American InterContinental University

More than 90% of all fatal mushroom poisonings worldwide are due to amatoxin containing species that grow abundantly in Europe, South Asia, and the Indian subcontinent. Many cases have also been reported in North America. Initial symptoms of abdominal cramps, vomiting, and a severe cholera-like diarrhea generally do not manifest until at least six to eight hours following ingestion and can be followed by renal and hepatic failure. Outcomes range from complete recovery to fulminant organ failure and death which can sometimes be averted by liver transplant. There are no controlled clinical studies available due to ethical reasons, but uncontrolled trials and case reports describe successful treatment with intravenous silibinin (Legalon® SIL). In nearly 1,500 documented cases, the overall mortality in patients treated with Legalon® SIL is less than 10% in comparison to more than 20% when using penicillin or a combination of silibinin and penicillin. Silibinin, a proven antioxidative and anti-inflammatory acting flavonolignan isolated from milk thistle extracts, has been shown to interact with specific hepatic transport proteins blocking cellular amatoxin re-uptake and thus interrupting enterohepatic circulation of the toxin. The addition of intravenous silibinin to aggressive intravenous fluid management serves to arrest and allow reversal of the manifestation of fulminant hepatic failure, even in severely poisoned patients. These findings together with the available clinical experience justify the use of silibinin as Legalon® SIL in Amanita poisoning cases.

MicroRNAs (miRs) are a novel class of small RNA molecules, the dysregulation of which can contribute to cancer. A combinatorial approach was used to identify miRs that promote prostate cancer progression in a unique set of prostate cancer cell lines, which originate from the parental p69 cell line and extend to a highly tumorigenic/metastatic M12 subline. Together, these cell lines are thought to mimic prostate cancer progression in vivo. Previous network analysis and miR arrays suggested that the loss of hsa-miR-125b together with the overexpression of hsa-miR-22 could contribute to prostate tumorigenesis. The dysregulation of these two miRs was confirmed in human prostate tumor samples as compared to adjacent benign glandular epithelium collected through laser capture microdissection from radical prostatectomies. In fact, alterations in hsa-miR-125b expression appeared to be an early event in tumorigenesis. Reverse phase microarray proteomic analysis revealed ErbB2/3 and downstream members of the PI3K/AKT and MAPK/ERK pathways as well as PTEN to be protein targets differentially expressed in the M12 tumor cell compared to its parental p69 cell. Relevant luciferase+3'-UTR expression studies confirmed a direct interaction between hsa-miR-125b and ErbB2 and between hsa-miR-22 and PTEN. Restoration of hsa-miR-125b or inhibition of hsa-miR-22 expression via an antagomiR resulted in an alteration of M12 tumor cell behavior in vitro. Thus, the dual action of hsa-miR-125b as a tumor suppressor and hsa-miR-22 as an oncomiR contributed to prostate tumorigenesis by modulations in PI3K/AKT and MAPK/ERK signaling pathways, key pathways known to influence prostate cancer progression.

Abstract The discipline of International Relations (IR) is increasingly being criticized for ignoring and marginalizing states and societies outside of the core countries of the West. The idea of a ‘Global IR’ has been proposed since 2014 a pathway toward a bridging the ‘West and the Rest’ divide and thus develop a more inclusive discipline, recognizing its multiple and diverse foundations. At the same time, there is a trend toward developing theories, or ‘schools’, on a national or regional basis, the leading examples of which come from China. This article examines some theoretical constructs emerging in China, such as the ‘Relational Theory’ of Qin Yaqing, who is the foundational scholar in the ‘Chinese School of IR’, the Tianxia (‘all under Heaven’) concept as applied to IR and world order by Zhao Tingyang, and ‘Moral Realism’ of Yan Xuetong, who is the leading figure of the ‘Tsinghua School’. To many scholars, both inside and outside China, the relationship among the various Chinese approaches and their overall contribution to the IR field remain unclear. Without claiming to capture all their nuances and complexity, this article hopes to stimulate a conversation among scholars, Chinese and foreign, with a view to generate greater clarity and highlight their importance to the study of IR. I argue that while making important contributions, the Chinese approaches to International Relations Theory (IRT) also face a number of challenges. This includes the need for them to offer more convincing proof that the concepts and explanations they propose can apply to other societies and to IR more generally. Moreover, there is the need for these approaches to attract a critical mass of followers worldwide, stimulate a research agenda for other, especially younger scholars, and distance themselves from the official Chinese policy framings. The Global IR approach offers a helpful framework for highlighting and perhaps addressing these challenges, especially in avoiding cultural exceptionalism and ensuring their wider relevance beyond China.

<p class="Style1" style="text-align: justify; margin: 0in 35.75pt 0pt 0.5in; mso-layout-grid-align: auto; mso-add-space: auto;"><span style="font-size: x-small;"><span style="font-family: Times New Roman;"><span style="mso-bidi-font-weight: bold;">Mass customization allows firms to produce only things their customers want (or produce after they have orders in hand). This approach, make-to-order, brings many benefits to firms in terms of cost and profit because of lower inventory levels, maximum sales, elimination </span>of material waste, flexible production and, most of all, customer satisfaction. However, mass customization may not be the panacea for <span style="mso-bidi-font-weight: bold;">all <span style="mso-bidi-font-style: italic;">organizations. </span>While some companies are very successful with mass customization, others are not. <span style="mso-spacerun: yes;"> </span>This paper illustrates that mass customization strategies depend on an understanding </span>of the conditions in each industry.</span></span></p>

The increasing prevalence of malware presents a critical challenge to cybersecurity, emphasizing the need for robust detection methods. This study uses a binary tabular classification dataset to evaluate the impact of feature selection, feature scaling, and machine learning (ML) models on malware detection. The methodology involves experimenting with three feature scaling techniques (no scaling, normalization, and min-max scaling), three feature selection methods (no selection, Linear Discriminant Analysis (LDA), and Principal Component Analysis (PCA)), and twelve ML models, including traditional algorithms and ensemble methods. A publicly available dataset with 11,598 samples and 139 features is utilized, and model performance is assessed using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Results reveal that the Light Gradient Boosting Machine (LGBM) achieves the highest accuracy of 97.16% when PCA and either min-max scaling or normalization are applied. Additionally, ensemble models consistently outperform traditional ML models, demonstrating their effectiveness in enhancing malware detection. These findings offer valuable insights into optimizing preprocessing and model selection strategies for developing reliable and efficient malware detection systems.

Early and accurate detection of breast cancer is essential for reducing mortality rates and improving clinical outcomes. However, deep learning (DL) models used in healthcare face significant challenges, including concerns about data privacy, domain-specific overfitting, and limited interpretability. To address these issues, we propose BreastSwinFedNetX, a federated learning (FL)-enabled ensemble system that combines four hierarchical variants of the Swin Transformer (Tiny, Small, Base, and Large) with a Random Forest (RF) meta-learner. By utilizing FL, our approach ensures collaborative model training across decentralized and institution-specific datasets while preserving data locality and preventing raw patient data exposure. The model exhibits strong generalization and performs exceptionally well across five benchmark datasets-BreakHis, BUSI, INbreast, CBIS-DDSM, and a Combined dataset-achieving an F1 score of 99.34% on BreakHis, a PR AUC of 98.89% on INbreast, and a Matthews Correlation Coefficient (MCC) of 99.61% on the Combined dataset. To enhance transparency and clinical adoption, we incorporate explainable AI (XAI) through Grad-CAM, which highlights class-discriminative features. Additionally, we deploy the model in a real-time web application that supports uncertainty-aware predictions and clinician interaction and ensures compliance with GDPR and HIPAA through secure federated deployment. Extensive ablation studies and paired statistical analyses further confirm the significance and robustness of each architectural component. By integrating transformer-based architectures, secure collaborative training, and explainable outputs, BreastSwinFedNetX provides a scalable and trustworthy AI solution for real-world breast cancer diagnostics.

This article provides a comparative analysis of the systems used for the ‘accreditation’ of degree-granting institutions in the USA (accreditation) and the UK (audit). The authors begin by outlining the similarities and differences between the two processes. They point out that audit is not the subject of political controversy in the way that accreditation currently is. However, they add that this does not necessarily mean that all is set fair and potential sources of disturbance are highlighted. The article then considers what changes can be expected in the two processes as a result of increased market pressures, and whether such changes will make them more effective vehicles for quality improvement. An alternative approach, which would involve merging the regulatory role of the QAA with the regulatory functions of the Funding Councils, as part of a complete scheme of risk assessment, is suggested. The article also asks to whom the accrediting agencies in both countries are accountable for their work. According to the authors, neither process is foolproof; both can be manipulated and are open to abuse. In addition, it is argued that on neither side of the Atlantic are there currently adequte arrangements to “inspect the inspectors”. The authors conclude by warning that with the growing tendency to resort to the legal process, more questions will be asked about the accountability of those whose judgements are being questioned. In their view, the stronger the market pressures, the greater will be the recourse to legal process in both countries.

Lung cancer continues to be a leading cause of cancer-related deaths worldwide due to its high mortality rate and the complexities involved in diagnosis. Traditional diagnostic approaches often face issues such as subjectivity, class imbalance, and limited applicability across different imaging modalities. To tackle these problems, we introduce Lung MobileVIT (LMVT), a lightweight hybrid model that combines a Convolutional Neural Network (CNN) and a Transformer for multiclass lung cancer classification. LMVT utilizes depthwise separable convolutions for local texture extraction while employing multi-head self-attention (MHSA) to capture long-range global dependencies. Furthermore, we integrate attention mechanisms based on the Convolutional Block Attention Module (CBAM) and feature selection techniques derived from the Simple Gray Level Difference Method (SGLDM) to improve discriminative focus and minimize redundancy. LMVT utilizes attention recalibration to enhance the saliency of the minority class, while also incorporating curriculum augmentation strategies that balance representation across underrepresented classes. The model has been trained and validated using two public datasets (IQ-OTH/NCCD and LC25000) and evaluated for both 3-class and 5-class classification tasks. LMVT achieved an impressive 99.61 % accuracy and 99.22 % F1-score for the 3-class classification, along with 99.75 % accuracy and 99.44 % specificity for the 5-class classification. This performance surpasses that of several recent Vision Transformer (ViT) architectures. Statistical significance tests and confidence intervals confirm the reliability of these performance metrics, while an analysis of model complexity supports its capability for potential deployment. To enhance clinical interpretability, the model is integrated with explainable AI (XAI) and is implemented within a web-based diagnostic application for analyzing CT and histopathology images. This study highlights the potential of hybrid ViT architectures in creating scalable and interpretable data-driven tools for practical use in lung cancer diagnostics.

Journal Article Kennedy's Alliance for Progress: countering revolution in Latin America. Part I: From the White House to the Charter of Punta del Este Get access MICHAEL DUNNE MICHAEL DUNNE 1Senior Research Associate at the Centre of Latin American Studies at the University of Cambridge, where he is working on inter-American history (see ‘Ending the hegemonic presumption? Recent writings on US—Cuban relations’, International Affairs 89: 1, 2013) Search for other works by this author on: Oxford Academic Google Scholar International Affairs, Volume 89, Issue 6, November 2013, Pages 1389–1409, https://doi.org/10.1111/1468-2346.12080 Published: 11 November 2013

Purpose The purpose of this paper is to explore academic library leadership behaviors and the methods for integrating the democratic and transformational leadership styles. Design/methodology/approach Eleven structured interviews were conducted with academic deans and directors. A thematic content analysis was conducted on their responses, analyzing the frequency of certain topics and identifying emergent themes. These themes were then used to construct a democratic communication model. Findings The interview responses were grouped into five general leadership themes: participation in decision-making, relationship building, frequent and honest communication, equality and knowing the environment. Research limitations/implications The structured interview format did not permit for unplanned follow-up questions, and some topics may not have come up in every interview unless specifically asked by an interview question. Due to the qualitative nature of this study, the perspectives of the participants may not be generalizable to the larger population. Practical implications This study identifies core themes of leadership practice that extend beyond the focus of transformational leadership alone. It suggests a democratic communication model to assist in integrating democratic leadership methods with transformational practices and goals. Social implications This study suggests a greater emphasis on the communication and engagement practices of democratic leadership. In doing so, it suggests that the American Library Association's emphasis on transformational leadership alone should be reconsidered and that library science schools should increase focus on democratic leadership practices. Originality/value Most library leadership style studies emphasize transformational leadership. While there are some studies that explore elements of democratic leadership such as engagement and a flattening of organizational hierarchy, there is limited research on the integration of democratic and transformational leadership practices.

Artificial Intelligence (AI) is revolutionizing digital marketing automation by enhancing efficiency, personalization, and predictive capabilities. This study examines the role of AI in transforming marketing practices, focusing on its applications, benefits, ethical considerations, and future directions. By leveraging AI tools such as predictive analytics, NLP, and chatbots, businesses can achieve improved customer segmentation, content personalization, and campaign optimization in marketing strategies. Secondary data from journals, articles, and conference papers were synthesized to provide insights into AI's impact on digital marketing automation. A systematic literature review utilizing the PRISMA methodology initially identified 2,850 records from database searches. Following the removal of duplicates and non-relevant studies, 1,035 records were screened for eligibility based on defined criteria, resulting in the inclusion of 150 relevant studies and 25 high-quality reports for detailed analysis. This robust approach ensured the inclusion of high-quality research, minimizing biases. The findings reveal that AI enhances digital marketing by streamlining processes, automating repetitive tasks, and delivering hyper-personalized customer experiences. Predictive analytics helps anticipate consumer behavior, while chatbots improve real-time customer engagement. However, challenges such as data privacy, algorithmic bias, and the high costs of AI adoption persist. AI adoption allows businesses to make data-driven decisions, improve customer retention, and maximize return on investment. Ethical AI practices, such as transparency and algorithm fairness, are essential for maintaining consumer trust. The study primarily focuses on existing literature, with limited empirical validation. Future research should explore long-term effects of AI-driven marketing on consumer behavior and investigate its integration with emerging technologies like the Internet of Things (IoT) and blockchain. Additionally, tailored AI solutions for SMEs and under-researched areas, such as B2B marketing, are critical for inclusive growth.

Purpose To outline the themes, topics and material used in a new course, Global business and sustainability, for business educators interested in integrating this emergingparadigm into their courses. Design/methodology/approach The structure, design and reference materials for the Global business and sustainability course are reviewed. Specific challenges in designing the course are discussed. Recommendations are provided on how key frameworks developed for this course can aid in the delivery of a business course integrating sustainability concepts. Findings Compared to a more traditional business course, in a business course integrating sustainability concepts there is greater need for delineating the relationships between institutional, industry and corporate level factors linked to sustainability issues. However, a business course developed around the principles of sustainability is also similar to any other course in the business curriculum in that it needs to demonstrate the link between competitive actions and outcomes. Finally, sustainable development and sustainability are concepts that, in principle, should appeal and apply across cultural and national boundaries. Practical implications A useful source of information for business educators planning to integrate sustainability issues and concepts into their courses. Originality/value This paper addresses an important emerging paradigm and offers practical advice on how to incorporate this paradigm into business curriculum.

The texture segmentation techniques are diversified by the existence of several approaches. In this paper, we propose fuzzy features for the segmentation of texture image. For this purpose, a membership function is constructed to represent the effect of the neighboring pixels on the current pixel in a window. Using these membership function values, we find a feature by weighted average method for the current pixel. This is repeated for all pixels in the window treating each time one pixel as the current pixel. Using these fuzzy based features, we derive three descriptors such as maximum, entropy, and energy for each window. To segment the texture image, the modified mountain clustering that is unsupervised and fuzzy c-means clustering have been used. The performance of the proposed features is compared with that of fractal features.

A multimodal biometric system amalgamates the information from multiple biometric sources to alleviate the limitations in performance of each individual biometric system. In this paper a multimodal biometric system employing hand based biometrics (i.e. palmprint, hand veins, and hand geometry) is developed. A general combination approach is proposed for the score level fusion which combines the matching scores from these hand based modalities using t-norms due to Hamacher, Yager, Weber, Schweizer and Sklar. This study aims at exploring the potential usefulness of t-norms for multimodal biometrics. These norms deal with the real challenge of uncertainty and imperfection pervading the different sources of knowledge (scores from different modalities). We construct the membership functions of fuzzy sets formed from the genuine and imposter scores of each of the modalities considered. The fused genuine score and imposter scores are obtained by integrating the fuzzified genuine scores and imposter scores respectively from each of the modalities. These norms are relatively very simple to apply unlike the other methods (example SVM, decision trees, discriminant analysis) as no training or any learning is required here. The proposed approach renders very good performance as it is quite computationally fast and outperforms the score level fusion using the conventional rules (min, max, sum, median) The experimental evaluation on a database of 100 users confirms the effectiveness of score level fusion. The preliminary results are encouraging in terms of decision accuracy and computing efficiency.

The purpose of this research was to examine the influence of selected social factors on the clothing buying behaviour patterns of black college consumers. The sample consisted of 333 students from two US universities. Results showed that social participation was significantly related to store patronage. No difference was found in patronage behaviour for the variables of reference group, social involvement, fashion involvement, clothing benefits sought, and social environment. In addition, social involvement, fashion involvement and clothing benefits sought were significantly related to time/frequency of clothing purchases. Results suggest implications for future research and retail stores.

Prosthetic limbs have historically been intended for replacing loss, and as a result, are usually functional or naturalistic in design. The basic, or “functional,” prosthesis meets the rudimentary operational needs of the user. A naturalistic prosthetic limb, or cosmesis, is focused on discretion, thereby disguising limb loss. However, a new type of artificial limb is appearing in the marketplace. These prostheses differ from the traditional designs, in that they solicit attention and express the personal style and self-concept of the individual with limb loss or absence. Based on the Lamb and Kallal (1992) FEA Consumer Needs Model, these prostheses fulfill the expressive needs of prosthetic limb users, which have historically been overlooked. This article explores the current literature in order to establish a context for what will be designated expressive prostheses. The analysis will begin by reviewing recent scholarship on prosthetic limbs. Since there is limited relevant research on prosthesis design and appearance, related literature from disability studies and fashion studies will be integrated. This expanded literature analysis will provide a larger framework with which to situate expressive prostheses within the existing body of knowledge. Attention will be focused on scholarship related to social psychology, including models of disability and appearance management. In addition, the authors will draw inferences and propose interpretations for the meaning and significance of these prostheses.

This paper proposes a fuzzy-based approach to edge detection in gray-level images. The proposed fuzzy edge detector involves two phases - global contrast intensification and local fuzzy edge detection. In the first phase, a modified Gaussian membership function is chosen to represent each pixel in the fuzzy plane. A global contrast intensification operator, containing three parameters, viz., intensification parameter t, fuzzifier f/sub h/ and the crossover point x/sub c/, is used to enhance the image. The entropy function is optimized to obtain the parameters f/sub h/, and x/sub c/ using the gradient descent function before applying the local edge operator in the second phase. The local edge operator is a generalized Gaussian function containing two exponential parameters, /spl alpha/ and /spl beta/. These parameters are obtained by the similar entropy optimization method. By using the proposed technique, a marked visible improvement in the important edges is observed on various test images over common edge detectors.

In the contemporary financial realm, safeguarding against banking fraud and managing associated risks is paramount. In this pursuit, the integration of artificial intelligence (AI) stands as a beacon of promise, offering multifaceted solutions that outshine traditional fraud detection mechanisms. This study delves into the expansive applications of AI, delineating its role in identifying, pre-empting, and navigating fraudulent activities within the banking sector, juxtaposed against conventional fraud detection methodologies. AI revolutionizes banking fraud prevention and risk management by leveraging its rapid analysis capabilities to detect anomalies and flag fraudulent activities in real-time. Deep learning, particularly through neural networks trained on historical fraud data, excels in discerning intricate patterns and forecasting fraudulent transactions with remarkable accuracy. Natural Language Processing (NLP) enhances Know Your Customer (KYC) protocols, ensuring the authenticity of customers by scrutinizing textual data from diverse sources. Graph analytics visually map transactional relationships, spotlighting suspicious activities like rapid fund transfers indicative of money laundering. Predictive analytics transcends conventional credit scoring by integrating diverse datasets, offering holistic insights into customer creditworthiness. User-friendly interfaces like AI-powered chatbots facilitate immediate reporting of suspicious activities alongside advanced biometric authentication mechanisms such as facial and voice recognition. Adaptability inherent in AI ensures dynamic updates to combat evolving fraud strategies, extending beyond fraud detection to phishing, IoT integration, and cross-channel analysis. Additionally, AI's capability to simulate economic scenarios empowers proactive risk management and streamlines regulatory compliance processes, marking a transformative shift in banking security and efficiency.

Mass customization allows firms to produce only things their customers want (or produce after they have orders in hand). This approach, make-to-order, brings many benefits to firms in terms of cost and profit because of lower inventory levels, maximum sales, elimination of material waste, flexible production and, most of all, customer satisfaction. However, mass customization may not be the panacea for all organizations. While some companies are very successful with mass customization, others are not. This paper illustrates that mass customization strategies depend on an understanding of the conditions in each industry.

Refusing to re-enact caricatures, Walker, at the turn of the 20th century, reclaimed the figure of the dandy from racist minstrelsy and rearticulated it within an African American context.