Exponential Growth Research Articles

The overdiagnosis of thyroid cancer should not be overinterpreted

Recently, the topic of "overdiagnosis" of thyroid cancer has once again sparked controversy in the medical academic community, as well as extensive discussions from patients and the general population. To some extent, the overdiagnosis of thyroid cancer has been over interpreted. This article provides a detailed discussion on how to correctly understand the overdiagnosis of thyroid cancer from the perspective of a thyroid surgeon. The author believes that there are two elements to the occurrence of overdiagnosis of thyroid cancer: the presence of a large "silent pool" of inert tumors in the human body and medical methods to improve detection rates. In recent years, thyroid cancer has become the fastest growing malignant tumor in the world in terms of incidence rate. The exponential growth mode makes thyroid cancer over diagnosed and inevitably becomes a hot topic in academia. However, the overdiagnosis of thyroid cancer is not contradictory to the early screening and diagnosis of thyroid cancer. The overdiagnosis of thyroid cancer should be viewed scientifically and rationally, especially avoiding overinterpretation.

Groundstates for Planar Schrödinger–Poisson System Involving Convolution Nonlinearity and Critical Exponential Growth

Groundstates for Planar Schrödinger–Poisson System Involving Convolution Nonlinearity and Critical Exponential Growth

GPSFun: geometry-aware protein sequence function predictions with language models.

Knowledge of protein function is essential for elucidating disease mechanisms and discovering new drug targets. However, there is a widening gap between the exponential growth of protein sequences and their limited function annotations. In our prior studies, we have developed a series of methods including GraphPPIS, GraphSite, LMetalSiteand SPROF-GO for protein function annotations at residue or protein level. To further enhance their applicability and performance, we now present GPSFun, a versatile web server for Geometry-aware Protein Sequence Function annotations, which equips our previous tools with language models and geometric deep learning. Specifically, GPSFun employs large language models to efficiently predict 3D conformations of the input protein sequences and extract informative sequence embeddings. Subsequently, geometric graph neural networks are utilized to capture the sequence and structure patterns in the protein graphs, facilitating various downstream predictions including protein-ligand binding sites, gene ontologies, subcellular locationsand protein solubility. Notably, GPSFun achieves superior performance to state-of-the-art methods across diverse tasks without requiring multiple sequence alignments or experimental protein structures. GPSFun is freely available to all users at https://bio-web1.nscc-gz.cn/app/GPSFun with user-friendly interfaces and rich visualizations.

A Parasite Odyssey: An RNA virus concealed in Toxoplasma gondii

Abstract We are entering a “Platinum Age of Virus Discovery”, an era marked by exponential growth in the discovery of virus biodiversity, and driven by advances in metagenomics and computational analysis. In the ecosystem of a human (or any animal) there are more species of viruses than simply those directly infecting the animal cells. Viruses can infect all organisms constituting the microbiome, including bacteria, fungi, and unicellular parasites. Thus the complexity of possible interactions between host, microbe, and viruses is unfathomable. To understand this interaction network we must employ computationally-assisted virology as a means of analyzing and interpreting the millions of available samples to make inferences about the ways in which viruses may intersect human health. From a computational viral screen of human neuronal datasets, we identified a novel narnavirus Apocryptovirus odysseus (Ao) which likely infects the neurotropic parasite Toxoplasma gondii. Previously, several parasitic protozoan viruses (PPVs) have been mechanistically established as triggers of host innate responses, and here we present in silico evidence that Ao is a plausible pro-inflammatory factor in human and mouse cells infected by T. gondii. T. gondii infects billions of people worldwide, yet the prognosis of toxoplasmosis disease is highly variable, and PPVs like Ao could function as a hitherto undescribed hypervirulence factor. In a broader screen of over 7.6 million samples, we explored phylogenetically-proximal viruses to Ao and discovered 19 Apocryptovirus species, all found in libraries annotated as vertebrate transcriptome or metatranscriptomes. While samples containing this genus of narnaviruses are derived from sheep, goat, bat, rabbit, chicken, and pigeon samples, the presence of virus is strongly predictive of parasitic Apicomplexa nucleic acid co-occurrence, supporting that Apocryptovirus is a genus of parasite-infecting viruses. This is a computational proof-of-concept study in which we rapidly analyze millions of datasets from which we distilled a mechanistically, ecologically, and phylogenetically refined hypothesis. We predict this highly diverged Ao RNA virus is biologically a T. gondii infection, and that Ao, and other viruses like it, will modulate this disease which afflicts billions worldwide.

Multiplicity and Concentration of Solutions for Kirchhoff Equations with Exponential Growth

Multiplicity and Concentration of Solutions for Kirchhoff Equations with Exponential Growth

Experimental study on inhibition of coal spontaneous combustion by carbon dioxide and nitrogen

ABSTRACT Injecting CO2 and N2 to reduce the concentration of O2 in the air is a commonly used measure to prevent coal spontaneous combustion (CSC). To study the difference in inhibitory effects of CO2 and N2 on coal oxidation, experiments in this study were conducted using a setup of self-developed gas-bath coal oxidation for low temperatures. The oxidation process of the coal-oxygen system was then analyzed using reaction kinetics. Two different coal samples were tested in experiments under two inert gas environments, i.e. CO2 and N2 with various O2 concentrations, respectively. The changes in O2 and CO concentrations were measured during the oxidation process. By studying the standard oxygen consumption rate (SOCR) and the apparent activation energy of the coal-oxygen reaction system, inhibitory effects of CO2 and N2 on CSC were analyzed and compared. The results showed that the SOCR of the same coal sample remained consistent and independent of the O2 concentration within the same inert gas environment although under different O2 levels. In both CO2-O2 and N2-O2 environments, the SOCR of the coal sample exhibits exponential growth with temperature. The SOCR was found to have a functional connection with temperature, it can be represented as y = a ⋅ e bT , and the R 2 (determination coefficients) of the fitting formulas are all greater than 0.94. In the correlation, the inert gas environment has minimal impact on coefficient “b,” while in the CO2-O2 environment, reducing the value of coefficient “a” can effectively lower the coal sample’s SOCR. The GBMK sample and YMWK sample have an “a” value that is 20% of the value in the N2-O2 environment when oxidizing in the CO2-O2 environment. The SOCR in the CO2-O2 environment showed a decrement of between 26.0% and 52.1% when compared to that in the N2-O2 environment, whereas the activation energy showed an increment between 1.4% and 18.6%. The findings in this work provide an in-depth understanding of the prevention of CSC in the gob for risk mitigation.

On a class of a coupled nonlinear viscoelastic Kirchhoff equations variable-exponents: global existence, blow up, growth and decay of solutions

In this work, we consider a quasilinear system of viscoelastic equations with dispersion, source, and variable exponents. Under suitable assumptions on the initial data and the relaxation functions, we obtained that the solution of the system is global and bounded. Next, the blow-up is proved with negative initial energy. After that, the exponential growth of solutions is showed with positive initial energy, and by using an integral inequality due to Komornik, the general decay result is obtained in the case of absence of the source term.

Glioblastoma: a comprehensive approach combining bibliometric analysis, Latent Dirichlet Allocation, and HJ-Biplot : Glioblastoma insights and trends: a 49-year bibliometric analysis.

Glioblastoma is a common and aggressive malignant central nervous system tumor in adults. This study aims to evaluate and analyze the scientific results, collaboration countries, main research topics, and topics over time reported about glioblastoma. A bibliometric analysis of glioblastoma publications was performed mainly using R and Multbiplot software for author, journal, and resume. Associated statistic methods Latent Dirichlet Allocation (LDA) and HJ-Biplot. Inclusion criteria were research articles from the PubMed database published in English between 1973 and December 2022. A total of 64,823 documents with an annual growth rate of 8.27% indicates a consistent increase in research output over time. The results for the number of citations and significant publications showed Cancer Res, J Neuro-Oncol, and Neuro-Oncology are the most influential journals in the field of glioblastoma. The countries that concentrated research were the tumor United States, China, Germany, and Italy. Finally, there has been a marked growth in studies on prognosis and patient survival, therapies, and treatments for glioblastoma. These findings reinforce the need for increased global resources to address glioblastoma, particularly in underdeveloped countries. Glioblastoma research's exponential growth reflects sustained interest in early diagnosis and patient survival.

Parking recommendation with meta-heuristic algorithms

Due to the exponential growth of cars in urban areas, parking problems have become a significant concern. Addressing this issue requires efficient methods for locating available parking spaces, enhancing the overall experience for drivers. This paper introduces a parking lot recommendation model leveraging meta-heuristic algorithms to generate a list of potential parking locations based on the users travel destinations. The primary objectives of these algorithms include minimizing travel distance, reducing total parking fees, and selecting parking lots with ample available spaces. The proposed model incorporates bio-inspired algorithms, including simulated annealing, genetic algorithms, and their adaptive variants. Our evaluation compares the performance of these algorithms, highlighting the adaptive simulated annealings superior quality of solutions and robustness against local minima. However, it is important to note that this approach comes with a trade-off, requiring longer execution times. In summary, this research contributes a novel parking lot recommendation model that effectively addresses the challenges posed by urban parking. The performance evaluation underscores the efficacy of the adaptive simulated annealing approach, showcasing its potential for practical implementation despite its relatively longer execution time.

Trends and Triggers: Analyzing the Co-Movement of Cryptocurrency and NFT Prices

This paper aims to explore the complex interrelationships between the prices of cryptocurrency, specifically Ethereum (ETH), and five top Non-Fungible Token (NFT) collections: Bored Ape Yacht Club, Mutant Ape Yacht Club, Azuki, Moonbirds, and Otherdeed. Motivated by the intertwining dynamics of these digital assets and the unexplored nature of their interdependencies, this study employs a Vector Autoregressive (VAR) model and utilizes Granger Causality to dissect the multifaceted interactions. The analysis period ranges from April 2021 to January 2023, a critical window of exponential growth and fluctuation in the digital asset market. The results demonstrate a statistically significant impact of ETH prices on NFT collection prices, but not vice versa, revealing the strong dependence of the NFT market on cryptocurrency volatility. Specifically, the research finds that changes in ETHs value are predictive of shifts in NFT prices, whereas NFT price fluctuations lack predictive power for ETH prices. In conclusion, this research represents an advancement in understanding price dynamics in the rapidly evolving digital economy. By innovatively analyzing the co-movement of cryptocurrencies and NFTs, it not only enriches existing knowledge but also paves the way for further exploration, offering practical insights for diverse stakeholders navigating this exciting, ever-changing field.

Notes on universality in short intervals and exponential shifts

AbstractWe improve a recent universality theorem for the Riemann zeta-function in short intervals due to Antanas Laurinčikas with respect to the length of these intervals. Moreover, we prove that the shifts can even have exponential growth. This research was initiated by two questions proposed by Laurinčikas in a problem session of a recent workshop on universality.

Simple Regression Models: A Comparison using Criteria Measures

The study is on simple regression models: a comparison using criteria measures. The source of the dataset used for this study was extracted from records of the Federal Medical Centre, Owerri, Imo State, on weight of babies and hemoglobin level of mothers. The response variable is weight of babies while the explanatory variable is hemoglobin level of mothers. Eleven simple regression models—Linear, Growth, Quadratic, Polynomial, Logarithmic, Hyperbolic, Power, Exponential Growth, Square Root, Sinusoidal and Arctangent—were stated and employed for the study. For ease of data analysis, E-views package was implemented. Three model selection criteria measures for comparison, known as Akaike Information Criterion (AIC), Schwarz Information Criterion (SIC) and Hannan-Quinn Information Criterion (HQIC), were employed. The result of the study showed that, when it comes to analyzing the association between baby weight and mothers' hemoglobin levels, the exponential growth regression model performs better than the other ten models that were examined. Therefore, researchers should investigate other models that were not included in this analysis and compare the findings using goodness of fit metrics other than the criteria measures used in this work.

Addressing Counterfeiting and Fraud Concerns in Healthcare Packaging and Labeling with Blockchain: Opportunities and Challenges

Blockchain technology (BT), originally developed to facilitate secure digital monetary transactions, has recently gained significant traction in various healthcare sectors. Characterized by the exponential growth of sensitive data, the healthcare sector is poised to witness the emergence of BT. This emergence is primarily driven by the pressing need to globally expose, protect against threats, ensure confidentiality, and establish traceability for the plethora of sensitive data continuously generated by the healthcare industry. The healthcare supply chain focuses on traceability due to the prevalence of counterfeit and recalled drugs. Managing operational constraints such as temperature, humidity, and air quality within specified parameters is paramount. The various processes involved in international trade transactions contribute to the creation of numerous records, each of which is meticulously entered into the systems of the companies involved. Therefore, the problem set for this study was: What are the challenges and prospects for BT in the healthcare sector? To answer this question, the following objective was set: describe and examine the challenges and prospects of BT in the healthcare sector. In addition, a key research objective was to identify specific applications and use cases that can benefit the most from this technological advancement. In line with the research objective, a systematic review of all studies BT for traceability, anti-counterfeiting, and fraud detection was conducted from January 2023 to September 2023. Using robust tools such as VosViewer, we used bibliometric metrics from the renowned medical repository PubMed to construct and visually represent data analysis networks. BT shows remarkable potential to improve traceability and optimize supply chain management within healthcare organizations. The study includes a deep analysis of blockchain capabilities, including smart contracts, identity management, access control, and zero-knowledge proofing.

The race for critical minerals in Africa: A blessing or another resource curse?

There is currently an exponential growth in the global demand for critical minerals, particularly lithium, to meet clean energy and decarbonisation objectives. However, sustainable supply of these minerals is at risk due to declining ore grades, available extraction and processing technologies, socio-environmental concerns, and geopolitical challenges. Africa hosts substantial critical minerals resources, and the continent is currently being positioned as a major global player in the critical minerals supply chain. As a result, there is a rush for Africa's critical mineral resources through investment in exploration activities and license acquisition by foreign mining companies. Drawing on the case of lithium mining in Africa, we analysed the urgency claims of critical minerals in the African context and assessed the inherent socio-ecological impacts. We found that the urgency claims of critical minerals largely serve the geostrategic and economic interests of western countries and China. The rush for Africa's critical minerals is producing significant socio-ecological impacts, including driving loss of rich biodiversity, displacement of communities and breeding new forms of illegalities in the resource sector. Based on our findings, we argue that the current race for Africa's critical mineral resources does not serve the interest of Africa and it is likely to create adverse long-term socio-ecological impacts rather than benefits for the continent unless appropriate sustainable measures, including strategic planning, are carefully considered, and fully implemented. Our findings have implications for policies seeking to promote sustainable and responsible mining in Africa, and elsewhere with similar challenges.

EMI teachers’ emotion labour: capturing classroom, institutional and sociocultural ecologies

ABSTRACT Although English medium instruction (EMI) has received exponential growth of attention in recent years, the scope of research on EMI teachers is still limited, and even much less on their emotions. Drawing on an ecological perspective as the theoretical framework, we explored Iranian EMI teachers’ emotion labour across the classroom, institutional and sociocultural ecologies. Data were collected from narrative frames and semi-structured interviews. Data analysis revealed three themes in relation to Iranian EMI teachers’ emotion labour: (1) classroom ecology: student expectations as impeding effective content enactment, (2) institutional ecology: workload as a hurdle for investment in personal growth and (3) sociocultural ecology: low international collaborations complicating knowledge dissemination. The study shows that beyond their personal proclivities, Iranian EMI teachers face socio-educational challenges that not only complicate their pedagogical effectiveness, but also stop them from imagining the identity of global citizens within the community of EMI practitioners. We also provide implications for EMI teacher educators to draw on the study findings to run teacher education courses that tap EMI teachers’ emotions.

Hyper-Flophet: A neural Prophet-based model for traffic flow forecasting in transportation systems

Nowadays, an accurate and reliable traffic forecast is meaningful in making the right decisions for traffic management systems in vehicular environments. Nevertheless, traffic flow prediction is a significant challenge in Vehicular Ad Hoc Networks (VANETs) that has taken much attention. Therefore, in this paper, we propose a hybrid traffic prediction model based on Prophet model and Long Short-Term Memory neural network (LSTM), called Hyper-Flophet, to predict next traffic flow. Hyper-Flophet model adopts the traditional neural prophet model but with major parameter tuning. First, we propose an efficient algorithm for predicting the traffic flow trend then, we develop an interactive LSTM (I-LSTM) model for auto-regression components. After that, we implement a new future regressor component called network mobility and finally, we enhance the event and holiday component by introducing exponential growth term. Through simulations with real VANET data, we show that the proposed hybrid approach can achieve superior forecasting performance over other models.

Doc‐KG: Unstructured documents to knowledge graph construction, identification and validation with Wikidata

AbstractThe exponential growth of textual data in the digital era underlines the pivotal role of Knowledge Graphs (KGs) in effectively storing, managing, and utilizing this vast reservoir of information. Despite the copious amounts of text available on the web, a significant portion remains unstructured, presenting a substantial barrier to the automatic construction and enrichment of KGs. To address this issue, we introduce an enhanced Doc‐KG model, a sophisticated approach designed to transform unstructured documents into structured knowledge by generating local KGs and mapping these to a target KG, such as Wikidata. Our model innovatively leverages syntactic information to extract entities and predicates efficiently, integrating them into triples with improved accuracy. Furthermore, the Doc‐KG model's performance surpasses existing methodologies by utilizing advanced algorithms for both the extraction of triples and their subsequent identification within Wikidata, employing Wikidata's Unified Resource Identifiers for precise mapping. This dual capability not only facilitates the construction of KGs directly from unstructured texts but also enhances the process of identifying triple mentions within Wikidata, marking a significant advancement in the domain. Our comprehensive evaluation, conducted using the renowned WebNLG benchmark dataset, reveals the Doc‐KG model's superior performance in triple extraction tasks, achieving an unprecedented accuracy rate of 86.64%. In the domain of triple identification, the model demonstrated exceptional efficacy by mapping 61.35% of the local KG to Wikidata, thereby contributing 38.65% of novel information for KG enrichment. A qualitative analysis based on a manually annotated dataset further confirms the model's excellence, outshining baseline methods in extracting high‐fidelity triples. This research embodies a novel contribution to the field of knowledge extraction and management, offering a robust framework for the semantic structuring of unstructured data and paving the way for the next generation of KGs.

Advancing Privacy and Confidentiality in the Digital Healthcare Era: The Image Encryption Solution

The rapid digitization of health care services has spurred exponential growth in medical image generation and exchange. Safe guarding the confidentiality and integrity of these sensitive E-Health care images is crucial for protecting patient privacy. This project proposes a robust solution within the AWS Cloud, implementing an efficient image encryption model. Advanced cryptographic techniques ensure that only authorized personnel can access and decrypt images, minimizing the risk of unauthorized data breaches. The project aims to advance secure E-Healthcare image sharing, facilitating improved collaboration among providers while prioritizing patient privacy and data integrity.

SPEED-MODE cell line development (CLD): Reducing Chinese hamster ovary (CHO) CLD timelines via earlier suspension adaptation and maximizing time spent in the exponential growth phase.

Chinese hamster ovary (CHO) cells are the preferred system for expression of therapeutic proteins and the majority of all biotherapeutics are being expressed by these cell lines. CHO expression systems are readily scalable, resistant to human adventitious agents, and have desirable post-translational modifications, such as glycosylation. Regardless, drug development as a whole is a very costly, complicated, and time-consuming process. Therefore, any improvements that result in reducing timelines are valuable and can provide patients with life-saving drugs earlier. Here we report an effective method (termed SPEED-MODE, herein) to speed up the Cell line Development (CLD) process in a targeted integration (TI) CHO CLD system. Our findings show that (1) earlier single cell cloning (SCC) of transfection pools, (2) speeding up initial titer screening turnaround time, (3) starting suspension adaptation of cultures sooner, and (4) maximizing the time CHO cultures spend in the exponential growth phase can reduce CLD timelines from ~4 to ~3 months. Interestingly, SPEED-MODE timelines closely match the theoretical minimum timeline for CHO CLD assuming that CHO cell division is the rate limiting factor. Clones obtained from SPEED-MODE CLD yielded comparable titer and product quality to those obtained via a standard CLD process. Hence, SPEED-MODE CLD is advantageous for manufacturing biotherapeutics in an industrial setting as it can significantly reduce CLD timelines without compromising titer or product quality.

Security and privacy challenges, issues, and enhancing techniques for Internet of Medical Things: A systematic review

AbstractThe Internet of Things (IoT) is a rapidly expanding network of interconnected things that use embedded sensors to gather and share data in real‐time. IoT technologies have given rise to many networking applications in our everyday life such as smart homes, smart cities, smart transport, and so forth. Smart healthcare is one such application that has been revolutionized by the IoT, introducing a new branch of IoT known as the Internet of Medical Things (IoMT). IoMT encompasses an entire ecosystem consisting of smart wearable, implantable sensing equipment's or devices, transmitters that are critical for monitoring the patients remotely and continuing the real‐time and has opened the door to new innovative smart healthcare approaches while improving patient care outcomes. IoMT wearable and embedded sensing devices are commonly utilized in smart healthcare to capture medical data and transmit the medical data in a communication network stored in the cloud. The large volume of data generated and transmitted by these IoMT devices is rising at an exponential rate, resulting in an increase in security and privacy vulnerabilities of healthcare data. To ensure the Confidentiality and integrity of the IoMT devices and the sensitive medical data, there should be proper security and privacy measures such as access control, passwords, multifactor authentication, and encryption of data generated, transmitted, or processed in the IoMT framework. In this paper, we identified the internet of things and its applications in smart healthcare systems. Additionally, the paper focuses on the architecture of IoMT, and several challenges, including the IoMT security and privacy requirements, and attack taxonomy. Furthermore, the paper thoroughly investigates both cryptographic and non‐cryptographic based security and privacy‐enhancing techniques for IoMT or healthcare systems with particular emphasis on advancements in key areas such as Homomorphic Encryption, Differential Privacy, and Federated Learning.