bluuebunny/crossref_metadata_2025 huggingface dataset:
General Characteristics of Pedagogical Conditions and the Structural and Functional Model for the Formation of Future Vocational Education Teachers’ Pedagogical Tolerance by Means of Interdisciplinary Integration
Authors: Didur Andrii
Published: March 2024

Modern social processes are characterized by a global trend towards the priority of fundamental educational needs in the formation of a developed personality with significant intellectual and creative potential. The article provides a general description of the main pedagogical conditions for the formation of future vocational education teachers’ pedagogical tolerance by means of interdisciplinary integration, as well as the features of the structural and functional model for the process. The purpose of the article is to characterize the theoretical foundations and conditions for the formation of future vocational education teachers’ pedagogical tolerance, with the involvement of means of interdisciplinary integration, as well as a description of the structural and functional model for the process. Research methods. During the implementation of the research, a number of general scientific research methods were used, in particular, methods of analysis, synthesis, concretization and formalization, abstraction, induction and deduction, comparison. Results. It is determined that among the main vectors for modernizing the national system of vocational education is the optimization of the process of theoretical and practical training of future teachers. The article establishes that the need to actualize the phenomenon of pedagogical tolerance is conditioned by the specific requirements for the professionally significant qualities of a vocational teacher, those are characterized by the specific content and specific focus of pedagogical activity. In the course of the study, it was found that one of the promising areas of pedagogical education is the formation of personal qualities of a future vocational education teacher as a specialist, including his/her pedagogical tolerance. Attention is focused on the importance of forming pedagogical tolerance of future vocational education teachers with the involvement of interdisciplinary integration. The essence of the phenomenon under study is identified as a personal quality and principle of a teacher's activity, which consists in recognizing the value of and expressing respect for the differences between the subjects of the educational process, recognizing the right to be different, refusing to change it in one's own image, the ability to adequately understand each other, and the desire to build democratic relations with all subjects of the educational process. Conclusions. The study highlights the main provisions of modern legal acts that legitimize the need to transform the algorithms for training future vocational education teachers, as well as the conceptual foundations for organizing the educational process in higher education. In addition, the basic principles of implementation and development of interdisciplinary integration of the educational process are characterized.
Formation of personal environmental awareness in the conditions of the new socio-cultural environment
Authors: Melnyk Iryna
Published: March 2024

The urgency of studying the problem of environmental awareness is due to the aggravation of the ecological crisis, negative phenomena inherent in the modern socio-cultural environment. In order to solve these problems, a modern person must first of all change his attitude to the surrounding reality, in particular to nature. It was determined that a person's ecological consciousness is understood as a separate form of human consciousness. Ecological consciousness is represented by a set of certain views, thoughts and emotions that reflect a specific way of relating to nature, which is based on the appropriate ratio of specific needs of society/group/person and natural opportunities. The ecological consciousness of a person is characterized from the standpoint of four psychological directions, namely: the direction in which a person's ecological consciousness is considered as a prerequisite for the formation of his ecological worldview; a direction that considers environmental consciousness as a complex mental structure that, first of all, illuminates a person's attitude to the surrounding reality, the activities of other subjects, society as a whole, as well as the consequences of these activities; the direction in which ecological consciousness is closely connected with the ecological thinking of a person, his creative potential; direction, in the context of which environmental awareness is defined as the highest personal value. The features of human interaction with the natural environment are described, taking into account the norms and rules existing in society and the consumer orientation of the individual.
Osoby z niepełnosprawnością jako interesariusze organizacji
Authors: Manczak Iryna , Bajak Maria
Published: March 2024

Celem artykułu jest przedstawienie problematyki osób z niepełnosprawnością jako interesariuszy organizacji. Opracowanie ma charakter koncepcyjny. Podjęty temat przeanalizowano w kontekście teorii interesariuszy. Zidentyfikowano obszary wsparcia osób z niepełnosprawnością przez organizacje, a także zwrócono uwagę na wartości dostarczane przez nie interesariuszom reprezentującym omawianą grupę społeczną. Ponadto w artykule wyszczególniono przykładowe mierniki realizacji koncepcji spo-łecznej odpowiedzialności biznesu wobec osób z dysfunkcjami.
Lok-Test and Capo-Test pullout for in-situ concrete strength
Authors: Petersen Claus Germann
Published: March 2024

Among the test systems for in-place concrete strength available today, two measure the in-place physical strength, pullout, and cores. Both systems are dealt with in detail in this paper, the pullout systems named LOK-TEST/CAPO-TEST (ASTM C900-19) and coring (ASTM C42/42M-18). Testing in-situ with accurate test systems will reveal effects on the final strength from a potential mix’s over transportation, pumping, consolidation, compaction, and curing. With the LOK-TEST system testing of the pre-installed inserts takes 4–5 minutes each, easily and with only one small suitcase brought along. The CAPO-TEST, originally designed to supplement the LOK-TEST, takes 15–20 minutes for each test to be performed anywhere on a structure without pre-installed inserts. No large holes are left in the structure from coring and thinner elements may be tested without weakening them structurally. The pullout test provides accurately the In-Place Strength without testing cores and the duration is about 15 minutes compared to 3–4 days for coring correctly cured. General robust correlations to strength of standard specimens exist no matter what parameter is considered for normal concrete, even for carbonation of the surface layer. With the systems the cover layer protecting the reinforcement may be checked efficiently and quickly, not at least in areas with dense reinforcement or on slim structures. Bad curing conditions are revealed and the consequences in terms of reduced service life for presence of chlorides or carbonation may be estimated swiftly. This paper is benchmarking 50 years of successful in-situ concrete strength measurements, from studies of the failure mechanism and laboratory/on-site correlations to full scale testing of structures in Europe, Scandinavia, and Canada. Six testing cases with emphasis on pullout and cores are illustrating different applications: Case 1. Production testing at the Great Belt Link, Denmark. Case 2. Service life of bridge pier, Great Belt Link, Denmark. Case 3. Curing of the cover layer evaluated by pullout and conductivity, Denmark. Case 4. Strength testing with CAPO-TEST for further loading of old bridges, Poland. Case 5. In-Situ compressive strength testing of quarantined precast concrete tunnel lining segments using CAPO-TEST, UK. Case 6. Safe and early loading with LOK-TEST, Canada. Other cases are given on www.NDTitans.com
Deep learning approaches for breast cancer detection in histopathology images: A review
Authors: Priya C V Lakshmi , V G Biju , B R Vinod , Ramachandran Sivakumar
Published: March 2024

BACKGROUND:

Breast cancer is one of the leading causes of death in women worldwide. Histopathology analysis of breast tissue is an essential tool for diagnosing and staging breast cancer. In recent years, there has been a significant increase in research exploring the use of deep-learning approaches for breast cancer detection from histopathology images.

OBJECTIVE:

To provide an overview of the current state-of-the-art technologies in automated breast cancer detection in histopathology images using deep learning techniques.

METHODS:

This review focuses on the use of deep learning algorithms for the detection and classification of breast cancer from histopathology images. We provide an overview of publicly available histopathology image datasets for breast cancer detection. We also highlight the strengths and weaknesses of these architectures and their performance on different histopathology image datasets. Finally, we discuss the challenges associated with using deep learning techniques for breast cancer detection, including the need for large and diverse datasets and the interpretability of deep learning models.

RESULTS:

Deep learning techniques have shown great promise in accurately detecting and classifying breast cancer from histopathology images. Although the accuracy levels vary depending on the specific data set, image pre-processing techniques, and deep learning architecture used, these results highlight the potential of deep learning algorithms in improving the accuracy and efficiency of breast cancer detection from histopathology images.

CONCLUSION:

This review has presented a thorough account of the current state-of-the-art techniques for detecting breast cancer using histopathology images. The integration of machine learning and deep learning algorithms has demonstrated promising results in accurately identifying breast cancer from histopathology images. The insights gathered from this review can act as a valuable reference for researchers in this field who are developing diagnostic strategies using histopathology images. Overall, the objective of this review is to spark interest among scholars in this complex field and acquaint them with cutting-edge technologies in breast cancer detection using histopathology images.
Revolutionizing travel experiences: An in-depth analysis of intelligent booking systems and behavioral patterns
Authors: Guo Qianzi , Mu Lin , Lou Shuai
Published: March 2024

This study delves into the intricacies of booking behaviors, focusing on the preference for tropical coastal cities during colder seasons, given the burgeoning need for personalized and intelligent travel recommendations in the modern tourism landscape. Traditional booking systems, with their static algorithms, have often neglected the dynamic interplay of environmental factors and user preferences, necessitating an evolution towards more sophisticated, adaptive models. To address this, the research employed advanced machine learning and artificial intelligence techniques, amalgamating data from diverse sources like major tourism websites, hotel platforms, and social media. The methodology involved meticulous data preprocessing to ensure data quality and depth, followed by the implementation of deep learning models and time series analysis, which were instrumental in deciphering complex patterns and offering predictive insights. This comprehensive approach facilitated the integration of various environmental factors such as weather conditions and temporal events, enabling the system to craft hyper-personalized recommendations that resonate with user preferences and environmental nuances. The findings were illuminating, unveiling a significant inverse relationship between temperature and bookings and highlighting the influential role of temporal events in driving bookings. The advanced predictive models showcased commendable accuracy, underscoring the transformative potential of such intelligent systems in enhancing user experiences and engagement in the tourism sector. These insights pave the way for the development of innovative strategies that can significantly elevate user satisfaction and industry growth. The study concludes that the integration of machine learning and AI in booking systems can revolutionize the tourism industry by offering unprecedented levels of personalization and adaptability, catering to the diverse and evolving needs of modern travelers.
Environmental risk assessment and management of nuclear power plants based on big data analysis
Authors: Zhang Qin , Wu Han , Ma Chi , Wang Yuebin , Zheng Xiangyang
Published: March 2024

In traditional research, monitoring data and samples are limited, and it is difficult to achieve ideal results in real-time monitoring and rapid response to environmental risks. By leveraging extensive environmental data gathered from nuclear power plants, the research employed machine learning methodologies for accurate feature selection and extraction of environmental parameters. An efficient environmental risk assessment model was successfully established by using a random forest algorithm. The 95% confidence interval for the area under the curve value spanned from 0.6894 to 0.9292. This provided a more dynamic and effective means for assessing and managing the environmental risks of nuclear power plants.
Power data quality assessment and verification governance based on knowledge graph
Authors: Wang Honggang , Ji Xin , Zhao Xiaolong , He Yude , Yu Ting
Published: March 2024

In addressing the challenges of scattered data and limited professional knowledge in traditional power data quality assessment and verification governance, our approach leveraged natural language processing (NLP) technology for text preprocessing, incorporating power system research findings and case analyses. Named entity recognition enhanced entity identification accuracy, while relationship weighting technology facilitated entity classification and relationship weight assignment. The resulting power system knowledge graph was seamlessly integrated into a graph database for real-time updates. Through the synergistic use of relationship weighting technology and graph convolutional networks (GCN), our method achieved precise representation and modeling of power system knowledge graphs. The research outcomes underscored the method’s exceptional performance in real-time anomaly detection, maintaining an anomaly detection rate between 1% and 10% and accuracy fluctuating from 80% to 98%. This method stands as a testament to its efficacy in processing power data and validating its robustness in power system assessment and verification.
Correlation Between Prefrontal Functional Connectivity and the Degree of Cognitive Impairment in Alzheimer’s Disease: A Functional Near-Infrared Spectroscopy Study
Authors: Zhang Mengxue , Qu Yanjie , Li Qian , Gu Chao , Zhang Limin , Chen Hongxu , Ding Minrui , Zhang Tong , Zhen Rongrong , An Hongmei
Published: March 2024

Background: The development of Alzheimer’s disease (AD) can be divided into subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia. Early recognition of pre-AD stages may slow the progression of dementia. Objective: This study aimed to explore functional connectivity (FC) changes of the brain prefrontal cortex (PFC) in AD continuum using functional near-infrared spectroscopy (fNIRS), and to analyze its correlation with cognitive function. Methods: All participants underwent 48-channel fNIRS at resting-state. Based on Brodmann partitioning, the PFC was divided into eight subregions. The NIRSIT Analysis Tool (v3.7.5) was used to analyze mean ΔHbO2 and FC. Spearman correlation analysis was used to examine associations between FC and cognitive function. Results: Compared with HC group, the mean ΔHbO2 and FC were different between multiple subregions in the AD continuum. Both mean ΔHbO2 in the left dorsolateral PFC and average FC decreased sequentially from SCD to MCI to AD groups. Additionally, seven pairs of subregions differed in FC among the three groups: the differences between the MCI and SCD groups were in heterotopic connectivity; the differences between the AD and SCD groups were in left intrahemispheric and homotopic connectivity; whereas the MCI and AD groups differed only in homotopic connectivity. Spearman correlation results showed that FCs were positively correlated with cognitive function. Conclusions: These results suggest that the left dorsolateral PFC may be the key cortical impairment in AD. Furthermore, there are different resting-state prefrontal network patterns in AD continuum, and the degree of cognitive impairment is positively correlated with reduced FC strength.
Association of Cancer History with Lifetime Risk of Dementia and Alzheimer’s Disease
Authors: Li Yaqi , Xu Xinming , Wang Peilu , Chen Xiqun , Yang Qishan , Sun Liang , Gao Xiang
Published: March 2024

Background: The literature presents conflicting results regarding the potential protective effect of prevalent cancer on the development of dementia and Alzheimer’s disease (AD). Objective: Association between cancer and subsequent risk of dementia and/or AD was reported previously, but survival bias has been of concern. Here, we aimed to calculate the lifetime risk of dementia and AD and evaluate the association of cancer history with these two conditions. Methods: In this retrospective analysis, we included 292,654 participants aged 60+ y during the follow-up and free of dementia at baseline, within the UK Biobank cohort. Lifetime risks of dementia and AD were estimated in individuals with and without cancer history, and different durations of cancer exposure and cancer types. Results: During a median of 12.5 follow-up years, 5,044 new dementia and 2,141 AD cases were reported. Lifetime risks of dementia and AD were lower in cancer survivors compared to those without cancer, and this effect was more pronounced in participants with cancer history exposure≥5 years. Similar relationship was observed in individual cancer types, except for breast cancer. Conclusions: Results suggested an inverse association between cancer history and lifetime risk of dementia and AD, which may be modified by different cancer types and cancer exposure time.
Review on Climate Smart Agriculture Practice: A Global Perspective
Authors: Barua Prabal , Mitra Anisa
Published: March 2024

Climate change is having a detrimental effect on the environment’s natural equilibrium. The population that depends on agriculture is suffering from rising temperatures, sporadic droughts and famines, unpredictable dry spells, and irregular rains. Deploying Climate Smart Agriculture (CSA) is a terrific strategy to cut greenhouse gas emissions and boost crop output for food security and climate change adaptation. The primary objective of this study is to provide an organized appraisal of current advancements in the field of climate-smart agriculture. For this study, the Scopus database was used to analyze 157 papers that were published between 2013 and 2022. However, the use of climate-smart agriculture technology that considers local knowledge is still quite low in developing countries. Therefore, raising the importance of indigenous knowledge in the context of climate change could aid smallholder agricultural groups in their adaptation. Improving adaptability, developing capacity, and fusing indigenous knowledge with climate-smart agricultural practices may all be necessary to increase a community’s effective resilience to climate change.
Anthropocene: Human Activity Impact on the Climate and Environment
Authors: Damor Parthvee R.
Published: March 2024

The environment undergoes shifts across various timescales, yet presently, human intervention remains the primary solution provider and is likely to persist in this role for the next few centuries. While it is widely recognised that human-induced, or anthropogenic, climate change contributes to global warming, what is often underestimated are the direct impacts of heavy rainfall, droughts, and storms, incurring significant costs for both society and the environment. The expansion of human populations has led to the conversion of natural ecosystems for agricultural, industrial, and residential purposes, creating a demand for environmental inputs such as fresh water, fiber, and soil fertility. This heightened demand puts increased pressure on the capacity of natural ecosystems. Deforestation, expanding agriculture, illegal fishing and hunting, unplanned tourism, and pesticide pollution have collectively led to the progressive degradation of natural habitats. The consequence is a loss of biodiversity and the removal of forests, disrupting the food and shelter sources for wildlife residing in these ecosystems. Scientific research aims to comprehend the scale of biodiversity, and land use, and contribute to mitigating the impacts of land use changes.
Identification of horizontal gene transference by means of anomaly detection and natural language-inspired interpretable embeddings
Authors: Lomas-Barrie Victor , Reyes-Camacho Michelle , Neme Antonio
Published: March 2024

Horizontal gene transference is a biological process that involves the donation of DNA or RNA from an organism to a second, unrelated organism. This process is different from the more common one, vertical transference, which is present whenever an organism or pair of organisms reproduce and transmit their genetic material to the descendants. The identification of segments of genetic material that are the result of horizontal transference is relevant to construct accurate phylogenetic trees, on one hand, and to detect possible drug-resistance mechanisms, on the other, since this movement of genetic material is the main cause behind antibiotic resistance in bacteria. Here, we describe a novel algorithm able to detect sequences of foreign origin, and thus, possible acquired via horizontal transference. The general idea of our method is that within the genome of an organism, there might be sequences that are different from the vast majority of the remaining sequences from the same organism. The former are candidate anomalies, and thus, their origin may be explained by horizontal transference. This approach is equivalent to a particular instance of the authorship attribution problem, that in which from a set of texts or paragraphs, almost all of them were written by the same author, whereas a minority has a different authorship. The constraint is that the author of each text is not known, so the algorithm has to attribute the authorship of each one of the texts. The texts detected to be written by a different author are the equivalent of the sequences of foreign origin for the case of genetic material. We describe here a novel method to detect anomalous sequences, based on interpretable embeddings derived from a common attention mechanism in humans, that of identifying novel tokens within a given sequence. Our proposal achieves novel and consistent results over the genome of a well known organism.
TrumpetNet: A Convolutional Neural Network with Self-Attention Mechanisms for visual detection of trumpet fingering
Authors: Valdez-Rodríguez José E. , Rangel Nahum , Moreno-Armendáriz Marco A.
Published: March 2024

Visual detection of fingering on the trumpet is an increasingly interesting topic in music research. The ability to recognize and track the movements of the trumpet player’s fingers during the performance of a musical piece can provide valuable information for analyzing and improving instrument technique. However, this is a largely unexplored task, as most works focus on audio quality rather than instrument fingering techniques. Developing techniques for identifying essential finger positions on a musical instrument is crucial, as poor fingering techniques can harm instrument performance. In this work, we propose the visual detection of this fingering using convolutional neural networks with a proprietary dataset created for this purpose. Additionally, to improve the results and focus on the essential parts of the instrument, we use self-attention mechanisms by extracting these features automatically.
Automated license plate authentication framework using multi-view vehicle images
Authors: Ganesh M.A. , Saravana Perumaal S. , Gomathi Sankar S.M.
Published: March 2024

The current framework for detecting Fake License Plates (FLP) in real-time is not robust enough for patrol teams. The objective of this paper is to develop a robust license plate authentication framework, based on the Vehicle Make and Model Recognition (VMMR) and the License Plate Recognition (LPR) algorithms that is implementable at the edge devices. The contributions of this paper are (i) Development of license plate database for 547 Indian cars, (ii) Development of an image dataset with 3173 images of 547 Indian cars in 8 classes, (iii) Development of an ensemble model to recognize vehicle make and model from frontal, rear, and side images, and (iv) Development of a framework to authenticate the license plates with frontal, rear, and side images. The proposed ensemble model is compared with the state-of-the-art networks from the literature. Among the implemented networks for VMMR, the Ensembling model with a size of 303.2 MB achieves the best accuracy of 89% . Due to the limited memory size, Easy OCR is chosen to recognize license plate. The total size of the authentication framework is 308 MB. The performance of the proposed framework is compared with the literature. According to the results, the proposed framework enhances FLP recognition due to the recognition of vehicles from side images. The dataset is made public at https://www.kaggle.com/ganeshmailecture/datasets.
FCTNet: Fusion of 3D CNN and transformer dance action recognition network
Authors: Ning Tao , Zhang Tingting , Huang Guowei
Published: March 2024

Folk dance is an important intangible cultural heritage in China. In the environment where movement recognition technology is widely used, there is still no research field on the protection and inheritance of folk dance culture. In order to better protect and inherit the minority dance, screening the typical movements of 5 types of minority dance, through the dance video frame processing, obtain the key movements of 19 class dance sequence, build the national dance typical action data set, put forward a 3D CNN fusion Transformer national dance recognition network model (FCTNet), the recognition rate of 96.7% in the experiment. The results show that the construction method of the folk dance data set is reasonable, the identification model has good performance for the classification of folk dance, and can effectively identify and record the folk dance movements, which also makes new contributions to the digital protection of folk dance.
An efficient two-heuristic algorithm for the student-project allocation with preferences over projects
Authors: Viet Hoang Huu , Uyen Nguyen Thi , Cao Son Thanh , Nguyen Long Giang
Published: March 2024

The Student-Project Allocation with preferences over Projects problem is a many-to-one stable matching problem that aims to assign students to projects in project-based courses so that students and lecturers meet their preference and capacity constraints. In this paper, we propose an efficient two-heuristic algorithm to solve this problem. Our algorithm starts from an empty matching and iteratively constructs a maximum stable matching of students to projects. At each iteration, our algorithm finds an unassigned student and assigns her/his most preferred project to her/him to form a student-project pair in the matching. If the project or the lecturer who offered the project is over-subscribed, our algorithm uses two heuristic functions, one for the over-subscribed project and the other for the over-subscribed lecturer, to remove a student-project pair in the matching. To reach a stable matching of a maximum size, our two heuristics are designed such that the removed student has the most opportunities to be assigned to some project in the next iterations. Experimental results show that our algorithm is efficient in execution time and solution quality for solving the problem.
RETRACTED: Potential functions of construction worker–machine interaction safety assessment
Authors: Bai Yu , Hu Qijun , Zhou Zhenxiang , Cai Qijie , He Leping
Published: March 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
RETRACTED: Pedestrian tracking method based on S-YOFEO framework in complex scene
Authors: Sheng Wenshun , Shen Jiahui , Huang Qiming , Liu Zhixuan , Ding Zihao
Published: March 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
RETRACTED: A study on the impact of full eco-smart home on the cognitive load and perceptual needs of elderly users
Authors: Huang Jinsong , Hou Hecheng , Li Xiaoying , Zhang Ziyi , Jia Qi
Published: March 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
RETRACTED: Dual generators and dual discriminators generative adversarial network for video anomaly detection
Authors: Chen Kang , Song Changming , Cheng Dongxu , Li Hao
Published: March 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
Medical delivery without representation?: Examining disability representation within the healthcare community
Authors: Ramsey Justin , Najarian Christopher Raffi
Published: March 2024

Individuals with disabilities comprise approximately 13% of the overall population. This editorial explores recent events that may involve ableism. The recent COVID pandemic created a rapid need and pressure to develop ventilator allotment policies. Many concluded several state policies were discriminatory in nature toward persons with disabilities (PWD). Lack of disability representation in medical fields may contribute to such discrimination within state and hospital medical policies. The underrepresented numbers of PWD in medical fields are explored. We conclude that improved education for all medical providers is needed. Possible strategies for improving healthcare representation and delivery within the United States are discussed.
smart-KG: Partition-Based Linked Data Fragments for querying knowledge graphs
Authors: Azzam Amr , Polleres Axel , D. Fernández Javier , Acosta Maribel
Published: March 2024

RDF and SPARQL provide a uniform way to publish and query billions of triples in open knowledge graphs (KGs) on the Web. Yet, provisioning of a fast, reliable, and responsive live querying solution for open KGs is still hardly possible through SPARQL endpoints alone: while such endpoints provide a remarkable performance for single queries, they typically can not cope with highly concurrent query workloads by multiple clients. To mitigate this, the Linked Data Fragments (LDF) framework sparked the design of different alternative low-cost interfaces such as Triple Pattern Fragments (TPF), that partially offload the query processing workload to the client side. On the downside, such interfaces still come with the expense of unnecessarily high network load due to the necessary transfer of intermediate results to the client, leading to query performance degradation compared with endpoints. To address this problem, in the present work, we investigate alternative interfaces, refining and extending the original TPF idea, which also aims at reducing server-resource consumption, by shipping query-relevant partitions of KGs from the server to the client. To this end, first, we align formal definitions and notations of the original LDF framework to uniformly present existing LDF implements and such “partition-based” LDF approaches. These novel LDF interfaces retrieve, instead of the exact triples matching a particular query pattern, a subset of pre-materialized, compressed, partitions of the original graph, containing all answers to a query pattern, to be further evaluated on the client side. As a concrete representative of partition-based LDF, we present smart-KG+, extending and refining our prior work (In WWW ’20: The Web Conference 2020 (2020) 984–994 ACM / IW3C2) in several respects. Our proposed approach is a step forward towards a better-balanced share of the query processing load between clients and servers by shipping graph partitions driven by the structure of RDF graphs to group entities described with the same sets of properties and classes, resulting in significant data transfer reduction. Our experiments demonstrate that the smart-KG+ significantly outperforms existing Web SPARQL interfaces on both pre-existing benchmarks for highly concurrent query execution as well as an accustomed query workload inspired by query logs of existing SPARQL endpoints.
Declarative generation of RDF-star graphs from heterogeneous data
Authors: Arenas-Guerrero Julián , Iglesias-Molina Ana , Chaves-Fraga David , Garijo Daniel , Corcho Oscar , Dimou Anastasia
Published: March 2024

RDF-star has been proposed as an extension of RDF to make statements about statements. Libraries and graph stores have started adopting RDF-star, but the generation of RDF-star data remains largely unexplored. To allow generating RDF-star from heterogeneous data, RML-star was proposed as an extension of RML. However, no system has been developed so far that implements the RML-star specification. In this work, we present Morph-KGCstar, which extends the Morph-KGC materialization engine to generate RDF-star datasets. We validate Morph-KGCstar by running test cases derived from the N-Triples-star syntax tests and we apply it to two real-world use cases from the biomedical and open science domains. We compare the performance of our approach against other RDF-star generation methods (SPARQL-Anything), showing that Morph-KGCstar scales better for large input datasets, but it is slower when processing multiple smaller files.
NME6 as a potential biomarker and therapeutic target involved in immune infiltration for lung adenocarcinoma
Authors: Luo Linjie , Li Ying , Zhang Lin , Yang Lijie
Published: March 2024

BACKGROUND: Lung adenocarcinoma (LUAD), a prevalent form of lung cancer, is characterized by its high global mortality rate. Previous studies have demonstrated the significance of Nucleoside diphosphate kinase (NME) in various cancers; however, the specific role of NME6 in LUAD remains inadequately understood. OBJECTIVE: This research aims to enhance our understanding of LUAD by investigating the expression level, epigenetic mechanism, signaling activities, and immune infiltrating characteristic immune cells of NME6 in patients. METHODS: The NME6 expression was explored between LUAD and normal tissue samples using GEPIA, UALCAN and HPA databases. The survival analysis was performed by Kaplan-Meier plotter. The Shiny Methylation Analysis Resource Tool was employed to examine the methylation characteristics of NME6. The Tumor Immune Single-cell Hub (TISCH) and CIBERSORT algorithm were utilized to analyze immune infiltrating characteristic immune cells between NME6 high- and low-expression group in LUAD. RESULTS: According to GEPIA, UALCAN, and HPA databases, NME6 is highly expressed in LUAD compared to normal tissues. At the same time, elevated levels of NME6 were found to be significantly correlated with inferior overall survival outcomes in LUAD patients. Subsequently, the top 10 genes interacted with NME6 were mainly involved in seven pathways, such as p53 signaling pathway, glutathione metabolism, thiamine metabolism, metabolic pathways, and drug metabolism. Notably, NME6 methylation in LUAD samples was lower than in normal samples. The methylation of cg04625862 has a significant impact on the regulation of NME6 expression in LUAD. Furthermore, high NME6 expression in LUAD was associated with tumor stages and relative abundance of tumor infiltrating immune cells, such as Macrophage M2, activated mast cell, and neutrophil. Moreover, NME6 regulated the expression of m6A modification of genes related to LUAD, including METTL3, WTAP, RBM15B, METTL14, RBMX, VIRMA, YTHDC1, RBM15, ZC3H13, YTHDF1, YTHDC2, IGF2BP2, YTHDF3, HNRNPA2B1, YTHDF2, HNRNPC, FTO, and ALKBH5. CONCLUSION: The analysis showed that NME6 is a crucial prognostic factor for LUAD patients. NME6 regulates genes related to m6A modification and immune cells infiltration. Furthermore, NME6 could sever as a potential therapeutic target for LUAD.
The Super-Critical Operational Modes in Robotic Systems
Authors: Humennyi Dmytro , Humennyi Oleksandr , Shabala Yevheniia
Published: March 2024

Amidst the swift advancements in robotics, the emergence of super-critical operational modes stands distinct, marking conditions that transcend conventional critical benchmarks. Such modes subject robotic systems to unparalleled strains, necessitating unmatched reliability, flexibility, and robustness. This paper probes into the intricacies of super-critical operations, underscoring the imperative for state-of-the-art sensors, heightened adaptability, resilience post-mishaps, intrinsic redundancy, unceasing surveillance, exhaustive pre-launch examinations, and all-encompassing operator education. Tending to these elements is indispensable for preserving the safety and efficacy of robotic systems under intense conditions, thereby fortifying both the apparatus and their associated industries.
Research on the use of sensor networks in underwater technologies
Authors: Delembovskyi Maksym , Korniychuk Boris , Klymenko Mykola , Diachenko Oleksandr
Published: March 2024

This study focuses on the application of sensor networks in the field of underwater technologies, opening new possibilities for monitoring and researching the marine environment. Main attention is given to the development and implementation of sensor systems capable of withstanding high pressure and corrosion in the underwater environment, as well as providing accurate and reliable data.

The work covers the theoretical aspects of sensor networks, including their design, functional capabilities, and challenges associated with their use in the underwater environment. Particular attention is paid to data transmission methods and energy conservation in conditions of limited access to energy.

The next component of the work is the analysis of the practical aspects of implementing sensor networks, particularly considering their application for monitoring water resources, studying marine fauna and flora, and in underwater archaeological research. The work emphasizes the importance of these technologies for collecting data about the seabed, its relief, and ecosystem.

This scientific work considers the prospects for the development of sensor networks in underwater research, including integration with other technologies, such as autonomous underwater vehicles, and the development of new materials and technologies to enhance the efficiency and durability of sensor systems.
Analysis of the state of development of the issue of protection of critical infrastructure facilities using engineered munitions
Authors: Lenkov Serhii , Kryvtsun Volodymyr , Miroshnichenko Oleg , Golushko Serhii , Ruslan Koltsov
Published: March 2024

The Russian-Ukrainian war at the stage of full-scale invasion has shown that one of the most difficult tasks of ensuring the survivability of troops and the functioning of critical infrastructure is to protect them from high-precision weapons, namely cruise and ballistic missiles and unmanned aerial vehicles.

The experience of warfare shows that the existing domestic air defense systems do not fully defeat air targets, while highly effective foreign-made air defense systems are supplied in insufficient quantities, and the created mobile fire groups to destroy air targets are not always effective. In these circumstances, it is advisable to consider new integrated approaches to the use of various types of weapons in the active defense of troops and critical infrastructure against air attack.

The complex construction of a system of active defense of troops and critical infrastructure against air attack is possible with the use of several types of weapons. Based on the analysis of the enemy's use of missiles of various classes and Shahed-type attack unmanned aerial vehicles, the paper proposes the use of engineered munitions as one of the means of defeating moving air targets that fly low at low speed. The use of special engineered ammunition will make it possible to increase the range of low-flying air targets and increase the likelihood of their defeat. This will improve the effectiveness of the existing system of protecting troops and critical infrastructure from air attack.

The paper analyzes the use of air attack weapons of the Russian Federation against troops and critical infrastructure facilities of Ukraine, substantiates proposals on the expediency of using engineered ammunition as an element of building a system of active protection of troops and critical infrastructure facilities from air attack, and presents their principle of operation and possible applications.
Hospital disease burden due to influenza and its comorbidities in Spain (2016-2021)
Authors: Díaz Gil A. , Gil-Prieto R. , Hernández Barrera V. , Gil de Miguel A.
Published: March 2024

Background: Influenza is an infectious disease that can lead to complications requiring hospitalization and may even result in death, particularly in patients at extreme ages. Our objective is to analyze hospitalized patients with an ICD-10 diagnostic code for influenza (codes J09, J10, and J11) in any diagnostic position from 2016 to 2021 in the Spanish hospital surveillance system.
Methods: Information on age, admissions, length of hospital stay, lethality, mortality, and comorbidity with a diagnostic code for influenza between October 1, 2016, and September 30, 2021, was obtained from the National Hospital Data Surveillance System (Conjunto Mínimo Básico de Datos, CMBD).
Results: A total of 118,585 hospital admissions with an influenza diagnose were recorded. Out of these, 57,671 patients (48.63%) were males, while 60,914 (51.37%) were females. The overall median age of admissions was 70.5 years. Significant differences were found among different age groups, with those over 65 years being the largest group in this study. The hospitalization rate for the entire period was 42.13 cases per 100,000 inhabitants. The overall mortality rate for the observed period was 2.45 deaths per 100,000 inhabitants, being higher in the group over 85 years. The most common comorbidity in the studied patients was diabetes mellitus, followed by chronic obstructive pulmonary disease and congestive heart failure. The latter group showed higher lethality than those patients with any other comorbidity. The average length of hospital stay was 9.59 days.
Conclusions: Influenza is an important cause of hospitalization in Spain. Male gender, older age groups, and associated clinical risk conditions showed a direct relationship with hospital mortality. This suggests that influenza vaccination should be implemented in the population to reduce associated comorbidities, as well as the mortality resulting from them.
Turist Rehberlerinin Alan Uzmanlığına Bakış Açıları Üzerine Görgül Bir Araştırma
Authors: Toksöz Derya
Published: March 2024

Araştırmanın amacı, Türkiye’nin farklı bölgelerinde mesleklerini icra eden turist rehberlerinin meslekte uzmanlaşmaya ve uzmanlık alanlarına/türlerine yönelik görüşlerini ortaya koymaktır. Bu noktada meslekte uzmanlaşma konusu özellikle son 10 yılda önem kazanmış ancak hala hakkettiği değeri/ilgiyi kazanamamış bir araştırma sahasıdır. Nitel araştırma desenlerinden durum çalışması kullanılarak yürütülen araştırmada, veri toplama yöntemi olarak görüşme tekniği tercih edilmiştir. Yarı yapılandırılmış görüşme formu aracılığıyla Mart 2023 tarihinde 16 turist rehberine ulaşılmıştır. Araştırmanın örnekleme yöntemi olarak tesadüfü olmayan örnekleme yöntemlerinden amaçlı örnekleme yöntemi kullanılmıştır. Elde edilen veriler üzerinden betimsel analiz yapılmıştır. Araştırma sonucunda temel olarak görüşmecilerin neredeyse hepsinin turist rehberliği mesleğinde uzmanlaşmanın bir gereklilik olduğunu düşündükleri tespit edilmiştir. Uzmanlaşmanın, ülkenin sahip olduğu değerlere (turistik ürünlere) ve ziyaretçi talebine göre şekillenmesi gerektiği ise diğer bir araştırma bulgusudur. Araştırmanın sonuç kısmında, değerlendirmelere ve önerilere yer verilmiştir.
Kültürel Miras Unsuru Olarak Ulusal Mutfağın Sürdürülebilirliği: Gastromilliyetçilik Çerçevesinde Nitel Bir Araştırma
Authors: Tekeli Mehmet , Yıldırım İrem , Çıtak Burak
Published: March 2024

Bu araştırmada mutfak şeflerinin ulusal mutfağın sürdürülebilirliği konusunda gastromilliyetçiliği ne derece önemsedikleri ve milli unsurlar çerçevesinde ulusal mutfağı nasıl koruyup kullandıklarının tespit edilmesi amaçlanmıştır. Bu kapsamda ölçüt örnekleme yöntemi kullanılarak mutfak şefleri ile yarı yapılandırılmış görüşmeler gerçekleştirilmiştir. Nitel yöntemin benimsendiği araştırmada fenomenoloji, araştırma deseni olarak belirlenmiştir. Araştırma verileri içerik analizine ve betimsel analize tabi tutulmuş olup beş ana tema, yirmi dokuz alt tema ve 234 kod elde edilmiştir. Araştırma sonucunda kültürel miras unsuru olarak ulusal mutfak, ulusal mutfağın sürdürülebilirliği, ulusal mutfak kültürüne ilişkin turist tutumları, ulusal mutfak kültürünün uluslararası bilinirliği ve devlet politikaları ve stratejileri ana temalar olarak belirlenmiştir. Ayrıca mutfak şeflerinin en sık dile getirdiği konunun ulusal mutfağın sürdürülebilirliği ana temasına, en uzun üzerinde durdukları konunun ise farkındalık/bilinçlenme alt temasına yönelik olduğu belirlenmiştir. Genel olarak ulusal mutfak kültürünün sürdürülebilirliğinde belirlenen ana temaların önemli rolünün olduğu sonucuna ulaşılmıştır.
Lisans Düzeyinde Turizm Eğitimi Alan Öğrencilerin Turizmde Kullanılan Otomasyon Sistemlerine Yönelik Algılarının Teknoloji Kabul Modeli Vasıtasıyla Ölçümü
Authors: Dulgaroglu Oguzhan , Solunoğlu Ali , İlban Mehmet Oğuzhan , Özgürel Gizem
Published: March 2024

Bilgi ve iletişim teknolojisinde gün geçtikçe artan gelişmelerle birlikte otomasyon sistemlerinin de çeşitlendiği görülmektedir. Otomasyon sistemlerinin fonksiyonlarında yaşanan artış işletmelere de önemli kolaylıklar sağlamaktadır. Turizm işletmeleri, bu sistemler sayesinde yönetsel faaliyetlerini kolaylıkla gerçekleştirebilmektedir. Çalışmada, turizm alanında lisans düzeyinde eğitim gören öğrencilerin turizm sektöründe kullanılan otomasyon sistemlerine ilişkin algıları incelenmiştir. Bu çalışma ile öğrencilerin turizmde kullanılan otomasyon sistemlerine ilişkin algılarının ne düzeyde olduğunun ve öğrencilerin otomasyon sistemlerine ilişkin algılarında belirleyici olan demografik özelliklerinin tespit edilmesi amaçlanmıştır. Çalışmada veriler nicel araştırma yöntemlerinden anket uygulama tekniğiyle toplanmıştır. Çalışma, 2022-2023 Eğitim-Öğretim Yılı içinde Balıkesir Üniversitesi Burhaniye Uygulamalı Bilimler Fakültesi turizm alanı öğrencileri örnekleminde gerçekleştirilmiştir. Uygulanan anket teknoloji kabul modeli ve demografik bilgiler olmak üzere iki kısımdan oluşmaktadır. Araştırma amacı doğrultusunda öncelikle elde edilen verilerin frekans dağılımları alınmış ve ölçeğe ilişkin açıklayıcı faktör analizi yapılmıştır. Daha sonra çalışmanın amacı dikkate alınarak oluşturulan hipotezler doğrultusunda t-testi ve tek yönlü varyans analizi yöntemlerinden yararlanılarak analizler gerçekleştirilmiştir. Analizler neticesinde öğrencilerin turizmde kullanılan otomasyon sistemlerine yönelik algılarının öğrenim gördükleri bölümlere ve mezun oldukları lisenin türüne göre farklılaştığı tespit edilmiştir. Alanyazın incelendiğinde, özellikle öğrencilerin turizm sektöründe kullanılan otomasyon sistemlerine yönelik algılarının demografik değişkenler üzerinde incelendiği az çalışma olduğu görülmektedir. Bu çalışmayla birlikte konuya ilişkin alanyazının çeşitlendirilmesi beklenmektedir.