Repository Of Information Research Articles

Securing cyber-physical robotic systems for enhanced data security and real-time threat mitigation

The convergence of data security and operational efficiency across various sectors, such as manufacturing, industry, logistics, agriculture, healthcare, and internet services, has been significantly enhanced using robotic-driven platforms and protocols. Notably, there has been a notable uptick in sophisticated cyberattacks targeting corporate and industrial robotic systems. These attacks are activated following the integration of the Internet of Things, the Internet, and organizational networks, as industrial units are interconnected. This study has formulated security-oriented criteria-based indicators for cyber-physical systems (CPS), encompassing industrial components and embedded sensors responsible for processing information logs and procedures. In this research, a robust security framework based on attack trees has been introduced, strategically focusing on addressing critical exploitable vulnerabilities rather than attempting to cover all CPS devices comprehensively. The systematic categorization of each physical device and its associated integrated sensors has been accomplished via data from logs and an information repository contained within a sensor index device library.

Semantic Memory Space Becomes Denser with Age.

Semantic memory, a repository for concepts and factual information, plays a vital role in acquiring and retrieving knowledge. This study explores the impact of age-related knowledge accumulation on semantic cognition, investigating whether a denser representational space affects retrieval processes. Using a semantic feature verification task, we employ both behavioral (reaction time; RT) and neurophysiological (event-related potential; ERP) measures to explore these dynamics across young and older adults. Findings revealed an age-related RT difference in retrieval of semantically incongruent features, indicative of increased semantic search demands with age. ERP results show attenuated N400 responses in older adults for congruent features, possibly reflecting increased semantic relatedness. The late frontal effect (LFE) shows sustained modulation in older adults, indicative of enhanced post-retrieval monitoring. We propose that this extended search through semantic memory reflects an increase in the number of features to evaluate. These results support the idea that aging leads to a more densely packed semantic space, impacting the speed and dynamics of semantic retrieval.

Transforming Medicine: Cutting-Edge Applications of Nanoscale Materials in Drug Delivery.

Since their inception in the early 1960s, the development and use of nanoscale materials have progressed tremendously, and their roles in diverse fields ranging from human health to energy and electronics are undeniable. The application of nanotechnology inventions has revolutionized many aspects of everyday life including various medical applications and specifically drug delivery systems, maximizing the therapeutic efficacy of the contained drugs by means of bioavailability enhancement or minimization of adverse effects. In this review, we utilize the CAS Content Collection, a vast repository of scientific information extracted from journal and patent publications, to analyze trends in nanoscience research relevant to drug delivery in an effort to provide a comprehensive and detailed picture of the use of nanotechnology in this field. We examine the publication landscape in the area to provide insights into current knowledge advances and developments. We review the major classes of nanosized drug delivery systems, their delivery routes, and targeted diseases. We outline the most discussed concepts and assess the advantages of various nanocarriers. The objective of this review is to provide a broad overview of the evolving landscape of current knowledge regarding nanosized drug delivery systems, to outline challenges, and to evaluate growth opportunities. The merit of the review stems from the extensive, wide-ranging coverage of the most up-to-date scientific information, allowing unmatched breadth of landscape analysis and in-depth insights.

An ontological model of the reproduction system of a mineral resource asset

One of the urgent problems in developing the subsoil mineral resource potential is the lack of reliable information on the volume of forecasted resources and the potential possibility of their transfer to conditional reserves for some types of mineral raw materials. The heterogeneity and diversity of information sources sometimes makes it impossible to create a model of a single information repository with its attributes and connections. The traditional approach to organizing an information storage system based on a relational database cannot be applied in this situation. Therefore, it is urgent to find an alternative to both the data storage system itself and the approaches that are used in its filling and updating. We propose to use an ontological model to solve this problem, as a way to organize a storage system and systematize heterogeneous sources of information. At the same time, taking into account a fairly large number of alternative options for constructing an ontological model, we propose to base it precisely on the concept of mineral resource potential.

Cross-Cultural Pragmatic Analysis of Politeness Strategies used by the Australian and Indian Characters in “Lion”

This analysis investigated the representation of characters in the cinematic work “Lion.” Used various kinds of politeness strategies. This study aims to analyze the positive and negative politeness strategies used by characters from Australia and India, in addition to determine the factors that shape these strategies. This study applies a qualitative approach. As a result, the conversations in the movie are carefully transcribed and analyzed descriptively through the perspective of politeness theory (Brown, 2020) and s cross-cultural pragmatics (Wierzbicka, 2009) to identify the influence of the characters' cultural backgrounds on their strategic communication choices. The conversations that occur between the Australian and Indian characters in the film serve as a valuable repository of information, that includes both positive and negative politeness strategies. To obtain the data, the researcher uses a few processes, including watching the movie, taking notes, and choosing conversations in which the actors use both positive and negative politeness strategies frequently. The findings of this study reveal that a total of 121 sentences were classified as either negative or positive politeness, with 68 instances identified as negative politeness and 53 as positive politeness strategies. The application of both negative and positive politeness strategies articulated by the characters is primarily influenced by their cultural context.

Digital Health for Asthma Management: Electronic Medication Monitoring for Adherence as a Case Example.

Digital health is an umbrella term for components of healthcare utilizing computer platforms, software, connectivity and sensors to augment the recording, documentation and communication of clinical information. The functions of digital health may be viewed in three domains: 1) the repository for patient information, 2) monitoring devices and 3) communication tools. Monitoring devices have provided robust information as diagnostic and prognostic tools in office and hospital settings. In this review, as a case study, we will discuss the research and our direct clinical experience of electronic medication monitoring technology and the potential benefits to patient care, and the opportunities and perils encountered in utilizing this approach for patients with moderate to severe asthma, including issues related to patient uptake and concerns for bias, impacts on the provider/patient relationship, and discussions regarding monitoring of rescue medication use in exacerbations. Additionally, despite the fact that there is evidence for improvements in various aspects of patient care afforded by electronic medication monitoring, these devices have not yet seen widespread uptake in clinical settings, and we will discuss the steps needed to address these barriers and keep these important devices available for patient use in the future.

Semantic Analysis of Natural Language Texts: Ontological Approach

The development of information (intelligent) learning systems, electronic document management systems, web-oriented systems working with text information in natural language has led to an increase in the volume of educational content and/or arrays of processed full-text documents. All this requires new means of organizing access to information, many of which should be classified as intelligent systems for knowledge processing. One of the effective approaches to identifying and processing the meaning of educational content (and/or text documents) is the use of ontologies. The purpose of the article is research, analysis of various approaches to determining the semantic content of texts in natural language, consideration of existing concepts of text analysis and prospects for using the proposed ontological approach to semantic analysis of texts in natural language. Research methods are methods of semantic analysis of the main concepts of the analyzed subject area (semantic analysis of texts in natural language). The article considers an approach to the linguistic analysis of texts based on ontological modeling. The novelty of the research is the application of the proposed ontological approach to the semantic analysis of texts in natural language to determine the meaning (semantics) of text information, which is used in intelligent systems of various classes. The conclusion of the research carried out in the article is as follows: an ontological approach to the semantic analysis of natural language text, its tasks and methods is proposed. The use of the proposed approach to text analysis leads to the understanding of semantic analysis as a single triad: <Ontology – Text – Meaning>. For effective and correct extraction of knowledge, it is suggested to use a multi-level ontology. The result of the interaction of a specific natural language text with an ontology is an ontological meaning – a set of interconnected subgraphs of the ontograph. The ontological content is extracted from the ontograph using a semantic analyzer. The dialogue processor examines the syntactic tree of sentence parsing (a connected element of the natural language text) and, based on the given question, finds a fragment in the text that is the answer to the question. Computer understanding (in information or intellectual systems) of natural language text is achieved, in particular, by: immersion of the text in a single environment of knowledge – ontology; formal presentation of meaning (semantics) in the knowledge base of the corresponding system; the possibility of operations on the ontological content. The proposed approach can be used to create intelligent information repositories that work in a single knowledge environment. The proposed approach to the semantic analysis of texts in natural language is focused on the automatic extraction of metadata from texts of various nature (for example, a text document, Internet content, educational content of relevant online courses, description of computer and board games). With further development of the proposed approach, it can be used in systems of automatic referencing of scientific publications, meaningful interpretation of multimedia content, training and testing (including elements of visual display of information and elements of gamification).

Historical Research on Aerosol Number Concentrations, Classifications of Air Pollution Severity and Particle Retention: Lessons for Present-Day Researchers

Research into the adverse health effects of air pollution exposure has repeatedly considered smaller particles, to the point where particle number concentration might be a more relevant metric than mass concentration. Here, we highlight some historical research which developed metrics for air pollution severity based on particle number concentration. Because this work was published in a national journal and prior to the internet and open access, this historical research is not easy to find, and it was more through the history of the aerosol research community in Ireland that this work is now being presented. Multiple online searches for published research papers on “particle number concentrations” and “air pollution severity” were undertaken. Even when specific searches were undertaken using the author names and publication year, these featured papers were not found on any internet search. O’Dea and O’Connor proposed that air pollution severity could be classified based on particle number concentration of condensation nuclei, with ‘little’ air pollution <50 × 103 particles per cm3, ‘mean’ 50–70 × 103 particles per cm3, ‘strong’ 70–100 × 103 particles per cm3, and ‘very strong’ >100 × 103 particles per cm3. Applying their assumptions on density and mean particle size, equated to mass concentrations for a mean of 6 µgm−3, strong at 8.5 µgm−3, and very strong >10 µgm−3. These are consistent with the current WHO guideline values for PM2.5. Additionally, we highlight the 1955 work by Burke and Nolan on the retention of inhaled particles, where ~40% of the inhaled number concentration is retained in the respiratory system. This is also consistent with the more recently published work on particle retention. In summary, the proposed categories of pollution severity, based on number concentrations, could form a basis for the development of future guidelines. This paper highlights that sometimes research has already been published, but it is difficult to find. We challenge researchers to find publications from their own countries which pre-date the WWW to inform current and future research. Additionally, there is scope for a repository for such information on historical publications. We have presented historical research on aerosol number concentrations, classifications of air pollution severity, and particle retention, which present lessons for current researchers.

Exploring how generative AI contributes to the motivated engagement and learning production of science-oriented students

Generative AI is transforming the educational landscape by offering new ways for students and educators to engage in personalized, adaptive learning. Unlike traditional tools, generative AI enables students to access a vast repository of information, interact with content in real-time, and generate responses, which collectively support individualized learning pathways. This study explored the role of generative AI, particularly ChatGPT, in students’ self-directed learning (SDL) process. College students (n=15) from science-oriented programs were purposively sampled to be interviewed. Findings revealed that students used AI to enhance efficiency in completing tasks, generate content, and engage in deeper learning experiences. Students reported that AI tools, such as ChatGPT, helped break down complex subjects, provided instant feedback, and allowed them to manage learning at their own pace. These features supported autonomy, motivation, and competence, core components of SDL, by enabling students to make independent learning choices and confidently tackle challenging content. Student narratives illustrated how generative AI aided in organizing study material, understanding science topics, and even learning to troubleshoot code, which supported mastery of complex science program skills. The findings also suggested that AI tools contributed to active learning, as students engaged more meaningfully with content, enhancing their analytical and problem-solving abilities. The integration of generative AI in education may shape future pedagogical approaches, enabling educators to promote personalized and adaptive learning environments that support students' intrinsic motivation, SDL, and critical thinking.

The future of big data: Remote monitoring of positive airway pressure treatment for obstructive sleep apnea - insights from adults and implications for pediatric care.

The advent of large expansive datasets has generated substantial interest as a means of developing and implementing unique algorithms that facilitate more precise and personalized interventions. This methodology has permeated the realm of sleep medicine and in the care of patients with sleep disorders. One of the large repositories of information consists of adherence and physiological datasets across long periods of time as derived from patients undergoing positive airway pressure (PAP) treatment for sleep-disordered breathing. Here, we evaluate the extant and yet scarce findings derived from big data in both adults and children receiving PAP for obstructive sleep apnea and suggest future directions towards more expansive utilization of such valuable approaches to improve therapeutic decisions and outcomes.

Herbaria

Herbaria, collections of preserved plant specimens, have existed for 500 years as repositories of information about plants. Many of these collections are now being digitized, making them available to a much broader audience including students and teachers. Specimens can be used in a variety of different contexts in teaching biology, including from the viewpoint of the history of science. There are plants collected on Captain Cook’s and Darwin’s round-the-world voyages, as well as on early explorations of the western United States. Besides being tied to the history of science and culture, older specimens also document environmental variations over time, such as changes in flowering or fruiting times due to global warming. With access to digital images and information, teachers can design activities in which students test hypotheses and search for supporting evidence as they learn about collectors and the indigenous peoples who assisted them.

Data Visualisation To Understand How Data Is Structured Using K-Means And Hierarchical Cluster Analyses With Interactive Graphics

This study examines the structure of geothermal geochemisty data in India using k-means and hierarchical cluster analyses. The first attempt to list hot springs in India was made by Schlagintweit in 1852. The Geological Survey of India published a special publication titled 'Geothermal Atlas of India', and the government of India constituted a 'Hot Spring Committee' to examine the possibility of developing geothermal plants for power generation and other uses. In the Puga valley and Parvati projects, it is estimated that it is possible to harness 5000 MWh of geothermal energy from Puga valley, sufficient to sustain a 20 MWe power plant. The GSI, the repository of most information concerning geological and related data in the country, included an R&D item No. 7/WB-5 for the development of a computerised system of geothermal database system referred to as GTHERMIS in its field season 1993-94 program. The computational strategy involves assigning each data point to the cluster with the nearest center (or "centroid"), recalculating the centroids after each assignment, and repeating the procedure until the clusters are no longer statistically different. The k-means method, developed by MacQueen (1967), is one of the most widely used non-hierarchical methods, particularly suitable for large amounts of data. The data is scaled before cluster analysis, and the results are visualized using interactive graphics.

Do Economic Crises Affect Medical Education? A Narrative Review

Objectives: This review aims to investigate the impact of economic sanctions, as a form of economic crisis, on medical education. Methods: This research is a review of studies published between 1995 and 2023. A search was conducted in electronic sources, databases, and information repositories. The initial search yielded 313 articles. In the first screening, which involved examining the titles, 173 unrelated articles were removed, leaving 158 articles for the abstract screening stage. During this stage, 95 unrelated articles were excluded, and after accessing and reviewing the full text of the remaining 63 articles, 35 were deemed eligible for review. Results: Economic sanctions are defined as restrictions imposed by one country on the international trade and investment of another to force a policy change. Academic and research systems are often overlooked areas that are severely impacted by sanctions. In Sudan, for example, sanctions led foreign organizations to refrain from cooperating with academics in the country, particularly regarding the publication of articles. Importantly, the negative effects of economic sanctions on education cannot be easily remedied, even long after the sanctions are lifted. Conclusions: The effects of sanctions on education and health have been proven in many countries, including Sudan, Haiti, Zimbabwe, and Iraq. There is an urgent need to specifically focus on the consequences of sanctions on medical education above all other aspects.

PharmFreq: a comprehensive atlas of ethnogeographic allelic variation in clinically important pharmacogenes.

Genetic polymorphisms in drug metabolizing enzymes, drug transporters as well as in genes encoding the human major histocompatibility complex contribute to inter-individual differences in drug efficacy and safety. The extent, pattern and complexity of such pharmacogenetic variation differ drastically across human populations. Here, we present PharmFreq, a global repository of pharmacogenetic frequency information that aggregates frequency data of 658 allelic variants from over 10 million individuals collected from>1200 studies across 144 countries. Most investigations were conducted in East Asian and European populations, accounting for 29.4 and 26.6% of all studies, respectively. We find that the number of studies per country and aggregated cohort size correlated significantly with population size (R=0.55, P=3*10-9) and country gross domestic product (R=0.43, P=2*10-6) with overall population coverage varying between 5% in Estonia to<0.001% in many countries in Sub-Saharan Africa and Asia. All frequency data are openly accessible via a web-based interactive dashboard at pharmfreq.com that facilitates the exploration, visualization and analysis of country- and population-specific data and their inferred phenotypic consequences. PharmFreq thus presents a comprehensive, freely available resource for pharmacogenetic variant frequencies that can inform about ethnogeographic pharmacogenomic diversity and reveal important inequities that help to focus future research efforts into underrepresented populations.

The secondary metabolism collaboratory: a database and web discussion portal for secondary metabolite biosynthetic gene clusters.

Secondary metabolites are small molecules produced by all corners of life, often with specialized bioactive functions with clinical and environmental relevance. Secondary metabolite biosynthetic gene clusters (BGCs) can often be identified within DNA sequences by various sequence similarity tools, but determining the exact functions of genes in the pathway and predicting their chemical products can often only be done by careful, manual comparative analysis. To facilitate this, we report the first release of the secondary metabolism collaboratory (SMC), which aims to provide a comprehensive, tool-agnostic repository of BGC sequence data drawn from all publicly available and user-submitted bacterial and archaeal genome and contig sources. On the website, users are provided a searchable catalog of putative BGCs identified from each source, along with visualizations of gene and domain annotations derived from multiple sequence analysis tools. SMC's data is also available through publicly-accessible application programming interface (API) endpoints to facilitate programmatic access. Users are encouraged to share their findings (and search for others') through comment posts on BGC and source pages. At the time of writing, SMC is the largest repository of BGC information, holding 13.1M BGC regions from 1.3M source sequences and growing, and can be found at https://smc.jgi.doe.gov.

HPB SO06 - An advanced clinical practitioner (ACP) led pancreatic cyst surveillance programme

Abstract Background The prevalence of pancreatic cysts (PCs), including intraductal papillary mucous neoplasms (IPMNs), has increased substantially over recent decades. This rising prevalence is due largely to the increasing volume and quality of medical imaging technology. Although PC's are often an incidental finding in asymptomatic individuals, a small number do harbour a certain malignant potential and thus raise warranted concern. The challenge therefore is identifying the high-risk lesions for early resection through robust, guideline-driven surveillance. To ensure a consistent and evidence-based approach to PC surveillance, our trust has adopted a dedicated surveillance programme led by an Advanced Clinical Practitioner (ACP) in gastroenterology. Method A PC surveillance clinic was designed and delivered from April 2023. The service underwent a formal inauguration and was promoted at the regional HPB MDT. All patients receive a standardised approach to surveillance in-line with the current international guidelines or regional MDT recommendation. Surveillance diagnostics are arranged by the ACP and patients are updated with regard to their results and next steps during a telephone or face-to-face consultation. A robust database was designed to capture relevant patient details and serve as a future research aid. Results 144 referrals have been received (04/23-04/24). 134 are undergoing surveillance, with discontinuation due to frailty, patient choice, diagnosis of an inflammatory cyst or relocation in 10 cases. Most PCs under surveillance are side-branch IPMNs (70.9%). Malignant transformation has not yet been observed, however 18 cases display worrisome features and 3 have high-risk stigmata. These are undergoing intensive surveillance or awaiting MDT discussion. 93% of surveyed patients (16) report a greater sense of involvement in their PC surveillance and a greater awareness of their surveillance protocol (93%). Referrer feedback (7) has been positive and staff are satisfied with this service (100%). Conclusion Robust surveillance of patients with PC will likely improve early detection and prognosis of pancreatic cancer by enabling early identification of resectable cases. A centralised service that adopts a single agreed guideline will reduce variation in the management of these cysts, ensure these complex lesions are under specialist observation and offer a single point of contact which will improve retainment and follow-up of these patients. It is intended that synchronous data collection will serve to generate a comprehensive repository of information relating to PC. This databases will aid future research into this evolving field and steer future guideline development.

Peptipedia v2.0: a peptide sequence database and user-friendly web platform. A major update.

In recent years, peptides have gained significant relevance due to their therapeutic properties. The surge in peptide production and synthesis has generated vast amounts of data, enabling the creation of comprehensive databases and information repositories. Advances in sequencing techniques and artificial intelligence have further accelerated the design of tailor-made peptides. However, leveraging these techniques requires versatile and continuously updated storage systems, along with tools that facilitate peptide research and the implementation of machine learning for predictive systems. This work introduces Peptipedia v2.0, one of the most comprehensive public repositories of peptides, supporting biotechnological research by simplifying peptide study and annotation. Peptipedia v2.0 has expanded its collection by over 45% with peptide sequences that have reported biological activities. The functional biological activity tree has been revised and enhanced, incorporating new categories such as cosmetic and dermatological activities, molecular binding, and antiageing properties. Utilizing protein language models and machine learning, more than 90 binary classification models have been trained, validated, and incorporated into Peptipedia v2.0. These models exhibit average sensitivities and specificities of 0.877±0.0530 and 0.873±0.054, respectively, facilitating the annotation of more than 3.6 million peptide sequences with unknown biological activities, also registered in Peptipedia v2.0. Additionally, Peptipedia v2.0 introduces description tools based on structural and ontological properties and user-friendly machine learning tools to facilitate the application of machine learning strategies to study peptide sequences. Database URL: https://peptipedia.cl/.

Analysis Of Using #1hari1oknum and #percumalaporpolisi Hashtags in The Computer Mediated Communication Function On Instagram

This research examines the pattern of incidents related to police institutions on Instagram's social media platform, focusing on the hashtags #percumalaporpolisi and #1hari1oknum. It also tries to explain the role and purpose of using these hashtags in the context of computer-mediated communication (CMC) practices on Instagram. This research uses a qualitative methodology, utilizing data collection procedures through social media analysis, analysis of hashtag use, literature review, and other similar methods. This research documentation involved examining digital content on Instagram's social media platform, primarily visual content accompanied by written explanations. This research analysis began by searching for phrase content using the hashtags #1hari1oknum and #percumalaporpolisi on Instagram social media. The findings show that CMC uses various strategies utilizing hashtags on the social media platform Instagram. The hashtag (#) functions as an identifier for themes and a repository for information about items related to police agency performance. This hashtag will collect data about incidents related to the credibility of police agencies. Apart from that, the hashtag contains the latest issues regarding violations of authority committed by police officers involved in violations or unethical behavior. This shows that individuals who utilize social media platforms produce personalized content.

Distributed misbehavior monitors for socially organized autonomous systems

In systems in which many heterogeneous agents operate autonomously, with competing goals and without a centralized planner or global information repository, safety and performance can only be guaranteed by “social” rules imposed on the behavior of individual agents. Social laws are structured in a way that they can be verified just by using local information made available to an agent by a small number of its neighbors. Automobile mobility with traffic rules and logistics robots in warehouses are canonical examples of such “regulated autonomy”, but many other fairly-competing autonomous systems are to be expected shortly. In these systems, detecting whether an agent is not abiding by the rules is crucial for raising an alert and taking appropriate countermeasures. However, the limited visibility due to the local nature of the information makes the problem of misbehavior detection hard for any single agent, and only an exchange of information between agents can provide sufficient clues to arrive at a decision. This paper attacks the misbehavior detection problem for a class of socially organized autonomous systems, where the behavior of agents depends on the presence or absence of other neighbors. We propose a solution involving a “local monitor”, which runs on each agent and includes a hybrid observer and a set-valued consensus node. Based on whatever visibility is available, it reconstructs a set-valued occupancy estimate of nearby regions and combines it with communicating neighbors to reach a shared view and a mismatch discovery. We provide a formal framework for describing social rules that unify many different applications and a tool to generate code automatically for local monitors. The technique is demonstrated in various systems, including self-driving cars, autonomous forklifts, and distributed power plants.

Something between a colleague and a tool: exploring the complex nexus between the role, responsibilities, and expectations of language assistants in multinational police settings in Kosovo (1999–2018)

ABSTRACT The United Nations Mission in Kosovo (UNMIK) and the European Union Rule of Law Mission in Kosovo (EULEX) included armed international police officers (IPOs) vested with transitional executive authorities from 1999 to 2018. IPOs initially assumed full policing responsibilities in cooperation with language assistants (LAs), until recruited Kosovo Police officers (KPOs) underwent training and integrated into multinational police teams. Drawing on twenty-three ethnographic interviews conducted between 2018 and 2021, this article explores stakeholders’ perspectives on the role and expectations of police LAs. The analysis reveals that, besides serving as communication facilitators, cultural mediators, fixers, and repositories of cultural knowledge and information, LAs transcended conventional translator and interpreter roles by proactively assuming police officers’ responsibilities, including statement-taking, assisting with reports, and proofreading. However, LAs encountered trust issues stemming from IPOs’ apprehensions about losing control due to the language gap and deviations from literalism, coupled with KPOs’ concerns about LAs’ potential influence on their performance evaluations and promotions. Moreover, IPOs from authoritarian states and militarized police backgrounds tended to perceive LAs as tools rather than equal team members. Interviews indicate that a professional LA, beyond linguistic proficiency, embodies strong interpersonal skills and professional qualities essential for fostering trust and effective communication.