bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lizard-Moth Flame Optimization Algorithm for Large-Scale Global Optimization Problems
Authors: Singh Prabhat Ranjan , Sing Vaibhav Kumar , Chaurasia Sachchida Nand , Singh Vivek Kumar 
Published: June 2024

Abstract

This paper introduces a disruptive strategy, namely a lizard-hunting approach, into the classical Moth Flame Optimization (MFO) algorithm. The conventional MFO emulates a moth's navigation pattern around artificial light at night but tends to face stagnation due to the flame's exploitative tendencies, often getting trapped in local optima, particularly in higher-dimensional problems. The research motivation stems from the need to disrupt small groups stuck at various local optima after a certain number of iterations. To address the limitations of the existing MFO, the proposed Lizard-Moth-Flame Optimization (L-MFO) algorithm is put forth. In L-MFO, moth positions are classified into outlier and non-outlier categories using a clustering method in each iteration. Following this categorization, non-outliers are divided into highly and less densely populated subgroups, with the densely crowded group considered closer to the solution. However, a distinctive aspect of the lizard's behaviour in L-MFO is its inclination towards the less crowded group, reflecting a slower update. When a moth detects a lizard within its range and at the same angle, moths within the group either flee in the opposite direction or move towards a densely crowded group. This strategic response mitigates the issue of stagnation, enhancing the algorithm's overall performance. The proposed L-MFO algorithm undergoes a comprehensive evaluation by being compared with other state-of-the-art meta-heuristic algorithms. The assessment involves testing on twenty-three CEC-2005 benchmarks across different dimensions (10, 30, 50, 100, 500, 1000, 2000, and 5000), eight engineering problems, and 36 CEC-2017 benchmark functions with 10, 50 and 100 dimensions. The robustness of the algorithm is examined through convergence and divergence analysis, Wilcoxon signed-rank test, Two-tailed Mann-Whitney U test, and boxplot analysis. The experimental and statistical results consistently demonstrate the superior performance of L-MFO over other algorithms.
WITHDRAWN: Evaluation of Spatial Performance in X-ray Imaging Detectors: CTF, MTF, LSF, and ESF Analysis
Authors: SOLOMON GIRMA BINIYAM , Diyor KHAZRATOV , Kuvondik KODIROV , Brombal LUCA 
Published: June 2024

Abstract

This study was conducted at the University of Trieste's Department of Physics, focusing on the comprehensive characterization of the spatial response of an X-ray imaging detector. The primary objective was to measure and analyze the Contrast Transfer Function (CTF), Modulation Transfer Function (MTF), Line Spread Function (LSF), and Edge Spread Function (ESF) to evaluate the detector's performance. Using a well-calibrated X-ray source, various test patterns and phantoms were imaged to acquire data. The CTF was assessed by analyzing the contrast response of the detector to a series of bar patterns with varying spatial frequencies. The MTF was derived from the Fourier transform of the LSF, which was obtained by scanning a narrow slit across the detector. The LSF provided detailed insights into the detector's response to a sharp line source. The ESF was measured by imaging a sharp edge, and subsequent differentiation yielded the LSF. These measurements were conducted under controlled conditions to ensure accuracy and repeatability. The results highlighted the spatial resolution capabilities and limitations of the X-ray imaging detector, providing a detailed understanding of its performance metrics. This comprehensive characterization is crucial for optimizing detector design and improving image quality in various applications, ranging from medical imaging to industrial inspection.
WEE Family Kinase Inhibitors Combined with Sorafenib Can Selectively Inhibit HCC Cell Proliferation
Authors: Chen Anling , Yin Ke , Liu Yu , Hu Lei , Cui Qianwen , Wan Xiaofeng , Yang Wulin 
Published: June 2024

Background:
Sorafenib is currently the first choice for the treatment of patients with advanced
hepatocellular carcinoma, but its therapeutic effect is still limited.

Objectives:
This study aims to examine whether WEE family kinase inhibitors can enhance the anticancer
effect of sorafenib.

Methods:
We analyzed the expression levels of PKMYT1 kinase and WEE1 kinase in HCC,
studied the inhibitory effect of PKMYT1 kinase inhibitor RP-6306, WEE1 kinase inhibitor
adavosertib combined with sorafenib on the proliferation of HCC cells, and detected the effect of
drug combination on CDK1 phosphorylation

Results:
We found that PKMYT1 and WEE1 were upregulated in HCC and were detrimental to
patient survival. Cell experiments showed that both RP-6306 and adavosertib (1-100 μM) inhibited
the proliferation of HCC cell lines in a dose-dependent manner alone, and the combination of
the two drugs had a synergistic effect. In HCC cell lines, sorafenib combined with RP-6306 or
adavosertib showed a synergistic antiproliferation effect and less toxicity to normal cells. Sorafenib
combined with RP-6306 and adavosertib further inhibited the proliferation of HCC cells
and caused complete dephosphorylation of CDK1.

Conclusion:
Taken together, our findings provide experimental evidence for the future use of sorafenib
in combination with RP-6306 or adavosertib for the treatment of HCC.
Supervised Learning based E-mail/ SMS Spam Classifier
Authors: Kumar Satendra , Kumar Raj , Saini Ashish 
Published: June 2024

Background:
One of the challenging problems facing the modern Internet is spam,
which can annoy individual customers and wreak financial havoc on businesses. Spam communications target customers without their permission and clog their mailboxes. They consume
more time and organizational resources when checking for and deleting spam. Even though
most web users openly dislike spam, enough are willing to accept lucrative deals that spam remains a real problem. While most web users are well aware of their hatred of spam, the fact
that enough of them still click on commercial offers means spammers can still make money
from them. While most customers know what to do, they need clear instructions on avoiding
and deleting spam. No matter what you do to eliminate spam, you won't succeed. Filtering is
the most straightforward and practical technique in spam-blocking strategies.

Method:
We present procedures for identifying emails as spam or ham based on text classification. Different methods of e-mail organization preprocessing are interrelated, for example, applying stop word exclusion, stemming, including reduction and highlight selection strategies to
extract buzzwords from each quality, and finally, using unique classifiers to Quarantine messages as spam or ham.

Results:
The Nave Bayes classifier is a good choice. Some classifiers, such as Simple Logistic
and Adaboost, perform well. However, the Support Vector Machine Classifier (SVC) outperforms it. Therefore, the SVC makes decisions based on each case's comparisons and perspectives.

Conclusion:
Many spam separation studies have focused on recent classifier-related challenges. Machine Learning (ML) for spam detection is an important area of modern research. Today,
spam detection using ML is an important area of research. Examine the adequacy of the proposed work and recognize the application of multiple learning estimates to extract spam from
emails. Similarly, estimates have also been scrutinized.
Optimal Systolic Blood Pressure for the Prevention of All-Cause and Cardiovascular Disease Mortality in Older Adults With Hypertension: Nationwide Population-Based Cohort Study
Authors: Lee Jae Jun , Lee Kyung Hee 
Published: June 2024

Background
Target systolic blood pressure (SBP) levels for older adults with hypertension vary across countries, leading to challenges in determining the appropriate SBP level.

Objective
This study aims to identify the optimal SBP level for minimizing all-cause and cardiovascular disease (CVD) mortality in older Korean adults with hypertension.

Methods
This retrospective cohort study used data from the National Health Insurance Service database. We included older adults aged 65 years or older who were newly diagnosed with hypertension and underwent a National Health Insurance Service health checkup in 2003-2004. We excluded patients who had a history of hypertension or CVD, were not prescribed medication for hypertension, had missing blood pressure or any other covariate values, and had fewer than 2 health checkups during the follow-up period until 2020. We categorized the average SBP levels into 6 categories in 10 mm Hg increments, from <120 mm Hg to ≥160 mm Hg; 130-139 mm Hg was the reference range. Cox proportional hazards models were used to examine the relationship between SBP and all-cause and CVD mortalities, and subgroup analysis was conducted by age group (65-74 years and 75 years or older).

Results
A total of 68,901 older adults newly diagnosed with hypertension were included in this study. During the follow-up period, 32,588 (47.3%) participants had all-cause mortality and 4273 (6.2%) had CVD mortality. Compared to older adults with SBP within the range of 130-139 mm Hg, individuals who fell into the other SBP categories, excluding those with SBP 120-129 mm Hg, showed significantly higher all-cause and CVD mortality. Subgroup analysis showed that older adults aged 65-74 years had higher all-cause and CVD mortality rates according to SBP categories than those aged 75 years or older.

Conclusions
The SBP levels within the range of 120-139 mm Hg were associated with the lowest all-cause and CVD mortality rates among older Korean adults with hypertension. It is recommended to reduce SBP to <140 mm Hg, with 120 mm Hg as the minimum value for SBP, for older Korean adults with hypertension. Additionally, stricter SBP management is required for adults aged 65-74 years.
Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review
Authors: Anderson Euan , Lennon Marilyn , Kavanagh Kimberley , Weir Natalie , Kernaghan David , Roper Marc , Dunlop Emma , Lapp Linda 
Published: June 2024

Background
Telecare and telehealth are important care-at-home services used to support individuals to live more independently at home. Historically, these technologies have reactively responded to issues. However, there has been a recent drive to make better use of the data from these services to facilitate more proactive and predictive care.

Objective
This review seeks to explore the ways in which predictive data analytics techniques have been applied in telecare and telehealth in at-home settings.

Methods
The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist was adhered to alongside Arksey and O’Malley’s methodological framework. English language papers published in MEDLINE, Embase, and Social Science Premium Collection between 2012 and 2022 were considered and results were screened against inclusion or exclusion criteria.

Results
In total, 86 papers were included in this review. The types of analytics featuring in this review can be categorized as anomaly detection (n=21), diagnosis (n=32), prediction (n=22), and activity recognition (n=11). The most common health conditions represented were Parkinson disease (n=12) and cardiovascular conditions (n=11). The main findings include: a lack of use of routinely collected data; a dominance of diagnostic tools; and barriers and opportunities that exist, such as including patient-reported outcomes, for future predictive analytics in telecare and telehealth.

Conclusions
All papers in this review were small-scale pilots and, as such, future research should seek to apply these predictive techniques into larger trials. Additionally, further integration of routinely collected care data and patient-reported outcomes into predictive models in telecare and telehealth offer significant opportunities to improve the analytics being performed and should be explored further. Data sets used must be of suitable size and diversity, ensuring that models are generalizable to a wider population and can be appropriately trained, validated, and tested.
Enhancing Cognitive Learning: A Comparative Analysis of E-Learning Media Tailored to Different Learning Styles
Authors: Winarti Atiek , Almubarak Almubarak , Sundari Titi , Sumardjoko Bambang , Nzuza Zakhele 
Published: June 2024

This study aims to analyze the effectiveness of e-learning media based on learning style in the form of e-posters, podcasts, and interactive videos on students' cognitive learning outcomes. This type of research is quantitative research. This study used a sample of 32 grade X PMIA 1 SMA Negeri 3 Banjarmasin students. This research uses Google Classroom as a platform for learning activities. Learning media in the form of e-posters, podcasts, interactive videos, and student learning outcomes can be downloaded and uploaded on Google Classroom. The research instrument used is a learning style questionnaire containing 30 statements. Data analysis in this study used a normality test, and Wilcoxon signed rank test. The cognitive learning outcomes of students with visual, auditory, and kinesthetic learning styles were 90.66%, 93.33%, and 87.33%, respectively, with ­N-gain values of 0.75, 0.80, and 0.71. Based on data analysis, the results of this study show that e-learning media based on learning style is efficacious in improving the cognitive learning outcomes of students with visual, auditory, and kinesthetic learning styles. This learning style-based e-learning media can be applied to other chemical materials or subjects. The study employed-N-gain values to analyst the learning outcomes of the students. The - N gain values for students with visual, auditory and kinesthetic learning styles were 0.75, 0,80 and 0.71 respectively. Overall, the data suggests that the learning outcomes of the students were quite positive and that the use of various learning media and Google Classroom as a platform can be effective in enhancing students learning outcomes.
Comparison the accuracy of thyroid sono-elastography vs. ultrasound-guided fine needle aspiration cytology with thyroid malignancy diagnosis histopathology
Authors: Mahmoud Sarah Abd Elmageed , Enaba Mohamed Elsayed , Shareef Mohamed Moustafa , Hafez Yasser Moustafa , Abbas Ibrahim 
Published: June 2024

Abstract

Objective. The intend of the present study was to assess the diagnostic performance of strain elastography in investigating the thyroid nodule malignancy taking the surgical biopsy as a gold standard reference test.

Methods. The study included 120 patients with 123 thyroid nodules, of which 67 had total thyroidectomy. The American College of Radiology Thyroid Imaging Reporting and Data Systems (ACR-TIRADS) were evaluated for all nodules. All suspicious nodules were referred for a fine needle aspiration cytology (FNAC) if they fulfilled the required size. Strain elastography was performed for each suspicious nodule. Ultrasound-guided FNAC was performed for all suspicious nodules. Total thyroidectomy was performed in those whom the suspicious nodules were proven by FNAC.

Results. Strain ratio had a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of 84%, 81%, 95%, 85%, and 84%, respectively, with a cut point 1.96. Elasticity score had a sensitivity, specificity, PPV, NPV, and diagnostic accuracy of 100%, 80%, 95%, 85% and 87%, respectively, with a cut point 0.96. The elasticity score had a statistically significantly odds ratio for detecting the benignity 3.9 C. I (1.6–9.3).

Conclusion. Strain elastography has a high diagnostic performance in detecting the malignant as well as benign nodules, thus it can limit the rate of unneeded FNAC or surgery especially among B3 and B4 groups with indeterminate cytology.
The dynamics of financial development, environmental degradation, economic growth and population health in the Economic Community of West African States
Authors: Prempeh Kwadwo Boateng , Frimpong Joseph Magnus , Yeboah Samuel Asuamah 
Published: June 2024

Abstract
By exploring the nexus between financial development and population health in the ECOWAS region we contribute to the debate on population health and also examine the direction of causality between population health and its determinants. We utilised a panel dataset of 11 ECOWAS nations from 1990-2019. The study used the augmented mean group (AMG) estimator to examine the long-run associations among the chosen variables. The empirical results indicate that financial development and economic growth are positive drivers of population health. The analysis also demonstrates that lower population health is associated with increased environmental degradation (CO2 emissions) in the ECOWAS region. However, regarding the country-specific analysis, the impact of the determinants of population health varied per country. The empirical estimates of the Dumitrescu Hurlin panel causality tests reveal a bidirectional causality between population health and its determinants. The empirical outcomes offer new insights for policymakers by means of financial development, environmental degradation, and income as economic tools to promote population health by guiding finance, growth, and environmental policies.
A spatial analysis of one of the regional logistics clusters in central Poland using GIS
Authors: Jażdżewska Iwona , Żerek Karolina 
Published: June 2024

Abstract
Logistics services are a consequence of increasingly rapid economic development and globalisation. More and more facilities with adequate transport accessibility are being created for the storage of goods, which form logistics clusters. They emerged later in Central and Eastern Europe than in the United States and Western Europe. Investment in Poland's first logistics parks can be linked to the political and economic transformation in 1989, Poland's accession to the European Union in 2004, the development of motorways, and Poland's integration into the global economy. In the second decade of the 21st century, there are more than a dozen places in Poland that can be called logistics clusters. The purpose of this research was to geographically analyse a selected regional cluster located in Lodz voivodeship, as well as its links to the transport network of Poland and Europe. Geostatistical and GIS methods were used for spatial analysis. It was shown that its spatial distribution is strongly related to the A1 highway, the intersection of the A1 and A2 highways and the voivodeship city of Lodz. The characteristics of the geographic environment give reason to suggest that it has great development potential and is worthy of further observation and comparative studies.
ISM model for assessing critical productivity factors in the Jordanian construction industry post-COVID-19 pandemic
Authors: Alshdiefat Ala’a Saleh 
Published: June 2024

Abstract
The construction industry is a human-intensive industry despite the massive development in technologies. Nowadays, after crossing COVID-19 pandemic, the construction industry is an important sector for saving the national economy. The COVID-19 pandemic has created new ways of thinking due to massive and unpredictable socioeconomic consequences. Thus, understanding the critical productivity factors after the COVID-19 pandemic will enhance the construction industry by improving the understanding of the professionals who are involved at an early stage of the project lifecycle. This study aims to determine the critical productivity factors after the COVID-19 pandemic for enhancing the construction industry in developing countries such as Jordan. A review of available literature similar to the related topics before the COVID-19 pandemic was explored, and then a questionnaire was distributed across the Jordanian construction industry to determine the main productivity factors post-COVID-19 pandemic. A focus group was used to determine the interrelationship among the factors with the Interpretive Structural Modelling (ISM) approach. The obtained results indicated that 22 main productivity factors affected the Jordanian construction industry. The hierarchy of these factors is categorised into six levels of ISM whereas the sixth level has the greatest factors that influence productivity in the construction industry. Thus, enhancing productivity in construction projects requires solving problems related to factors in level 1, which will help to solve problems at the next level and so on.
The Nature of Airspace Surveillance Through Intelligence to the Defence and Security of the Territory of the Republic of Indonesia
Authors: Abbas Ilham , Bangsawan Andi , Rahman Sufirman , Husen La Ode 
Published: June 2024

Objective: This research aims to analyze, criticize, and determine the nature of airspace surveillance through intelligence concerning the Defense and Security of the Territory of the Republic of Indonesia. It further seeks to assess the effectiveness of such surveillance and identify the factors influencing its success.

Theoretical framework: In line with its objectives, this research adopts a descriptive approach with a normative legal research methodology, supplemented by sociological legal research. By employing this framework, the study endeavors to provide a comprehensive understanding of airspace surveillance within the Indonesian context.

Method: To achieve its objectives, this study utilizes descriptive research methods, drawing upon normative legal research complemented by sociological legal research. Through this methodological approach, the research aims to gather empirical data and analyze legal frameworks to shed light on airspace surveillance practices.

Results and conclusion: The findings of this research indicate several key insights. Firstly, the nature of airspace surveillance through intelligence is primarily framed within the context of preventive measures within the Air Defense Identification Zone (ADIZ). Secondly, while efforts are made, the effectiveness of airspace surveillance concerning the Defense and Security of the State Territory of the Republic of Indonesia is deemed inadequate. Thirdly, factors impacting effectiveness range from legal limitations to challenges in human resources coordination, infrastructure issues, community dynamics, and cultural attitudes.

Implications of the research: The implications of this study are multifaceted. Firstly, it highlights the urgent need to optimize airspace surveillance conducted by the Indonesian Air Force, supporting the enhancement of air defense capabilities through equipment reinforcement. Secondly, it underscores the necessity of drafting legislation on Airspace Management to strengthen law enforcement against airspace violations. Finally, it emphasizes the importance of community involvement in airspace regulation, including participation in spatial development planning processes.

Originality/value: This research contributes original insights into the nature and effectiveness of airspace surveillance through intelligence within the Indonesian context. By offering recommendations for improving airspace management strategies, the study provides valuable guidance for policymakers and stakeholders involved in national defense and security initiatives.