bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of Critical Thinking Skills among Secondary School Science Students: An Analysis of Chemistry Textbook Grade IX (2020)
Authors: Jamil Muhammad , Mehmood Wahid , Shah Faiz ul Hassan
Published: May 2024

This study focuses on a Chemistry textbook for grade IX to determine how effective it is at fostering critical thinking skills in students. The researchers did a qualitative content analysis using NVivo 12 software, and they picked the textbook with purposive sampling. It was found that the textbook does a good job of promoting critical thinking through different strategies. It encourages inquiry and questioning, relates abstract concepts to real-life applications, prompts students to compare and contrast ideas, promotes problem-solving, talks about the history behind scientific concepts, and addresses misconceptions. At the end of every topic are "Test Yourself" sections that ask thought provoking questions meant to challenge students' analytical thinking. Relating chemical concepts to real-world situations fosters critical thinking by connecting abstract with concrete knowledge. Lastly, there are lots of examples and problems for students to work through which helps them apply what they know while developing problem-solving abilities too.
Relationship of Teachers' Academic Guidance Competence and Students' Satisfaction at Secondary School Level
Authors: Parveen Afia , Idris Muhammad , Mehmood Samreen
Published: May 2024

This study focused on the association between teachers' academic guidance competence and students' satisfaction at the secondary school level in district Mardan, KP, Pakistan. Teachers and students at government secondary schools provided data, and a quantitative survey, more precisely a survey-type research design was used. Based on the stratum-wise distribution of schools, stratified random sampling was used applying Krejesic and Morgan's table to calculate the sample size. In district Mardan, both urban and rural teachers and students from government secondary schools made up the study's population. A self-created questionnaire with a five-option Likert scale was used to collect the data. Supervisor feedback was utilized to guarantee face validity, and a pilot study was carried out to confirm reliability. Students' academic satisfaction, teachers' guidance competence, and the relationship between the two variables were examined by independent sample t-tests using SPSS V-22. In light of the importance of teacher-student interactions in secondary education, the study finds that excellent academic mentoring correlates to better levels of student satisfaction.
Exploring Promotion-Linked Training Impact on the Professional Development of Educational Managers in Punjab
Authors: Abbas Muhammad , Sarwar Muhammad , Rehman Raza Ur
Published: May 2024

This study aimed to explore the effectiveness of in-service professional development training provided by the Quaid-e Azam Academy for Educational Development (QAED) to 53 educational managers in Punjab, Pakistan. A qualitative feedback design was utilized to gather participants' perspectives on the most and least valuable training content, relevance to professional needs, suggested modifications, effective methodologies, and overall impressions. Data were collected through an open-ended post-training questionnaire and analyzed thematically. Findings revealed the training was well-aligned to develop financial, managerial, and IT skills but needed more practical application. Being peer-learning enabled with expansion of hands-on simulation task as budgeting and audit was suggested. The class timelines and redistributing sessions were suggested to be reduced. Activity based methods were focused on like group discussions, presentations, and hands on practice considered effective by most of the participants.
Descriptive Study of Transformational Leadership Influence on Professional Development of Teachers at Higher Education Level
Authors: Sultana Nayyar , Chaudhry Aleena , Shaukat Hina
Published: May 2024

The descriptive research study examined the impact of transformational leadership on university teachers' professional development. My study set out to evaluate the impact of transformational leadership on university-level teacher professional development. The aim of the research was to ascertain the impact of transformational leadership on the professional development of university teachers. This change in self-interest raises the ideals, maturity, and concern for the goal of the follower. This is an essential part of the Full Range Leadership Model. Transformational leadership has been suggested by researchers as a crucial component of a teacher's effectiveness. The researcher used a quantitative research design with the target population of 30 teachers of the psychology department of NUML University Islamabad. The sample size for the target population was 20 teachers. The delimitation of the study was teachers of the psychology department evening shift at NUML University Islamabad. The researcher developed a questionnaire and used a manual percentage formula for the research findings.
PENGARUH UKURAN BANK, KREDIT, KREDIT BERMASALAH, MODAL DAN PRODUK DOMESTIK BRUTO TERHADAP PENGAMBILAN RISIKO BANK
Authors: Tandeas Fiona , Setyawan Agustinus
Published: May 2024

Penelitian ini mempunyai tujuan untuk menguji pengaruh dari Ukuran Bank, Kredit, Kredit Bermasalah, Modal, Produk Domestik Bruto pada pengambilan risiko Bank. Data pada penelitian ini diperoleh dari laporan keuangan perbankan selama 5 periode, dari tahun 2018–2022 yang diunduh di situs www.idx.co.id. Untuk jumlah sample yang digunakan sebanyak 50 data dari 10 entitas subsektor perbankan, setelah dilakukan purposive sampling dengan jenis data sekunder. Hasil penelitian menggunakan PYTHON yang menunjukkan bahwa ukuran bank (SIZE) dan kredit bermasalah (NPL) berpengaruh negatif terhadap pengambilan risiko bank. Sedangkan untuk kredit (LDR), modal (CAR), dan produk domestik bruto (PDB) tidak berpengaruh signifikan terhadap pengambilan risiko Bank.
Kemampuan Mengajukan Masalah Direlasikan dengan Kemampuan Berpikir Logis Matematik
Authors: Puspitasari Nitta
Published: May 2024

Kajian ini memaparkan tentang kemampuan mengajukan masalah matematis direlasikan dengan kemampuan berpikir logis matematis. Kemampuan mengajukan masalah serta berpikir matematis merupakan kemampuan esensial yang dimiliki dan dikembangkan oleh siswa untuk bisa menghadapi kemajuan IPTEK yang semakin pesat. Karena kedua kemampuan tersebut menjadi hal yang penting, maka diperlukan panduan pengembangan kemampuan tersebut jika dikaitkan satu dengan yang lainnya. Pada kajian ini juga menyajikan bagaimana rubrik penilaian mengajukan masalah direlasikan dengan kemampuan berpikir logis. Hal ini diharapkan dapat menjadi stimulus untuk mengembangkan kemampuan mengajukan masalah matematis secara optimal.
Analisis Kesulitan Belajar Operasi Hitung Pada Siswa Kelas Satu Sekolah Dasar
Authors: Suwarto
Published: May 2024

Penelitian ini bertujuan menganalisis kesulitan belajar operasi hitung pada siswa kelas satu Sekolah Dasar. Subyek penelitian ini adalah siswa kelas 1 SD Negeri Ketapang Kecamatan Mauk Kabupaten Tangerang, dengan metode diskriptif kualitatif. Teknik pengumpulan data menggunakan tes. Hasil analisis didapat: (1) Letak kesulitan siswa dalam memahami operasi bilangan adalah dalam hal fakta, hal ini ditunjukan ketika menyelesaikan soal bentuk cerita, dalam operasi penjumlahan soal cerita dalam katagori kurang baik yaitu sebesar 25% siswa dapat mengerjakan soal dengan benar. Pada soal cerita operasi pengurangan kemapuan penyelesaian siswa dalan katagori kurang baik yaitu sebesar 22,5% siswa yang dapat mengerjakan soal dengan benar. (2) Siswa masih mengalami kesulitan jika dimodelkan dalam bentuk abstrak, hal ini terlihat pada soal yang dimodelkan abstrak pada operasi pengurangan sebesar 50% siswa dapat menyelesaikan soal dengan baik dan pada operasi pengurangan terdapat 37,5%.
This study aims to study how to learn to count on the first-grade students of Primary School. The subjects of this study were the students of grade 1 of the State Elementary School of Ketapang, Mauk Sub-District, Tangerang Regency, with a qualitative descriptive method. The technique of writing data using tests. The results of the analysis obtained: (1) The location of the student's difficulties in understanding the operation of numbers is in fact facts, this shows a connection between the form of the story, in the process of summing up the story in less good category that is equal to 25% of students can do the problem correctly. On the matter of operation story to trap the release of students in less good category that is equal to 22,5% students who can do the problem correctly. (2) Students are still difficult if modeled in abstract form, this is seen in the problem that is moderated. A total of 50% of students can solve the problem well and on the operation, there are 37.5% subtractions there.
Penerapan Pendekatan Realistic Mathematics Education Dalam Pembelajaran Matematika Materi Persamaan Garis Lurus di SMP Kristen Tomohon
Authors: Kaunang Derel Filandy
Published: May 2024

Penelitian ini dilaksanakan dengan tujuan untuk mengetahui perbedaan hasil belajar siswa yang diajarkan dengan pendekatan Realistic Mathematics Education (RME) dan siswa yang diajarkan dengan pembelajaran langsung pada materi persamaan garis lurus di kelas VIII SMP. Penelitian ini menggunakan metode true experimental. Populasi yang diambil adalah seluruh siswa kelas VIII SMP Kristen Tomohon yang terdiri dari enam kelas sedangkan sampel yang diambil secara acak adalah dua kelas yang homogen yaitu kelas VIIIA sebagai kelas eksperimen dan kelas VIIIB sebagai kelas kontrol. Kelas eksperimen terdiri dari 24 orang siswa dan kelas kontrol terdiri dari 24 orang siswa. Penelitian ini merupakan model rancangan Pretest-Posttest Control Group Design. Data diambil dan dikumpulkan dari tes hasil belajar siswa. Data dalam penelitian ini adalah selisih hasil belajar siswa yang diperoleh dari skor pretes dan posttest pada materi persamaan garis lurus. Berdasarkan analisis data dengan uji t diperolah bahwa thitung = 2,824 > ttabel = 2,013 untuk α = 0,05. Berdasarkan hasil penelitian maka dapat diambil kesimpulan bahwa rata-rata hasil belajar siswa yang diajarkan dengan pendekatan Realistic Mathematics Education (RME) lebih tinggi dari rata-rata hasil belajar siswa yang diajarkan dengan dengan pembelajaran langsung.
This research aims to investigate the difference between students' learning outcomes taught by the Realistic Mathematics Education (RME) approach and the students taught by direct learning on the equation of a straight line in class VIII SMP. This research uses a true experimental method. The population is all students of class VIII SMP Kristen Tomohon consists of six classes while the the random samples are two homogeneous classes which are class VIIIA as an experiment class and class VIIIB as a control class. The experimental class consists of 24 students and the control class consists of 24 students. This research is a design model of Pretest-Posttest Control Group Design. The data are gathered and collected from students' learning outcomes. The data in this research are the differences in the students' learning results obtained from the pretest and post-test scores on the equation of straight-line equation. Furthermore, the data analysis with t-test is tcount = 2,824 > ttable = 2.013 for α = 0,05. Based on the result of this research, it can be concluded that the average of student learning outcomes taught with the Realistic Mathematics Education (RME) approach is higher than the average of student learning outcomes taught with direct learning.
Erzählen und Aufarbeiten? Eine kritische Reflexion über narrative Interviews in Forschungen zu sexualisierter Gewalt im kirchlichen Kontext
Authors: Justke Sebastian , Sigl Johanna
Published: May 2024

Der Artikel thematisiert Herausforderungen qualitativer Forschung im Kontext sexualisierter Gewalt, insbesondere bei der Erhebung von narrativen Interviews und den damit verbundenen Erwartungen auf Seiten der Wissenschaftler:innen und der Interviewpartner:innen. Grundlage der Analyse stellen die in einem Forschungsprojekt zu sexualisierter Gewalt in den evangelischen Kirchen in Deutschland erhobenen narrativen Interviews mit Betroffenen sexualisierter Gewalt dar, welche zwischen 2021 und 2023 von den Autor:innen des Beitrags geführt wurden. Anhand der Analyse des Materials und der Reflexion der Interviewsituationen greift der Artikel die unterschiedlichen Erwartungshaltungen und Perspektiven auf die Forschung auf und fragt nach ihrer Bedeutung für den Forschungsprozess. Ausgelotet dabei wird der Möglichkeitsraum für Empowerment der Betroffenen durch die Wissenschaftler:innen. Im Ergebnis wird ein Spannungsfeld konstatiert, das durch Forschung, Aufarbeitung und den impliziten Wunsch nach Empowerment der Betroffenen definiert wird.
Massage on the prevention of breast cancer through stress reduction and enhancing immune system
Authors: Siregar Zilhana , Usman Andi Nilawati , Ahmad Mardiana , Ariyandy Andi , Ilhamuddin Ilhamuddin , Takko A.B.
Published: May 2024

INTRODUCTION: Housewives are a population at high risk of breast cancer due to repeated or chronic exposure to stress. Prevention in a simple yet evidence-based manner is needed. METHODS: This study is a narrative review of the potential of massage as breast cancer prevention through stress and immune system mechanisms. RESULTS: Massage is able to prevent chronic stress through improved sleep and fatigue and lower stress levels. Prevention of chronic stress will maximize the function of cells that eliminate cancer cells, such as B cells, T cells, and natural killer (NK) cells, and improve the balance of Foxp3 Tregulator cells. Partnered delivery massage will bring effective benefits for stress reduction. CONCLUSIONS: Massage can provide indirect prevention of breast cancer, and partnered delivery massage can be a good choice to reduce stress.
Quantification of mechanical behavior of rat tail under compression
Authors: Moore Kevin D. , Wu John Z. , Krajnak Kristine , Warren Christopher , Dong Renguang G.
Published: May 2024

BACKGORUND: The development of vibration-induced finger disorders is likely associated with combined static and dynamic responses of the fingers to vibration exposure. To study the mechanism of the disorders, a new rat-tail model has been established to mimic the finger vibration and pressure exposures. However, the mechanical behavior of the tail during compression needs to be better understood to improve the model and its applications. OBJECTIVE: To investigate the static and time-dependent force responses of the rat tail during compression. METHODS: Compression tests were conducted on Sprague-Dawley cadaver rat tails using a micromechanical system at three deformation velocities and three deformation magnitudes. Contact-width and the time-histories of force and deformation were measured. Additionally, force-relaxation tests were conducted and a Prony series was used to model the force-relaxation behavior of the tail. RESULTS: The rat tails’ force-deformation and stiffness-deformation relationships were strongly nonlinear and time-dependent. Force/stiffness increased with an increase in deformation and deformation velocity. The time-dependent force-relaxation characteristics of the tails can be well described using a Prony series. CONCULSIONS: We successfully quantified the static and time-dependent force responses of rat tails under compression. The identified mechanical behavior of the tail can help improve the rat-tail model and its applications.
Neutrophil/HDL-C, Lymphocyte/HDL-C and Monocyte/HDL-C in subjects with asymptomatic carotid atherosclerosis
Authors: Caimi Gregorio , Lo Presti Rosalia , Urso Caterina , Brucculeri Salvatore , Carlisi Melania
Published: May 2024

BACKGROUND: Leukocyte count is a prognostic marker for cardiovascular diseases, with key role in atherosclerosis development. Specific number of neutrophils, lymphocytes and monocytes can predict cardiovascular risk, also in asymptomatic subjects. Among the lipoprotein fractions, HDL-C is a protective factor in the cardiovascular disorders. For the above reason, we have examined the peripheral count of leukocytes, neutrophils, lymphocytes and monocytes, and the ratios between neutrophils/HDL-cholesterol, lymphocytes/HDL-cholesterol, and monocytes/HDL-cholesterol, to evaluate the possible utility of the obtained values in progression of asymptomatic carotid atherosclerosis. METHODS: We performed our analysis in a cohort of 100 subjects with asymptomatic carotid atherosclerosis, of which 43 men and 57 women. The data were expressed as medians and IQR. To analyse the differences in leukocyte, neutrophil, lymphocyte, monocytes count and their ratio with HDL-cholesterol the Mann-Whitney test was employed. RESULTS: The peripheral count of leukocyte subtypes and the ratios, they change in relation to the number of cardiovascular risk factors and the degree of insulin resistance. CONCLUSIONS: In this cohort of subjects, the percentage of observed cardiovascular risk factors significantly affect some leukocyte parameters. These results, allow us to underline the importance of the leukocyte indices in the evaluation of subjects with asymptomatic vascular atherosclerosis.
EFS-XGBoost: A robust framework for precision classification of COVID-19 cases
Authors: Abidi Mustufa Haider , Khare Neelu , D. Preethi , Alkhalefah Hisham , Umer Usama
Published: May 2024

The emergence of the novel COVID-19 virus has had a profound impact on global healthcare systems and economies, underscoring the imperative need for the development of precise and expeditious diagnostic tools. Machine learning techniques have emerged as a promising avenue for augmenting the capabilities of medical professionals in disease diagnosis and classification. In this research, the EFS-XGBoost classifier model, a robust approach for the classification of patients afflicted with COVID-19 is proposed. The key innovation in the proposed model lies in the Ensemble-based Feature Selection (EFS) strategy, which enables the judicious selection of relevant features from the expansive COVID-19 dataset. Subsequently, the power of the eXtreme Gradient Boosting (XGBoost) classifier to make precise distinctions among COVID-19-infected patients is harnessed.The EFS methodology amalgamates five distinctive feature selection techniques, encompassing correlation-based, chi-squared, information gain, symmetric uncertainty-based, and gain ratio approaches. To evaluate the effectiveness of the model, comprehensive experiments were conducted using a COVID-19 dataset procured from Kaggle, and the implementation was executed using Python programming. The performance of the proposed EFS-XGBoost model was gauged by employing well-established metrics that measure classification accuracy, including accuracy, precision, recall, and the F1-Score. Furthermore, an in-depth comparative analysis was conducted by considering the performance of the XGBoost classifier under various scenarios: employing all features within the dataset without any feature selection technique, and utilizing each feature selection technique in isolation. The meticulous evaluation reveals that the proposed EFS-XGBoost model excels in performance, achieving an astounding accuracy rate of 99.8%, surpassing the efficacy of other prevailing feature selection techniques. This research not only advances the field of COVID-19 patient classification but also underscores the potency of ensemble-based feature selection in conjunction with the XGBoost classifier as a formidable tool in the realm of medical diagnosis and classification.
MEGANET: Improved framework with nature inspired approach for colorectal cancer polyp classification
Authors: Kumar Ravi , Singh Amritpal , Khamparia Aditya
Published: May 2024

BACKGROUND: Polyps are tumorous growths in the colon or rectal area which can turn into cancer at later stages, thus detection of polyps is very important for timely prevention of colorectal cancer. The aim of the study is to develop a framework to accurately detect polyp images in colonoscopy images. OBJECTIVE: Development of an intelligent framework for classification of colorectal cancer from colon and rectal images. The standard machine learning, convolutional neural networks and ensemble models with nature inspired approach were implemented for this study. Model optimization was performed by varying hyper parameters. The main objective was to find an optimal model with high accuracy, optimized weights and less parameters. METHODS: The deep learning Convolutional Neural Network (CNN) models such as VGG19, ResNet50, EfficientNet, Ensemble Model (EM), and Modified Ensemble CNN with Genetic Algorithm (MEGANET) were implemented for the classification of colon images. RESULTS: Ensemble model was also created with two best performing deep learning models to further achieve higher accuracy of 96%. The ensemble model outperformed the other models in terms of accuracy, precision, recall, and F1 score. But this model has more complexity. The MEGANET, nature inspired evolutionary ensemble CNN model was implemented with transfer learning and genetic algorithms for weights optimization and parameter reduction. It achieved accuracy of 95%, on training data. CONCLUSION: The MEGANET performed similar to EM with less number of parameters on training, validation and test dataset. In future different methods will be implemented to further reduce the parameters and attain reasonable accuracy using MEGANET.
A Multidimensional, Person-Centered Framework for Functional Assessment in Dementia: Insights from the ‘What’, ‘How’, ‘To Whom’, and ‘How Much’ Questions
Authors: Slachevsky Andrea , Grandi Fabrissio , Thumala Daniela , Baez Sandra , Santamaria-García Hernando , Schmitter-Edgecombe Maureen , Parra Mario A.
Published: May 2024

Dementia is a syndrome characterized by cognitive and neuropsychiatric symptoms associated with progressive functional decline (FD). FD is a core diagnostic criterion for dementia, setting the threshold between its prodromal stages and the full-blown disease. The operationalization of FD continues to generate a great deal of controversy. For instance, the threshold of FD for the diagnosis of dementia varies across diagnostic criteria, supporting the need for standardization of this construct. Moreover, there is a need to reconsider how we are measuring FD to set boundaries between normal aging, mild cognitive impairment, and dementia. In this paper, we propose a multidimensional framework that addresses outstanding issues in the assessment of FD: i) What activities of daily living (ADLs) are necessary to sustain an independent living in aging? ii) How to assess FD in individuals with suspected neurocognitive disorders? iii) To whom is the assessment directed? and iv) How much does FD differentiate healthy aging from mild and major neurocognitive disorders? Importantly, the To Whom Question introduces a person-centered approach that regards patients and caregivers as active agents in the assessment process of FD. Thus, once impaired ADLs have been identified, patients can indicate how significant such impairments are for them in daily life. We envisage that this new framework will guide future strategies to enhance functional assessment and treatment of patients with dementia and their caregivers.
Informal Caregivers of People with Dementia in Germany: Psychosocial Characteristics and Unmet Needs
Authors: Thyrian Jochen René , Boekholt Melanie , Biernetzky Olga , Blotenberg Iris , Teipel Stefan , Killimann Ingo , Hoffmann Wolfgang
Published: May 2024

Background:

Caregivers of people with dementia living at home (CPwDh) are likely to be affected by a range of health problems. However, CPwDh are often regarded as accompanying persons and receive less attention in research and care. Little is known about this population and their needs in Germany. However, better knowledge of CPwDH is needed to design effective interventions.

Objective:

The objective of this report is to describe the situation of CPwDh and highlight differences based on sex and living situation.

Methods:

This was a cross-sectional analysis of the psychosocial characteristics of participants in the GAIN trial, a cluster-randomized, controlled intervention trial investigating the effectiveness of a care management program. A total of n = 192 CPwDh were recruited in GP offices, memory clinics or through public campaigns in the German federal state of Mecklenburg-Western Pomerania. The inclusion criteria were an age of 18 years or above, being a CPwDh, written informed consent. In a comprehensive digital assessment, psychosocial variables, burden, and care needs were assessed.

Results:

Partners, women, and people living in the same household represented the majority of caregivers, and their mean number of needs was 8.7. Overall, participants indicated a mild to moderate burden. There are differences in burden based on sex and living situation, with caregivers living with people with dementia showing less burden and different psychosocial demographics.

Conclusions:

There is a need for interventions to reduce caregivers’ unmet needs in the CPwDh. Such interventions should consider differences in sex and living situation to better address individual caregiver needs.
Semantic Harmonization of Alzheimer’s Disease Datasets Using AD-Mapper
Authors: Wegner Philipp , Balabin Helena , Ay Mehmet Can , Bauermeister Sarah , Killin Lewis , Gallacher John , Hofmann-Apitius Martin , Salimi Yasamin ,   for the Alzheimer’s Disease Neuroimaging Initiative,   the Japanese Alzheimer’s Disease Neuroimaging Initiative,   the Aging Brain: Vasculature, Ischemia, and Behavior Study,   the Alzheimer’s Disease Repository Without Borders Investigators,   the European Prevention of Alzheimer’s Disease (EPAD) Consortium
Published: May 2024

Background:

Despite numerous past endeavors for the semantic harmonization of Alzheimer’s disease (AD) cohort studies, an automatic tool has yet to be developed.

Objective:

As cohort studies form the basis of data-driven analysis, harmonizing them is crucial for cross-cohort analysis. We aimed to accelerate this task by constructing an automatic harmonization tool.

Methods:

We created a common data model (CDM) through cross-mapping data from 20 cohorts, three CDMs, and ontology terms, which was then used to fine-tune a BioBERT model. Finally, we evaluated the model using three previously unseen cohorts and compared its performance to a string-matching baseline model.

Results:

Here, we present our AD-Mapper interface for automatic harmonization of AD cohort studies, which outperformed a string-matching baseline on previously unseen cohort studies. We showcase our CDM comprising 1218 unique variables.

Conclusion:

AD-Mapper leverages semantic similarities in naming conventions across cohorts to improve mapping performance.
Using paradata to assess respondent burden and interviewer effects in household surveys: Evidence from low- and middle-income countries
Authors: Hasanbasri Ardina , Kilic Talip , Koolwal Gayatri , Moylan Heather
Published: May 2024

Over the past decade, national statistical offices in low- and middle-income countries have increasingly transitioned to computer-assisted personal interviewing and computer-assisted telephone interviewing for the implementation of household surveys. The byproducts of these types of data collection are survey paradata, which can unlock objective, module- and question-specific, actionable insights on survey respondent burden, survey costs, and interviewer effects – all of which have been understudied in low- and middle-income contexts. This study uses paradata generated by Survey Solutions, a computer-assisted personal interviewing platform used in recent national household surveys implemented by the national statistical offices of Cambodia, Ethiopia, and Tanzania. Across countries, the average household interview, based on a socioeconomic household questionnaire, ranges from 82 to 120 minutes, while the average interview with an adult household member, based on a multi-topic individual questionnaire, takes between 13 to 25 minutes. The paper further provides guidelines on the use of paradata for module-level analysis to aid in operational survey decisions, such as using interview length to estimate unit cost for budgeting purposes as well as understanding interviewer effects using a multilevel model. Our findings, particularly by module, point to where additional interviewer training, fieldwork supervision, and data quality monitoring may be needed in future surveys.
Crop sequence boundaries using USDA National Agricultural Statistics Service historic cropland data layers
Authors: Hunt Kevin A. , Abernethy Jonathon , Beeson Peter C. , Bowman Maria , Wallander Steven , Williams Ryan
Published: May 2024

Gridded landcover datasets like the NASS Cropland Data Layer (CDL) provide a useful resource for analyses of cropland management. However, many farm operation decisions are made at the field level, not the pixel level. To capture relationships between land cover and field characteristics – size, contiguity, etc. – some method is needed to aggregate gridded data into crop fields. To provide a uniform and consistent approach for aggregation of gridded data at the field level over a series of years, this research project developed a set of Crop Sequence Boundaries (CSBs), which are polygons that delineate areas of homogeneous cropping sequences for the contiguous US. The CSBs are open-sourced algorithm-based, geospatial polygons derived using historic CDLs together with road and rail networks to capture areas with common cropping sequences. The CSB approach used geospatial functions in Google Earth Engine (GEE) and in the ArcGIS Pro application. These geospatial functions are run in parallel by sub-dividing the contiguous US into smaller regions based on road and rail boundaries to prevent overlaps or gaps in the data. As a new set of algorithmically delineated field polygons, the CSBs enhance applications requiring large-scale crop mapping with vector-based data.
Effect of double rocking jump rope training on lower limb muscle strength of badminton players
Authors: Li Jun , Li Rong
Published: May 2024

BACKGROUND: Double rocking jump rope training can effectively enhance physical recovery, adaptability to exercise load, and lower limb muscle strength of badminton players in sports colleges, thus offering valuable insights for improving training methods in sports colleges and universities. OBJECTIVE: To investigate the effect of double rocking jump rope training on the lower limb muscle strength of badminton players specializing in badminton in sports colleges. METHODS: An experimental study was conducted through a ten-week teaching intervention experiment with badminton players. Relevant heart rate indexes and badminton related lower limb muscle strength indexes were measured before and after the experiment. The data of the measured relevant indexes were statistically and analytically analyzed. At the end of the experiment, the physical recovery level and the heart’s adaptability to the exercise load of the control group were improved, and the lower limb muscle strength test indexes and sports performance were better than before the experiment. In the experimental group, badminton players’ physical function, anaerobic metabolism of the body and other aspects also improved. RESULTS: The physical function of the experimental group of badminton players, the energy supply capacity of the body anaerobic metabolism and aerobic work capacity all have an enhancement effect, enabling badminton players to adapt to large exercise loads quickly and improve the recovery rate of physical fitness. CONCLUSION: The introduction of double rocking jump rope into badminton training classes in sports colleges and universities as a means of lower limb muscle strength training is conducive to improving the level of lower limb muscle strength of special badminton players, enriching the teaching and training means of lower limb muscle strength in sports colleges and universities, and broadening the research field of lower limb muscle strength in badminton in sports colleges and universities.
Computer-aided detection of prostate cancer in early stages using multi-parameter MRI: A promising approach for early diagnosis
Authors: Tang Jianer , Zheng Xiangyi , Wang Xiao , Mao Qiqi , Xie Liping , Wang Rongjiang
Published: May 2024

BACKGROUND: Transrectal ultrasound-guided prostate biopsy is the gold standard diagnostic test for prostate cancer, but it is an invasive examination of non-targeted puncture and has a high false-negative rate. OBJECTIVE: In this study, we aimed to develop a computer-assisted prostate cancer diagnosis method based on multiparametric MRI (mpMRI) images. METHODS: We retrospectively collected 106 patients who underwent radical prostatectomy after diagnosis with prostate biopsy. mpMRI images, including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), and dynamic-contrast enhanced (DCE), and were accordingly analyzed. We extracted the region of interest (ROI) about the tumor and benign area on the three sequential MRI axial images at the same level. The ROI data of 433 mpMRI images were obtained, of which 202 were benign and 231 were malignant. Of those, 50 benign and 50 malignant images were used for training, and the 333 images were used for verification. Five main feature groups, including histogram, GLCM, GLGCM, wavelet-based multi-fractional Brownian motion features and Minkowski function features, were extracted from the mpMRI images. The selected characteristic parameters were analyzed by MATLAB software, and three analysis methods with higher accuracy were selected. RESULTS: Through prostate cancer identification based on mpMRI images, we found that the system uses 58 texture features and 3 classification algorithms, including Support Vector Machine (SVM), K-nearest Neighbor (KNN), and Ensemble Learning (EL), performed well. In the T2WI-based classification results, the SVM achieved the optimal accuracy and AUC values of 64.3% and 0.67. In the DCE-based classification results, the SVM achieved the optimal accuracy and AUC values of 72.2% and 0.77. In the DWI-based classification results, the ensemble learning achieved optimal accuracy as well as AUC values of 75.1% and 0.82. In the classification results based on all data combinations, the SVM achieved the optimal accuracy and AUC values of 66.4% and 0.73. CONCLUSION: The proposed computer-aided diagnosis system provides a good assessment of the diagnosis of the prostate cancer, which may reduce the burden of radiologists and improve the early diagnosis of prostate cancer.
Brain tumor segmentation based on the U-NET+⁣+ network with efficientnet encoder
Authors: Chen Yunyi , Quan Lan , Long Chao , Chen Yuxuan , Zu Li , Huang Chenxi
Published: May 2024

BACKGROUND: Brain tumor is a highly destructive, aggressive, and fatal disease. The presence of brain tumors can disrupt the brain’s ability to control body movements, consciousness, sensations, thoughts, speech, and memory. Brain tumors are often accompanied by symptoms like epilepsy, headaches, and sensory loss, leading to varying degrees of cognitive impairment in affected patients. OBJECTIVE: The study goal is to develop an effective method to detect and segment brain tumor with high accurancy. METHODS: This paper proposes a novel U-Net+⁣+ network using EfficientNet as the encoder to segment brain tumors based on MRI images. We adjust the original U-Net+⁣+ model by removing the dense skip connections between sub-networks to simplify computational complexity and improve model efficiency, while the connections of feature maps at the same resolution level are retained to bridge the semantic gap. RESULTS: The proposed segmentation model is trained and tested on Kaggle’s LGG brain tumor dataset, which obtains a satisfying performance with a Dice coefficient of 0.9180. CONCLUSION: This paper conducts research on brain tumor segmentation, using the U-Net+⁣+ network with EfficientNet as an encoder to segment brain tumors based on MRI images. We adjust the original U-Net+⁣+ model to simplify calculations and maintains rich semantic spatial features at the same time. Multiple loss functions are compared in this study and their effectiveness are discussed. The experimental results shows the model achieves a high segmention result with Dice coefficient of 0.9180.
Development of optimized cascaded LSTM with Seq2seqNet and transformer net for aspect-based sentiment analysis framework
Authors: Ramasamy Mekala , Elangovan Mohanraj
Published: May 2024

The recent development of communication technologies made it possible for people to share opinions on various social media platforms. The opinion of the people is converted into small-sized textual data. Aspect Based Sentiment Analysis (ABSA) is a process used by businesses and other organizations to assess these textual data in order to comprehend people’s opinions about the services or products offered by them. The majority of earlier Sentiment Analysis (SA) research uses lexicons, word frequencies, or black box techniques to obtain the sentiment in the text. It should be highlighted that these methods disregard the relationships and interdependence between words in terms of semantics. Hence, an efficient ABSA framework to determine the sentiment from the textual reviews of the customers is developed in this work. Initially, the raw text review data is collected from the standard benchmark datasets. The gathered text reviews undergo text pre-processing to neglect the unwanted words and characters from the input text document. The pre-processed data is directly provided to the feature extraction phase in which the seq2seq network and transformer network are employed. Further, the optimal features from the two resultant features are chosen by utilizing the proposed Modified Bird Swarm-Ladybug Beetle Optimization (MBS-LBO). After obtaining optimal features, these features are fused together and given to the final detection model. Consequently, the Optimized Cascaded Long Short Term Memory (OCas-LSTM) is proposed for predicting the sentiments from the given review by the users. Here, the parameters are tuned optimally by the MBS-LBO algorithm, and also it is utilized for enhancing the performance rate. The experimental evaluation is made to reveal the excellent performance of the developed SA model by contrasting it with conventional models.
Success rate and predictors of return to work after implementation of a formal return-to-work trajectory: A retrospective cohort study
Authors: Boets Isabelle , Luyten Steven , Vandenbroeck Sofie , Godderis Lode
Published: May 2024

BACKGROUND: Long term sick leave (SL) is increasing in Europe, several countries have legislative initiatives to reduce long-term absenteeism. OBJECTIVE: We evaluated the impact of a legally defined return-to-work (RTW) trajectory on the RTW of employees on sick leave in Belgium. METHODS: This was a retrospective register-based cohort study of employees (n = 1416) who followed an RTW trajectory in 2017. We linked workers’ data from a prevention service with social security data. By multinomial logistic regression, we analysed which characteristics predicted the RTW with the same or another employer. RESULTS: One year after their RTW trajectory, 69.2% of the 1416 employees did not RTW; 10.7% returned to work with the same employer and 20.1% with a new employer. Duration of SL was an important predictor for the RTW with both the same employer and another employer. The odds of RTW were lower when the SL duration was >  6 months compared to <  6 months. Marital status, organization-size, and the occupational physician decision had a significant impact on the RTW with the same employer. Age and who initiated the RTW-trajectory were important predictors on the RTW with another employer. CONCLUSIONS: Overall, 30.8% of employees returned to work after their RTW trajectory. A one-size-fits-all approach is not recommended. A stepped approach with an early, informal start of the RTW process is advised. When employees or employers fail to initiate the RTW on their own, a legally defined RTW trajectory could be useful. In particular, RTW with another employer seemed a positive effect of the RTW-trajectory.
Technology trends in practice: A survey of occupational therapy practitioners
Authors: Segal Addie , Doyle Nancy W.
Published: May 2024

BACKGROUND: Given the evolving digital revolution, technology and digital tools are becoming inseparable from work and daily occupations. Occupational therapy practitioners (OTPs) focus on supporting individuals in their desired and needed activities. Many of these daily activities (i.e., online banking, shopping, social media, smartphone use) require digital skills at a growing rate, and insufficient technological skills may subject individuals to limited engagement and social isolation. OBJECTIVE: To understand OTPs attitudes regarding technology utilization. METHODS: We surveyed 109 OTPs about their attitudes, work practices, and barriers to addressing their clients’ digital technological functioning. RESULTS: The findings of our study suggest that while OTPs acknowledge the importance and potential benefit of technology for their clients, there is a notable professional gap in the training and skills of OTPs to support their clients’ digital functioning effectively and to integrate technology into their practice. CONCLUSIONS: There is a need to improve and expand OTP’s digital skills and knowledge regarding addressing clients’ digital functioning and technology implementation in occupational therapy practice.
Design of special technological equipment with elements of artificial intelligence
Authors: Polshchikov Ivan
Published: May 2024

Особенности дизайна лазерных и оптоэлектронных систем инструментов всех уровней в условиях применения в их узлах и модулях управления и контроля элементов искусственного интеллекта и искусственных нейронных сетей , включая перспективу последовательного перехода на квантовые компьютеры и их эквиваленты Надёжность надсистем базовых технических решений в оптических приборах и инструментах , полученная за счёт повышения эффективности и надёжности подсистем установки и монтажа , полученных при использовании капсулированных композитных материалов Надёжность надсистем базовых технических решений в оптических приборах и инструментах , полученная за счёт повышения эффективности и надёжности подсистем электронных блоков , полученных при использовании капсулированных композитных материалов и технологий производства электронных печатных плат , применяющих принципы иерархии построения присущие РИТМ технологиям и их комбинациям Обеспечение требуемого уровня быстродействия процессорной техники в узлах управления и контроля с использованием передовых методов производства плат тонкоплёночных микросборок в том числе с применением Гибких автоматизированных производственных модулей и скоростных методов гальванических и электрохимических покрытий , реализующих процессы без негативного влияния краевого эффекта электродов и с электрохимическими ячейками , работающими в направленном потоке электролита.
A Cognitive Investigation of the Zen Meditation through the Lens of Thousand-Brain Intelligence and the Cerebral Reward System
Authors: Wang Zhiqing
Published: May 2024

Zen Buddhism is perceived by many Buddhists as the path to transcendent truth, and Zen meditation is considered as an effective and representative approach. This article seeks to decipher the Zen meditation experience with the latest findings from Hawkins' theory of thousand-brain intelligence and the cerebral rewarding system. According to the findings, it suggests that Zen meditation might involve both the construction and integration of cortical column models and the liberation from everyday addictive behavior. Therefore, it may be interpreted as a form of cultural art and skill from a pre-scientific era, rather than religious mysterious activities in the traditional understanding.
Explorando Horizontes Académicos: Innovación que Inspira en la Universidad Autónoma de Chile
Authors: Hernán Viguera F , Patricia Ibañez P , Salinas Barrera Vicente , Muñoz Buzeta Romina , López Tamaya Jorge , Arán Jara María Angélica , Samuel Sánchez Marjorie , Beltrán Cabezas Marcela , Pohels Bustos Karen , Puebla Rojas Pamela , Matus Marques Erich , Ibáñez Huenur Leroy , Muñoz Pincheira Carmen , Ortega Roda Myriam , Sánchez Schmidt Consuelo , Sepúlveda Ibarra Consuelo , Yáñez Pereira Víctor , Cereceda Otárola Marcos , Retamal Aravena Alan , Gajardo Cáceres Pablo , Contreras Mu Alicia , Alcina de Fortoul Sandra , Faundez Poblete Marcelo , Muñoz Sepúlveda Fernando , Vásquez Cerda Pablo , ContePozo Víctor , Soto Moscoso Matías , Almonacid Marcos , Castillo Manuel , González Martínez Darío
Published: May 2024

Para la Universidad Autónoma de Chile, la innovación educativa se define como el proceso sistemático y reflexivo de introducción de cambios significativos y relevantes en las metodologías o tecnologías empleadas en la enseñanza y el aprendizaje. Este proceso tiene como objetivo agregar valor y mejorar la calidad, eficacia y eficiencia de los procesos educativos, atendiendo a las particularidades de cada una de las sedes. La innovación educativa no consiste simplemente en el cambio por el cambio, sino en la introducción de modificaciones sustantivas y pertinentes para el contexto educativo específico. Estos cambios pueden estar relacionados con las prácticas de enseñanza y aprendizaje, los materiales y herramientas educativas, las estructuras y sistemas organizativos, o cualquier otro aspecto del proceso educativo.
Análisis de desempeño microinstitucional: reflexiones sobre el uso de la tecnología. El caso del sistema de justicia (penal) colombiano
Authors: Mendez Reategui Ruben
Published: May 2024

La necesidad de una mejora tecnológica continua de la administración de justicia fue particularmente evidente en el caso de Colombia, país utilizado como ejemplo por su dinámica social y ajustes institucionales. Por ello, este artículo se centra en examinar y argumentar este escenario desde un enfoque jurídico-económico heterodoxo.
Predictive Music Based on Mood
Authors: Ganesh B. Regulwar  , Nikhila Kathirisetty
Published: May 2024

It is often difficult for a person to choose which mu- sic to listen to from a vast array of available options. Relatively, this paper focuses on building an efficient music recommendation system based on the user’s mood which determines the emotion of user using Facial Recognition technique. The model is build using the transfer learning approach for which MobileNet model and Cascade classifier are used. Analyzing the user’s face expression might help you better comprehend their current emotional or mental condition. Music and video are one area where there is a lot of potential to present clients with a variety of options depending on their interests and data. More than 60% of users anticipate that the number of songs in their music collection will grow to the point where they will be unable to find the song they need to play at some point in the future. The user would save time by not having to search for or look up tunes. The image of the user is captured using a webcam. Then, depending on the user’s mood, an appropriate song from the user’s playlist or a movie is shown.