bluuebunny/crossref_metadata_2025_split huggingface dataset:
PENGARUH ZAKAT PRODUKTIF TERHADAP PENINGKATAN EKONOMI MUSTAHIQ PADA DOMPET DHUAFA WASPADA MEDAN
Authors: Pane Nurleliana , Nasution Ahmad Adib , Parianto Parianto 
Published: June 2023

Penelitian ini bertujuan untuk menganalisis pengaruh zakat produktif terhadap Peningkatan ekonomi mustahiq di Dompet Dhuafa Waspada Medan. Variabel yang digunakan dalam penelitian ini yaitu, zakat produktif (X) dan peningkatan ekonomi mustahiq (Y). Rumusan Masalah dalam penelitian ini adalah : (1) Apakah Program Zakat Produktif Berpengaruh Terhadap Peningkatan Ekonomi Mustahiq di Dompet Dhuafa Waspada Medan. (2) Bagaimanakah Pengelolaan Program Zakat Produktif di Dompet Dhuafa Waspada Medan. Penelitian ini menggunakan pendekatan penelitian kuantitatif. Data yang digunakan dalam penelitian adalah data primer, responden yang dijadikan sampel dalam penelitian ini sebanyak 30 orang. Metode yang digunakan adalah analisis regresi linear sederhana dengan bantuan SPSS 25 untuk mendapatkan gambaran umum tentang hubungan antara satu variabel dengan variabel lainnya. Hasil dari penelitian menunjukkan bahwa variabel zakat produktif memiliki pengaruh yang signifikan terhadap peningkatan ekonomi mustahiq. Hal ini diperoleh dari hasil uji T yaitu = 0.840 dan = 1,671 dengan taraf signifikan 0,05 dan juga R Square Value sebesar 0.818
Pelatihan Pembuatan Video Pembelajaran Interaktif dengan Memaksimalkan PowerPoint bagi Guru SDN Kemayoran 1 Bangkalan
Authors: Retnawati Lestari , Saurina Nia , Pratama Firman Hadi Sukma , Karyanto Nonot Wisnu , Prasetiya Noven Indra 
Published: June 2023

Pendidikan sekolah dasar merupakan.bagian dari sistem pendidikan nasional yang mempunyai peranan sangat penting dalam menggali dan meningkatkan potensi anak sedini mungkin, termasuk di SDN Kemayoran 1 Bangkalan. Sekolah dasar merupakan lembaga pendidikan yang diperuntukkan bagi anak usia 6–12 tahun agar anak memiliki kemampuan dasar, pengetahuan, dan keterampilan sebagai tahap awal untuk melanjutkan ke jenjang pendidikan selanjutnya. Karena peranan penting ini, kemampuan guru Sekolah Dasar Negeri (SDN) Kemayoran 1 Bangkalan dalam membuat sebuah media pembelajaran yang menarik minat siswa menjadi kunci dalam keberhasilan proses belajar mengajar dalam sekolah ini. Karakter siswa SDN Kemayoran 1 Bangkalan yang masih senang bermain merupakan tantangan bagi para guru. Dengan demikian, dibutuhkan kreativitas dalam membuat media pembelajaran yang menarik minat siswa sekolah dasar untuk memperhatikan. Ketika guru sekolah dasar hanya menulis di papan tulis atau menampilkan slide presentasi dengan banyak tulisan, hal tersebut bisa membuat tujuan pembelajaran dari sebuah materi tidak tersampaikan dengan baik. Oleh karena itu, seorang guru sekolah dasar membutuhkan tambahan pengetahuan untuk membuat media pembelajaran yang menarik dan interaktif. Media pembelajaran berbentuk video dengan memaksimalkan PowerPoint bisa menjadi solusi untuk membuat media pembelajaran yang menarik bagi siswa sekolah dasar di SDN Kemayoran 1 Bangkalan. Melalui pelatihan pembuatan video pembelajaran interaktif dengan memaksimalkan PowerPoint ini diharapkan para guru di SDN Kemayoran 1 Bangkalan dapat menambah ilmu pengetahuan dalam memaksimalkan PowerPoint sehingga proses belajar mengajar bisa dilakukan dengan efektif.
LEVENSHTEIN UZAKLIK ÖLÇEĞİNE GÖRE ÇAĞDAŞ TÜRK YAZI DİLLERİNİN FONETİK UZAKLIĞI
Authors: SAĞLAM Bilgit , ÖZEK Fatih 
Published: June 2023

Bu çalışmanın temel amacı Oğuz grubu yazı dillerinin birbirlerine karşı fonetik uzaklıklarını tespit etmektir. Çalışmada fonetik uzaklıkların tespiti için bilgisayarlı dil bilim alanında sıkça kullanılan niceliksel ölçüm tekniklerinden Levenshtein uzaklık algoritması kullanılmıştır. Bu bağlamda, çalışmanın Türkiye’deki lehçe bilim araştırmalarında kullanılabilecek bir yöntemi tanıtarak ilgili alanda yeni değerlendirmelerin yapılması adına katkı sunması hedeflenmektedir.
Vladimir Levenshtein tarafından geliştirilen ve birçok alanda kullanılan Levenshtein uzaklık algoritması, bir sözcük dizisini diğerine dönüştürürken kullanılabilecek ekleme, çıkarma ve yer değiştirme işlemlerinden matematiksel olarak en az işlem gerektiren sayısal değeri tespit etmede kullanılan bir algoritmadır. Levenshtein uzaklık algoritması, lehçe bilim çalışmalarında ilk olarak Brett Kessler tarafından kullanılmıştır. Bu çalışmadaki ilk denemenin başarılı olmasının ardından birçok lehçe bilimcinin algoritmaya ilgisi artmış, aynı yöntemle birçok dil değişkesinin fonetik uzaklığını ele alan çalışmalar yapılmıştır.
Bu çalışmada Türkiye Türkçesi, Azerbaycan Türkçesi, Türkmen Türkçesinin birbirlerine fonetik uzaklığı Levenshtein uzaklık algoritması ile ölçülecektir. Çalışmanın genellenebilir sonuçlar vermesi adına Türkçe Sıklık Sözlüğü’ne göre en sık kullanılan 40 fiil ve 13 tematik başlıktan (hayvan, beden, yiyecek, giyim, ulaşım, aile, madde, zaman, spor, tabiat, hava, meslek ve renk) 216 adet isim olmak üzere toplam 256 Türkçe madde başı sözcük belirlenmiştir. Sözcükler hedef dillere ana dili konuşurları tarafından tercüme edilmiştir. Sözcükler, ana dili konuşurlarına seslettirilmiş ve sözcük telaffuzları uluslararası fonetik alfabeye aktarılmıştır. Kaynak dilden her sözcük ile hedef dildeki karşılığı LUA ile hesaplanarak sözcüklerin fonetik uzaklıkları tespit edilmişir. Örnek setindeki tüm sözcük çiftlerine bu işlem uygulandıktan sonra hesaplanan ortalama değer temel alınarak Oğuz grubu Türk yazı dillerinin birbirlerine olan ortalama fonetik uzaklığı hesaplanmıştır.
Rancang Bangun Alat Parut Modifikasi sebagai Teknologi Tepat Guna
Authors: Gumay Ovilia Putri utami , Afan Reno Ali 
Published: June 2023

Daging buah kelapa merupakan salah satu jenis bahan baku yang paling sering digunakan indutri kecil catering makanan, dimana kelapa proses pengolahannya dilakukan dengan cara diparut. Proses pemarutan kelapa cukup dilakukan dengan manual dengan papan parut sederhana jika berjumlah sedikit, untuk menghasilkan parutan bagus, kecepatan parutan manual yang dibutuhkan kurang lebih 3000 gerakan parut setiap jam. Alat pemarut kelapa adalah salah satu produk alat untuk kebutuhan rumah tangga yang berfungsi sebagai alat untuk menghancurkan buah kelapa menjadi butiran-butiran kecil, dengan tujuan untuk memperoleh santan yang terkandung di daging buah kelapa. Penelitian ini bertujuan untuk merancang teknologi tepat guna berupa alat parut kelapa modifikasi skala menengah yang lebih ergonomis. Dari hasil pengujian yang diperoleh semakin tinggi kecepatan putar silinder atau kecepatan potong pisau parut maka semakin tinggi kapasitas pemarutan. Hal ini disebabkan karena semakin cepat kecepatan potong pisau parut maka siklus atau frekuensi pemarutan berlangsung lebih cepat.
CONHECIMENTO DE ACADÊMICOS DE FISIOTERAPIA SOBRE O CONSUMO DE NARGUILÉ
Authors: Pires Julia Magalhães , De Morais Elizabeth Rodrigues , Nascimento Leonardo Lopes , De Oliveira Valéria Rodrigues Costa 
Published: June 2023

OBJETIVO: Avaliar o nível de conhecimento de acadêmicos de Fisioterapia sobre os efeitos do narguilé, relacionar o nível de conhecimento com o perfil sociodemográfico e com o período cursado. MÉTODOS: Estudo transversal e analítico, realizado com 153 alunos de uma universidade privada de Goiás. Os instrumentos empregados foram um formulário com dados sociodemográficos e um questionário com 20 questões objetivas, sendo 18 sobre os efeitos do narguilé na saúde, e 2 sobre o papel do profissional da saúde no controle ao tabagismo. RESULTADOS: O conhecimento foi alto nas questões relacionadas ao prejuízo do hábito de fumar narguilé na saúde, às doenças pulmonares causadas pelos agentes tóxicos e às doenças infecciosas transmitidas pelo bocal. Em relação aos níveis de alcatrão e nicotina, e a influência do uso contínuo do narguilé no desenvolvimento de Diabetes Mellitus e infertilidade o conhecimento foi baixo. A maioria reconhece a importância do papel do profissional da saúde no controle do tabagismo. A média de acertos dos acadêmicos com companheiros e daqueles com renda mensal familiar de até 4 salários-mínimos foi significativamente maior (p = 0,01), enquanto o período cursado não interferiu nos resultados. CONCLUSÃO: Os estudantes apresentam baixo conhecimento sobre os efeitos do narguilé em relação a questões relevantes, o que desperta a necessidade de rever os conteúdos programáticos e os objetivos pedagógicos das disciplinas afins, a fim de melhor preparar o aluno para desempenhar efetivamente seu papel no controle do tabagismo.
Palavras-chave: Fisioterapia. Cachimbo de água. Estudantes. Tabagismo.
Reflections on the Circulation of Normative Models and Legal Works in the 1936 Argentine Civil Code Draft on Possession
Authors: Polotto María Rosario , Cacciavillani Pamela Alejandra 
Published: June 2023

Abstract
This article explores the process of circulation of normative models and legal theories between Europe and Latin America. It analyzes the circulation of the German possessory model in the Argentinian legal experience between the enactment of the Civil Code of 1869 and the Civil Code Draft of 1936. We argue that the prominence of possession in nineteenth-century local legal culture and practices both conditioned and encouraged the reception of this model. The criticisms that arose at the beginning of the twentieth century regarding the liberal property law of the Civil Code were channelled, in the Civil Code Draft of 1936, through an expansion of the protection of possession to situations not previously contemplated, such as rural and urban leaseholds. German law, which served among other things, as the basis for this legal innovation, was adapted to specific legal traditions operating in provincial regulations.
Developing nonlinear k-nearest neighbors classification algorithms to identify patients at high risk of increased length of hospital stay following spine surgery
Authors: Shahrestani Shane , Chan Andrew K. , Bisson Erica F. , Bydon Mohamad , Glassman Steven D. , Foley Kevin T. , Shaffrey Christopher I. , Potts Eric A. , Shaffrey Mark E. , Coric Domagoj , Knightly John J. , Park Paul , Wang Michael Y. , Fu Kai-Ming , Slotkin Jonathan R. , Asher Anthony L. , Virk Michael S. , Michalopoulos Giorgos D. , Guan Jian , Haid Regis W. , Agarwal Nitin , Chou Dean , Mummaneni Praveen V. 
Published: June 2023

OBJECTIVE
Spondylolisthesis is a common operative disease in the United States, but robust predictive models for patient outcomes remain limited. The development of models that accurately predict postoperative outcomes would be useful to help identify patients at risk of complicated postoperative courses and determine appropriate healthcare and resource utilization for patients. As such, the purpose of this study was to develop k-nearest neighbors (KNN) classification algorithms to identify patients at increased risk for extended hospital length of stay (LOS) following neurosurgical intervention for spondylolisthesis.

METHODS
The Quality Outcomes Database (QOD) spondylolisthesis data set was queried for patients receiving either decompression alone or decompression plus fusion for degenerative spondylolisthesis. Preoperative and perioperative variables were queried, and Mann-Whitney U-tests were performed to identify which variables would be included in the machine learning models. Two KNN models were implemented (k = 25) with a standard training set of 60%, validation set of 20%, and testing set of 20%, one with arthrodesis status (model 1) and the other without (model 2). Feature scaling was implemented during the preprocessing stage to standardize the independent features.

RESULTS
Of 608 enrolled patients, 544 met prespecified inclusion criteria. The mean age of all patients was 61.9 ± 12.1 years (± SD), and 309 (56.8%) patients were female. The model 1 KNN had an overall accuracy of 98.1%, sensitivity of 100%, specificity of 84.6%, positive predictive value (PPV) of 97.9%, and negative predictive value (NPV) of 100%. Additionally, a receiver operating characteristic (ROC) curve was plotted for model 1, showing an overall area under the curve (AUC) of 0.998. Model 2 had an overall accuracy of 99.1%, sensitivity of 100%, specificity of 92.3%, PPV of 99.0%, and NPV of 100%, with the same ROC AUC of 0.998.

CONCLUSIONS
Overall, these findings demonstrate that nonlinear KNN machine learning models have incredibly high predictive value for LOS. Important predictor variables include diabetes, osteoporosis, socioeconomic quartile, duration of surgery, estimated blood loss during surgery, patient educational status, American Society of Anesthesiologists grade, BMI, insurance status, smoking status, sex, and age. These models may be considered for external validation by spine surgeons to aid in patient selection and management, resource utilization, and preoperative surgical planning.
Radiomic signatures of meningiomas using the Ki-67 proliferation index as a prognostic marker of clinical outcomes
Authors: Khanna Omaditya , Fathi Kazerooni Anahita , Arif Sherjeel , Mahtabfar Aria , Momin Arbaz A. , Andrews Carrie E. , Hafazalla Karim , Baldassari Michael P. , Velagapudi Lohit , Garcia Jose A. , Sako Chiharu , Farrell Christopher J. , Evans James J. , Judy Kevin D. , Andrews David W. , Flanders Adam E. , Shi Wenyin , Davatzikos Christos 
Published: June 2023

OBJECTIVE
The clinical behavior of meningiomas is not entirely captured by its designated WHO grade, therefore other factors must be elucidated that portend increased tumor aggressiveness and associated risk of recurrence. In this study, the authors identify multiparametric MRI radiomic signatures of meningiomas using Ki-67 as a prognostic marker of clinical outcomes independent of WHO grade.

METHODS
A retrospective analysis was conducted of all resected meningiomas between 2012 and 2018. Preoperative MR images were used for high-throughput radiomic feature extraction and subsequently used to develop a machine learning algorithm to stratify meningiomas based on Ki-67 indices < 5% and ≥ 5%, independent of WHO grade. Progression-free survival (PFS) was assessed based on machine learning prediction of Ki-67 strata and compared with outcomes based on histopathological Ki-67.

RESULTS
Three hundred forty-three meningiomas were included: 291 with WHO grade I, 43 with grade II, and 9 with grade III. The overall rate of recurrence was 19.8% (15.1% in grade I, 44.2% in grade II, and 77.8% in grade III) over a median follow-up of 28.5 months. Grade II and III tumors had higher Ki-67 indices than grade I tumors, albeit tumor and peritumoral edema volumes had considerable variation independent of meningioma WHO grade. Forty-six high-performing radiomic features (1 morphological, 7 intensity-based, and 38 textural) were identified and used to build a support vector machine model to stratify tumors based on a Ki-67 cutoff of 5%, with resultant areas under the curve of 0.83 (95% CI 0.78–0.89) and 0.84 (95% CI 0.75–0.94) achieved for the discovery (n = 257) and validation (n = 86) data sets, respectively. Comparison of histopathological Ki-67 versus machine learning–predicted Ki-67 showed excellent performance (overall accuracy > 80%), with classification of grade I meningiomas exhibiting the greatest accuracy. Prediction of Ki-67 by machine learning classifier revealed shorter PFS for meningiomas with Ki-67 indices ≥ 5% compared with tumors with Ki-67 < 5% (p < 0.0001, log-rank test), which corroborates divergent patient outcomes observed using histopathological Ki-67.

CONCLUSIONS
The Ki-67 proliferation index may serve as a surrogate marker of increased meningioma aggressiveness independent of WHO grade. Machine learning using radiomic feature analysis may be used for the preoperative prediction of meningioma Ki-67, which provides enhanced analytical insights to help improve diagnostic classification and guide patient-specific treatment strategies.
Machine learning for automated and real-time two-dimensional to three-dimensional registration of the spine using a single radiograph
Authors: Abumoussa Andrew , Gopalakrishnan Vivek , Succop Benjamin , Galgano Michael , Jaikumar Sivakumar , Lee Yueh Z. , Bhowmick Deb A. 
Published: June 2023

OBJECTIVE
The goal of this work was to methodically evaluate, optimize, and validate a self-supervised machine learning algorithm capable of real-time automatic registration and fluoroscopic localization of the spine using a single radiograph or fluoroscopic frame.

METHODS
The authors propose a two-dimensional to three-dimensional (2D-3D) registration algorithm that maximizes an image similarity metric between radiographic images to identify the position of a C-arm relative to a 3D volume. This work utilizes digitally reconstructed radiographs (DRRs), which are synthetic radiographic images generated by simulating the x-ray projections as they would pass through a CT volume. To evaluate the algorithm, the authors used cone-beam CT data for 127 patients obtained from an open-source de-identified registry of cervical, thoracic, and lumbar scans. They systematically evaluated and tuned the algorithm, then quantified the convergence rate of the model by simulating C-arm registrations with 80 randomly simulated DRRs for each CT volume. The endpoints of this study were time to convergence, accuracy of convergence for each of the C-arm’s degrees of freedom, and overall registration accuracy based on a voxel-by-voxel measurement.

RESULTS
A total of 10,160 unique radiographic images were simulated from 127 CT scans. The algorithm successfully converged to the correct solution 82% of the time with an average of 1.96 seconds of computation. The radiographic images for which the algorithm converged to the solution demonstrated 99.9% registration accuracy despite utilizing only single-precision computation for speed. The algorithm was found to be optimized for convergence when the search space was limited to a ± 45° offset in the right anterior oblique/left anterior oblique, cranial/caudal, and receiver rotation angles with the radiographic isocenter contained within 8000 cm3 of the volumetric center of the CT volume.

CONCLUSIONS
The investigated machine learning algorithm has the potential to aid surgeons in level localization, surgical planning, and intraoperative navigation through a completely automated 2D-3D registration process. Future work will focus on algorithmic optimizations to improve the convergence rate and speed profile.
Surgical classification using natural language processing of informed consent forms in spine surgery
Authors: Shost Michael D. , Meade Seth M. , Steinmetz Michael P. , Mroz Thomas E. , Habboub Ghaith 
Published: June 2023

OBJECTIVE
In clinical spine surgery research, manually reviewing surgical forms to categorize patients by their surgical characteristics is a crucial yet time-consuming task. Natural language processing (NLP) is a machine learning tool used to adaptively parse and categorize important features from text. These systems function by training on a large, labeled data set in which feature importance is learned prior to encountering a previously unseen data set. The authors aimed to design an NLP classifier for surgical information that can review consent forms and automatically classify patients by the surgical procedure performed.

METHODS
Thirteen thousand two hundred sixty-eight patients who underwent 15,227 surgeries from January 1, 2012, to December 31, 2022, at a single institution were initially considered for inclusion. From these surgeries, 12,239 consent forms were classified based on the Current Procedural Terminology (CPT) code, categorizing them into 7 of the most frequently performed spine surgeries at this institution. This labeled data set was split 80%/20% into train and test subsets, respectively. The NLP classifier was then trained and the results demonstrated its performance on the test data set using CPT codes to determine accuracy.

RESULTS
This NLP surgical classifier had an overall weighted accuracy rate of 91% for sorting consents into correct surgical categories. Anterior cervical discectomy and fusion had the highest positive predictive value (PPV; 96.8%), whereas lumbar microdiscectomy had the lowest PPV in the testing data (85.0%). Sensitivity was highest for lumbar laminectomy and fusion (96.7%) and lowest for the least common operation, cervical posterior foraminotomy (58.3%). Negative predictive value and specificity were > 95% for all surgical categories.

CONCLUSIONS
Utilizing NLP for text classification drastically improves the efficiency of classifying surgical procedures for research purposes. The ability to quickly classify surgical data can be significantly beneficial to institutions without a large database or substantial data review capabilities, as well as for trainees to track surgical experience, or practicing surgeons to evaluate and analyze their surgical volume. Additionally, the capability to quickly and accurately recognize the type of surgery will facilitate the extraction of new insights from the correlations between surgical interventions and patient outcomes. As the database of surgical information grows from this institution and others in spine surgery, the accuracy, usability, and applications of this model will continue to increase.
Comparison of machine learning models to predict long-term outcomes after severe traumatic brain injury
Authors: Arefan Dooman , Pease Matthew , Eagle Shawn R. , Okonkwo David O. , Wu Shandong 
Published: June 2023

OBJECTIVE
An estimated 1.5 million people die every year worldwide from traumatic brain injury (TBI). Physicians are relatively poor at predicting long-term outcomes early in patients with severe TBI. Machine learning (ML) has shown promise at improving prediction models across a variety of neurological diseases. The authors sought to explore the following: 1) how various ML models performed compared to standard logistic regression techniques, and 2) if properly calibrated ML models could accurately predict outcomes up to 2 years posttrauma.

METHODS
A secondary analysis of a prospectively collected database of patients with severe TBI treated at a single level 1 trauma center between November 2002 and December 2018 was performed. Neurological outcomes were assessed at 3, 6, 12, and 24 months postinjury with the Glasgow Outcome Scale. The authors used ML models including support vector machine, neural network, decision tree, and naïve Bayes models to predict outcome across all 4 time points by using clinical information available on admission, and they compared performance to a logistic regression model. The authors attempted to predict unfavorable versus favorable outcomes (Glasgow Outcome Scale scores of 1–3 vs 4–5), as well as mortality. Models’ performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) with 95% confidence interval and balanced accuracy.

RESULTS
Of the 599 patients in the database, the authors included 501, 537, 469, and 395 at 3, 6, 12, and 24 months posttrauma. Across all time points, the AUCs ranged from 0.71 to 0.85 for mortality and from 0.62 to 0.82 for unfavorable outcomes with various modeling strategies. Decision tree models performed worse than all other modeling approaches for multiple time points regarding both unfavorable outcomes and mortality. There were no statistically significant differences between any other models. After proper calibration, the models had little variation (0.02–0.05) across various time points.

CONCLUSIONS
The ML models tested herein performed with equivalent success compared with logistic regression techniques for prognostication in TBI. The TBI prognostication models could predict outcomes beyond 6 months, out to 2 years postinjury.