bluuebunny/crossref_metadata_2025_split huggingface dataset:
Skrining senyawa Eurycoma longifolia terhadap siklus sel dengan metode docking melalui interaksi cylcin
Authors: Hadi Samsul , Anwar Khoerul , Komari Noer , Azhara Desiya Ramayanti , Rosanti Diah Aulia 
Published: September 2023

Latar belakang: Pasak bumi telah dikenal oleh masyarakat secara turun-temurun untuk meningkatkan gairah seksual pada pria (afrodisiaka). Disamping itu dapat meningkatkan performa atlet olahraga dan sebagai antistress, menurunkan tegangan, kemarahan, dan kebingungan.
Tujuan: Penggunaan yang relatif banyak ini mendorong peneliti berkeinginan melakukan skrining senyawa aktif terhadap antitumor secara in silico dengan melalui 2 tahap yaitu melalaui PASS online dan docking.
Metode: Metode docking dipakai untuk melihat interaksi stabilitas senyawa dengan cylin. Dalam pengujian ini, Pyrx digunakan sebagai alat docking dengan berbagai target yang berhubungan dengan siklus sel, yaitu cyclin D3 (PDB ID: 3G33), cyclin A (PDB ID: IJSU), cyclin C (PDB ID: 1ZP2), cyclin D (ID PDB: 2W9F), cyclin H (ID PDB: 1KXU), dan cyclin T (ID PDB: 3BLR).
Hasil: Hasil docking pada masing-masing cyclin menunjukkan interaksi terbaik dengan senyawa berikut ini: cyclin A dan 9-Methoxycanthin-6-one 3-N-oxide (-8,6 Kkal/mol), cyclin H dan Niloticin (-8,0 Kkal/mol), cyclin D3 dan 9-Methoxycanthin-6-one 3-N-oxide (-7,2 Kkal/mol), cyclin D dan Eurycolactone F (-8,4 Kkal/mol), cyclin C dan Niloticin (-7,5 Kkal/mol), serta cyclin T dan Niloticin (-8,2 Kkal/mol).
Kesimpulan: Berdasarkan skor docking yang diperoleh, kestabilan interaksi diprediksi akan terjadi pada cyclin A dengan 9-Methoxycanthin-6-one 3-N-oxide (delta G: -8.6 Kkal/mol).
A MODEL PEMBELAJARAN ACTIVE LEARNING TIPE SNOWBALL THROWING DAN TIPE INDEX CARD MATCH (ICM) TERHADAP PEMAHAMAN KONSEP SISWA PADA PEMBELAJARAN IPS DI SEKOLAH DASAR
Authors: Rifqi Abdul Basit  , Enok Maryani  
Published: September 2023

Penguasaan materi pembelajaran adalah salah satu tujuan utama dalam pendidikan. Tujuan dari penelitian ini adalah untuk mempelajari model pembelajaran lemparan bola salju dengan tipe kartu indeks yang sesuai dengan kemampuan pemahaman peserta didik di sekolah dasar. Penelitian ini dilakukan pada siswa kelas IV SDN Cikampek Barat II, Kecamatan Cikampek, Kota Karawang. Jumlah peserta dalam penelitian ini sebanyak 26 siswa di kelas eksperimen 1 yang menerapkan model pembelajaran snowball throwing dan 26 siswa di kelas eksperimen 2 yang menggunakan model pembelajaran indeks kartu pertandingan. Metode penelitian yang digunakan dalam penelitian ini adalah metode kuasi eksperimen dengan desain pretest postest control group design control. Data penelitian dikumpulkan menggunakan instrumen tes dan rubrik. Berdasarkan hasil penelitian, dapat disimpulkan bahwa model kartu indeks pembelajaran cocok lebih efektif dalam meningkatkan pemahaman peserta didik tentang bahan rumah dan pakaian adat Indonesia dengan model pembelajaran melempar bola salju .
Kata Kunci: Pembelajaran Aktif , Pelemparan Bola Salju, Pencocokan Kartu Indeks , Pemahaman Konsep.
Are We There Yet? Tight-Oil Producers Still Chipping Away at Parent-Child Problems
Authors: Jacobs Trent 
Published: September 2023

In June, ExxonMobil’s CEO announced at an investor conference that the oil giant is experimenting with “very promising new technologies” poised to substantially enhance its recovery rates in the Permian Basin.
Darren Woods was light on specifics. Nonetheless, ears perk up when the head of the largest oil producer in the US suggests that it’s making progress on its goal to double the oft-cited and oft-derided 10% recovery factor of shale resource plays.
What Woods clued the outside world into is ExxonMobil’s optimism around drilling extra-long horizontal wells (17,500-ft laterals vs. the more-typical 10,000 ft) and the work that engineers are doing to make sure such wells can be effectively propped. Both count as compelling kernels to chew on.
But the CEO alluded to another area of notable progress when he said, “There are very few companies or organizations out there that could tell you exactly how fracs propagate and what that looks like underground—our view is that’s just a hard science project problem to solve.”
A couple of weeks later, the largest oil and gas producer in the US shared a much more in-depth assessment of that science problem at a different industry gathering—this time the Unconventional Resources Technology Conference (URTeC).
In URTeC 3860704, two dozen authors from ExxonMobil describe a four-well test used to steer decision making in the Midland Basin side of the Permian where it is among those drilling massive multiwell projects known as cube developments.
The experiment raised enough red flags for ExxonMobil to rethink its plans for a tight-well design. Multiple sources of data showed the wells were too close, their fracture networks in too much communication.
“These results suggest the pilot location was overcapitalized and that future developments should be drilled at fewer wells per section,” the authors concluded.
Those are not words that an oil company generally likes to hear. However, the point is that ExxonMobil learned of a poor outcome on a small scale which ostensibly preempted further financial pitfalls at the location.
Cautionary tales such as Concho Resource’s 23-well “Dominator Project” in 2019 have demonstrated to all tight-rock producers—and particularly Permian cube developers—the consequences of insufficient subsurface analysis.
In the words of ExxonMobil’s engineers and other specialists, “This integrated project clearly established the value of running diagnostic pilots and collecting high-resolution data before, during, and after completions to quantify the size and shape of the fractures created and propped extent of the stimulated rock.”
ExxonMobil’s latest study is one of several published this year to suggest that while the work is far from complete, significant leverage is being gained over the parent-child phenomenon.
Another big operator known for keeping a close eye on fracture propagation announced it has developed a tandem of new models to help improve parent-child relationships in the Eagle Ford Shale of south Texas.
Study Compares Fixed Wind Architectures for Offshore Green Hydrogen Production
Authors: Carpenter Chris 
Published: September 2023

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 32317, “Fixed Wind Architecture Comparison for Offshore Green Hydrogen Production,” by Dominic Pliszka, Catherine Gourlay, and Nihad Abdullazada, Subsea7, et al. The paper has not been peer reviewed. Copyright 2023 Offshore Technology Conference. Reproduced by permission.

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The complete paper is a summary of a study that covers through-life economics for producing green hydrogen from offshore fixed wind turbines. The options compare production of hydrogen on the turbine itself (decentralized) with production on a dedicated production platform (centralized) and production from an onshore facility. The results of the economic analysis show that decentralized hydrogen production can be competitive against onshore production at longer offshore distances.

Introduction
The paper contains a summary of an offshore green-hydrogen-production study performed by a group of companies. The field architectures for each of the evaluated cases are described and the results of the economic analysis are presented in the complete text.

Field Architecture Layouts
The hydrogen production cases in the study are divided into three architectural layouts.
Case 1: Onshore Hydrogen Production. The electricity produced at the offshore wind farm is gathered by interarray cables and directed to an offshore substation. From this location, the combined power is transported to an onshore hydrogen production facility through offshore export cables. Case 1 (the base case) is used to benchmark and compare the results against Cases 2 and 3.
Case 2: Offshore Hydrogen Production (Decentralized). The electricity generated by each offshore wind turbine is directed to a hydrogen production unit specific to each of the turbines, and green hydrogen is produced. Hydrogen is collected and exported through a dedicated hydrogen transport line to onshore facilities. Case 2 also is referred to as a wind farm with a decentralized hydrogen production system. In this scenario, a gas compressor located on each wind turbine is considered.
Case 3: Offshore Hydrogen Production (Centralized). The electricity produced at the offshore wind farm is directed to a hydrogen-production facility at a centralized offshore substation. The hydrogen from the substation is exported through a dedicated hydrogen transportation line to onshore facilities. This scenario is also referred to as a wind farm with centralized hydrogen production system.

Wind-Farm Overview
A single wind turbine with a maximum power output of 15 MW is assumed as the basis of this study. The wind farm has 67 turbines on fixed monopile foundations, giving a power rating of 1 GW. The wind turbines will be connected in a daisy-chain manner, each chain containing eight or nine turbines.
Automatic Gait Events Detection with Inertial Measurement Units: Healthy Subjects and Moderate to Severe Impaired Patients
Authors: Voisard Cyril , De l'Escalopier Nicolas , Ricard Damien , Oudre Laurent 
Published: September 2023

Abstract

Background: Recently, the use of inertial measurement units (IMUs) in quantitative gait analysis has been widely developed in clinical practice. Numerous methods have been developed for the automatic detection of gait events (GEs). While many of them have achieved high levels of efficiency in healthy subjects, detecting GEs in highly degraded gait from moderate to severely impaired patients remains a challenge. In this paper, we aim to present a method for improving GE detection from IMU recordings in such cases.
Methods: We recorded 10-meter gait IMU signals from 13 healthy subjects, 29 patients with multiple sclerosis, and 21 patients with post-stroke equino varus foot. An instrumented mat was used as the gold standard. Our method detects GEs from filtered free acceleration and gyration signals. Firstly, we use autocorrelation and pattern detection techniques to identify a reference stride pattern. Next, we apply multiparametric Dynamic Time Warping to annotate this pattern from a model stride, in order to detect all GEs in the signal.
Results: We analyzed 16,819 GEs recorded from healthy subjects and achieved an F1-score of 100%, with a median absolute error of 8 ms (IQR [3-13] ms). In multiple sclerosis and equino varus foot cohorts, we analyzed 6067 and 8951 GEs, respectively, with F1-scores of 99.4% and 96.3%, and median absolute errors of 18 ms (IQR [8-39] ms) and 26 ms (IQR [12-50] ms).
Conclusions: Our results are consistent with the state of the art for healthy subjects and demonstrate a good accuracy in GEs detection for pathological patients. Therefore, our proposed method provides an efficient way to detect GEs from IMU signals, even in degraded gaits. However, it should be evaluated in each cohort before being used to ensure its reliability.
SMOTE based Ensemble model for educational data mining
Authors: M stalin , S Kalyani 
Published: September 2023

Abstract
Data mining in the classroom is one of the prominent fields which involves data mining concepts, statistical analysis, and machine learning concepts which all gets applied on the educational data. These EDM processed data are widely used for analysing the various aspects of the business and process model. Existing and conventional process model involves the usage of conventional statistical techniques to process the data which in-turn needs a lot of manual interventional for data modelling and pre-processing. To address the above-mentioned issues this paper proposes a novel technique which combines the machine learning model along with the statistical approaches. This machine learning combination involves the ensembling different classifiers such as Decision tree, logistic regression, K nearest Neighbour, Random Forest, multiplayer perceptron etc. The data which utilized in the experimentation is highly imbalanced due to the limited data availability. Hence the above claimed technique is combined with the universally benchmarked model called synthetic minority oversampling technique (SMOTE) to address the problem of class imbalance. Further, the performance evaluation is also statistically performed in order to prove the efficacy of the proposed technique.
Numerical simulation and optimization study of glass fiber transport processes using multi-region coupling schemes
Authors: Qian Xu , Yang Su , Liu Chunbao 
Published: September 2023

Abstract
Governing the flow of air can be a challenge in many conditions. In chemical production, the airflow disturbance occurring during glass fiber transport can be abstracted as a multi-fluid domain coupling problem, resulting from a superposition of aerodynamic behaviors induced by multiple moving boundaries. Taking air disturbance during glass fiber transport as an example, a numerical analysis method for coupled airflow in multiple fluid domains considering dynamic meshing boundaries is proposed. A computational fluid dynamics (CFD) approach is developed which combines twodimensional analysis and three-dimensional analysis of a fiber thrower (including a finger wheel and a pull wheel). The presented numerical approach obtains the flow field data which is difficult to acquire by the experimental method. The flow structure of the pull wheel wake during fiber transport is described. Findings show that the pull wheel, but not the finger wheel, dominates the flow structure of the external flow field of the fiber thrower. The airflow around the fiber thrower is weakened by reducing the height of the trapezoidal section of the pull wheel. This analysis of airflow disturbances in the chemical industry is representative.
Improving the search ability of GP using semantic diversity and local search
Authors: Liang Jiayu , Lu Yaxin , Wu Han 
Published: September 2023

Abstract
Genetic programming (GP) is one of evolutionary computation algorithms, which simulates the evolution procedure in nature. To improve the search ability of GP, the integration of semantics and/or local search into GP has attracted significant attention. Since the existing works have shown the advantage of preserving semantic diversity through genetic operators (crossover and mutation) during evolution in GP, this work focuses on investigating new genetic operators aware of semantic diversity for GP. However, the existing semantic diversity operators rarely consider local search that may further improve their search ability. To address this issue, two new genetic operators (a crossover and a mutation) are designed, which involve both a mechanism of increasing semantic diversity and a step of local search. They are termed as semantic crossover with local search (SCls) and semantic mutation with local search (SMls), based on which two new GP-based methods are developed, i.e. GP_SCls and GP_SMls. The proposed methods are compared with GP, three reference semantic diversity methods and five non-GP regression methods for regression tasks. Results show that GP_SCls and GP_SMls are generally better than both GP and the reference semantic diversity methods, since their evolved solutions achieve lower RMSE (root mean square error) values and they converge faster for most test cases. In addition, compared with five non-GP regression methods, the proposed methods perform consistently better for the given tasks.
Brain-inspired emotional learning algorithm enhanced with type-one and interval type-two fuzzy extreme learning machine in noisy data
Authors: Golshan Mehdi , Teshnehlab Mohammad , Sharifi Arash 
Published: September 2023

Abstract
One of the advanced machine learning branches is emotional learning based on brain emotional learning (BEL), which has been widely used for almost three decades. BEL mimics the emotional learning mechanism in the mammalian brain, which has the superior features of fast learning, quick reacting, and low computational complexity. The original BEL model inspired by the limbic system is composed of two neural network components, namely the amygdala (AMYG) and orbitofrontal cortex (ORBI), which interact with each other. Using a fuzzy extreme learning machine (FELM), a brain-inspired emotional learning model was developed in this study to predict noisy, chaotic time series. The exchange of information between AMYG and ORBI is facilitated by an online sequential type-one FELM with interactive recurrent memory (OIRMS-T1FELM). The CenteroMedial (CM) section of AMYG uses type-one FELM (T1FELM) and interval type-two FELM (IT2FELM). Hence, the proposed model is named BEL-OIRMS-T1/IT2 FELM. The impact of noise on time series targets was determined by altering the various standard deviation of Gaussian noise levels (Sig). The experimental results show that the interaction between AMYG and ORBI leads to the reduction of RMSE in their outputs. Also, when IT2FELM is used instead of T1FELM in the CM of AMYG, the RMSE and MAPE of the final prediction output are lower, and the R is higher. In this state, by increasing the noise level, the proposed method with MF=2 performs better than other states.
Sudan Geodetic and Geospatial Reference System
Authors: Sami Kamal Abdellatif 
Published: September 2023

Abstract
The development in information and satellite technologies and geospatial data productions of today, let the Sudan Survey Authority to study all available options for a change from its local datum, Adindan to a global reference frame to be in line with the United Nations Global Geospatial Information (UNGGIM) committee of expert recommendations. Sudan Survey Authority (SSA) finally adopted the ITRF2008 (IGS2008) to be as the official Sudan Reference System (SRS). The SRS shall be an accurate reference system for the unification of Sudan existing many geospatial reference systems and datums, geodetic networks and mapping products and to work closely with the international and regional geospatial communities. The Sudan adopted reference system shall assist the public and private sector institutions for improving their organizational integration, data sharing and data exchange capabilities as well as increasing the ability to link geospatial data infrastructure data sets based on common location data, including Sudan National Basemap, property and building surveys, utility surveys and setting outs, natural resources surveys, roads and infrastructure surveys, map productions and map updatings, as well as all what can be considered as geospatial and survey practices in Sudan. The new Sudan reference system (ITRF2008) will be considered as a national Geodetic Reference Frame (NGRF), which, shall help the geospatial community in Sudan to adopt the best practices in the fields of geomatics and geoinformation by enhancing the existing systems and adopting new technologies associated with common standards and specifications. The paper overviewed the technical considerations of the adopted Sudan Reference System and outlined the benefits of the unification of previous georeferencing systems and to eliminate the drawbacks of using many datums within the entire boundaries of Sudan.
Existence results for novel sequential phi-Caputo fractional differential equations via concept of measures of noncompactness
Authors: Darzi Rahmat , Agheli Bahram 
Published: September 2023

Abstract
In this paper, by assuming certain assumptions, a novel class of sequential ⅁-Caputo fractional differential equations (S⅁-CFDE) featuring anti-periodic and ⅁-Riemann-Liouville (⅁- R-L) fractional integral boundary conditions has been studied.
The primary goal of this paper has been to explore whether there exists a uniqueness solution to the proposed class of problem utilizing the manner of the theory of topological degree for Banach contraction principle and the curtailing maps.
Eventually, two specific instances of the study results have been offered to demonstrate its performance and effectiveness.
Could fecal calprotectin serve as an indicator of gastrointestinal (GI) complications in individuals with systemic sclerosis?
Authors: Fatemi Alireza , Hedayatmofidi Hale , Abdolahi Nafiseh , Gharib Mohammad Hadi , Livani Somayeh , Aghaie Mehrdad 
Published: September 2023

Abstract
Introduction
: Systemic sclerosis is a connective tissue disease with unpredictable symptoms. The GI tract is often affected, with calprotectin levels indicating inflammation. We are examining fecal calprotectin as a simple method to diagnose GI disorders and disease activity.
Materials and methods
We gathered information from the 39 patients by filling out a UCLA SCTS 2.0 questionnaire regarding their GI symptoms. Additionally, we examined their skin and determined their skin score. Blood samples were taken to assess malnutrition, and stool samples were collected to measure the calprotectin level. We also conducted a non-aggressive Barium Swallow and CT Enterography to check for GI disorders.
Results
Increased levels of FC didn’t have any correlation with age (P Value = 0.79), disease type (P Value = 0.59), slight intestine wall thickening (P Value = 0.10), esophageal dilatation (P Value = 0.18), skin score (P Value = 0.25), final GI score (P Value = 0.30) and none of the serum tests. In our study, just 5 cases had evidence of wall thickening in CT Enterography, and all those 5 cases had increased levels of calprotectin. Also, as most of our patients take vitamin and mineral supplements, we couldn’t find any signs of micronutrient deficiencies or correlation with FC level.
Discussion and Conclusion:
While no statistical correlation was found between FC level and the variables, the results suggest that FC might be a specific tool for assessing intestinal wall thickness. However, due to the limited sample size, further studies are necessary to validate these findings.
The short-term and long-term prognosis of discharged COVID-19 patients in Guangdong during the first wave of pandemic
Authors: Li Pei-hong , Xu Hui , Xie Cheng-yuan , Ji Zhong-liang , Hu Bei , Deng Yi-yu , Jiang Wen-qiang , Li Xin , Fang Ming 
Published: September 2023

Abstract
BACKGROUD: People are increasingly concerned about the rehabilitation and sustained sequelae of Coronavirus Disease 2019 (COVID-19) infection. Our study aimed to investigate the sequelae of patients’ psychological and physical condition and its related risk factors in the early and late stages.
METHODS: This longitudinal study was conducted on 281 COVID-19 patients discharged from the first wave of pandemic. Patients were followed up for 12 months with constantly evaluation of psychological and physical condition, the follow-up was divided into early and late stage to observe the development of psychological and physical condition, data were collected and analyzed to find out its risk factors.
RESULTS: COVID-19 survivors had psychological and physical sequelae in the early and late stages, such as depression, anxiety, Post-Traumatic Stress Disorder (PTSD), sleep disorder, and functional damage in heart, liver, kidney and lung. The incidence of sequelae in the late stage decreased comparing to the early stage, the proportion of depression decreased by 10.0%, anxiety by 3.6%, PTSD by 2.9%, liver abnormality by 13.6%, cardiac by 2.2%, renal by 9.6%, and pulmonary by 27.4%. In two stages, gender, age, severity of COVID-19, hospitalization time and various comorbidities were significantly associated with psychological or physical sequelae.
CONCLUSION: We noticed that psychological and physical sequelae occurred to COVID-19 survivors in short and long stages, and these would gradually decrease as time went on. Male gender, age >50 years old, severe clinical condition, longer hospitalization time and comorbidity history were risk factors that significantly affected the rehabilitation of COVID-19 patients.