizontal rectangular model anchor plates in loose granular soil and to study the variation of breakout factors and critical depths of embedment (for shallow and deep anchor behavior) with length-to-width ratios of the plates.

Microfluidic lumen-based systems are microscale models that recapitulate the anatomy and physiology of tubular organs. These technologies can mimic human pathophysiology and predict drug response, having profound implications for drug discovery and development. Herein, we review progress in the development of microfluidic lumen-based models from the 2000s to the present. The core of the review discusses models for mimicking blood vessels, the respiratory tract, the gastrointestinal tract, renal tubules, and liver sinusoids, and their application to modeling organ-specific diseases. We also highlight emerging application areas, such as the lymphatic system, and close the review discussing potential future directions.

In the digital era, online platforms serve as crucial hubs for social interactions and idea exchange. However, these platforms are continually shadowed by toxic comments that undermine genuine discourse and have the potential to harm participants. While machine learning provides an avenue for detecting such toxic content, a significant challenge arises when these models, influenced by biased training datasets, inadvertently propagate or amplify inherent biases. Such unintentional biases are especially disconcerting when they disadvantage or misrepresent identities already vulnerable in online spaces. Addressing this complex landscape, our research presents a model meticulously designed to detect toxic comments, aiming to achieve a higher degree of accuracy while striving to minimize such unintended biases. Our approach is underpinned by a combination of a tailored data preprocessing technique and the integration of Long Short-Term Memory networks (LSTM) with Attention mechanisms. Preliminary evaluations reveal our model's AVC score to be 0.93524, indicating its efficacy in toxicity detection. While there's always room for improvement, the design and results of our model emphasize the importance and feasibility of developing more nuanced and unbiased machine learning solutions for the challenges posed in the digital domain.

). We investigated the effect of ECM density on LV morphology, growth, cytokine secretion, and barrier function. LVs cultured in HD matrices showed morphological changes as compared to LVs cultured in a LD matrix. Specifically, LVs cultured in HD matrices had a 3-fold higher secretion of the pro-inflammatory cytokine, IL-6, and a leakier phenotype, suggesting LVs acquired characteristics of activated vessels. Interestingly, LV leakiness was mitigated by blocking the IL-6 receptor on the lymphatic ECs, maintaining endothelium permeability at similar levels of LV cultured in a LD matrix. To recreate a more in vivo microenvironment, we incorporated metastatic breast cancer cells (MDA-MB-231) into the LD and HD matrices. For HD matrices, co-culture with MDA-MB-231 cells exacerbated vessel leakiness and secretion of IL-6. In summary, our data suggest that (1) ECM density is an important microenvironmental cue that affects LV function in the breast tumor microenvironment (TME), (2) dense matrices condition LVs towards an activated phenotype and (3) blockade of IL-6 signaling may be a potential therapeutic target to mitigate LV dysfunction. Overall, modeling LVs and their interactions with the TME can help identify novel therapeutic targets and, in turn, advance therapeutic discovery.

This letter demonstrates that a network composed of a current conveyor and RC one-ports is capable of realizing any real rational voltage transfer function.

Recent studies (1, 2, 3, 5) on shallow vertical anchors have been primarily concerned with the ultimate pullout capacity of the anchor and have not concerned themselves with the load-displacement relationship. In bulkheads or other lateral load bearing structures where vertical anchors are used, tolerable displacement may impose restrictions on the design load.

Stroke prediction plays a crucial role in preventing and managing this debilitating condition. In this study, we address the challenge of stroke prediction using a comprehensive dataset, and propose an ensemble model that combines the power of XGBoost and xDeepFM algorithms. Our work aims to improve upon existing stroke prediction models by achieving higher accuracy and robustness. Through rigorous experimentation, we validate the effectiveness of our ensemble model using the AUC metric. Through comparing our findings with those of other models in the field, we gain valuable insights into the merits and drawbacks of various approaches. This, in turn, contributes significantly to the progress of machine learning and deep learning techniques specifically in the domain of stroke prediction.

Bank lending to SMEs plays a vital role in economic growth, contributing significantly to employment and GDP. Access to bank lending is crucial for small- and medium-sized enterprises (SMEs), as they contribute significantly to global employment and GDP. New financial technologies promise better bank operations, fewer costs, and enhanced credit supply to SMEs. However, there is still a lack of empirical findings on how these technologies can solve demand-side bank lending problems for small- and medium-sized firms. This study gathered data from a sample of 381 respondents, comprising CEOs, managers, officers, loan managers, IT consultants, and other relevant stakeholders. The findings indicate that the adoption of blockchain technologies, as well as the adoption of Big Data technologies encompassing cloud computing, data analytics, algorithms, and programming, along with the adoption of mobile banking technologies, have had a substantial positive impact on bank credit supplies for small- and medium-sized enterprises (SMEs) in Pakistan. This novel study contributes to existing knowledge in two ways. First, it provides knowledge to SMEs looking to adopt new technologies; second, it provides knowledge to a manager looking to finance the SMEs with information asymmetries. This research also provides key findings for researchers and policymakers.

Pyrolysis is a thermal process that converts biosolids into biochar (a soil amendment), py-oil and py-gas, which can be energy sources. The objectives of this research were to determine the product yield of dried biosolids during pyrolysis and the energy requirements of pyrolysis. Bench-scale experiments revealed that temperature increases up to 500 °C substantially decreased the fraction of biochar and increased the fraction of py-oil. Py-gas yield increased above 500 °C. The energy required for pyrolysis was approximately 5-fold less than the energy required to dry biosolids (depending on biosolids moisture content), indicating that, if a utility already uses energy to dry biosolids, then pyrolysis does not require a substantial amount of energy. However, if a utility produces wet biosolids, then implementing pyrolysis may be costly because of the energy required to dry the biosolids. The energy content of py-gas and py-oil was always greater than the energy required for pyrolysis.

This paper introduces a novel spatiotemporal feature representation model designed to address the limitations of traditional methods in multidimensional time series (MTS) analysis. The proposed approach converts MTS into one-dimensional sequences of spatially evolving events, preserving the complex coupling relationships between dimensions. By employing a variable-length tuple mining method, key spatiotemporal features are extracted, enhancing the interpretability and accuracy of time series analysis. Unlike conventional models, this unsupervised method does not rely on large training datasets, making it adaptable across different domains. Experimental results from motion sequence classification validate the model's superior performance in capturing intricate patterns within the data. The proposed framework has significant potential for applications across various fields, including backend services for monitoring and optimizing IT infrastructure, medical diagnosis through continuous patient monitoring and health trend analysis, and internet businesses for tracking user behavior and forecasting sales. This work offers a new theoretical foundation and technical support for advancing time series data mining and its practical applications in human behavior recognition and other domains.