Prime Feature Research Articles

Scholarly apps: enhancing research mobility and accessibility

Purpose This paper aims to explore how scholarly apps are changing the landscape of academic research by increasing mobility, accessibility and user engagement. It assesses the role of mobile-based applications in supporting research activities, enabling access to scholarly content and facilitating seamless communication between researchers and libraries. Design/methodology/approach A brief content analysis of prominent scholarly apps was performed to evaluate their prime features and usability. Findings Scholarly apps improve research mobility by offering on-the-go access to academic resources, databases, communication tools, personalized content recommendations and integrated automated citation tools. The integration of AI and machine learning into these apps holds potential for further personalizing and streamlining research processes. Research limitations/implications Although certain discussed mobile apps are well known and frequently used by users, their value in the field of mobile technology remains significant. Despite their vast popularity, these apps continue to play a significant part in the mobile app ecosystem due to their proven efficacy, widespread acceptance and continuous relevance in meeting user demands. Originality/value To the best of the authors’ knowledge, this is one of the first studies to comprehensively evaluate the scholarly apps and contributes to the literature on mobile-based library services.

Energy density inhomogeneities with self-gravitating charged fluid in modified teleparallel gravity

In this paper, we analyze energy density inhomogeneities for charged fluid configuration in the background of [Formula: see text] theory and recognize its prime features as computed in GR. The dynamical equations are composed employing Bianchi identities for the standard, [Formula: see text] extra terms, and energy-momentum tensor for the electromagnetic field. We evaluate various mathematical models of dissipative and anisotropic fluid distributions in-plane symmetry under [Formula: see text] gravity. To proceed with the investigation, we design the [Formula: see text] field equations, kinematical quantities, and mass function. We analyzed dynamical variables and Ellis equations in terms of our considered theory. To examine the associated inhomogeneity factors, specific scenarios are illustrated alongside and without dissipation. Within a non-radiating situation, we analyze dust and isotropic and anisotropic matter in the state of electric charge. We examine the inhomogeneity factor of a dissipative fluid via a charged dust haze. We derive that the electromagnetic field fosters matter inhomogeneity, but additional curvature factors make the entire structure more homogenous as time passes.

A Novel Approach for Bluetooth Mesh Network Detection using Machine Learning and Correlation Analysis

Bluetooth (BT) mesh networks are used in several ad hoc network communication scenarios, including natural disasters, calamities, war zones, etc. However, there are situations where Bluetooth mesh networks are misused by anti-social elements during riots etc. As of now there no such research article that showcases detection of BT mesh networks using machine learning tools. Therefore, there is a need to build novel methods for detecting the very presence of Bluetooth mesh networks to aid the law enforcement agencies. This paper contributes to building a new dataset for the aid of machine learning tools to detect the presence of Bluetooth mesh networks. The dataset is extracted from a simulated Bluetooth mesh network with 52 BT network traffic attributes, using the Bridgefy application. Further, the dataset is optimized by identifying prime features, using label encoding, one-hot encoding, correlation analysis and evaluated by various Machine Learning (ML) tools. An accuracy of 99.6% is achieved after optimizing the feature set.

Prey selection along a predators' body size gradient evidences the role of different trait-based mechanisms in food web organization.

An increase in prey richness, prey size and predator trophic position with predator body size has been consistently reported as prime features of food web organization. These trends have been explained by non-exclusive mechanisms. First, the increase in energy demand with body size determines that larger predators must reduce prey selectivity for achieving the required number of resources, being consumption relationships independent of prey traits. Second, when consumption is restricted by gape limitation, small predators are constrained to select among small prey. However, this selection weakens over large predators, which progressively consume more and larger prey. Finally, the optimal foraging mechanism predicts that larger predators optimize their diet by selecting only large prey with high energy reward. Each one of these mechanisms can individually explain the increase in prey richness, prey size and predator trophic position with predator body size but their relative importance or the direct evidence for their combined role was seldom considered. Here we use the community assembly by trait selection (CATS) theory for evaluating the support for each one of these mechanisms based on the prey selection patterns that they predict. We analyzed how prey body size and trophic guild determine prey selection by predators of increasing body size in a killifish guild from a temporary pond system. Results support the combination of the three mechanisms to explain the structural trends in our food web, although their strength is contingent on prey trophic group. Overall, high energy prey are preferred by larger predators, and small predators select small prey of all trophic status. However, large predators prefer large primary producers and avoid large carnivorous prey, probably because of the inherent risk of consuming other carnivorous. Our study provides a mechanistic understanding of how predator traits determine the selection of prey traits affecting food web assembly.

Intention and Interpretation, Now and Then

Almost every interpretative university discipline in or adjacent to the Humanities makes routine, unproblematic appeal to intention as an interpretative move. By proscribing intentionalism as an instrument of interpretation, Literary Criticism is the outlier among adjacent and not so adjacent disciplines. The introduction to the special issue “Intention and Interpretation, Now and Then” maps the prime features of the intellectual landscape concerning intention and literary criticism in Anglo-American and French traditions since the late eighteenth century. It then highlights the losses that Literary Criticism incurs by its repudiation of intention as a heuristic tool. Apart from its loss of intellectual esteem by peers, any interpretive practice that refuses to intuit intention also loses significant cognitive and ethical purchase. The nature and magnitude of these losses can be measured in many ways. Articles in this special issue measure these losses by looking to the later medieval period in particular (with contributions also from the early medieval and early modern periods), when intention rose dramatically as a heuristic tool in many discursive fields, notably in criminal law, penitential ethics, and biblical hermeneutics. The contributions also explicate the way a premodern or early modern discourse deploys and sometimes defines intentionalism.

Otherness and suspiciousness: a comparative study of public opinions between the Confucius Institute and Goethe-Institut in developing countries

This study aims to examine the differences in lexical priming features between Confucius Institutes and Goethe-Instituts in developing countries using lexical priming theory and natural language processing techniques. By collecting news media coverage from 2014 to 2023, this study analyses the corpus through collocation, colligation, semantic association, and semantic prosody. In this study, it was found that the Goethe-Institut has a more stable institutional identity, while the Confucius Institute is still largely recognized for language teaching and cultural dissemination activities. The association of the Confucius Institute with China and its government creates a stronger sense of "otherness" and leads to negative perceptions. Overall, this study contributes to a better understanding of public perception and institutional image in developing countries.

Comparison to Perceptions of Teachers and Head Teachers about Accountability Measures for Public School Improvement in Punjab

Every government faces the pressure of quality and accountability as it has become an international agenda. Countries are shaping their policies according to the interest of stakeholders in globalization and international competition. They plan to establish high-performing school systems. Developed and developing countries are also struggling with this movement to ensure the quality and accountability of schools. Different accountability approaches are in practice. Mixed methods research design opted for using an interview protocol and sur\vey with 2220 participants from public elementary schools. A pragmatic approach selected for reliability, and validity was assured while using a triangulation design. Descriptive statistics and thematic analysis were performed for the analysis of data. It concluded that educational accountability is a challenging job. Data revealed an ambiguity in the roles and responsibilities of different stakeholders, teachers, head teachers, parents, students, and district administration. Assign duties may be clarified, and responsible persons may be considered answerable. Some policies are there, but these are in practice in a different sense. Implementation of policies may be authenticated. When students' achievement is considered the main drive of school effectiveness, and schools are rated accountable for results to nurture positive change. More than achievement in numbers alone is required for quality education, as this can easily be achieved through misreporting. For quality and improvement over time, some prime features like creativity and higher-order thinking skills are mandatory. In different situations, there may be other yardsticks to judge students' personality traits like teamwork, commitment, and emotional adjustment. School teachers and head teachers had recommended replacing test-based accountability with performance-based accountability to amplify student learning and success.

Enhanced Binary Black Hole algorithm for text feature selection on resources classification

Exploring an optimal combination of features from high-dimensional text vectors is an essential step for classification on Scientific and Technical Service (STS) resources. However, existing metaheuristic feature selection methods exhibit deteriorated performance and increased computational costs when dealing with high-dimensional datasets. Based on these issues, a novel Enhanced Binary Black Hole (EBBH) algorithm is investigated to improve the performance of mining the optimal subset of high-dimensional attributes in STS resources text. In the proposed EBBH, all operators employ a novel binary encoding framework to reduce the number of features, which provides a more efficient way to search the solution space. As a pre-processing step, prime feature attention assigns different selection weights based on the variable importance ranking from random forests, ensuring a high-quality initial population is generated. Moreover, we incorporate an adaptive enhancement strategy to maintain the balance between exploitation and exploration in the search process. The proposed EBBH is assessed on eight high-dimensional benchmark datasets and subsequently demonstrated successful application to five STS resource datasets. Comparative experiments with 12 state-of-the-art algorithms for high-dimensional problems revealed the superiority of EBBH in terms of fitness, classification accuracy, and selected features.

Brain Imaging Databases.

The availability of neuroimaging-based databases is helping immensely to understand the brain function in healthy and diseased conditions. This viewpoint highlights the objectives, commonalities, and differences within these existing databases and pointers for researchers to choose a particular database. We introduce a multimodal multidisease database, SWADESH, and its comparison with the existing databases. A futuristic database blueprint is proposed for housing multidisease, multimodal, and longitudinal brain imaging data systematically organized in a matrix form along with neuropsychological assessment scores for the identification of causal disease processes. The information-rich databases will ultimately assist with the systematic identification of prime features linked to causal disease processes, leading to the design of appropriate clinical trials for successful therapeutic interventions.

Progress and Prospective of the Industrial Development and Applications of Eco-Friendly Colorants: An Insight into Environmental Impact and Sustainability Issues

Color is the prime feature directly associated with the consumer's attraction and choice of their food. The flavor, safety, and nutritional value of any food product are directly associated with the food color. Natural and synthetic colorants (dyes and pigments) have diversified applications in various sectors such as food, feed, pharmaceutical, textiles, cosmetics, and others. Concerning the food industry, different types of natural and synthetic colorants are available in the market. Synthetic food colorants have gained popularity as they are highly stable and cheaply available. Consumers worldwide prefer delightful foodstuffs but are more concerned about the safety of the food. After its disposal, the colloidal particles present in the synthetic colorants do not allow sunlight to penetrate aquatic bodies. This causes a foul smell and turbidity formation and gives a bad appearance. Furthermore, different studies carried out previously have presented the toxicological, carcinogenic effects, hypersensitivity reactions, and behavioral changes linked to the usage of synthetic colorants. Natural food colorings, however, have nutraceutical qualities that are valuable to human health such as curcumin extracted from turmeric and beta-carotene extracted from carrots. In addition, natural colorants have beneficial properties such as excellent antioxidant properties, antimutagenic, anti-inflammatory, antineoplastic, and antiarthritic effects. This review summarizes the sources of natural and synthetic colorants, their production rate, demand, extraction, and characterization of food colorants, their industrial applications, environmental impact, challenges in the sustainable utilization of natural colorants, and their prospects.

What is behind partial repetition costs? Event-files do not fully occupy bound feature codes.

Feature binding accounts state that features of perceived and produced events are bound into event-files. Performance while responding to an event is impaired when some, as opposed to all or none, of this event's features already belong to a previous event-file. While these partial repetition costs are generally considered to be indicators of feature binding, their cause is still unclear. Possibly, features are fully occupied when bound in an event-file and must be unbound in a time-consuming process before they can enter a novel event-file. In this study, we tested this code occupation account. Participants responded to the font color (target) of a word (distractor) by pressing one of three keys (response) while ignoring the word meaning. We measured partial repetition costs from prime to probe while introducing an intermediate trial. We compared sequences in which this intermediate trial did not repeat any prime features and sequences in which it repeated either the prime response or distractor. Partial repetition costs occurred in the probe, even when one (vs. none) of the prime features repeated in the intermediate trial, although significantly reduced. Thus, single bindings do not fully occupy feature codes. By ruling out a possible mechanism behind partial repetition costs, the present study contributes to the further specification of feature binding accounts.

An active memristor based rate-coded spiking neural network

Neuromorphic computing is a novel computing paradigm that aims to mimic the behavior of biological neural networks for efficiently solving complex problems. While CMOS based neurons and synapses have been developed, they are limited in their ability to demonstrate bio-realistic dynamics. This, coupled with the fact that a huge number of these individual devices are required to build neurons and synapses, limits the scaling and power efficiency of such systems. A viable answer to this problem is neuromemristive systems that are based on memristor devices. These devices exhibit physical behaviors that can be related to the bio-physical dynamics of synapses and neurons. In this paper, a rate-coded all memristive “spiking neural network” (SNN) is presented. The proposed SNN is built with an active memristor neuron based on vanadium dioxide (VO2) coupled with a non-volatile memristor synapse. The results are validated by first simulating spiking versions of two Boolean functions viz., AND and XOR gates in SPICE. With features extracted from the small neural nets, a large-scale 3-layer spiking neural network is then simulated in Python which yields a validation accuracy of 87% on the MNIST dataset of handwritten digits. One of the prime features of this work is the realization of the XOR function using a single neuron which is not possible without the use of 2-layers of neurons in traditional neural networks. Another significant contribution is the utilization of a gradient-based learning approach for online training of a large-scale SNN. For this, we use the inherent activation function (Sigmoid/ReLU) of the proposed neuron design.

Risk-Sensitive Maximum Principle for Controlled System with Delay

Risk-sensitive maximum principle and verification theorem for controlled system with delay is obtained by virtue of classical convex variational technique. The prime feature in the research is that risk-sensitive parameter ϑ seriously affects adjoint equation and variational inequality. Moreover, a verification theorem of optimality is derived under some concavity conditions. An example is given to illustrate our theoretical result.

Scalable Scientific Interactive Research Computing With Project Scinco

Interactive computing with Jupyter notebooks has transformed the state-of-the-art of scientific research computing. Users can perform a multitude of computational tasks in real time, including data cleansing, analysis, visualization, and post-processing, with Jupyter notebooks. Additionally, the ability to write and execute code and include supporting text and images in the same document has made it popular for use in scholarly articles and teaching. These capabilities complement the batch computing services provided by HPC centers exposed through traditional science gateways. However, integrating Jupyter into science gateways and other advanced computing ecosystems introduces new challenges related to scalability, collaboration, and reproducibility. In this paper, we discuss Project Scinco (Scalable Interactive Computing), an open-source platform for scalable, reproducible, interactive scientific computing, designed to be run in academic computing centers and incorporated into science gateways. We describe the prime features, architecture, deployment choices, and challenges of project Scinco.

Micromorphological studies of the leaf and stem of Camellia sinensis (L.) Kuntze with reference to their taxonomic significance.

The micro-morphological examinations of the leaf lamina, petiole and stem for Camellia sinensis (L.) Kuntze (Theaceae) was carried out using a variety of microscopic techniques widely implemented in the area of medicine. The objective of this study was the micromorphological characterization of stem, petiole, lamina, stomata, leaf trichomes and other internal characters. The anatomical studies included the cross section of stem and leaf of Camellia sinensis thereby exhibiting a typical pattern of arrangement of tissues similar to woody plants. Some idioblastic sclereids like astrosclereids, osteosclereids were found in the medullary parenchyma of tea stem and leaf. Large numbers of sclereids were found mainly in the parenchymatous tissue of leaf petiole. Other micro-morphological features like trichomes, stomata, and different tissue layer were also recorded. The leaf trichomes were unicellular, long and densely present in the lower surface of immature leaf but a decrease in amount of trichomes was seen in the mature leaf making it a prime taxonomic feature of the tea leaf. The microscopic morphological analysis of the stem, petiole, lamina, stomata, leaf trichomes of Camellia sinensis can be used for its identification. In addition, these techniques can be further implemented for the taxonomic characterization thereby establishing a genetic relationship and solving taxonomic disputes in the field of plant systematics.

Interaction of spatial-Gaussian lasers with the magnetized CNTs in the presence of DC electric field and enhanced THz emission

The interaction of spatial-Gaussian lasers with magnetized carbon nanotubes (CNTs) in the presence of a DC electric field is shown to broaden the surface plasmon resonance effectively. The prime feature of the interaction of lasers with magnetized CNTs is that when the electron cylinder of CNT is shifted with respect to the ion cylinder, the space charge electric field is developed in the overlapped region and results in the restoration force of the electrons. The restoration force along with the ponderomotive and magnetic forces assist to increase the nonlinear current which is further enhanced by the drift velocity acquired by the electrons of CNTs due to the applied static electric field. This action is also responsible for the enhanced THz yield at surface plasmon resonance condition where is the polarization coefficient, is the relative permittivity of the dielectric substrate, is the cyclotron frequency of CNT electrons and is the plasma frequency. The study reveals that THz generation from the magnetized CNTs may be a promising source for the detection of dangerous chemicals like royal demolition explosive (RDX), pentaerythritol tetra-nitrate (PETN), trinitrotoluene (TNT), etc.

Aïda Muluneh's Homebound: A Journey in Photography & Addis Foto Fest: Nine Years Survey

Aïda Muluneh's Homebound: A Journey in Photography & Addis Foto Fest: Nine Years Survey

Analisis Perbandingan Algoritma Forecasting dalam Prediksi Harga Saham LQ45 PT Bank Mandiri Sekuritas (BMRI)

Economic development in Indonesia has slowed in recent years. This resulted in the movement of the index for several stocks listed on BEIm, especially LQ45 which also experienced increases and decreases. Therefore, it is necessary to analyze stock price movements so that the results of the analysis can be used by investors to make investment decisions. This study will apply several Forecasting algorithms such as Linear Regression and Neural Network to predict the stock price of LQ45 in the case study of Bank Mandiri Sekuritas (BMRI). By using four attributes, namely open, high, and low values as predictors and close as a class, this study focuses on determining the accuracy value, namely Root Mean Squared Error (RMSE) by optimizing parameter values. The test results obtained an RMSE value of 0.034 on the Neural Network method with the addition of a hidden layer and an RMSE value of 0.052 on the Linear Regression method with M5 Prime and Greedy Feature Selection with a min-tolerance value of 0.05.

Hybrid cumulative approach for localization of nodes with adaptive threshold gradient feature on energy minimization using federated learning

PurposeLocalization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed adaptive threshold gradient feature (ATGF) model. A received signal strength indicator (RSSI) model with node estimated features is implicated with localization problem and enhanced with hybrid cumulative approach (HCA) algorithm for node optimizations with distance predicting.Design/methodology/approachUsing a theoretical or empirical signal propagation model, the RSSI (known transmitting power) is converted to distance, the received power (measured at the receiving node) is converted to distance and the distance is converted to RSSI (known receiving power). As a result, the approximate distance between the transceiver node and the receiver may be determined by measuring the intensity of the received signal. After acquiring information on the distance between the anchor node and the unknown node, the location of the unknown node may be determined using either the trilateral technique or the maximum probability estimate approach, depending on the circumstances using federated learning.FindingsImprovisation of localization for wireless sensor network has become one of the prime design features for estimating the different conditional changes externally and internally. One such feature of improvement is observed in this paper, via HCA where each feature of localization is depicted with machine learning algorithms imparting the energy reduction problem for each newer localized nodes in Section 5. All affected parametric features on energy levels and localization problem for newer and extinct nodes are implicated with hybrid cumulative approach as in Section 4. The proposed algorithm (HCA with AGTF) has implicated with significant change in energy levels of nodes which are generated newly and which are non-active for a stipulated time which are mentioned and tabulated in figures and tables in Section 6.Originality/valueLocalization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed ATGF model. An RSSI model with node estimated features is implicated with localization problem and enhanced with HCA algorithm for node optimizations with distance predicting.

Papillon-Lefevre syndrome with arachnodactyly and associated aggressive periodontitis

Papillon-Lefevre Syndrome (PLS) is a autosomal recessive genetic disorder. The prevalence of this disorder has been reported to be 1 to 4 per million in the general population. The prime features of PLS include palmar-plantar hyperkeratosis along with premature loss of deciduous and permanent teeth due to aggressive periodontitis. Radiological feature involves severe destruction of alveolar bone giving teeth a typical “floating in air” appearance along with intra-cranial calcifications. Clinical findings of disease specific dermatologic manifestations and periodontal involvement forms the framework of the diagnosis. Clinical significance: As PLS involves severe irreparable destruction of alveolar bone and tooth loss, both dental clinicians and dermatologists opinion is required for both initial and long term management of this disease followed by psychological counselling.