Pieces Of Information Research Articles

Resource Organizer

There are a lot of problems with the current system because everything is done manually. The present system's biggest flaw is the enormous physical volume of data, which makes searching for and retrieving specific pieces of information a pain. Papers containing information are susceptible to harm. It takes a long time to find out whether documents arrived or not when you have to handle everything by hand. At the time of product application, it will be rejected if any document is missing. It is really time-consuming to reapply for the product process. All these issues with the current method led them to believe that a computerised system would be better. All of these issues will be resolved by the automated system. There is a good probability that more people's time will be wasted in this communication scenario. It is quick, adaptable, and easy to use. Analysing a system is looking closely at all the different things it does and how they relate to each other and the world around them. Analysing a system involves doing things like drawing lines around it and checking to see if a potential system has to take other systems into account. Decision points and transactions processed by the current system are the subjects of data acquired during analysis. The project's backend was built in SQL Server and frontend was built in Asp.Net. A large number of managers are in charge of each and every one of an organization's operations. Manufacturing, production, marketing, and other facets of organisational management are overseen by these managers. An important problem with management is that, as time went on, they shifted their focus to human capital. Now, every company has a hierarchical structure, and each structure has its own unique purpose and character. Given that their people are their most valuable asset, larger companies in this situation must devote significant resources to human resource management. Organisational data evolved over time away from its core functions in this way. To sum up, a resource organiser is a piece of software that helps with the management and coordination of different resources. Assets can be either concrete, like machinery and buildings, or intangible, like human capital and time.

Ising string beyond the Nambu-Goto action

A major result of the effective string theory (EST) description of confinement is the so-called “low-energy universality”, which states that the first few terms of the large distance expansion of any EST are universal and coincide with those of the Nambu-Goto action. Going beyond this approximation is one of the most interesting open problems in the EST. In the higher-order terms beyond Nambu-Goto several important pieces of physical information are encoded, which could improve our understanding of the physical mechanisms behind confinement and of the physical degrees of freedoms which originate the EST. In this paper we evaluate numerically the first two of these corrections in the case of the three-dimensional gauge Ising model. The first of them turns out to be negative; γ3=−0.00048(4), similar (but not equal) to the one recently measured in the SU(2) Yang-Mills theory in three dimensions and compatible with the bootstrap bound γ3≥−1768. Published by the American Physical Society 2024

Intellectual Capital And Firm Value: Moderating Roles Of Tax Incentives In R&D

This study investigates the effect of intellectual capital on firms’ value with tax incentives in Research and development as a moderating variable. Utilizing the moderating variable becomes the novelty of this study since research that uses the moderating variable has never been conducted. The sample used in this study is 144 firm-year companies in the manufacturing sector listed on the IDX during the 2017-2022 period. The study used panel data and multiple linear regression analysis methods with a random effect model. The result of this study indicated that intellectual capital aggregately positively affects the firms’ value. Individually, capital employed and human capital positively affect firms’ value, while structural capital does not significantly affect firms’ value. Furthermore, after the moderation, the tax incentives are proven to weaken the effect of intellectual capital aggregately and capital employed on firms’ value. Conversely, tax incentives strengthen the relationship between structural capital and firms’ value. Meanwhile, tax incentives are not moderating the relationship between human capital and firms’ value. The result of this study can be a piece of additional information for OJK to understand firms’ intrinsic value and consideration in formulating the policy about regulation and supervision.

The Useful Cinema of State Secrecy. On Some Socialist Romanian Spy Films

A decree passed by the socialist Romanian state at the beginning of the 1970s stipulated the need to defend state secrets as a condition for the country’s economic and social progress in the context of its growing participation in world commerce. While the law operated with a wide notion of state secrecy, referring to any piece of information which, if disclosed, could jeopardize the Romanian state’s interests, it included harsh regulations with regard to personal contacts with foreign citizens and served primarily for establishing a heightened operative control over the local economic life. Following the Romanian State Security Council’s encouragement of campaigns for popular education, which should help prevent any trespassing of this decree, the early 1970s saw the commissioning of several instruction films made by the newly founded Film Service of the Ministry of the Interior as a form of ‘counterintelligence training of the working class’. The present paper contextualizes and closely analyzes some of these films staging fictional cases of industrial espionage and their subsequent investigation by State Security as a form of useful cinema, arguing that, aside from their main educational mission of raising awareness about the legal responsibilities ensuing from the decrees concerning state secrecy, these films also acted as an instrument of national, geopolitical and institutional self-representation.

Examining the neural basis of unitization: A review.

Associative memory refers to the ability to form and remember associations between individual pieces of information rather than memory for a single object or word. Encoding associations in memory tends to be a more difficult task than item (only) encoding, because associative memory requires encoding multiple items as well as the specific links amongst the items. Accordingly, researchers have worked to identify interventions and strategies to reduce the effort and neural resources required for successful associative memory processing. Unitization is one such strategy that has traditionally been defined as the process by which two or more discrete items are processed, or encoded, such that they are perceived as a single ensemble. The current review explores the neural research on unitization while considering the behavioral benefits that accompany the process.

Communication Contribution of IPNU Organization Gebang Kec. Cirebon District in the Development of Da'wah

The NU organization among students is IPNU-IPPNU; of course, it has a role in spreading the wings of Islamic da'wah, primarily through the Internet. Its function is to prevent the internet from being filled with content that does not contain education and people who are not responsible, especially people who can mislead the younger generation today. This study aims to analyze the contribution of IPNU organizational communication in Gebang District, Cirebon Regency, in developing da'wah science. The research method used in this study is the qualitative descriptive method. These sources are gathered based on discussion and linked from one piece of information to another. The data collection techniques used in this study were observation, interviews, and research. This data is analyzed, and then conclusions are drawn. Based on the results of the analysis, it was found that IPNU Gebang District plays a role in the development of da'wah in its area, with various activities held, such as monthly and weekly routines. The forms of activity are Narayanan, Yasin, recitation of Al-Barjanji, and general recitation. Other activities supporting the development of da'wah are developing adolescents' interests and talents, such as journalistic training, graphic design, and public speaking.

Imaging Urban Hidden Faults with Ambient Noise Recorded by Dense Seismic Arrays

Abstract The identification of preexisting near-surface faults represents a piece of crucial information needed to correctly assess the seismic hazard of any area. The mapping of these structures is particularly challenging in densely populated and heavily urbanized areas. We use ambient seismic noise recorded by a dense array in Seal Beach, California, to image shallow fault lines via a reflected surface-wave analysis. Our results highlight the presence of previously unknown shallow faults that correlate remarkably well with shallow seismicity and active survey images.

Impact of hemB mutations on 5-aminolevulinic acid production in Escherichia coli

Background Microbial production of 5-aminolevulinic acid (ALA) attracts attention due to a wide range of biotechnological and medical applications of ALA, including cancer treatment and diagnosis. Various genetic engineering approaches have been employed to improve ALA production in bacterial hosts such as Escherichia coli possessing the C5 pathway. Glutamyl-tRNA reductase (GluTR) encoded by hemA, glutamate-1-semialdehyde aminotransferase (GSA-AT) encoded by hemL, and ALA dehydratase (ALAD) encoded by hemB play important roles in ALA metabolism including the C5 pathway. Attenuation of the intercellular ALAD activity, which condensates 2 molecules of ALA to synthesize porphobilinogen (PBG), has been employed by various measures. However, a mutation approach by substituting catalytically important residues in ALAD encoded by hemB has never been attempted. The aim of this study is to assess the impact of hemB mutations on the ALA production in E. coli. Methods In this study, the authors mutated the amino acid residues potentially related to the enzymatic activity of E. coli ALAD by referring to a mutation experiment of human ALAD. The authors created five types of mutated hemB genes, introduced these genes to the hemB-deleted mutant strain of E. coli, and assessed the impact of the ALAD mutations on ALA production. In addition, hemA, hemL, and rhtA encoding an ALA exporter were introduced to the E. coli possessing a mutated hemB. Results The authors revealed that the mutations of ALAD employed in this study did not significantly enhance ALA production. Overexpression of hemA, hemL, and rhtA substantially increased ALA production in any E. coli strain possessing mutated hemB, while a difference in ALA production of the strain could be rather attributed to its growth behaviour than ALAD inactivation. Conclusions This study provides an important piece of information to design the bioprocess of ALA production using E. coli engineered through the C5 pathway.

A machine learning driven automated system for safety data sheet indexing

Safety Data Sheets (SDS) are foundational to chemical management systems and are used in a wide variety of applications such as green chemistry, industrial hygiene, and regulatory compliance, among others within the Environment, Health, and Safety (EHS) and the Environment, Social, and Governance (ESG) domains. Companies usually prefer to have key pieces of information extracted from these datasheets and stored in an easy to access structured repository. This process is referred to as SDS “indexing”. Historically, SDS indexing has always been done manually, which is labor-intensive, time-consuming, and costly. In this paper, we present an automated system to index the composition information of chemical products from SDS documents using a multi-stage ensemble method with a combination of machine learning models and rule-based systems stacked one after the other. The system specifically indexes the ingredient names, their corresponding Chemical s Service (CAS) numbers, and weight percentages. It takes the SDS document in PDF format as the input and gives the list of ingredient names along with their corresponding CAS numbers and weight percentages in a tabular format as the output. The system achieves a precision of 0.93 at the document level when evaluated on 20,000 SDS documents annotated for this purpose.

DIF Analysis with Unknown Groups and Anchor Items

Ensuring fairness in instruments like survey questionnaires or educational tests is crucial. One way to address this is by a Differential Item Functioning (DIF) analysis, which examines if different subgroups respond differently to a particular item, controlling for their overall latent construct level. DIF analysis is typically conducted to assess measurement invariance at the item level. Traditional DIF analysis methods require knowing the comparison groups (reference and focal groups) and anchor items (a subset of DIF-free items). Such prior knowledge may not always be available, and psychometric methods have been proposed for DIF analysis when one piece of information is unknown. More specifically, when the comparison groups are unknown while anchor items are known, latent DIF analysis methods have been proposed that estimate the unknown groups by latent classes. When anchor items are unknown while comparison groups are known, methods have also been proposed, typically under a sparsity assumption – the number of DIF items is not too large. However, DIF analysis when both pieces of information are unknown has not received much attention. This paper proposes a general statistical framework under this setting. In the proposed framework, we model the unknown groups by latent classes and introduce item-specific DIF parameters to capture the DIF effects. Assuming the number of DIF items is relatively small, an L1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L_1$$\\end{document}-regularised estimator is proposed to simultaneously identify the latent classes and the DIF items. A computationally efficient Expectation-Maximisation (EM) algorithm is developed to solve the non-smooth optimisation problem for the regularised estimator. The performance of the proposed method is evaluated by simulation studies and an application to item response data from a real-world educational test.

Malware2ATT&CK: A sophisticated model for mapping malware to ATT&CK techniques

MITER Corporation presents the ATT&CK Matrix, which maps malware behavior to different tactics, techniques, and procedures (TTPs) providing a comprehensive view to clarify the inner mechanism of malware more accurately. However, manual mapping methods are time-consuming, while rule-based feature-based mapping methods often under- or misreport many attacks. Inspired by the successful application of image multi-label classification techniques, we propose a method called Malware2ATT&CK to automatically map malware to ATT&CK techniques. The method applies pre-trained models to extract features from the two pieces of information — the static analysis information of assembly instruction and API calls from malware. The malicious techniques are identified by a multi-label classifier based on the graph neural network and knowledge graph. In the experiments over two test sets, Malware2ATT&CK shows excellent performance achieving an average F1 score of 83.6% in the technology mapping task. Further evaluation indicates that the high accuracy of the prediction is due to our ability to accurately capture the correlation between behaviors.

Exploring Challenges Faced by Engineering Students in Graphic Preparations of Perspective Geometry

Perspective geometry is a fundamental, challenging, and captivating subject within the engineering bachelor's degree program. It holds significant importance in developing graphic skills, analytical abilities, sketching proficiency, and comprehension of drawings. Moreover, it plays a crucial role in organizing the architectural components of a bachelor's thesis. However, the teaching and learning of perspective geometry often reveal various difficulties and shortcomings. Moreover, one of the various systems of symbols and languages created by global cultures is the graphical language, which is an exceptional and unparalleled language for understanding scientific and technical information. This language is considered the oldest international language. Every visual piece of information in various processes of human life has been formed through the graphical language, which is composed of various geometric shapes. In this research, data has been gathered from the perspectives of first-year students from the 2013 batch regarding the difficulties encountered in perspective geometry. A questionnaire was distributed to collect their opinions, and three graphical tasks with different levels of complexity were given to the students. The results obtained from this study indicate that employing suitable teaching methods and providing adequate resources can alleviate most of the learning difficulties associated with graphic skills. Instructors also play a pivotal role in resolving learning obstacles. Therefore, efforts should be made to enhance the quality of teaching graphic subjects by updating the content and curriculum of educational programs and improving teaching methods through the use of technology. In higher education institutions, conditions for fostering students' professional knowledge, enhancing their ability to draw maps and create technical documents can be facilitated through computer graphics education. Updating the teaching methods plays a crucial role in improving the quality of graphic education. The mentioned factors provide a suitable environment for the growth of graphic knowledge and the implementation of projects related to professional subjects.

Fractal based feature extraction technique for classifying EEG signal for color visualization tasks

Exploring and finding Significant features for colour visualization tasks using the EEG signals is crucial in developing a robust Brain-machine Interface (BMI). The visually evoked potential carries multiple pieces of information, and finding its best feature is a tedious task. The main objective of this research is to concentrate on various linear and non-linear features which classifies the visually evoked potential when visualizing various colours for a certain period with reduced computational time and with higher accuracy. The feature extraction techniques utilized for extracting the features of EEG signals while visualizing various colours are Power Spectral Intensity (PSI), Spectral Entropy (SE), Detrended Fluctuation analysis (DFA), Higuchi Fractal Dimension (HFD), Petrossian Fractal Dimension (PFD), Multifractal Detrended Fluctuation Analysis (MFDFA). The extracted features were classified using the Multiclass classifier using one vs rest technique Support Vector Machine algorithm. The result shows that the MFDFA method with multiclass classifier combination has achieved 97.4 percent of classification accuracy when compared with other features.

Quantification of brain-wide vascular resistivity via ultrafast Doppler in human neonates helps early detection of white matter injury

Preterm birth is associated with cerebrovascular development disruption and can induce white matter injuries (WMI). Transfontanellar ultrasound Doppler is the most widely used clinical imaging technique to monitor neonatal cerebral vascularisation and haemodynamics based on vascular indexes such as the resistivity index (RI); however, it has poor predictive value for brain damage. Indeed, these RI measurements are currently limited to large vessels, leading to a very limited probing of the brain’s vascularisation, which may hinder prognosis. Here we show that ultrafast Doppler imaging (UfD) enables simultaneous quantification, in the whole field of view, of the local RI and vessel diameter, even in small vessels. Combining both pieces of information, we defined two new comprehensive resistivity parameters of the vascular trees. First, we showed that our technique is more sensitive in the early characterisation of the RI modifications between term and preterm neonates and for the first time we could show that the RI depends both on the vessel diameter and vascular territory. We then showed that our parameters can be used for early prediction of WMI. Our results demonstrate the potential of UfD to provide new biomarkers and pave the way for continuous monitoring of neonatal brain resistivity.

A Multi-Information Spreading Model for One-Time Retweet Information in Complex Networks.

In the realm of online social networks, the spreading of information is influenced by a complex interplay of factors. To explore the dynamics of one-time retweet information spreading, we propose a Susceptible-Infected-Completed (SIC) multi-information spreading model. This model captures how multiple pieces of information interact in online social networks by introducing inhibiting and enhancement factors. The SIC model considers the completed state, where nodes cease to spread a particular piece of information after transmitting it. It also takes into account the impact of past and present information received from neighboring nodes, dynamically calculating the probability of nodes spreading each piece of information at any given moment. To analyze the dynamics of multiple information pieces in various scenarios, such as mutual enhancement, partial competition, complete competition, and coexistence of competition and enhancement, we conduct experiments on BA scale-free networks and the Twitter network. Our findings reveal that competing information decreases the likelihood of its spread while cooperating information amplifies the spreading of mutually beneficial content. Furthermore, the strength of the enhancement factor between different information pieces determines their spread when competition and cooperation coexist. These insights offer a fresh perspective for understanding the patterns of information propagation in multiple contexts.

Intent-Oriented Dynamic Interest Modeling for Personalized Web Search

Given a user, a personalized search model relies on her historical behaviors, such as issued queries and their clicked documents, to generate an interest profile and personalize search results accordingly. In interest profiling, most existing personalized search approaches use “static” document representations as the inputs, which do not change with the current search. However, a document is usually long and contains multiple pieces of information, a static fix-length document vector is usually insufficient to represent the important information related to the original query or the current query, and makes the profile noisy and ambiguous. To tackle this problem, we propose building dynamic and intent-oriented document representations which highlight important parts of a document rather than simply encode the entire text. Specifically, we divide each document into multiple passages, and then separately use the original query and the current query to interact with the passages. Thereafter we generate two “dynamic” document representations containing the key information around the historical and the current user intent, respectively. We then profile interest by capturing the interactions between these document representations, the historical queries, and the current query. Experimental results on a real-world search log dataset demonstrate that our model significantly outperforms state-of-the-art personalization methods.

The contribution of difficulty of an irrelevant task to task conflict.

In the standard colour-word Stroop task, participants are presented with colour words and required to respond to their colour while ignoring their meaning. Two types of conflict might occur in such experiments: information conflict and task conflict. Information conflict reflects the processing of two contradicting pieces of information and is indicated by shorter reaction times (RTs) in congruent than in incongruent trials. Task conflict reflects the additional effort associated with performing two tasks, as opposed to one, and is indicated by shorter RTs in neutral trials than in congruent trials (termed reverse facilitation). While information conflict is commonly seen in Stroop and Stroop-like tasks, task conflict is rarely observed. In the present study, participants were presented with coloured segments that, by applying Gestalt principles, could be perceived as colour words. We found that incongruent trials were slower than congruent trials, suggesting that participants successfully perceived the colour words, which led to involuntary reading. In addition, reversed facilitation was found so that neutral trials (i.e., trials that only consist of one task) were faster than congruent trials (as well as incongruent trials; both consist of two tasks). The presence of both interference from the incongruent trials and reverse facilitation suggests that involuntary reading could also occur in scenarios requiring cognitive effort.

Deep image prior inpainting of ancient frescoes in the Mediterranean Alpine arc

The unprecedented success of image reconstruction approaches based on deep neural networks has revolutionised both the processing and the analysis paradigms in several applied disciplines. In the field of digital humanities, the task of digital reconstruction of ancient frescoes is particularly challenging due to the scarce amount of available training data caused by ageing, wear, tear and retouching over time. To overcome these difficulties, we consider the Deep Image Prior (DIP) inpainting approach which computes appropriate reconstructions by relying on the progressive updating of an untrained convolutional neural network so as to match the reliable piece of information in the image at hand while promoting regularisation elsewhere. In comparison with state-of-the-art approaches (based on variational/PDEs and patch-based methods), DIP-based inpainting reduces artefacts and better adapts to contextual/non-local information, thus providing a valuable and effective tool for art historians. As a case study, we apply such approach to reconstruct missing image contents in a dataset of highly damaged digital images of medieval paintings located into several chapels in the Mediterranean Alpine Arc and provide a detailed description on how visible and invisible (e.g., infrared) information can be integrated for identifying and reconstructing damaged image regions.

Reading as Media Consumption: Modern Russian Features

In recent years, the processes of media consumption have exposed a significant transformation. The place of reading in its structure also has changed. The article discusses the similarities between reading books and magazines as a process itself and sociocultural practice. The author suggests that book, magazine and news media consumption is in a relationship of mutual support and mutual influence. The author argues that the habit of such "long" reading is being influenced by crisis phenomena in the periodical segment: a sharp decline in readership, a decline in print circulation and difficulty in accessing. In recent years, the focus on electronic formats of publications has been the main direction in the transformation of reader practices. If e-books are considered as the same "long” reading practice, the similarity with media consumption practices on the Internet, especially in social networks, is doubtful. Despite the fact that in the Internet and social networks the audience also "reads", the intellectual ethic of consuming quick unrelated pieces of information from a variety of sources is changing this sociocultural practice itself. Soon, book reading, quite possibly, will depend on the accumulated practices of Internet surfing, Internet reading and the ability of people to deal with long texts.

A Comprehensive Review on Exosome: Recent Progress and Outlook.

Exosomes are intrinsic membrane-based vesicles that play a key role in both normal and pathological processes. Since their discovery, exosomes have been investigated as viable drug delivery systems and clinical indicators because of their magnitude and effectiveness in delivering biological components to targeted cells. Exosome characteristics are biocompatible, prefer tumor recruitment, have tunable targeting efficiency, and are stable, making them outstanding and eye-catching medication delivery systems for cancer and other disorders. There is great interest in using cell-released tiny vesicles that activate the immune system in the age of the fast development of cancer immunotherapy. Exosomes, which are cell-derived nanovesicles, have a lot of potential for application in cancer immunotherapy due to their immunogenicity and molecular transfer function. More significantly, exosomes can transfer their cargo to specified cells and so affect the phenotypic and immune-regulation capabilities of those cells. In this article, we summarize exosomes' biogenesis, isolation techniques, drug delivery, applications, and recent clinical updates. The use of exosomes as drug-delivery systems for small compounds, macromolecules, and nucleotides has recently advanced. We have tried to give holistic and exhaustive pieces of information showcasing current progress and clinical updates of exosomes.