bluuebunny/crossref_metadata_2025 huggingface dataset:
An optimized fuzzy based FP-growth algorithm for mining temporal data
Authors: Praveen Kumar B. , Padmavathy T. , Muthunagai S.U. , Paulraj D.
Published: October 2023

Data mining is one of the emerging technologies used in many applications such as Market analysis and Machine learning. Temporal data mining is used to get a clear knowledge about current trend and to predict the upcoming future. The rudimentary challenge in introducing a data mining procedure is, processing time and memory consumption are highly increasing while trying to improve the accuracy, precision or recall. As well as, while trying to reduce the processing time or memory consumption, accuracy, precision and recall values are reducing significantly. So, for improving the performance of the system and to preserve the memory and processing time, Three-Dimensional Fuzzy FP-Tree (TDFFPT) is proposed for Temporal data mining. Three functional modules namely, Three-Dimensional Temporal data FP-Tree (TTDFPT), Fuzzy Logic based Temporal Data Tree Analyzer (FTDTA) and Temporal Data Frequent Itemset Miner (TDFIM) are integrated in the proposed method. This algorithm scans the database and generates frequent patterns as per the business need. Every time a client purchases a new item, it gets stored in the recent database layer instead of rescanning the entire records which are placed in the old layer. The results obtained shows that the performance of the proposed model is more efficient than that of the existing algorithm in terms of overall accuracy, processing time, reduction in the memory utilization, and the number of databases scans. In addition, the proposed model also provides improved decision making and accurate pattern prediction in the time series data.
AQU-FRC Net: Automated soil prediction based on faster RCNN with aquila optimization
Authors: Sathish E. , Muthukumar R.
Published: October 2023

In agriculture, selecting an “appropriate plant for an appropriate soil” is a crucial stage for all sorts of lands. There are different types of soil found in India. It is necessary to understand the features of the soil type to predict the types of crops cultivated in a particular soil. This leads to significant inconsistencies and errors in large-scale soil mapping. However, manually analyzing the soil type in the laboratory is cost-effective and time-consuming, yet it produces an inaccurate classification result. To overcome these challenges, a novel AQU-FRC Net (Aquila – Faster Regional Convolutional Neural Neural) is proposed for the automatic prediction of soil and recommending suitable crops based on a soil-crop relationship database. The soil images were pre-processed using a Scalable Range-based Adaptive Bilateral Filter (SCRAB) for eliminating the noise artifacts from the images. The pre-processed images were classified using Faster-RCNN, which utilized MobileNet as a feature extraction network. The classification results were optimized by the Aquila optimization (AQU) algorithm that normalizes the parameters of the network to achieve better results. The proposed AQU-FRC Net achieves a high accuracy of 98.16% for predicting soil. The experimental results demonstrate that the model successfully predicts the soil when compared to other meta-heuristic-based methods.
Hidden Markov model-based modeling and prediction for implied volatility surface1
Authors: Guo Hongyue , Deng Qiqi , Jia Wenjuan , Wang Lidong , Sui Cong
Published: October 2023

The implied volatility plays a pivotal role in the options market, and a collection of implied volatilities across strike and maturity is known as the implied volatility surface (IVS). To capture the dynamics of IVS, this study examines the latent states of IVS and their relationship based on the regime-switching framework of the hidden Markov model (HMM). The cross-sectional models are first built for daily implied volatilities, and the obtained regression factors are regarded as the proxies of the IVS. Then, having these latent factors, the HMM is employed to model the dynamics of IVS. Take the advantages of HMM, the hidden state for each daily data is identified to achieve the corresponding time distribution, the characteristics, and the transition between the hidden states. The empirical study is conducted on the Shanghai 50ETF options, and the analysis results indicate that the HMM can capture the latent factors of IVS. The achieved states reflect different financial characteristics, and some of their typical features and transfer are associated with certain events. In addition, the HMM exploited to predict the regression factors of the cross-sectional models enables the further forecasting of implied volatilities. The autoregressive integrated moving average model, the vector auto-regression model, and the support vector regression model are regarded as benchmarks for comparison. The results show that the HMM performs better in the implied volatility prediction compared with other models.
Intelligent fuzzy edge computing for real-time decision making in IoT-based digital twin environments
Authors: Prasath N. , Arun A. , Saravanan B. , Kamaraj Kanagaraj
Published: October 2023

Intelligent Fuzzy Edge Computing (IFEC) has emerged as an innovative technology to enable real-time decision-making in Internet of Things (IoT)-based Digital Twin environments. Digital Twins provide virtual models of physical systems, facilitating predictive maintenance and optimization. However, implementing real-time decision-making in these environments is challenging due to massive data volumes and need for quick response times. IFEC addresses this by offering a flexible, scalable and efficient platform for real-time decision-making. This paper presents an overview of key aspects of IFEC including fuzzy logic, edge computing and Digital Twins. The use of fuzzy logic in IFEC provides an adaptive framework for handling uncertainties in data. Edge computing enables localized processing, reducing latency. The integration of Digital Twins allows system monitoring, analysis and optimization. Potential applications of IFEC are highlighted in domains such as manufacturing, healthcare, energy management and transportation. Recent advancements in IFEC are also discussed, covering new fuzzy inference systems, edge computing architectures, Digital Twin modeling techniques and security mechanisms. Overall, IFEC shows great promise in enabling real-time decision-making in complex IoT-based Digital Twin environments across various industries. Further research on IFEC will facilitate the ongoing digital transformation of industrial systems.
Reliable cluster based data collection framework for IoT-big data healthcare applications
Authors: Pughazendi N. , Valarmathi K. , Rajaraman P.V. , Balaji S.
Published: October 2023

Internet of Things (IoT) devices installed in hospital direct data unceasingly; in this manner, energy usage augments with the number of broadcasts too. In this paper, Reliable Cluster based Data Collection Framework (RCDCF) for IoT-Big Data Healthcare Applications (HA) is developed. During clustering process, the connected IoT devices are grouped into clusters. In clustering technique, the available IoT devices are gathered into groups. The device with high battery capacity and processing ability is selected as a cluster head (CH). Each member of the cluster is allocated multiple slots by applying a general function pooled by the Fog node and the entire devices. To perceive and eliminate outliers from the sensor data, Density-based spatial clustering of applications with noise (DBSCAN) method is utilized. To forecast the objective and subjective behaviours of the equipments, a Random Forest Deep Neural Network (RF-DNN) based classification model is utilized. By experimental results, it has been shown that RCDCF achieves 19% and 20% reduced energy consumption at Cloud and Fog centers, respectively. Moreover, RCDCF has 2.1% and 1.3% increased correctness of data at Cloud and Fog data centers, respectively, when compared to the existing framework.
Multimodal fusion sensitive information classification based on mixed attention and CLIP model1
Authors: Huang Shuaina , Zhang Zhiyong , Song Bin , Mao Yueheng
Published: October 2023

Social network attackers leverage images and text to disseminate sensitive information associated with pornography, politics, and terrorism,causing adverse effects on society.The current sensitive information classification model does not focus on feature fusion between images and text, greatly reducing recognition accuracy.To address this problem, we propose an attentive cross-modal fusion model (ACMF), which utilizes mixed attention mechanism and the Contrastive Language-Image Pre-training model.Specifically, we employ a deep neural network with a mixed attention mechanism as a visual feature extractor. This allows us to progressively extract features at different levels. We combine these visual features with those obtained from a text feature extractor and incorporate image-text frequency domain information at various levels to enable fine-grained modeling. Additionally, we introduce a cyclic attention mechanism and integrate the Contrastive Language-Image Pre-training model to establish stronger connections between modalities, thereby enhancing classification performance.Experimental evaluations conducted on sensitive information datasets collected demonstrate the superiority of our method over other baseline models. The model achieves an accuracy rate of 91.4% and an F1-score of 0.9145. These results validate the effectiveness of the mixed attention mechanism in enhancing the utilization of important features. Furthermore, the effective fusion of text and image features significantly improves the classification ability of the deep neural network.
Hybrid ResGRU: Effective brain tumour classification approach using of abnormal images
Authors: Rajendran Aishwarya , Ganesan Sumathi , Rathis Babu T.K.S.
Published: October 2023

Brain tumor is observed to be grown in irregular shape and presented deep inside the tissues that led to cancer. Human brain tumor identification and categorization are performed with high latency, but also an essential task for the medical experts. The assistance through the automated diagnosis is generally utilized for the advancement in the diagnosis ability in order to get superior accuracy in brain tumor detection. Although the researches are enhancing the brain tumor detection performance, the highly challenging is to segment the brain tumor since it has variability concerning the tumor type, contrast, image modality and also in other factors. To meet up all the challenges, a novel classification method is introduced using segmentation and machine learning approaches. Initially, the required images are collected from benchmark data sources. The input images are undergone for pre-processing stage, where it is done via “Contrast Limited Adaptive Histogram Equalization (CLAHE) and filtering methods”. Further, the pre-processed imagesare given as input to two classifier models as “Residual Network (ResNet) and Gated Recurrent Unit (GRU)”, in which the model provide the result as normal and abnormal images. In the second part, obtained abnormal image acts an input for segmentation step. In segmentation, it is needed to extract the relevant features by texture and spatial features. The resultant features are subjected for optimizing, where the optimal features are acquired through Adaptive Coyote Optimization Algorithm (ACOA). Then, the extracted features are fed into machine learning model like “Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF)” to render the segmented image. Finally, the hybrid classification named Hybrid ResGRUis developed by integrating the ResNet and GRU, where the hyper parameters are tuned optimally using developed ACOA, thus it is used for classifying the abnormal image that belongs to benign stage or malignant stage. The experimental results are evaluated, and its performance is analyzed by various metrics. Hence, the proposed classification model ensures effective segmentation and classification performance.
A model-free autonomous performance testing for human recognition using different types of software-intensive autonomous systems
Authors: Prasad Mal Hari , Swarnalatha P.
Published: October 2023

The model-based methods were utilized in order to produce the test cases for the behavioral model of a software system. Run test cases habitually or physically facilitates premature identification of requirement errors. Regression test suite design is thought-provoking as well as significant task in this automated test design. General techniques of regression testing comprise rerunning formerly accomplished tests as well as inspecting whether program behavior has modified as well as formerly fixed faults have recurred. Regression testing is carried out with the intension of assessing a system skillfully by means of logically picking the right least set of tests essential to suitably cover a particular modification. Then again, the relapse testing occasions of experiment prioritization, test suite decrease, and relapse test choice are commonly focused on conditions, which recognize the experiments to pick or the experiment to run thusly in independent framework. As indicated by experiment prioritization, experiments are very much arranged ward upon some condition just as experiments with greatest need are run first to achieve a presentation objective. If there should be an occurrence of test suite decrease/minimization, experiment, which end up being ended over the long haul are dismissed from the test suite with the intension of making a minor arrangement of experiments. In the event of relapse test determination, from a prevalent unique suite, a subset of experiments is picked.
EI-RNN-based text generation for the static and dynamic isolated sign language videos
Authors: Subburaj S. , Murugavalli S. , Muthusenthil B.
Published: October 2023

SLR, which assists hearing-impaired people to communicate with other persons by sign language, is considered as a promising method. However, as the features of some of the static SL could be the same as the feature in a single frame of dynamic Isolated Sign Language (ISL), the generation of accurate text corresponding to the SL is necessary during the SLR. Therefore, Edge-directed Interpolation-based Recurrent Neural Network (EI-RNN)-centered text generation with varied features of the static and dynamic Isolated SL is proposed in this article. Primarily, ISL videos are converted to frames and pre-processed with key frame extraction and illumination control. After that, the foreground is separated with the Symmetric Normalised Laplacian-centered Otsu Thresholding (SLOT) technique for finding accurate key points in the human pose. The human pose’s key points are extracted with the Media Pipeline Holistic (MPH) pipeline approach and to improve the features of the face and hand sign, the resultant frame is fused with the depth image. After that, to differentiate the static and dynamic actions, the action change in the fused frames is determined with a correlation matrix. After that, to engender the output text for the respective SL, features are extracted individually as of the static and dynamic frames. It is obtained from the analysis that when analogized to the prevailing models, the proposed EI-RNN’s translation accuracy is elevated by 2.05% in INCLUDE 50 Indian SL based Dataset and Top 1 Accuracy 2.44% and Top 10 accuracy, 1.71% improved in WLASL 100 American SL.
Integrated triangular fuzzy KE-GRA-TOPSIS method for dynamic ranking of products of customers’ fuzzy Kansei preferences
Authors: Liu Dashuai , Zhang Jie , Wang Chenlu , Ci Weilin , Wu Baoxia , Quan Huafeng
Published: October 2023

As society evolves, companies produce more homogeneous products, shifting customers’ needs from functionality to emotions. Therefore, how quickly customers select products that meet their Kansei preferences has become a key concern. However, customer Kansei preferences vary from person to person and are ambiguous and uncertain, posing a challenge. To address this problem, this paper proposes a TF-KE-GRA-TOPSIS method that integrates triangular fuzzy Kansei engineering (TF-KE) with Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Firstly, a Kansei evaluation system is constructed based on KE and fuzzy theory. A dynamic triangular fuzzy Kansei preference similarity decision matrix (TF-KPSDM) is defined to quantify customer satisfaction with fuzzy Kansei preferences. Secondly, dynamic objective weights are derived using Criteria Importance Though Intercrieria Correlation (CRITIC) and entropy, optimized through game theory to achieve superior combined weights. Thirdly, the GRA-TOPSIS method utilizes the TF-KPSDM and combined weights to rank products. Finally, taking the case of Kansei preference selection for electric bicycles, results indicate that the proposed method robustly avoids rank reversal and achieves greater accuracy than comparative models. This study can help companies dynamically recommend products to customers based on their Kansei preferences, increasing customer satisfaction and sales.
Construction and application of logistics scheduling model based on heterogeneous graph neural network
Authors: Wang Lei
Published: October 2023

The core of logistics is scheduling and monitoring. After the modern interprise logistics development concept change, the development prospect of enterprise logistics is more optimistic. Major enterprises have begun to use intelligent logistics scheduling platforms. In order to solve the problem that heterogeneous information fusion is complex in the temporal heterogeneous graphs, this paper proposes to dynamically store and update node representation through an augmented memory matrix in a memory network. At the same time, the model also designs a novel read-write module for the memory matrix, which can effectively capture the timing information in the long interaction sequence and has high flexibility. The model has significantly improved in tasks such as node classification, timing recommendation and visualization. This paper studies the logistics supply chain of modern enterprises and establishes the mathematical model of vehicle scheduling. This paper takes the non-full load scheduling model as the critical research object. Based on the research of logistics supply chain, the vehicle scheduling model is established. The intelligent heuristic algorithm is applied to solve it, and the effective vehicle distribution scheme and driving route are formed. The simulation results show that the approximate Pareto optimal solution obtained by our designed model and algorithm has good robustness. NSGAIIROELSDR can get a better solution in small-scale scheduling. However, in large-scale numerical experiments, the final solution obtained by MOEA/DROELSDR is obviously better than that of NSGAIIROELSDR, and the running time of MOEA/DROELSDR is also shorter. Therefore, we conclude that MOEA/DROELSDR is more suitable for large-scale scheduling, and NSGAIIROELSDR is more suitable for more minor scheduling.
Clinical application of neuromodulation therapy in patients with disorder of consciousness: A pooled analysis of 544 participants
Authors: Zheng Ruizhe , Qi Zengxin , Thibaut Aurore , Wang Zhe , Xu Zeyu , Di Haibo , Wu Xuehai , Mao Ying , Laureys Steven
Published: October 2023

BACKGROUND: The number of patients with disorders of consciousness (DoC) has increased dramatically with the advancement of intensive care and emergency medicine, which brings tremendous economic burdens and even ethical issues to families and society. OBJECTIVE: To evaluate the effectiveness of neuromodulation therapy for patients with DoC. METHODS: First, we conducted a literature review of individual patient data (IPD) on PubMed, EMBASE, and Cochrane-controlled trials following PRISMA guidelines. Then, we collected neuromodulation cases from our institution. Finally, we conducted a pooled analysis using the participants from the medical literature (n = 522) and our local institutions (n = 22). RESULTS: In this pooled analysis of 544 patients with DoC with a mean age of 46.33 years, our results revealed that patients have improved CRS-R scores [1.0 points (95% CI, 0.57–1.42)] after neuromodulation. Among them, patients have better effectiveness in traumatic than non-traumatic etiology (P < 0.05). The effectiveness of consciousness improvement could be affected by the age, baseline consciousness state, and duration of stimulation. Compared with non-invasive intervention, an invasive intervention can bring more behavioral improvement (P < 0.0001) to MCS rather than UWS/VS patients. Importantly, neuromodulation is a valuable therapy even years after the onset of DoC. CONCLUSION: This pooled analysis spotlights that the application of neuromodulation can improve the behavioral performance of patients with DoC. A preliminary trend is that age, etiology, baseline consciousness state, and stimulation duration could impact its effectiveness.
The influence of perceived financial security on social intention entrepreneurs
Authors: Mohamed Fitouri
Published: October 2023

Although a small number of studies have suggested that financial factors weigh heavily on social entrepreneurs, it is unclear how much financial incentives affect the likelihood of starting a new social venture. Furthermore, the socio-economic context in which an organization operates significantly shapes the propensity to embark on a new venture. This study adds a new SEI determinant: perceived financial stability, in order to analyze the conceptual underpinnings of SEI in a particular social setting. To validate this model and introduce the factor of perceived financial stability into SEI, this research uses two distinct measurement tools and performs separate statistical analyses using two groups of samples (n = 340 and 214). The findings show that perceived social support, perceived financial stability, and entrepreneurial social self-efficacy are all independent direct predictors of SEI and further attenuate the association between past experience and SEI. The Hockerts model is created as a result. This study sheds fresh light on the potential that worries about future financial security may be what drives social entrepreneurs, emphasizing the significance of perceived financial stability as a significant prelude to SEI.
Approach to Data Science with Multiscale Information Theory
Authors: Nawaz Shahid , Saleem Muhammad , Kusmartsev F. V. , Anjum Dalaver H.
Published: October 2023

Data Science is a multidisciplinary field that plays a crucial role in extracting valuable insights and knowledge from large and intricate datasets. Within the realm of Data Science, two fundamental components are Information Theory (IT) and Statistical Mechanics (SM), which provide a theoretical framework for understanding dataset properties. IT enables efficient storage and transmission of information, while SM focuses on the behavior of systems comprising numerous interacting components. In the context of data science, SM allows us to model complex interactions among variables within a dataset. By leveraging these tools, data scientists can gain a profound understanding of data properties, leading to the development of advanced models and algorithms for analysis and interpretation. Consequently, data science has the potential to drive accurate predictions and enhance decision-making across various domains, including finance, marketing, healthcare, and scientific research.

In this paper, we apply this data science framework to a large and intricate quantum mechanical system composed of particles. Our research demonstrates that the dynamic and probabilistic nature of such systems can be effectively addressed using a Multiscale Entropic Dynamics (MED) approach, derived from the Boltzmann methods of SM. Through the MED approach, we can describe the system's dynamics by formulating a general form of the Nonlinear Schrodinger equation and how it can be applied to various systems with particles and quasi-particles, such as electrons, plasmons, polarons, and solitons. By employing this innovative approach, we pave the way for a deeper understanding of quantum mechanical systems and their behaviors within complex materials.
Knowledge, Practices and Attitude of Residents towards Buruli Ulcer in Jasikan Municipality: An Ethnographic Study
Authors: Baba Atubiga Alobit , Alale Aweeya Joseph , Aloba Atubiga John
Published: October 2023

BACKGROUND: Buruli ulcer is one of the neglected tropical diseases in the world. The mode of transmission and the identification of source reservoirs of the causative organism of the disease are still largely unknown. Buruli ulcer often starts with a pre-ulcerative nodule, a plaque or oedema, which breaks down to form characteristic ulcers with undermined edges. The first probable case of Buruli ulcer in Ghana was reported in the Greater Accra Region in 1971.

PURPOSE: This study explored the knowledge, practices and attitudes of residents towards Buruli ulcer in Jasikan Municipality.

METHOD: The study employed an ethnographic approach. Data were collected from 20 study participants using an in-depth interview guide. The data were presented using thematic analysis. Ethical approval was obtained from the Research Ethics Committee (UHAS-REC: UHAS-REC A.11 [103] 2l-22).

RESULTS: The knowledge of respondents on Buruli ulcer was moderate. The study found that respondents sought treatment for Buruli ulcer in health facilities, prayer camps, herbalist homes, and practised self-medication. The study found that residents did not want to be seen with persons living with Buruli ulcer and the practice in terms of cleanliness of the wound was found to be low.

CONCLUSIONS: Respondents had varied social constructions for Buruli ulcer and employed several methods to clean and dress the wound. The study recommended more health education by health workers to improve residents’ knowledge and practices on Buruli ulcer in the study setting.
Последствия COVID-19: интегративный обзор литературы
Authors: Lopes Luiz Thiago Oliveira , Oliveira Marcelus de Andrade , Gonçalves Willian Guilherme Lobato , Daher Donizete Vago , Brito Irma da Silva , Dendasck Carla Viana , Dias Cláudio Alberto Gellis de Mattos , Fecury Amanda Alves , Araújo Maria Helena Mendonça de
Published: October 2023

Большая аффинность между белком-шипом вируса SARS-CoV-2 и рецептором ангиотензин-превращающего фермента 2 предполагается одной из основных причин высокой скорости вирусной передачи, что привело ВОЗ к объявлению COVID-19 чрезвычайной общественной здравоохранительной угрозой международного значения, а также к принятию мер по ограничению вирусной распространенности. Кроме того, благодаря физиопатологическому механизму вируса, обычно наблюдаются неспецифические симптомы, атипичное течение болезни, особенно у пожилых и иммунодефицитных пациентов, и более быстрое и летальное развитие. Аналогично, с расширением знаний о природе инфекции вирусом, были обнаружены устойчивые симптомы и/или последствия, вызывающие дисфункцию органов и негативное воздействие на качество жизни пациентов. В связи с этим целью статьи было представить интегративный обзор основных последствий COVID-19 за 2021 и 2022 годы. Для этого были исследованы полные статьи в базах данных Scientific Electronic Library Online, Biblioteca Virtual em Saúde и PubMed на португальском и английском языках, и использована методология PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), в результате было выбрано 14 статей. Среди результатов можно выделить следующее: физиопатогенез стойкой COVID-19 базируется на теории иммунных явлений, возникающих после инфекции, а также на аномальном иммунном ответе и присутствии вируса в иммунологически привилегированных местах; усталость, одышка, субъективное нарушение когнитивных функций, неврологические последствия, воспалительные заболевания центральной нервной системы и поствирусное нарушение обоняния – распространенные осложнения; были представлены доказательства причинной связи между COVID-19 и субакутным тиреоидитом; было продемонстрировано большее количество осложнений и госпитализаций у пациентов с дефицитом витамина D; а также присутствие нейропсихиатрических симптомов у группы профессионалов в области здравоохранения. В заключение была выявлена недостаточность исследований, непосредственно посвященных последствиям COVID-19, что делает необходимым углубление этих исследований с целью разработки более конкретных протоколов для диагностики.
Palliative Sedation: Responding to Severe Suffering in a metastatic breast cancer patient
Authors: Tahmasebi Mamak , Shamsi-Gooshki Ehsan
Published: October 2023

Abstract is not applicable for this type of article.
Relationship of Health Literacy and Social Support with Health-Promoting Behaviors in Women with Breast Cancer: Mediating Role of Cancer Self-Efficacy
Authors: Ameneh Etemadi Asl  , Sasan Bavi  , Johari Fard Reza
Published: October 2023

Background: Women diagnosed with breast cancer experience high levels of psychological disorders ranging from depression, anxiety, maladjustment to the disease, and decreased self-confidence to different emotional disturbances and fears of recurrence and mortality. The present study aimed to investigate the mediating role of cancer self-efficacy in the relationship between health literacy and social support with health-promoting behaviors in women with breast cancer.

Methods: In this descriptive-correlational study, the convenience sampling method was employed to select 282 women diagnosed with breast cancer in Ahvaz, Khuzestan Province (Iran) in 2021. The participants were asked to answer the Health-Promoting Lifestyle Profile II (HPLP-II), Iranian Health Literacy Questionnaire, Social Support Survey, and Cancer Behavior Inventory. Path analysis was adopted to analyze data in AMOS software.

Results: The results revealed there was a significant relationship between perceived social support with health-promoting behaviors (P=0.004) and cancer self-efficacy (P=0.001) in women with breast cancer. There was a significant relationship between health literacy and cancer self-efficacy in women. According to the results, the mediating role of cancer self-efficacy in the relationship between perceived social support and health literacy with health-promoting behaviors was found to be significant in women with breast cancer (P=0.001).

Conclusion: Accordingly, the research model had a desirable fit. The findings of this research showed the effective role of cancer self-efficacy in the outcome of social support and health literacy to increase health-promoting behaviors in women with breast cancer.
ПУБЛІЧНО-ПРИВАТНЕ ПАРТНЕРСТВО У ПРОФЕСІЙНІЙ ОСВІТІ ЄВРОПЕЙСЬКИХ КРАЇН: ПРОВІДНІ ТЕНДЕНЦІЇ РОЗВИТКУ
Authors: Радкевич Валентина
Published: October 2023

Актуальність статті зумовлена необхідністю виявлення та аналізу провідних тенденцій розвитку публічно-приватного партнерства у професійній освіті європейських країн для їх урахування в налагодженні державно-приватного партнерства в системі професійної освіти України.

Метою статті є виявлення й аналіз провідних тенденцій розвитку публічно-приватного партнерства у професійній освіті європейських країн; з’ясування можливостей їх урахування у вітчизняній практиці налагодження державно-приватного партнерства у сфері професійної освіти.

Методи: вивчення наукових джерел, законодавчих, нормативно-правових документів, емпіричних даних щодо публічно-приватного партнерства у професійній освіті – для виявлення його провідних тенденцій розвитку в європейських країнах; формування висновків і рекомендацій.

Результати: на основі аналізу сучасних законодавчих, нормативно-правових документів, наукових джерел, емпіричних даних виявлено та проаналізовано провідні тенденції розвитку публічно-приватного партнерства у професійній освіті європейських країн: розширення учасників партнерства і розвиток його нових форм; вплив партнерства на підвищення якості професійної освіти; розроблення спільно з партнерами навчальних програм відповідно до вимог ринку праці; збільшення обсягів фінансування професійної освіти шляхом інвестицій державного та приватного секторів, а також міжнародних організацій тощо.

Висновки: провідні тенденції розвитку ППП у професійній освіті європейських країн орієнтовані на: підвищення ефективності взаємодії між навчальними закладами, державними та приватними установами, громадськими, професійними й міжнародними організаціями; залучення малих, середніх і великих компаній до процесу розроблення нових кваліфікацій, стандартів, навчальних програм, впровадження інноваційних технологій професійного навчання; забезпечення рівного доступу до професійної освіти, робочих місць на ринку праці; збільшення фінансової підтримки навчальних закладів та підприємств, які здійснюють професійне навчання студентів тощо. Сформульовано пропозиції українським закладам професійної  освіти для налагодження державно-приватного партнерства (ДПП), а саме: забезпечення ефективної комунікації між учасниками партнерства – для обміну досвідом і спільного використання ресурсів; визначення чітких умов, зобов’язань і ризиків між партнерами – для підвищення рівня їхньої відповідальності за якість реалізації проєктів ДПП; вдосконалення механізмів контролю та оцінювання результатів співпраці – для своєчасного коригування напрямів розвитку партнерства;  збільшення обсягів фінансової підтримки проєктів ДПП – для забезпечення стабільного функціонування закладів професійної освіти на основі додаткових ресурсів; розширення участі громадськості в реалізації проєктів ДПП – для підвищення престижності професійної освіти та її впливу на відновлення України в повоєнний час тощо.
Wolność a dezinformacja: o konieczności przyjęcia postawy odpowiedzialnej. Rozważania nad stanem badań
Authors: Bąkowicz Katarzyna
Published: October 2023

Szeroka kategoria wolności połączona z postępem technologicznym stawia współczesnego człowieka przed wieloma wyzwaniami. Konwergencja mediów i egalitaryzm internetu spowodowały nieograniczony dostęp do informacji. Wiąże się to z ich wzrastającą ilością, ale także często wątpliwą jakością, co tworzy podatny grunt dla przekłamań, manipulacji i fałszu. Celem artykułu jest próba zrozumienia związku i wzajemnego wpływu dezinformacji oraz odpowiedzialności. Hipoteza, którą autorka próbuje zweryfikować, dotyczy tego, czy odpowiedzialność za zwiększoną ilość treści dezinformacyjnych jest zindywidualizowana. Ważną kwestią jest w związku z tym analiza związku mediów i narzędzi technologicznych z indywidualnymi decyzjami oraz odpowiedzialność za kryzysy wywołane fałszywymi informacjami. Metodologia: wybrane zagadnienia poddane zostaną analizie na podstawie dostępnych źródeł z różnych dziedzin: socjologii, komunikologii, medioznawstwa oraz filozofii. Wyniki i wnioski: zjawisko zwiększającej się ilości treści o charakterze dezinformacyjnym stanowi wyzwanie dla wszystkich uczestników ekosystemu informacyjnego. Świadomość potrzeby przyjęcia postawy odpowiedzialnej w tym zakresie po obu stronach procesu komunikowania może znacząco poprawić jakoś informacji, a przez to funkcjonowania społeczeństw. Oryginalność i wartość poznawcza: konieczne jest przyjrzenie się dezinformacji jako zjawisku złożonemu, którego przejawy są osadzone nie tylko w języku, ale również w obrazie. Taka zmiana perspektywy pomogłaby wypracować postawę odpowiedzialną oraz wpłynąć na zmniejszenie skali dezinformacji.
Transformation of Type I Fuzzy Logic into Interval Type II Fuzzy Logic-based Unit Voltage Template with Controlled Load
Authors: Drak Alsebai Muhannad , Pedapenki Kishore Kumar
Published: October 2023

The interval type II fuzzy logic controller (IT2FL) is a recent artificial intelligent technology which is applied in many aspects of electrical power engineering and controlling strategies these days. The improving of Distribution Static Synchronous Compensator (DSTATCOM) control algorithms is still ongoing, because the control strategy of DSTATCOM device is considered as a decisive factor which affects the performance of DSTATCOM device in enhancing the quality of power in the distribution grids. The improving process focuses in two main aspects the DC capacitor voltage response to the control system and the mitigation of the harmonics which are produced by the controlled and uncontrolled rectified loads. In this paper, the interval type II fuzzy logic controller (IT2FL) based the Unit Voltage Template strategy (UVT) is examined by the MATLAB program with two different ranges of input signals, and compared with the type I Fuzzy logic controller (T1FL). The IT2FL based UVT shows promising results in improving the deformed shape of source current waves in comparison with the other controllers.
The Effect of Learning on Assembly Line Balancing: A Review
Authors: Zine El Abidine Zakaria , Koltai Tamás
Published: October 2023

Classical assembly line balancing (ALB) models assume constant cycle times during production. However, this assumption oversimplifies the actual situation, especially in small batch production of up to a few hundred units, since employees can significantly improve their performance thanks to the learning effect, causing task times to decrease. Several researchers have realised the importance of the effect of learning in ALB. However, only a limited number of papers have so far addressed this issue. This is problematic, since ignoring the learning effect in ALB may lead to inaccurate results and by extension misleading conclusions. This study summarises the main contributions in the field of ALB that focus on the learning effect. First, assembly lines (ALs) and ALB problems are characterised. Next, the importance of the learning effect in ALB is highlighted, and the main learning curve (LC) models are introduced. Finally, an exhaustive review of the main contributions in the field of ALB and learning effect is provided. The results highlight that many problems in this area need to be investigated further, in relation to both conceptual model building and the development of algorithms for solving practical size problems.
Post-Modernist Kuramın Neo-Muhafazakar Aparatı: Araçsal Orta Çağ
Authors: Reyhan Cenk , Doğar M. Oğuzhan , Saday Barış
Published: October 2023

“Karanlık ortaçağ” metaforu post-modernizmin de etkisiyle bugün, birçok tarihçi tarafından bir yanılsama olarak görülmektedir ve yoğun eleştiri altındadır. Dahası, alegorik bir “aydınlık ortaçağ ütopyası” dahi sunulmaktadır. Ortaçağın “karanlığına” yönelik eleştiriler, üç madde altında gruplandırılabilir. İlki, tarihteki sürekliliklere atıfta bulunarak Rönesans’ın aslında ortaçağdan kopuşu değil, ortaçağın sonucunu teşkil etmekte olduğunu iddia eder. İkinci eleştirinin kaynağı olan Avrupa-merkezcilik karşıtı teorilere göre ise ortaçağ karanlıksa bile bu Avrupa için geçerlidir, dünyanın geri kalanı için değil. Son olarak ve diğer iki argümanı da kapsayan bir diğer itiraz ise ortaçağın karanlığı ya da geriliği paradigmasının Aydınlanma felsefesinin ve hümanizmaya dönüşün getirdiği bir önyargı olduğudur. Bu üç eleştiri başlığından hareketle, bu çalışma, modernitenin distopik bir geleceğe hizmet ettiğini savunan yukarıdaki “araçsallaştırıcı” iddiaları tartışacak. Batı - ortaçağ öğretim-kültür sisteminin, dogmanın ve skolastik düşünce tarzının öznel ortaçağ güzellemeleri (çoğunlukla mikro monografiler) üzerinden meşrulaştırılmasının, romantizm tepkiselliğinin ve ardından gelen post-modernizmin bir sonucu olduğunu değerlendirmekteyiz.
“Swamped with information”: a qualitative study of family physicians' experiences of managing and applying pandemic-related information
Authors: Young Gillian , Mathews Maria , Hedden Lindsay , Lukewich Julia , Marshall Emily Gard , Gill Paul , McKay Madeleine , Ryan Dana , Spencer Sarah , Buote Richard , Meredith Leslie , Moritz Lauren , Brown Judith B. , Christian Erin , Wong Eric
Published: October 2023

Introduction

Providing family physicians (FPs) with the information they need is crucial for their participation in a coordinated pandemic or health emergency response, and to allow them to effectively run their practices. Most pandemic planning documents do not address communication plans specific to FPs. This study describes FPs' experiences and challenges with information management during the COVID-19 pandemic in Canada.

Methods

We conducted semi-structured qualitative interviews with FPs across four Canadian regions and asked about their roles during different pandemic stages, as well as facilitators and barriers they experienced in performing these roles. We transcribed the interviews, used a thematic analysis approach to develop a unified coding template across the four regions, and identified recurring themes.

Results

We interviewed 68 FPs and identified two key themes specifically related to communication. The first is FPs' experiences obtaining and managing information during the COVID-19 pandemic. FPs were overwhelmed by the volume of information and had difficulty applying the information to their practices. The second is the specific attributes FPs need from the information sent to them. Participants wanted summarized and consistent information from credible sources that are relevant to primary care.

Discussion

Providing clear, collated, and relevant information to FPs is essential during pandemics and other health emergencies. Future pandemic plans should integrate strategies to deliver information to FPs that is tailored to primary care. Findings highlight the need for a coordinated communication strategy to effectively inform FPs in health emergencies.
Phenotypic plasticity in Pinus canariensis seedlings growing at chilling and freezing temperatures
Authors: Miranda José Carlos , López de Heredia Unai , Fernández Victoria , Pita Pilar
Published: October 2023

Global change is affecting the frequency and temporal distribution of frost events. We can expect damage caused by low temperatures to increase specially at the upper limit of a species’ elevation range. Here, we assessed how Pinus canariensis two-year old seedlings from different islands and elevations responded differently to chilling and freezing temperatures. Seedlings from six populations were grown either in a greenhouse (tmin = 5 ̊C, chilling conditions) or outdoors (tmin = −3.3 ̊C, freezing conditions) for 104 days in winter. Seedlings from one of these populations belonged to three half-sib families. Maximum quantum yield of Photosystem II and evapotranspiration (ETP) were determined four times throughout the study. All populations suffered photoinhibition under freezing but not under chilling conditions. We measured the lowest ETP in the population from the warmest and driest environment and lowest elevation (250 m) both under chilling and freezing conditions. The three populations from the higher elevations reached the highest ETP under chilling, a likely adaptation to the shortening of the vegetative period. The effect of elevation remained elusive for other parameters, which could be partly attributed to high intrapopulation variability. Epicuticular wax concentration increased and needle water content decreased in plants growing outdoors. All populations except the one most affected by cold dehydration showed osmotic adjustment and a simultaneous decrease in tissue elasticity under freezing. This response was stronger in the population from the highest elevation (2,241 m). Our results highlight the coexistence of adaptive processes, genetic variation and plasticity conditioning the response to low temperatures in this species.
Serum cytokines and neutrophil-to-lymphocyte ratio as predictive biomarkers of benefit from PD-1 inhibitors in gastric cancer
Authors: Hou Yidan , Li Xiaoli , Yang Yudan , Shi Hao , Wang Shaofang , Gao Ming
Published: October 2023

Background

Immunotherapy is significantly revolutionizing cancer treatment and demonstrating promising efficacy in gastric cancer (GC) patients. However, only a subset of patients could derive benefits from targeted monoclonal antibody therapy against programmed death receptor 1 (PD-1). This study aims to identify suitable serum cytokines and blood cell ratios as predictive biomarkers to aid in the selection of GC patients likely to benefit from PD-1 inhibitors.

Materials and methods

This retrospective study included 41 GC patients who received PD-1 inhibitors combined with chemotherapy, 36 GC patients treated solely with chemotherapy, and 33 healthy controls. The study assessed the levels of seven cytokines: interleukin-2 (IL-2), IL-4, IL-6, IL-10, IL-17A, tumor necrosis factor-alpha (TNF-α), interferon-gamma (IFN-γ), and various inflammatory markers, including the neutrophil-to-lymphocyte ratio (NLR), total lymphocyte count (TLC), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR). Measurements were obtained using the inpatient system. Univariate and multivariate Cox regression analyses were performed to evaluate the predictive significance of these hematologic parameters for clinical outcomes.

Results

Levels of IL-6, IL-10, TNF-α, NLR, and PLR were significantly elevated in GC patients compared to healthy controls, while TLC and LMR were higher in the control group. Among the 41 patients receiving PD-1 inhibitors and chemotherapy, baseline IL-2 was associated with OS and PFS. Additionally, IL-6 and IL-17A correlated with OS, while NLR was linked to PFS (all P<0.05). These factors were identified as independent prognostic indicators in both univariate and multivariate analyses. Furthermore, almost all cytokine levels increased following the initiation of PD-1 inhibitor treatment.

Conclusions

The introduction of PD-1 inhibitors alongside chemotherapy in GC impacts serum cytokine levels. IL-2, IL-6, IL-17A, and NLR exhibit potential as reliable circulating predictive biomarkers for identifying patients who may benefit from PD-1 inhibitors combined with chemotherapy.
ATP-dependent transporters: emerging players at the crossroads of immunity and metabolism
Authors: Balasubramanian Akshaya , Sundrud Mark S.
Published: October 2023

Nearly 50 ATP-binding cassette (ABC) transporters are encoded by mammalian genomes. These transporters are characterized by conserved nucleotide-binding and hydrolysis (i.e., ATPase) domains, and power directional transport of diverse substrate classes – ions, small molecule metabolites, xenobiotics, hydrophobic drugs, and even polypeptides – into or out of cells or subcellular organelles. Although immunological functions of ABC transporters are only beginning to be unraveled, emerging literature suggests these proteins have under-appreciated roles in the development and function of T lymphocytes, including many of the key effector, memory and regulatory subsets that arise during responses to infection, inflammation or cancers. One transporter in particular, MDR1 (Multidrug resistance-1; encoded by the ABCB1 locus in humans), has taken center stage as a novel player in immune regulation. Although MDR1 remains widely viewed as a simple drug efflux pump in tumor cells, recent evidence suggests that this transporter fills key endogenous roles in enforcing metabolic fitness of activated CD4 and CD8 T cells. Here, we summarize current understanding of the physiological functions of ABC transporters in immune regulation, with a focus on the anti-oxidant functions of MDR1 that may shape both the magnitude and repertoires of antigen-specific effector and memory T cell compartments. While much remains to be learned about the functions of ABC transporters in immunobiology, it is already clear that they represent fertile new ground, both for the definition of novel immunometabolic pathways, and for the discovery of new drug targets that could be leveraged to optimize immune responses to vaccines and cancer immunotherapies.