Search Results (141)

This study investigates the presence of the MAX effect, as defined by Bali et al. (2011), in the stock market of Borsa Istanbul, aiming to validate and extend previous findings in international markets. A comprehensive analysis of 439 firms from December 2013 to November 2023 reveals that stocks with low performance in previous periods tend to show strong performance in subsequent periods. This finding indicates that the MAX effect is also applicable to Borsa Istanbul and suggests that this effect can significantly influence stock price movements in the market. Additionally, this study highlights that past maximum returns, especially those accumulated over long periods, have a distinct impact on future returns. These findings contribute to a deeper understanding of the MAX effect’s presence in and impact on financial markets and offer valuable guidance for market participants. Full article

The increasing integration of renewable energy, electric vehicles, and industrial applications demands efficient power converter control strategies that reduce switching losses while maintaining high waveform quality. This paper presents a Finite-Control-Set Model Predictive Control (FCS-MPC) strategy for three-phase, two-level voltage source inverters (VSIs), incorporating a secondary objective for switching frequency minimization. Unlike conventional MPC approaches, the proposed method optimally balances control performance and efficiency trade-offs by adjusting the weighting factor (${\lambda }_{min}$). Real-time implementation using the OPAL-RT platform validates the effectiveness of the approach under both linear and non-linear load conditions. Results demonstrate a significant reduction in switching losses, accompanied by improved waveform tracking; however, trade-offs in distortion are observed under non-linear load scenarios. These findings provide insights into the practical implementation of real-time predictive control strategies for high-performance power converters. Full article

The landscape for the screening of colorectal cancer (CRC) has witnessed multiple triumphs over the past decades from policy-level interventions. In the United States (US), the most prominent intervention of this nature is the Patient Protection and Affordable Care Act (ACA), enacted more than a decade ago. Since its enactment, the ACA has seen multiple legal challenges, and its impact on CRC screening has been relatively well studied. However, a consolidated, concise analysis of the data on this subject is lacking. Herein, we evaluate the impact of the ACA on CRC screening through the lens of a policy analysis, highlighting its strengths and shortcomings, and suggest policy-level interventions to address these shortcomings and improve CRC screening adoption. Full article

Model Predictive Control (MPC) has become very attractive for the efficient control of power converters. This paper compares Classical MPC (C-MPC) and Sequential MPC (S-MPC) for a three-level NPC converter. Although C-MPC is simple to implement, it faces challenges such as switching frequency variations and complex weighting factor tuning. S-MPC addresses these issues by prioritizing control objectives sequentially, eliminating weighting factors, and simplifying controller design. Simulation results show that S-MPC improves the tracking of output currents, reduces harmonic distortion, and enhances the balancing of dc–link voltages under steady-state and transient conditions. These findings establish S-MPC as a robust alternative to C-MPC, improving power quality and system performance in multilevel converter applications. Full article

The fire at the Notre Dame Cathedral accentuated the sense that this architectural building is a symbol of unity and identity for the population. This symbol extends beyond its French and Christian origins, reflecting the universal nature of cultural heritage, which has the extraordinary ability to unite people across nations, transcending ideologies, religions, and political divisions. The reopening of the Notre Dame Cathedral marks a profound milestone in the preservation of cultural heritage, embodying resilience, restoration, and the enduring significance of shared human history. This research examines the impacts of the restoration of the cathedral at national and global scales through the implementation of questionnaires to visitors to the cathedral’s inauguration. The restoration process, which gained extensive media coverage, not only revived the cathedral’s architectural grandeur but also sparked international discussions about heritage conservation. Through an analysis of the questionnaire results for different factors, this study highlights the complexities of managing a monumental heritage of this magnitude in a globalized world. It reflects the ethical, financial, and social challenges involved in major restoration projects, using Notre Dame as a paradigmatic example. The paper concludes that the reopening of the cathedral is not only a triumph of physical restoration but also a testament to humanity’s resilience in the face of adversity. It serves as a powerful reminder of the importance of safeguarding cultural heritage for future generations while fostering a sense of collective responsibility. Full article

The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data, making it challenging and expensive to deploy data analytics platforms in new environments. Transfer learning (TL) mechanisms promise to mitigate data sparsity and model complexity by utilizing pre-trained models for a new task. Despite the triumph of TL in fields like computer vision and natural language processing, efforts on complex ST models for anomaly detection (AD) applications are limited. In this study, we present the potential of TL within the context of high-dimensional ST AD with a hybrid autoencoder architecture, incorporating convolutional, graph, and recurrent neural networks. Motivated by the need for improved model accuracy and robustness, particularly in scenarios with limited training data on systems with thousands of sensors, this research investigates the transferability of models trained on different sections of the Hadron Calorimeter of the Compact Muon Solenoid experiment at CERN. The key contributions of the study include exploring TL’s potential and limitations within the context of encoder and decoder networks, revealing insights into model initialization and training configurations that enhance performance while substantially reducing trainable parameters and mitigating data contamination effects. Full article

Obesity and type 2 diabetes mellitus (T2DM) are global health crises with significant morbidity and mortality. Retatrutide, a novel triple receptor agonist targeting glucagon-like peptide-1 (GLP-1), Glucose-Dependent Insulinotropic Polypeptide (GIP), and glucagon receptors, represents a groundbreaking advancement in obesity and T2DM pharmacotherapy. This review synthesizes findings from preclinical and clinical studies, highlighting retatrutide’s mechanisms, efficacy, and safety profile. Retatrutide’s unique molecular structure enables potent activation of GLP-1, GIP, and glucagon receptors, leading to significant weight reduction, improved glycemic control, and favorable metabolic outcomes. Animal studies demonstrate retatrutide’s ability to delay gastric emptying, reduce food intake, and promote weight loss, with superior efficacy compared to other incretin-based therapies. Phase I and II clinical trials corroborate these findings, showing dose-dependent weight loss, reductions in Glycated Hemoglobin (HbA1c) levels, and improvements in liver steatosis and diabetic kidney disease. Common adverse effects are primarily gastrointestinal and dose-related. Ongoing Phase III trials, such as the TRIUMPH studies, aim to further evaluate retatrutide’s long-term safety and efficacy in diverse patient populations. While retatrutide shows immense promise, considerations regarding cost and the quality of weight loss beyond BMI reduction warrant further investigation. Retatrutide heralds a new era in obesity and T2DM treatment, offering hope for improved patient outcomes. Full article

To meet the stringent demands of next-generation flexible optoelectronic devices, a novel fabrication approach is employed that integrates the spray-coating of copper nanowires (Cu NWs) with the magnetron sputtering of SrTiO₃ thin films, thereby yielding SrTiO₃/Cu NWs/SrTiO₃ hybrid thin films. The incorporation of the SrTiO₃ layers results in improved optical performance, with the transmittance of the Cu NW network increasing from 83.5% to 84.2% and a concurrent reduction in sheet resistance from 16.9 Ω/sq to 14.5 Ω/sq. Moreover, after subjecting the hybrid thin films to 100 repeated tape-peeling tests and 2000 bending cycles with a bending radius of 5.0 mm, the resistance remains essentially unchanged, which underscores the films’ exceptional mechanical flexibility and robust adhesion. Additionally, the hybrid thin films are subjected to rigorous high-temperature, high-humidity, and oxidative conditions, where the resistance exhibits outstanding stability. These results substantiate the potential of the SrTiO₃/Cu NWs/SrTiO₃ hybrid thin films for integration into flexible and wearable electronic devices, delivering enhanced optoelectronic performance and long-term reliability under demanding conditions. Full article

This study aims to explore the factors influencing word-of-mouth (WOM) intentions among cultural tourists in Thailand’s southern border provinces. This study develops a comprehensive framework predicting WOM intentions by combining the Stimulus–Organism–Response (S-O-R) model, the theory of planned behavior (TPB), and the cognitive–affective–conative (CAC) model, given the critical role of WOM in tourism marketing. Data were collected from 386 cultural tourists through purposive sampling using a self-administered questionnaire. The study employs structural equation modeling (PLS-SEM) to investigate relationships between trust, attitude, satisfaction, and WOM intentions. The findings reveal that trust, satisfaction, and attitude significantly impact WOM intentions. Social influence strongly predicts trust, while the perceived quality of behavior influences attitude. Satisfaction is driven by perceived value, the quality of the experience, the quality of the physical environment, and tourist engagement. This study contributes to cultural tourism literature by integrating different behavioral theories to provide a robust WOM model. This study’s theoretical contributions and practical implications for scholars, policymakers, tourism marketers, and cultural site operators will be further discussed. Full article

In Ten Little Indians, Sherman Alexie presents nine poignant and emotionally resonant stories about Native Americans’ struggle with alienation and stereotypes. Instead of focusing merely on the ethnic identity of American Indians, Alexie writes about a particular group of people sharing similar circumstances and addresses their common humanity, namely their search for love and respect in urban spaces. Alexie questions the authenticity of Indian identity and asserts that a “mental reservation” exists in the minds of Indian people which significantly influences their perceptions of self and community. Race, as a medium of seeing “the other” permeates U.S. society, especially in the wake of terrorist attacks. However, racial profiling has proven to be an ineffective means of detecting criminals and criminal activities, and has obstructed social relationships, bringing emotions of fear, loneliness and grief to urban Indians. In response to the modernity crisis, Alexie explores the American Indian cosmopolitanism in Ten Little Indians, and envisions a human social community based on reciprocity and mutual respect. His concerns regarding ordinary people’s life experiences and their ways of forming healthy relationships exhibit his considerable hope for “the triumph of the ordinary”. Full article

The task of image captioning in low-resource languages like Tamil is fraught with challenges due to limited linguistic resources and complex semantic structures. This paper addresses the problem of generating contextually and linguistically coherent captions in Tamil. We introduce the Dynamic Context-Aware Transformer (DCAT), a novel approach that synergizes the Vision Transformer (ViT) with the Generative Pre-trained Transformer (GPT-3), reinforced by a unique Context Embedding Layer. The DCAT model, tailored for Tamil, innovatively employs dynamic attention mechanisms during its Initialization, Training, and Inference phases to focus on pertinent visual and textual elements. Our method distinctively leverages the nuances of Tamil syntax and semantics, a novelty in the realm of low-resource language image captioning. Comparative evaluations against established models on datasets like Flickr8k, Flickr30k, and MSCOCO reveal DCAT’s superiority, with a notable 12% increase in BLEU score (0.7425) and a 15% enhancement in METEOR score (0.4391) over leading models. Despite its computational demands, DCAT sets a new benchmark for image captioning in Tamil, demonstrating potential applicability to other similar languages. Full article

Pear fruit quality is a key determinant of consumer preference, yet it remains insufficiently characterized in many newly developed cultivars. This study aimed to evaluate 25 pear genotypes (Pyrus communis L.), internationally renowned cultivars and new cultivars developed through Romanian breeding programs, with distinct ripening periods, using an integrative approach based on morphological, biochemical, and sensory traits. Standardized methods were applied to assess attributes including fruit size, firmness, soluble solids, organic acid composition, skin color, and hedonic sensory responses for taste, aroma, texture, and visual appeal. Results revealed significant variability across ripening groups, with several cultivars, such as ‘Paradox’, ‘Pandora’, ‘Isadora’, and ‘Daciana’, displaying favorable combinations of appearance, internal quality, and consumer-rated acceptability. ‘Paradox’ and ‘Pandora’ achieved the highest sensory scores, comparable to or surpassing those of commercial standards like ‘Packham’s Triumph’. Multivariate analyses confirmed trait correlations and distinct phenotypic profiles among cultivars. These findings underscore the utility of multidimensional quality assessment for selecting cultivars suited to modern market demands, both for fresh consumption and processing. Moreover, several genotypes demonstrated potential as parental sources in pear breeding programs targeting improved organoleptic and physicochemical traits. The study suggests that a differentiated sensory scoring approach, rather than a uniform 1–9 hedonic scale, may more effectively identify high-quality pear cultivars for breeding programs. Full article

Integrating artificial intelligence (AI), particularly large language models (LLMs), into the healthcare industry is revolutionizing the field of medicine. LLMs possess the capability to analyze the scientific literature and genomic data by comprehending and producing human-like text. This enhances the accuracy, precision, and efficiency of extensive genomic analyses through contextualization. LLMs have made significant advancements in their ability to understand complex genetic terminology and accurately predict medical outcomes. These capabilities allow for a more thorough understanding of genetic influences on health issues and the creation of more effective therapies. This review emphasizes LLMs’ significant impact on healthcare, evaluates their triumphs and limitations in genomic data processing, and makes recommendations for addressing these limitations in order to enhance the healthcare system. It explores the latest advancements in LLMs for genomic analysis, focusing on enhancing disease diagnosis and treatment accuracy by taking into account an individual’s genetic composition. It also anticipates a future in which AI-driven genomic analysis is commonplace in clinical practice, suggesting potential research areas. To effectively leverage LLMs’ potential in personalized medicine, it is vital to actively support innovation across multiple sectors, ensuring that AI developments directly contribute to healthcare solutions tailored to individual patients. Full article

The cancer chemotherapy regimen of a taxane and platinum combination was developed more than forty years ago, yet remains the cornerstone of treatment for several major cancer types today. Although many new agents targeting cancer genes and pathways have been developed and evaluated, none have been sufficient to replace the long-established taxane/platinum combination. This leads us to ponder why, after four decades of colossal efforts, multiple discoveries, and tremendous advances in understanding gene mutations and biology, the development of conceptually superior targeted therapies has not yet achieved overwhelming success in replacing cytotoxic chemotherapy. The concept of targeted therapy is based on the idea that blocking the altered pathway(s) crucial for cancer development (and maintenance), the disturbance in cellular signaling, metabolism, and functions will make the targeted cancer cells unfit and trigger programmed cell death in cancer cells, but without the significant side effects that limit chemotherapy. We propose that the lack of anticipated triumphs of targeted therapy stems from the desensitization of programmed cell death pathways during neoplastic transformation and malignant progression of cancer cells. This renders the targeting drugs largely ineffective at killing cancer cells and mostly insufficient in clinical implements. Recent advances in understanding suggest that, in contrast to targeted therapies, taxanes and platinum agents kill cancer cells by physical rupturing nuclear membranes rather than triggering apoptosis, making their effect independent of the intrinsic cellular programmed cell death mechanism. This new recognition of the non-programmed cell death mechanism in the success of chemotherapeutic agents, such as taxanes and platinum, may inspire oncologists and cancer researchers to focus their efforts more productively on developing effective non-programmed cell death cancer therapies to replace or significantly improve the application of the current standard taxane/platinum regimens. Full article

This article discusses the conversion of Hagia Sophia to a mosque in 2020. Examining this act through the prism of the neo-Ottoman political platform and with consideration of the meaning and importance of this historic cultural monument, it is inferred that the reconversion constitutes a political decision par excellence, intended to symbolically mark the beginning of a new era for Turkey while closing the chapter of Kemalism. In doing so, the current political establishment seeks to communicate its resolution to invert the process of secularisation, as a form of revanche for the Westernisation of the country and the identity erosion that it caused. Further, the reconversion symbolically connotes the conquest of Constantinople and the triumph of Islam over Christianity anew, harking back to past glories and upholding them as guidelines for the future, thus hinting to a revisionist political agenda, applicable both domestically and abroad, intended, according to rhetoric at least, to consolidate Turkey as a regional power and a worthy successor of the Ottoman Empire. Full article