1,721,172 research outputs found
Identification and new records of tick species on livestock from district Dera Ismail Khan, Pakistan
No full textUllah, Naimat, Jamil, Muhammad, Ramzan, Muhammad, Arshad, Aisha, Zeeshan, Muhammad, Haq (2022): Identification and new records of tick species on livestock from district Dera Ismail Khan, Pakistan. Persian Journal of Acarology 11 (1): 159-162, DOI: 10.22073/pja.v11i1.7097
Understanding Oxidation Mechanism of MoS2 Slab and Ribbon: Frist Principles Calculations
No full textEconomic Resilience Under Sustainability Uncertainty: Wavelet Quantile Insights From Energy Crises, Oil Market Volatility, and Supply Chain Disruptions
No full textABSTRACT Understanding the resilience of global economies amidst increasing natural and man‐made disruptions is crucial for effective policymaking in an increasingly uncertain and interconnected world. However, most existing studies analyze such disruptions in isolation, overlooking their compounded and interactive effects on economic resilience. This study addresses that gap by examining how sustainability uncertainty, energy market volatility, supply chain pressures, and oil market shocks collectively influence global economic resilience. Using monthly data from November 2002 to December 2023, we analyze six key indicators: the sustainability uncertainty index (SUI), the energy uncertainty index (EUI), real commodity factor prices (RCF), oil supply shocks (OSS), oil inventory demand shocks (ODS), and global supply chain pressure (SCP). These indicators are employed to assess their dynamic effects on the Global economic condition index (ECI), a proxy for resilience. A wavelet‐based approach is utilized to capture time‐frequency interactions and nonlinear causality among the variables. The findings reveal that the ECI is most strongly influenced by sustainability and energy uncertainties, particularly during periods of economic weakness, while SCP and OSS amplify downturn effects in the short term. In contrast, RCF shows positive long‐term associations with resilience, and SCP and the SUI display increasingly supportive long‐run relationships, which may reflect adaptive adjustments rather than causal stabilizing effects. These results underscore the importance of robust ESG frameworks, diversified energy strategies, and resilient supply chains with regard to supporting long‐term economic stability in line with global sustainability goals
A survey on using neural network based algorithms for hand written digit recognition
No full textAbstract—The detection and recognition of handwritten content is the process of converting non-intelligent information such as images into machine edit-able text. This research domain has become an active research area due to vast applications in a number of fields such as handwritten filing of forms or documents in banks, exam form filled by students, users’ authentication applications. Generally, the handwritten content recognition process consists of four steps: data preprocessing, segmentation, the feature extraction and selection, application of supervised learning algorithms. In this paper, a detailed survey of existing techniques used for Hand Written Digit Recognition(HWDR) is carried out. This review is novel as it is focused on HWDR and also it only discusses the application of Neural Network (NN) and its modified algorithms. We discuss an overview of NN and different algorithms which have been adopted from NN. In addition, this research study presents a detailed survey of the use of NN and its variants for digit recognition. Each existing work, we elaborate its steps, novelty, use of dataset and advantages and limitations as well. Moreover, we present a Scientometric analysis of HWDR which presents top journals and sources of research content in this research domain. We also present research challenges and potential future work
Brain tumor classification using MRI images and deep learning techniques
Full text linkBrain tumors pose a significant medical challenge, necessitating early detection and precise classification for effective treatment. This study aims to address this challenge by introducing an automated brain tumor classification system that utilizes deep learning (DL) and Magnetic Resonance Imaging (MRI) images. The main purpose of this research is to develop a model that can accurately detect and classify different types of brain tumors, including glioma, meningioma, pituitary tumors, and normal brain scans. A convolutional neural network (CNN) architecture with pretrained VGG16 as the base model is employed, and diverse public datasets are utilized to ensure comprehensive representation. Data augmentation techniques are employed to enhance the training dataset, resulting in a total of 17,136 brain MRI images across the four classes. The accuracy of this model was 99.24%, a higher accuracy than other similar works, demonstrating its potential clinical utility. This higher accuracy was achieved mainly due to the utilization of a large and diverse dataset, the improvement of network configuration, the application of a fine-tuning strategy to adjust pretrained weights, and the implementation of data augmentation techniques in enhancing classification performance for brain tumor detection. In addition, a web application was developed by leveraging HTML and Dash components to enhance usability, allowing for easy image upload and tumor prediction. By harnessing artificial intelligence (AI), the developed system addresses the need to reduce human error and enhance diagnostic accuracy. The proposed approach provides an efficient and reliable solution for brain tumor classification, facilitating early diagnosis and enabling timely medical interventions. This work signifies a potential advancement in brain tumor classification, promising improved patient care and outcomes
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Tunable Exciton Modulation and Efficient Charge Transfer in MoS 2 /Graphene van der Waals Heterostructures
Full text linkMonolayer transition metal dichalcogenides (TMDs) are direct gap semiconductors where the optical properties are dominated by strongly interacting electron–hole quasi-particles. Understanding the interactions among these quasi-particles is crucial for advancing optoelectronic applications. Here, we examine the electrical tunability of light emission from the A and B excitons in monolayer MoS 2 and MoS 2 /graphene heterostructures and unravel the competition between the A exciton to trion formation and charge transfer processes. Our results show significant gate-tunable quenching of the photoluminescence intensity from A excitons with notable differences due to charge transfer in the heterostructure. Furthermore, we observe a distinct superlinear correlation between the A exciton photoluminescence intensity and high doping levels in MoS 2 , which continues until the density of photoexcited excitons exceeds and saturates the free carrier density. This phenomenon ceases to occur in MoS 2 /graphene, where MoS 2 remains almost undoped across all values of the applied external voltage. In contrast, the B exciton photoluminescence is unaffected by doping in MoS 2 , while it decreases analogously to that of the A excitons in the MoS 2 /graphene heterostructure, indicating the relevance of gate-tunable charge transfer from hot electrons before any internal recombination
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
- …
