bluuebunny/crossref_metadata_2025 huggingface dataset:
Ransomware : le dossier comprendre l’ennemi
Authors: Fronczak Stéphane
Published: April 2024

Il était une fois un bandit de grand chemin nommé Ransomware … Voilà qui pourrait débuter la narration qui va suivre concernant ce malandrin, un brigand des Temps modernes. S’il n’est plus question des guerres qui ont opposé si longtemps la France à l’Angleterre, l’analogie avec les cybercriminels n’est pas si éloignée de cela. Sans doute cela est-il dû au fait qu’il s’agisse d’attaques perpétrées par des bandes d’assaillants ayant pour unique objectif la remise de fonds, l’obtention d’un butin, et ce, sur les chemins que sont nos autoroutes de l’information, nos réseaux sociaux, nos routes d’échanges numériques. L’actualité SI & IT, cyber et criminelle fait la part belle depuis plusieurs années à ce phénomène. Le rançongiciel ne distingue pas ses victimes (quelques exceptions dernièrement) mais s’affranchit de toutes frontières, règles, lois, pour n’user que de celles lui permettant d’atteindre son objectif. Car ne perdons pas de vue que les auteurs d’attaques par rançongiciels poursuivent des objectifs liés à la déstabilisation et à la terreur au sein d’une entreprise ou chez un particulier.
Conformité et sécurité : un cran au-dessus ?
Authors: Coupez François
Published: April 2024

Ayant pendant longtemps suivi des chemins parallèles, sécurité des systèmes d’information et conformité, notamment en matière de protection des données, sont deux concepts qui sont aujourd’hui fortement liés, que ce soit par la réglementation ou les régulateurs. La sécurité des systèmes d’information faisant régulièrement la une des journaux, la « culture sécurité » infusant dans tous les secteurs économiques, les régulateurs ont changé de vision et intègrent cet élément de façon cruciale dans leurs audits des entreprises dont ils ont la supervision. Pour la CNIL par exemple, la sécurité est un angle d’audit d’autant plus important que plusieurs facteurs se coagulent : l’afflux d’anciens de l’ANSSI dans ses rangs a accru sa compétence, le RGPD s’inscrit dans la droite ligne de la loi du 6 janvier 1978 et se fonde peu ou prou sur la sécurisation des systèmes d’information, la protection des données ne peut être assurée que si la sécurité des systèmes d’information et des infrastructures est elle-même renforcée, etc.
Apocalypse JO
Authors: Perelman Marc
Published: April 2024

Malgré une propagande sans relâche pour aller chercher l’adhésion aux jo de Paris 2024, les Français regardent ailleurs. Ils observent avec inquiétude les conflits en Ukraine, en Israël, les tensions entre la Chine et Taïwan et les deux Corées, qui rendent paradoxaux des jo se déroulant sous l’étendard des trois principales valeurs de l’olympisme : l’« excellence », l’« amitié » et le « respect ». Tandis que le cio a finalement décidé d’accepter la participation de quelques athlètes russes et biélorusses sous bannière neutre, la question du boycott reste posée par l’Ukraine. Et sauf décision tardive, la Seine sera transformée en scène d’un spectacle kitsch aussi monumental que dangereux et désuet .
Paris 2024 : récit d’une candidature
Authors: Morteau Alexandre
Published: April 2024

Après l’échec de Paris 2012, les décideurs politiques, administratifs et sportifs identifient un « problème » des candidatures olympiques françaises. Ils établissent un cadre d’action commun et une mise en administration des grands événements sportifs internationaux pour produire un environnement institutionnel et des réseaux propices à la construction d’une nouvelle candidature. Une fois celle de Paris 2024 officiellement lancée, le travail de ces entrepreneurs de l’olympisme consiste à éviter une politisation qui mettrait en péril le projet .
Visual Quality Assessment After FS-LASIK Using Customized Aspheric Ablation Profile for Age-Related Accommodation Deficiency Compensation
Authors: Zhang Ruiyu , Yuan Yifei , Zhang Yu , Chen Yueguo
Published: April 2024

Purpose:

To evaluate clinical outcomes and visual quality 12 months after femtosecond laser–assisted laser in situ keratomileusis (FS-LASIK) performed with the Custom-Q algorithm for correction of myopia with or without astigmatism and compensate for age-related accommodation deficiency.

Methods:

Patients who had Custom-Q FS-LASIK for myopia and myopic astigmatism with age-related accommodation deficiency were included in this retrospective study. Distance, intermediate, and near visual acuities, objective and subjective refractions, Q-factor, corneal higher order aberrations (HOAs), accommodation function, defocus curve, contrast sensitivity, and a subjective questionnaire assessing visual quality were evaluated 12 months postoperatively.

Results:

Clinical data of 43 cases were analyzed. The mean age was 42.02 ± 1.85 years (range: 40 to 48 years). At the 12-month follow-up visit, there were 43 (100%), 42 (97%), and 33 (77%) patients who achieved a binocular uncorrected distance, intermediate, and near visual acuity, respectively, better than 20/20 separately. Defocus curves revealed better distance vision in the dominant eyes, and the nondominant eyes performed better at intermediate and near vergence (
P
< .001). The Q-value and corneal spherical aberration coefficient were more positive in the dominant eyes than those in the nondominant eyes (
P
< .001). The accommodative amplitude and relative accommodation improved binocularly (
P
< .001). The questionnaire demonstrated high patient satisfaction with near vision, and no one reported having severe visual disturbance.

Conclusions:

For myopic patients with age-related accommodation deficiency, the Custom-Q algorithm proved to be an effective way to achieve acceptable near vision without compromising distance vision.

[
J Refract Surg
. 2024;40(4):e245–e252.]
A novel computational fractional modeling approach for the global dynamics and optimal control strategies in mitigating Marburg infection
Authors: Medjoudja Meroua , El hadi Mezabia Mohammed , Riaz Muhammad Bilal , Boudaoui Ahmed , Ullah Saif , Awwad Fuad A.
Published: April 2024

Marburg virus disease poses a significant risk to global health, impacting both humans and non-human primates. This study has yielded an optimal control model for potentially mitigating the transmission of the Marburg infection. The proposed mathematical model includes fractional-order derivatives in the Caputo sense. Initially, we analyzed the model without control measures, examining its key characteristics regarding local and global stabilities. Subsequently, we extended the model by incorporating suitable time-dependent optimal control variables. We have also introduced two time-dependent control measures: $ \Psi_1 $ for the prevention of human-to-human Marburg transmission, and $ \Psi_2 $ to enhance the rate of quarantine of exposed individuals. We performed simulation analysis for both cases i.e., with and without optimal controls using the two-step Newton polynomial approximation method, considering both fractional and classical orders. The numerical findings of the comparative study between classical and fractional cases validate the biological significance of the fractional operator and effectiveness of the proposed optimal control strategies.
Research on the Construction and Application of an Intelligent Education Learner Model Based on the UTAUT Theory
Authors: Gao Qing
Published: April 2024

With the rapid development of computer technology, a new educational model, the innovative education learner model, has emerged as a product of the deep integration of technology and education. In this paper, we will begin by organizing the theories and models related to technology acceptance. We will select the UTAUT model, known for its high explanatory power, as the theoretical framework. Subsequently, we will comprehensively analyze the dataset and conduct in-depth habit mining. The effectiveness of applying K-means concepts to address the classification of clusters of learners’ learning habits is confirmed. The feasibility of the LSTM algorithm in predicting learners for exercise responses is also demonstrated. Next, a learning cluster construction method based on intelligent learner clustering is proposed. The methods of MDS+K-means and spectral clustering are selected for clustering. Learning clusters are constructed, and the performance of the two types of algorithms is compared and analyzed. Finally, the enhanced text feature extraction algorithm is utilized to design and implement the corresponding system for the practical application of the innovative educational learner model. The final experiment proves that the text features extracted by the model are effective, with an error rate of only about 2.8%, thus demonstrating that the intelligent educational learning model in this paper is reasonable.
Shipping Applications of Digital Twins
Authors: Deligianni Angeliki , Drikos Leonidas , Mantalos George , Kaklamanis Eleftherios
Published: April 2024

This chapter presents three applications of digital twin in shipping, namely the predictive maintenance of ship machinery, cargo load area cleaning, and hull biofouling treatment. The chapter discusses the business importance of each of these applications and surveys the current state of the art practices. The chapter then illustrates novel approaches that utilise digital twins and bring improvements in terms of costs, safety, and environmental impact.
Role of University Libraries in Imparting Information Literacy Skills Among Indigenous Post Graduate Students in India
Authors: Chhetri Monishankar , Bhattacharyya Sahu Nivedita
Published: April 2024

The chapter examines the role of university libraries in imparting information literacy skills to indigenous students in three Indian universities. It explores the many facets of information literacy on indigenous knowledge and culture, how it is integrated into academic curricula, and how academic librarians' roles as information literacy educators are changing. It highlights the importance of preserving indigenous culture, traditions, and customs. The chapter also describes various stages and channels by which information literacy can be analyzed to create awareness of library services at the university level. Information literacy of indigenous PG students has not been found satisfactory of the three universities. Also, the library has to take proper initiative to enhance IL among these students. The chapter also looks to what extent the university library has a collection of tribal literature and whether the tribal students use the collection to enhance their subject knowledge.
Deep-Mental Workload Intelligent System
Authors: Ng Guo Foong , Keikhosrokiani Pantea , Isomursu Minna
Published: April 2024

Recently, the measurement of mental workload has emerged as a crucial aspect of well-being process in socio-technical organizations. Excessive mental workload reduces work productivity, whereas insufficient mental workload leads to the underutilization of human resources. Significant research utilizing machine learning algorithms, such as SVM and KNN, based on electroencephalogram (EEG) signals, has been used for stress classification in the past few years. However, issues with mental workload and classification accuracy persist across these studies. Therefore, this chapter proposes an AI-augmented web-based information system to assess mental workload by employing a deep learning model. The proposed deep learning algorithm is used for the classification and feature extraction from EEG signals to accurately classify mental workload status. The proposed AI-augmented system would become a significant tool for making more effective and precise predictions about employees' mental workload in organizations.
COVID-19 or Russia-Ukraine Conflict, Which Is Informative in Defining the Dynamic Relationship Between Bitcoin and Major Energy Commodities?
Authors: Derbali Abdelkader Mohamed Sghaier
Published: April 2024

This chapter examines an essential methodology to evaluate the influence of the COVID-19 and Russia-Ukraine conflict surprises and conception statements employed for the dynamic conditional correlation between returns and volatilities of energy commodity indices and Bitcoin. To assess analytically the unexpected component of COVID-19 and Russia-Ukraine conflict surprises, the authors use GARCH-DCC(1,1) model by incorporating a dummy variable which measures the surprise factor during the period of study from January 4, 2016, to April 4, 2022. The results suggest significant and considerable dynamic conditional correlation between energy commodities indices and Bitcoin if COVID-19 pandemic and Russia-Ukraine conflict shocks are incorporated in variance assessments. Additionally, these outcomes demonstrate the financialization phenomena of energy commodities indices and Bitcoin. The authors find that the dynamic conditional correlation between energy commodities indices and Bitcoin start to respond considerably more in the situation of Russia-Ukraine conflict shocks than COVID-19 surprises.
Does Gold React as a Safe Haven and Hedge for G7 Stock Markets?
Authors: Ghorbali Bassem , Naoui Kamel , Derbali Abdelkader Mohamed Sghaier
Published: April 2024

This chapter investigates the safe haven property and hedge of gold especially during GFC and COVID-19 for G7 stock markets by using DCC-GARCH and wavelet coherence analysis. The findings reveal that the dynamic conditional correlation between gold and each G7 stock market decreased significantly during extreme market conditions, especially during GFC and COVID-19. The results show that gold served as a strong safe haven asset for all G7 stock markets except Nikkei225 during GFC. However, gold has maintained this traditional role as a safe haven only for CAC40, S&PTSX, FTSE/MIB, and FTSE100 during the COVID-19 pandemic. The optimal portfolio weights of gold in each G7 stock market significantly increased during GFG and COVID-19 pandemic, meaning that investors should invest more in the gold as a ‘flight-to-safety asset' during market turmoil. The decomposition of the coherence between gold and each G7 stock market into three investment horizons demonstrates a weak correlation in both the short term and the long term for the normal and extreme conditions market.
The Impact of COVID-19 on Financial Market Volatility in Saudi Arabia
Authors: Jamel Lamia
Published: April 2024

This study aims to investigate the impact of unexpected events related to new cases, confirmed cases, and death cases of COVID-19 on the volatility of the financial market in Saudi Arabia. To conduct the analysis, the authors employ a GARCH(1,1) model and incorporate the surprise component into the estimated model. The data used in this study spans from January 1, 2019 to December 31, 2020. The empirical findings reveal that the daily returns and volatilities of the stock indexes in the financial market of Saudi Arabia exhibit significant peaks, particularly during the first quarter of 2020 when the COVID-19 pandemic was most widespread. Interestingly, they observe that the announcements of death cases related to COVID-19 in the United States have a substantial impact on the stock market index in Saudi Arabia. This study is the first of its kind to examine the effect of unexpected events in financial markets, specifically those related to new cases, confirmed cases, and death cases of COVID-19 on the volatility of the financial market in Saudi Arabia.
Blockchain Health
Authors: Vetrivel S. C. , Sowmiya K. C. , Sabareeshwari V.
Published: April 2024

Blockchain technology has emerged as a revolutionary force in various industries, and healthcare is no exception. This chapter explores the transformative potential of blockchain-powered smart contracts in healthcare operations. Smart contracts, self-executing agreements with coded rules, offer unprecedented transparency, security, and efficiency in managing healthcare processes. The decentralized and tamper-resistant nature of blockchain ensures the integrity and privacy of health data, addressing concerns related to data breaches and unauthorized access. Smart contracts automate and streamline complex healthcare workflows, reducing administrative overhead and enhancing operational efficiency. Through real-time data synchronization and a distributed ledger, stakeholders across the healthcare ecosystem can access a single version of truth, minimizing discrepancies and improving collaboration.
Chatbots vs. AI Chatbots vs. Virtual Assistants
Authors: Darwish Dina
Published: April 2024

The terms “chatbot,” “AI chatbot,” and “virtual agent” are used in a way that often does not differentiate between them, which might lead to possible misunderstandings. Despite the fact that the technologies associated with these terms are inextricably linked to one another, the subtle differences that exist between them result in considerable differences in their unique capabilities. The term “chatbot” is often used to refer to a wide variety of artificial intelligence systems that are designed to carry on conversations. The development of artificial intelligence chatbot software has resulted in the creation of virtual agents, which constitutes a significant step forward. These agents participate in conversation via the use of AI methods known as conversational AI, and they make use of deep learning algorithms in order to improve their performance over time. This chapter discusses the differences between chatbots, AI chatbots and virtual assistants, and other related topics.
Analysis of Chatbots
Authors: Biswas Neepa , Biswas Sudarsan , Maity Suchismita
Published: April 2024

A chatbot is a computer program or software application that is designed to communicate with humans via text or speech-based interfaces. A chatbot's main objective is to mimic human conversation and deliver immediate responses to user inquiries. Chatbots are used in a variety of industries in various cases, including customer support, sales and marketing, appointment scheduling, retrieving information, virtual assistants, and even more. They can be used on websites, chat apps (such as WhatsApp, Facebook Messenger, or Slack), mobile applications, and voice-activated platforms such as Google Assistant, Siri, and Alexa.This chapter offers a thorough investigation of chatbots, chronicling their historical evolution, looking at their numerous applications, and sketching forth a complete classification scheme. This chapter seeks to provide a comprehensive overview of the evolution, significance, and classification of chatbots in the field of human-computer interaction from its genesis to modern uses.
Exploring the Potential of Augmented Reality in Applying and Experiencing Intangible Cultural Heritage
Authors: Jingru Zhang , Yahaya Wan Ahmad Jaafar Wan , Sanmugam Mageswaran
Published: April 2024

Initially, a brief overview of intangible cultural heritage and augmented reality is provided. Subsequently, the document search and selection criteria, software and data extraction process, and analysis of findings and trends are emphasized in the following section. The third and central section of the review concentrates on the most prevalent uses of augmented reality in intangible cultural heritage, and it is structured around five leading topics: education, digital heritage, crafts, entertainment, and museums. Finally, the study concludes that this perspective on the future course of technological advancements in the transmission of intangible cultural heritage.
Circular Economy and Supply Chain Sustainability
Authors: Karim Rejaul , Roshid Mustaqim , Waaje Abdul
Published: April 2024

Pollution, climate change, and the loss of natural resources are still major concerns that must be addressed immediately. More and more, industries are realizing they need to include sustainability, social responsibility, circular economy, and ethical sourcing in their operations. This chapter aims to highlight how principles of the circular economy ideas—reducing, reusing, recycling, and recovering—may help to develop a sustainable supply chain, which is crucial in addressing these urgent concerns from the industry revolution. Apart from environmental considerations, industrial innovation, and sustainability inside supply chains, this chapter also describes those techniques (data analytics, blockchain, internet of things [IoT], artificial intelligence [AI], etc.) with the principles of the circular economy. Practicing circular economy with supply chain management sustainability within the industry and working towards a future that is more egalitarian, ecologically aware, and resilient is highlighted in this chapter.
Review of Terrace Gardening Literature and Suggestions for Increasing Adoption
Authors: Dey Chitra , Monica V. A. , Gurpur Amit Karanth , Venugopal Swetha , Lavanya M. , Samudrala Sai Choornika Chandana , Aakash Raja A. , Aishwar Haris J.
Published: April 2024

Agricultural lands are swiftly transforming into residential complexes and high-rise buildings, leaving limited space for traditional gardening. A sustainable and creative venture, terrace gardening (TG) focuses on using rooftop areas for the growth of plants, vegetables, and herbs. To give people and communities access to fresh food and a green haven in urban settings, it entails converting underutilized rooftops into gardens. The authors review and present relevant literature on the topic of TG. The review identifies that papers on TG mainly cover themes like motivations to participate in TG, advantages of TG, sustainability and innovation in TG, operations and stakeholder management in TG, and limitations of TG. The review shows that time management, project management, networking, and stakeholder involvement are essential for TG. A SWOC analysis reveals that TG offers potential for business owners seeking to have a positive environmental impact while supplying the rising demand for fresh, locally grown food.
Cluster of vortex helices in the incompressible three-dimensional Euler equations
Authors: Guerra Ignacio , Musso Monica
Published: April 2024

In an inviscid and incompressible fluid in dimension

3

, we prove the existence of several helical filaments, or vortex helices, collapsing into each other.
Impacto espacial de las estructuras humanas del comercio callejero informal sobre el espacio público
Authors: Ojeda Ledesma Lautaro , Lavín Oviedo Fernanda , Jirón Paola , Margarit Daisy , Toledo Durán Sara
Published: April 2024

El comercio callejero informal moviliza afectos, emociones, conocimientos, objetos, mercancías, dineros y/o artefactos, y se produce y reproduce en el espacio público de la ciudad. En ese escenario, los puestos de venta –fijos y/o móviles– de los comerciantes callejeros informales impactan sobre los espacios públicos de una urbe. Este artículo presenta un análisis comparativo –antes y durante la pandemia del COVID 19– del impacto espacial del comercio callejero informal (CCI) de Valparaíso (Chile), para lo cual nos enfocamos en lo que hemos denominado estructuras humanas del CCI, entendidas como sistemas compuestos por los cuerpos de los comerciantes y todos los objetos que conforman sus puestos de venta. Los resultados revelaron que, durante la pandemia, el comercio callejero aumentó en 133%, y que las estructuras humanas desarrollaron una tendencia a mejorar y agrandar sus puestos.
Hacia una formación arquitectónica más sostenible y responsable: Laboratorios de Urbanismo Táctico en Quito
Authors: Ríos Mantilla Renato , Trovato Graziella , Muñoz Parra Carlos
Published: April 2024

Se contrasta el modelo de gestión de Responsabilidad Social Universitaria (RSU) con la metodología de aprendizaje-servicio (ApS) en los Laboratorios Itinerantes de Urbanismo Táctico de Quito (LIUTS), para analizar y evaluar las relaciones comunitarias de las instituciones educativas. Generalmente, las intervenciones se enmarcan en la sustantiva de vinculación universitaria, siendo acciones externas que no repercuten en las transformaciones internas de gestión y de currículo universitario. Mediante el análisis, se busca reflexionar sobre las prácticas y los procesos, para que no queden aisladas y tengan mayor incidencia en los aprendizajes y la sociedad. La propuesta de investigación nace por las constantes desigualdades sociales y espaciales existentes en Latinoamérica, lo que evidencia la necesidad de implementar con más fuerza los modelos de RSU en la formación académica y profesional. Se demuestra de esta manera que la RSU y el ApS forman ciudadanos comprometidos con su entorno, aportando desde la arquitectura mayor sensibilización espacial.
Novel milestones for early esophageal carcinoma: From bench to bed
Authors: Qi Ji-Han , Huang Shi-Ling , Jin Shi-Zhu
Published: April 2024

Esophageal cancer (EC) is the seventh most common cancer worldwide, and esophageal squamous cell carcinoma (ESCC) accounts for the majority of cases of EC. To effectively diagnose and treat ESCC and improve patient prognosis, timely diagnosis in the initial phase of the illness is necessary. This article offers a detailed summary of the latest advancements and emerging technologies in the timely identification of ECs. Molecular biology and epigenetics approaches involve the use of molecular mechanisms combined with fluorescence quantitative polymerase chain reaction (qPCR), high-throughput sequencing technology (next-generation sequencing), and digital PCR technology to study endogenous or exogenous biomolecular changes in the human body and provide a decision-making basis for the diagnosis, treatment, and prognosis of diseases. The investigation of the microbiome is a swiftly progressing area in human cancer research, and microorganisms with complex functions are potential components of the tumor microenvironment. The intratumoral microbiota was also found to be connected to tumor progression. The application of endoscopy as a crucial technique for the early identification of ESCC has been essential, and with ongoing advancements in technology, endoscopy has continuously improved. With the advancement of artificial intelligence (AI) technology, the utilization of AI in the detection of gastrointestinal tumors has become increasingly prevalent. The implementation of AI can effectively resolve the discrepancies among observers, improve the detection rate, assist in predicting the depth of invasion and differentiation status, guide the pericancerous margins, and aid in a more accurate diagnosis of ESCC.
Human β-defensin-1 affects the mammalian target of rapamycin pathway and autophagy in colon cancer cells through long non-coding RNA TCONS_00014506
Authors: Zhao Yu-Xin , Cui Yan , Li Xin-Hong , Yang Wen-Hong , An Shi-Xiang , Cui Jia-Xian , Zhang Min-Yu , Lu Jing-Kun , Zhang Xuan , Wang Xiu-Mei , Bao Li-Li , Zhao Peng-Wei
Published: April 2024

BACKGROUND

Colorectal cancer has a low 5-year survival rate and high mortality. Human β-defensin-1 (hBD-1) may play an integral function in the innate immune system, contributing to the recognition and destruction of cancer cells. Long non-coding RNAs (lncRNAs) are involved in the process of cell differentiation and growth.

AIM

To investigate the effect of hBD-1 on the mammalian target of rapamycin (mTOR) pathway and autophagy in human colon cancer SW620 cells.

METHODS

CCK8 assay was utilized for the detection of cell proliferation and determination of the optimal drug concentration. Colony formation assay was employed to assess the effect of hBD-1 on SW620 cell proliferation. Bioinformatics was used to screen potentially biologically significant lncRNAs related to the mTOR pathway. Additionally, p-mTOR (Ser2448), Beclin1, and LC3II/I expression levels in SW620 cells were assessed through Western blot analysis.

RESULTS

hBD-1 inhibited the proliferative ability of SW620 cells, as evidenced by the reduction in the colony formation capacity of SW620 cells upon exposure to hBD-1. hBD-1 decreased the expression of p-mTOR (Ser2448) protein and increased the expression of Beclin1 and LC3II/I protein. Furthermore, bioinformatics analysis identified seven lncRNAs (2 upregulated and 5 downregulated) related to the mTOR pathway. The lncRNA TCONS_00014506 was ultimately selected. Following the inhibition of the lncRNA TCONS_00014506, exposure to hBD-1 inhibited p-mTOR (Ser2448) and promoted Beclin1 and LC3II/I protein expression.

CONCLUSION

hBD-1 inhibits the mTOR pathway and promotes autophagy by upregulating the expression of the lncRNA TCONS_00014506 in SW620 cells.
Long noncoding RNAs HAND2-AS1 ultrasound microbubbles suppress hepatocellular carcinoma progression by regulating the miR-873-5p/tissue inhibitor of matrix metalloproteinase-2 axis
Authors: Zou Qiang , Wang Hao-Wen , Di Xi-Liang , Li Yuan , Gao Hui
Published: April 2024

BACKGROUND

Increasing data indicated that long noncoding RNAs (lncRNAs) were directly or indirectly involved in the occurrence and development of tumors, including hepatocellular carcinoma (HCC). Recent studies had found that the expression of lncRNA HAND2-AS1 was downregulated in HCC tissues, but its role in HCC progression is unclear. Ultrasound targeted microbubble destruction mediated gene transfection is a new method to overexpress genes.

AIM

To study the role of ultrasound microbubbles (UTMBs) mediated HAND2-AS1 in the progression of HCC, in order to provide a new reference for the treatment of HCC.

METHODS

In vitro , we transfected HAND2-AS1 siRNA into HepG2 cells by UTMBs, and detected cell proliferation, apoptosis, invasion and epithelial-mesenchymal transition (EMT) by cell counting kit-8 assay, flow cytometry, Transwell invasion assay and Western blotting, respectively. In addition, we transfected miR-837-5p mimic into UTMBs treated cells and observed the changes of cell behavior. Next, the UTMBs treated HepG2 cells were transfected together with miR-837-5p mimic and tissue inhibitor of matrix metalloproteinase-2 (TIMP2) overexpression vector, and we detected cell proliferation, apoptosis, invasion and EMT. In vivo , we established a mouse model of subcutaneous transplantation of HepG2 cells and observed the effect of HAND2-AS1 silencing on tumor formation ability.

RESULTS

We found that UTMBs carrying HAND2-AS1 restricted cell proliferation, invasion, and EMT, encouraged apoptosis, and HAND2-AS1 silencing eliminated the effect of UTMBs. Additionally, miR-873-5p targets the gene HAND2-AS1, which also targets the 3’UTR of TIMP2. And miR-873-5p mimic counteracted the impact of HAND2-AS1. Further, miR-873-5p mimic solely or in combination with pcDNA-TIMP2 had been transformed into HepG2 cells exposed to UTMBs. We discovered that TIMP2 reversed the effect of miR-873-5p mimic caused by the blocked signalling cascade for matrix metalloproteinase (MMP) 2/MMP9. In vivo results showed that HAND2-AS1 silencing significantly inhibited tumor formation in mice.

CONCLUSION

LncRNA HAND2-AS1 promotes TIMP2 expression by targeting miR-873-5p to inhibit HepG2 cell growth and delay HCC progression.
Clinical pathological characteristics of “crawling-type” gastric adenocarcinoma cancer: A case report
Authors: Xu Yong-Wei , Song Yan , Tian Jun , Zhang Ba-Cui , Yang Yu-Sheng , Wang Jing
Published: April 2024

BACKGROUND

Gastric cancer (GC) is a significant health problem worldwide, and early detection and accurate diagnosis are crucial for improving patient outcomes. Crawling-type gastric adenocarcinoma is a rare subtype of GC that has unique histopathological and clinical characteristics, and its diagnosis and management can be challenging. This pathological type of GC is also rare.

CASE SUMMARY

Here, we report the case of a patient who underwent ordinary endoscopy, narrow-band imaging, and endoscopic ultrasonography intending to determine the extent of tumor invasion and upper abdominal enhanced computed tomography and whether there was tumor metastasis. Then, endoscopic submucosal dissection was performed. After pathological and immunohistochemical examination, the pathological diagnosis was crawling-type gastric adenocarcinoma. This is a very rare and special pathological type of tumor. This case highlights the importance of using advanced endoscopic techniques and pathological examination in diagnosing and managing gastric crawling-type adenocarcinoma. Moreover, the findings underscore the need for continued research and clinical experience in this rare subtype of GC to improve patient outcomes.

CONCLUSION

The “crawling-type” GC is a rare and specific tumor pathology. It is difficult to identify and diagnose gliomas via endoscopy. The tumor is ill-defined, with a flat appearance and indistinct borders due to the lack of contrast against the background mucosa. Pathology revealed that the tumor cells were hand-like, so the patient has diagnosed with “crawling-type” gastric adenocarcinoma.
Performance Assessment of Treponemal and Nontreponemal Tests for the Diagnosis of Acquired Syphilis
Authors: Silva Ângelo Antônio Oliveira , Lima Ayla Araújo , Vasconcelos Larissa de Carvalho Medrado , de Almeida Rosângela Andrade , de Freitas Natália Erdens Maron , Habib Fernanda Lopes , Oliva Talita Andrade , da Silva Miralba Freire de Carvalho Ribeiro , de Siqueira Isadora Cristina , Santos Fred Luciano Neves
Published: April 2024

There are a variety of nontreponemal test (NTT) and treponemal test (TT) kits for the serologic diagnosis of syphilis. Because of the complexity of the infection (multiple clinical stages) and the different antigens used in these kits, a systematic evaluation of the accuracy of the currently available commercial tests is warranted. Our objective was to evaluate the performance of commercially available tests for the diagnosis of syphilis infection. In this study, we analyzed one NTT (Venereal Disease Research Laboratory [VDRL] test, Wiener Laboratories, Rosario, Argentina) and two TTs (fluorescent treponemal antibody absorption [FTA-ABS] test, Euroimmun, Lübeck, Germany, and syphilis recombinant ELISA v. 4.0 test [ELISA], Wiener Laboratories, Rosario, Argentina) using a panel of 187 samples, including serum samples from 31 individuals with primary syphilis, 77 with secondary syphilis, and 79 with latent syphilis. An additional 192 samples from uninfected individuals and 323 serum samples from individuals with other diseases were included. The sensitivities of the VDRL, ELISA, and FTA-ABS tests were 97.9%, 100%, and 96.3%, respectively. The VDRL and ELISA tests showed a specificity of 100%, and the FTA-ABS test showed a specificity of 99.5%. Accuracy was 98.9% for the VDRL test, 100% for the ELISA, and 97.9% for the FTA-ABS test. For primary, secondary, and latent syphilis, the ELISA achieved a diagnostic performance of 100%, whereas the sensitivity for the VDRL and FTA-ABS tests ranged from 96.8% to 98.7% and 93.7% to 98.7%, respectively. No difference was observed when the tests were used as traditional or reverse algorithms. In general, all three tests are able to discriminate positive and negative samples for syphilis, regardless of the diagnostic algorithm.
Evaluation of Difficulty for Autonomous Vehicles Testing Roads based on Multiple Criteria Decision Analysis
Authors: Lin Da-Jie , Liu Hsin Hsien , CHOU AI-CHEN , Huang Pin-Cheng , WU CHENG HSIN , CHANG CHUN-YI , CHEN MING-HSU
Published: April 2024

Autonomous Vehicles are being widely tested under diverse conditions with expectations that they will soon be a regular feature on roads. The development of Autonomous Vehicles has become an important policy in countries around the world, and the technologies developed by countries and car manufacturers are different, and at the same time to adapt to the road environment and traffic management facilities of different countries, so some countries have built self-driving test sites, and the test content is also different, so it is impossible to compare its relative difficulty. This study surveyed experts and scholars to develop a means of weighting the respective difficulty of various autonomous vehicle testing conditions based on the analytic hierarchy process and fuzzy analytic hierarchy process, applied to a sample of 33 sets of testing conditions based on road type, management actions and operational capabilities. Weights are also adjusted in response to environmental impact factors to determine the relative difficulty of different scenarios for each test route. Research results show that "multiple intersections", "on-site traffic control" and " emergency stop " exert the highest weights. Increased overall route complexity or uncertainty results creates greater operational challenges for autonomous vehicles. The results provide a useful reference for future road testing systems and route classifications for autonomous vehicles. In addition, this study conducts a trial calculation of the difficulty of an autonomous vehicle test route. Total difficulty is calculated using six indicators, including the route’s total score, number of scenarios, along with the mean, standard deviations and maximum and minimum scores for each scenario. The results of this study can be used to evaluate the difficulty of test scenarios in existing test sites or as a guideline for future test site planning and design.
Research on Garbage Recognition of Road Cleaning Vehicle Based on Improved YOLOv5 Algorithm
Authors: Liu XinHong , Wen Zihao , Kang Kailei , Liu Xingchen
Published: April 2024

As a key tool to maintain urban cleanliness and improve the road environment, road cleaning vehicles play an important role in improving the quality of life of residents. However, the traditional road cleaning vehicle requires the driver to monitor the situation of road garbage at all times and manually operate the cleaning process, resulting in an increase in the driver 's work intensity. To solve this problem, this paper proposes a road garbage recognition algorithm based on improved YOLOv5, which aims to reduce labor consumption and improve the efficiency of road cleaning. Firstly, the lightweight network MobileNet-V3 is used to replace the backbone feature extraction network of the YOLOv5 model. The number of parameters and computational complexity of the model are greatly reduced by replacing the standard convolution with the deep separable convolution, which enabled the model to have faster reasoning speed while maintaining higher accuracy. Secondly, the attention mechanism in MobileNet-V3 is improved, and a more efficient coordinate attention module is embedded to enhance the model 's attention to key features and further improve the accuracy of garbage recognition. Thirdly, in order to better improve the detection effect of garbage recognition, the K-means clustering algorithm is used to adjust and re-cluster the anchor box of the original model, so that the generated anchor box is closer to the ground truth box.Finally, we conducted experiments on the self-made road garbage dataset to verify the effectiveness of the improved algorithm. The garbage recognition accuracy rate reached 94.1%, and compared with the original YOLOv5 model, the number of model parameters was reduced by 47.1%, and the detection speed was increased by 35%. Therefore, the improved algorithm achieves the balance between detection accuracy and speed, which lays a foundation for future deployment and testing in actual road cleaning vehicles.
Insides to Trustworthy AI-Based Embedded Systems
Authors: Blazevic Romana , Veledar Omar , Macher Georg
Published: April 2024

In an era characterized by the rapid proliferation and advancement of AI-based technologies across various domains, the spotlight is placed on the integration of these technologies into trustworthy autonomous systems. The integration into embedded systems necessitates a heightened focus on dependability. This paper combines the findings from the TEACHING project, which delves into the foundations of humanistic AI concepts, with insights derived from an expert workshop in the field of dependability engineering. We establish the body of knowledge and key findings deliberated upon during an expert workshop held at an international conference focused on computer safety, reliability and security. The dialogue makes it evident that despite advancements, the assurance of dependability in AI-driven systems remains an unresolved challenge, lacking a one-size-fits-all solution. On the other hand, the positive outcome of this dialogue about the dependability of AI in embedded systems is that experts foster a shared understanding across diverse domains of expertise. We enhance the outcomes by considering the entirety of the PESTEL analysis framework encompassing political, environmental, social, technological, economic and legal dimensions. Therefore, this work synthesizes insights aiming to provide a comprehensive view informed by a multitude of perspectives and factors.
Predicting Vehicle Engine Performance: Assessment of Machine Learning Techniques and Data Imputation
Authors: Giraldo Delgado Juan Camilo , Kuzhagaliyeva Nursulu , Gorbatenko Inna , Sarathy Mani
Published: April 2024

The accurate prediction of engine performance maps can guide data-driven optimization of engine technologies to control fuel use and associated emissions. However, engine operational maps are scarcely reported in literature and often have missing data. Assessment of missing-data resilient algorithms in the context of engine data prediction could enable better processing of real-world driving cycles, where missing data is a more pervasive phenomenon. The goal of this study is, therefore, to determine the most effective technique to deal with missing data and employ it in prediction of engine performance characteristics. We assess the performance of two machine learning approaches, namely Artificial Neural Networks (ANNs) and the extreme tree boosting algorithm (XGBoost), in handling missing data. Zero imputation and three statistical methods, specifically K-Nearest Neighbors (KNN), Expectation Maximization (EM) and Multivariate Imputation Chained Equations (MICE), were employed as a pre-processing step with ANN model. The efficiency of each technique was evaluated at various degrees of missing data in the input (25%, 50 and 75%). In addition, the imputation quality of missing data was estimated using Kernel Density Estimation (KDE) and Raw Bias. The ANN model’s ability to predict fuel consumption and thermal efficiency was limited by missing data input with the zero-imputation technique. Among statistical methods coupled with ANN, EM exhibited the least satisfying performance while KNN performed the best. XGBoost with sparsity aware algorithm outperformed other methods by demonstrating the best adaptability to input’s missing information.
Optimization of Body Parts Specifications Using A.I Technology
Authors: Kim Hyungtae , Lee Youngho , Kim Taejin
Published: April 2024

Optimizing the specifications of the parts that make up the vehicle is essential to develop a high performance and quality vehicle with price competitiveness. Optimizing parts specifications for quality and affordability means optimizing various factors such as engineering design specifications and manufacturing processes of parts. This optimization process must be carried out in the early stages of development to maximize its effectiveness. Therefore, in this paper, we studied the methodology of building a database for parts of already developed vehicles and optimizing them on a data basis. A methodology for collecting, standardizing, and analyzing data was studied to define information necessary for specification optimization. In addition, AI technology was used to derive optimization specifications based on the 3D shape of the parts. Through this study, body parts specification optimization system using AI technology was developed. Then, the simulation confirmed that using this system improves work efficiency and has a great effect on specification optimization.
Development of an Automatic Pipeline for Data Analysis and Pre-Processing for Data Driven-Based Engine Emission Modeling in a Real Industrial Application
Authors: Petrone Boris , Giovannardi Emanuele , Brusa Alessandro , Cavina Nicolò , Kitsopanidis Ioannis
Published: April 2024

During the development of an Internal Combustion Engine-based powertrain, traditional procedures for control strategies calibration and validation produce huge amount of data, that can be used to develop innovative data-driven applications, such as emission virtual sensing. One of the main criticalities is related to the data quality, that cannot be easily assessed for such a big amount of data. This work focuses on an emission modeling activity, using an enhanced Light Gradient Boosting Regressor and a dedicated data pre-processing pipeline to improve data quality. First thing, a software tool is developed to access a database containing data coming from emissions tests. The tool performs a data cleaning procedure to exclude corrupted data or invalid parts of the test. Moreover, it automatically tunes model hyperparameters, it chooses the best set of features, and it validates the procedure by comparing the estimation and the experimental measurement. The proposed pre-processing pipeline shows an improvement in terms of accuracy, demonstrating the utility of using large training data which cover a wide set of vehicle maneuvers. Thus, custom designed tests are performed for dataset enrichment, allowing the model to predict non-conventional conditions of aftertreatment systems inefficiency. Real case applications of the proposed model are exposed, such as emission estimation in non-measurable conditions, virtual assessment of the impact of new control strategy calibration on emissions, alignment of emission measurements with all other vehicle signals. Finally, a Principal Component Analysis-based algorithm is developed, to assess the epistemic uncertainty of the model and the prediction reliability during inference.
Temperature Accurate Prediction Method of Electric Drive Transmission Considering Spatio-Temporal Correlation Characteristics under High Speed and Heavy Load Working Conditions
Authors: Tang Peng , Zhao Zhiguo , Li Haodi , Yang Jianyu
Published: April 2024

Accurate prediction temperature variation of electric drive transmission (EDT) can effectively monitor its abnormal temperature rise that may occur under high speed and heavy load working conditions, so as to ensure the vehicles’ safe operation. In this paper, combined with real temperature and input/output characteristic data collected from EDT test platform under different working conditions, a spatio-temporal relationship dynamic graph convolution neural network based on least square method (OLS-DRGCN) for temperature prediction is proposed. Firstly, OLS is used to estimate the EDT’s internal temperature based on partial sensor information as the input of OLS-DRGCN. Secondly, the spatial dependence relationship of each temperature node is dynamically learned through node embedding and the dynamic thermal network topology of EDT is constructed. Meanwhile, the timing rule of each temperature node is obtained through the gated recurrent unit. The analysis results show that the proposed OLS-DRGCN can effectively predict the temperature changes in the future period based on only a few sensor information when the predicted time is 30s, the mean absolute error of OLS-DRGCN can be controlled within 1.07°C under three different working conditions.
Validation and Comparison of Alignment Methodologies for the SAE Wireless Power Transfer, J2954 Standard
Authors: Schneider Jesse , Sealy Ky , Boettigheimer Mike , Laemmle Timo , Teerlinck Ivo , Hollenbach Maximilian , Rappholz Bastian , Wendt Andreas , Joos Simon
Published: April 2024

Notice of Concern:
The Publisher has put a notice of caution and inquiry on this paper because of an expression of concern about the figures and so have temporarily withdrawn the article. We will update after our investigation.