bluuebunny/crossref_metadata_2025_split huggingface dataset:
Production performance and water quality of Caulerpa lentillifera with fermented fruit vegetable fertilizer
Authors: Supriyono Eddy , Chrisliana Chrisliana , Puji Hastuti Yuni , Lila Antara Kadek , Ee Ling Yong , Nurussalam Wildan 
Published: September 2024

The use of fermented vegetable and fruit waste (FVF) based fertilizers as an effort to reduce waste has the potential to be used to support aquaculture activities. This study aims to evaluate the effectiveness of Caulerpa lentillifera cultivation with the addition of fertilizer based on fruit and vegetable waste fermentation on water quality parameters and production performance. The experimental design used was a completely randomized design with three replications for four different doses of FVF fertilizer treatments, namely 0, 0.05, 0.1, and 0.2 µl/L. This study used an initial weight of 500 g which was reared in a concrete tank with a capacity of 1000 L for 30 days growth period. The results showed that the use of FVF of 0.1 µl/L gave a better net harvest weight (p<0.05), namely 0.08 ± 0.06 kg.

Keywords: Caulerpa lentillifera, fermentation, fertilizer, seaweed, waste

ABSTRAK

Penggunaan pupuk berbasis fermentasi limbah sayur dan buah (FVF) sebagai salah satu upaya pengurangan limbah memberikan potensi sebagai larutan pendukung kegiatan budidaya. Penelitian ini bertujuan untuk mengevaluasi efektivitas budidaya Caulerpa lentillifera dengan penambahan pupuk berbasis fermentasi limbah buah dan sayur terhadap parameter kualitas air dan kinerja produksi. Rancangan percobaan yang digunakan adalah rancangan acak lengkap dengan tiga kali ulangan pada empat perlakuan pupuk, yaitu 0, 0.05, 0.1, dan 0.2 µl/L. Penelitian ini menggunakan bobot awal 500 g yang dipelihara dalam bak beton berkapasitas 1000 L. Hasil pengamatan menunjukkan bahwa penggunaan FVF sebesar 0.1 µl/L memberikan bobot panen bersih yang lebih baik (p<0.05) yaitu 0.08 ± 0.06 kg.

Kata kunci: Caulerpa lentillifera, fermentasi, limbah, pupuk, rumput laut
Spiritual Emotional Freedom Technique untuk Mereduksi Psikosomatis Pada Ibu Rumah Tangga
Authors: Mahmuddah Dewi Edmawati  , Hadi Warsito  , Aldila Fitri RNM  , Akhmad Setyawan  
Published: September 2024

Tujuan penelitian ini adalah untuk mengetahui apakah Spiritual Emotional Freedom Technique efektif dalam mereduksi Psikosomatis Pada Ibu Rumah Tangga. Metode penelitian yang digunakan yaitu penelitian Pre-Experimental Design dengan model desain One-Group Pretest-Posttest Design. Pengambilan data sampel dalam penelitian ini menggunakan metode purposive sampling. Purposive sampling adalah teknik penentuan sampel dengan pertimbangan tertentu dalam penentuan sampel. Alasan menggunakan teknik purposive sampling ini karena sesuai untuk digunakan untuk penelitian kuantitatif, atau penelitian-penelitian yang tidak melakukan generalisasi. Pengumpulan data diperoleh melalui pengisian psychosomatic problem scale. Analisis yang digunakan oleh peneliti untuk mengetahui pengaruh masing-masing variabel menggunakan teknik uji analisis non parametrik yaitu Wilcoxon Signed Rank Test. Adapun pengujian hipotesis yang berbunyi Spiritual Emotional Freedom Technique efektif untuk mereduksi psikosomatis pada Ibu Rumah Tangga, berdasarkan hasil pada tabel berdasarkan hasil pada tabel didapat Z hitung sebesar sebesar -2,023 dengan Asymp.sig (2-tailed) sebesar 0.043. Sehingga dapat disimpulkan bahwa Spiritual Emotional Freedom Technique efektif untuk mereduksi psikosomatis pada ibu rumah tangga.
Valorisation of white wine lees: optimisation of subcritical water extraction of antioxidant compounds
Authors: Poulain Benjamin , Winstel Delphine , Massot Arnaud , Moine Virginie , Nioi Claudia 
Published: September 2024

This study explored the extraction of antioxidant compounds from white wine lees using subcritical water extraction (SWE). First, SWE was compared to extraction using conventional solvents mixed with water: 50 % methanol, 50 % ethanol and 50 % acetonitrile. The antiradical activity of the extract increased to 12.8 mg TE/g of extract dry matter (DM) using conventional solvents and to 29.2 mg TE/g DM with SWE, compared to the antiradical activity of dried lees, which was 5.7 mg Trolox Equivalent/g of dry matter (mg TE/g DM). Second, SWE conditions were optimised to obtain lees extracts with a high antioxidant activity by applying a Doehlert design and response surface methodology (RSM). The extraction parameters modulated during RSM optimisation were extraction time (t, 15 – 60 min), temperature (T, 100 – 250 °C) and stirring speed (S, 100 – 1000 RPM). Antioxidant activity was assessed using DPPH and FRAP tests, as well as by measuring the Oxygen Consumption Rate (OCR) of lees extracts. Temperature was found to be the main parameter that had a significant effect on antioxidant activity. The results of the RSM showed the optimal conditions (temperature, duration and speed) for attaining maximum antioxidant activity to be: 240 °C, 15 min and 550 RPM respectively. The best-known antioxidant compounds in white wine lees, Total Polyphenol Content (TPC), Glutathione (GSH) and Total Sulfhydryl groups (TSH) were also quantified. Extracts with the highest antioxidant capacity had the highest TPC and TSH concentrations. In conclusion, this study showed (i) that subcritical water extraction is a green process that has potential as an alternative to using conventional solvents for extracting white wine lees, (ii) the chemical components involved in their antioxidant capacity, and (iii) the potential of the lees for oenological and other industrial applications.
Antioxidant Effects of Aromatic Plant Extracts in Neurodegenerative Diseases: A Review
Authors: Zhu Youyang , Tian Miao , Qin Yuliang , Zhao Ting , Li Zhaofu , Qin Dongdong 
Published: September 2024

Neurodegenerative diseases (NDDs) are a class of cognitive and motor disorders that include Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington&#039;s disease (HD), amyotrophic lateral sclerosis (ALS), and others. They are caused by lesions in cells and tissues of the central nervous system, resulting in corresponding dysfunctions and consequent decline in cognitive and motor functions. Neural tissues are extremely vulnerable to oxidative stress, which plays critical biological roles in NDDs. Aromatic compounds are found extensively in natural plants and have substantial effects of anti-oxidative stress damage, which not only have a wide range of research applications in cosmetics, foods, etc., but are also frequently utilized in the treatment of various central nervous system diseases. This review summarizes the relevant oxidative stress mechanisms in NDDs (AD, PD, HD, and ALS) and reviews aromatic compounds such as polyphenols, terpenoids, and flavonoids that can be used in the management of neurodegenerative diseases, as well as their specific mechanisms of antioxidant action. This review will serve as a reference for future experimental studies on neurodegenerative illnesses while also offering fresh insights into clinical therapy.
Protein Set Transformer: A protein-based genome language model to power high diversity viromics
Authors: Anantharaman Karthik , Martin Cody , Gitter Anthony 
Published: September 2024

Abstract

Exponential increases in microbial and viral genomic data demand transformational advances in scalable, generalizable frameworks for their interpretation. Standard homology-based functional analyses are hindered by the rapid divergence of microbial and especially viral genomes and proteins that significantly decreases the volume of usable data. Here, we present Protein Set Transformer (PST), a protein-based genome language model that models genomes as sets of proteins without considering sparsely available functional labels. Trained on >100k viruses, PST outperformed other homology- and language model-based approaches for relating viral genomes based on shared protein content. Further, PST demonstrated protein structural and functional awareness by clustering capsid-fold-containing proteins with known capsid proteins and uniquely clustering late gene proteins within related viruses. Our data establish PST as a valuable method for diverse viral genomics, ecology, and evolutionary applications. We posit that the PST framework can be a foundation model for microbial genomics when trained on suitable data.
In-Situ Microstructural Evolution and Mechanical Performance of Nickel-Containing Steels During High-Temperature Heat Treatment: Precipitation Behavior and Its Impact on Material Properties
Authors: Sun Guojin , Wang Qi 
Published: September 2024

Abstract

This study investigates the microstructural evolution and mechanical properties of steel subjected to high-temperature heat treatment, with a focus on the formation and effects of second phase precipitates. High Temperature Confocal Laser Scanning Microscopy (HTCLSM) was employed to observe the in-situ changes in microstructure as the steel was heated from room temperature to 900°C. The observations revealed that second phase precipitates, identified as chromium carbides (Cr7C3), began forming at approximately 820°C and increased in quantity and size up to 900°C. Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectroscopy (EDX) confirmed that these precipitates significantly enhance the material's hardness, with microhardness values of 320 HV for the precipitates compared to 270 HV for the matrix. Tensile tests showed that the tensile strength of the steel increases with soaking time at 900°C, reaching a maximum after 3 minutes due to the strengthening effect of the precipitates. Beyond this point, strength decreases due to grain coarsening.
Intelligent Energy Optimization in Wind-PV-Battery Microgrids Using AI
Authors: Pandey Amit Kumar , Tiwari P. , Nishad D. K. , Khalid Saifullah 
Published: September 2024

Abstract

This research presents a novel swarm intelligence-based energy management framework for autonomous microgrids integrating wind, photovoltaic, and battery storage resources. Krill Herd, Moth-Flame, Particle Swarm, and Whale Optimization algorithms are employed for adaptive tuning of control parameters, maximizing renewable energy utilization, ensuring power balance, and maintaining voltage/frequency stability under dynamic conditions. A MATLAB/Simulink model of the wind-PV-battery microgrid is developed to evaluate the performance of the proposed AI-driven control approach. Simulations validate the superior performance of swarm-optimized controllers compared to conventional methods, demonstrating improved efficiency, renewable energy harvesting, power quality, and dynamic response. The AI-based energy management significantly enhances the reliability, sustainability, and economic viability of hybrid renewable microgrids. This work presents a significant advancement in optimizing energy flow and enabling intelligent, resilient operation of microgrids under variable conditions, paving the way for wider adoption of sustainable energy systems.
Cargo carrying model of high-speed railway express considering transportation capacity sharing and carbon trading
Authors: Yang Jingshuai , Yan Jiechan , Yang Yu’e 
Published: September 2024

Abstract

In order to make full use of high-speed railway (HSR) transportation capacity and facilitate the low-carbon development of transportation infrastructure, this paper examines the cargo carrying method in the context of transportation capacity sharing of HSR. With carbon trading incorporated into the profit of HSR express, a cargo carrying decision-making model with consideration of carbon trading is developed, considering constraints such as loading capacity of HSR and work ability of stations. Using genetic algorithm as the framework, a multi-loading rules genetic algorithm is designed to solve the model, considering the effect of cargo service types, origin-destination (OD) pairs, and loading priority of HSR trains. The numerical case of Xi'an-Chengdu HSR line is implemented to validate the proposed model by Gurobi solver, and the performance of different algorithm is compared. The results show that the three loading rules proposed are reasonable and the multi-loading rule genetic algorithm outperforms them. From the sensitivity analysis, it was determined that enhancing station work ability and considering train transfer can increase total profit.
Anomalous US-wide prevalence of reversion mutants in the emergence of Omicron BA.1
Authors: Kakeya Hideki 
Published: September 2024

Abstract

Surface glycoprotein sequences of the Omicron BA.1 lineage that included only reverse mutations were searched for in NCBI GenBank, where the dates and locations of collection were retrieved for each mutant. Data from BA.1.x lineages that emerged during the same period of time were used as a control, and the early spread of these mutants was compared. The number of states where the first 20 samples were found was compared between the group of reversion mutants and the control group. The result reveals that the reversion mutants were widespread from the early days of their emergence, showing statistically significant differences compared to the control group. Whether the origin of the reversion mutants is natural infection from abroad, domestic natural mutation, or leakage from a laboratory, it is usually possible to make a rough estimation of the epicenter by tracing its spread. The wide prevalence of reversion mutants of BA.1 from the beginning of their emergence is highly anomalous, which cannot be explained by any theories of molecular biology, virology, or infectiology currently known.
A classroom behavior recognition method based on Temporal Attention Mechanism and Fragment-based Mechanism
Authors: Lai Xiang , Fu Ruijie , Jiang Hongbo , Peng Yue , Tang Qiang , Yao Jinghua , Liu Tao 
Published: September 2024

Abstract

Classroom behavior detection aims to enhance classroom efficiency by collecting information on students' behavior and developing strategies for improved teaching methods. However, existing models suffer from low efficiency, subjectivity, and recognition accuracy. To address these issues, we propose a classroom behavior recognition model utilizing a dataset from monitoring devices, based on the Temporal Attention Mechanism (TAM) and Fragment-based mechanism of a 3D convolutional neural network. Our TAM applies attention distribution in the time dimension, combined with 3D CAM, as the Channel and temporal attention module (CTAM), resulting in a 10\% accuracy improvement. We integrate 3D-CAM and 3D-SAM with TAM, validating the portability and universality of our time attention mechanism. Furthermore, we introduce the Fragment-based mechanism, synthesizing feature outputs of different time scales for improved classification. By incorporating this mechanism, the model achieves a 30\% accuracy improvement on the classroom behavior dataset. Evaluation on a public dataset demonstrates significant improvements compared to the original model.
Perception of Undergraduate Medical Students and Examiners Towards Grand Objective Structured Clinical Examination
Authors: Hawezy Dawan J. , Barzinjy Saman Tahir , Saadadin Govand , Hawezy Gasheen Abdilwahid , Bakir Waleed 
Published: September 2024

Abstract

Background
The assessment of undergraduate medical students consists of a written component and an advanced clinical competency test that evaluates the students' skills. The Objective Structured Clinical Examinations (OSCE) was only implemented in most medical schools in the last ten years, similar in many developing countries. It was first used with other clinical assessment methods
This study was designed to investigate how medical students in the fourth and sixth grades and examiners perceived the unique format, features, and quality of the GRAND OSCE, a novel method for assessing students. The aim was to understand how this innovative method, distinct from traditional assessment methods, prepares students for real-world scenarios where they encounter different specialty cases.
Additionally, the study aimed to understand the significant role and challenges faced by the Faculty of General Medicine at Koya University in implementing this type of OSCE and to identify potential improvements to enhance the quality of the assessment process.
Method
Following the administration of two distinctGRAND OSCE examinations for the fourth and sixth stages, feedback forms were created using Google Forms and distributed to both students and examiners. The feedback forms covered all aspects of the examination process. The responses were then rigorously analyzed using the scientific tool SPSS, allowing for a comparison between students from both stages and the faculty and external examiners.
Results
In the survey of 104 undergraduate students (78% of attendants), the majority (84n,80%) rated the fairness of the assessment as good, which is the primary goal of this version of OSCE. A large percentage (89n,85.6%) agreed that the examination covered a wide range of clinical skills. Eighty students (76.85%) believed the patients cooperated and found the findings precise; on the other hand, The obtained responses from 24 out of 33 faculty staff members (75%) and 29 out of 35 external examiners (82%) strongly agreed that the examination adequately covered a wide variety of clinical abilities. Only two of 68 examiners (2.9%) believed the examination needed more to provide a learning opportunity.
Conclusion
With the insightful suggestions for improvement provided by the participants, this version of OSCE has a promising potential to evolve into a fair, objective clinical tool for assessing medical students, instilling a sense of optimism for its future development.
Novel Approaches to Reducing Gastric Cancer Risk through Electromagnetic Signal Treatment of Pork Meat and Its Effects on MAPK Pathway Modulation
Authors: Justine Elsa Easter , Samad Abdus , Dhandapani Sanjeevram , Lee Dongbin , Kim Yeon-Ju 
Published: September 2024

Abstract

The consumption of red meat, such as pork, beef, and lamb, has been associated with an incidence of gastric cancer (GC) due to certain processing and preparation methods. However, electromagnetic signal (EMS) exposure has demonstrated beneficial effects on food preparation, potentially improving food quality and reducing risk factors associated with GC. In our study, pork meat was treated with EMS, and this meat was investigated for its potential to reduce GC risk viain vitroand transcriptomic approaches. 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide and quantitative reverse transcription polymerase chain reaction assays were used to explore the cytotoxic and apoptotic effects of EMS-treated (EMS-T) and untreated (EMS-UT) pork meat extract on GC cells (KATO-III and S1M). The results indicated that KATO-III and S1M cells exhibited the highest inhibitory effect, with 32.8% and 24.53% inhibition, respectively, following treatment with 500 μg/mLEMS-T pork meat extract as compared to the EMS-UT meat extract treatment. Additionally, the apoptotic markers,CASP3andCASP9, and theBAX/BCL2ratio suggested that apoptosis was initiated upon treatment with EMS-T pork meat extract. Transcriptomic analysis further revealed that EMS-T pork treatment led to 217 uniquely differentially expressed genes in KATO-III cells, with 135 suppressed genes and 82 stimulated genes. Notably, the mitogen-activated protein kinase (MAPK) pathway emerged as a notably interconnected signaling pathway, with key genes, such asDUSP5, MAP4K2, TGFB3, CACNA2D2, CD14, CACNA1H, andEREG, showing significant interactions. Specifically, EMS-T pork meat extract treatment resulted in the suppression ofMAP4K2, TGFB3, andCD14, whileDUSP5andCACNA2D2were stimulated in KATO-III cells, contrasting with the gene expression profiles observed in cancer patients compared to healthy individuals according to The Cancer Genome Atlas data. Overall, these findings suggest that EMS-T meat may offer a novel approach for reducing GC risk by modulating apoptosis and the MAPK signaling pathway
CV Content Recognition and Organization Framework based onYOLOv8 and Tesseract-OCR Deep Learning Models
Authors: Sarhan Amany M. , Ali Hesham A. , Wagdi Mariam , Ali Bassant , Adel Aliaa , Osama Rahf 
Published: September 2024

Abstract

The growing number and variety of resumes in the job market makes it necessary to have better systems for sorting them efficiently. The task of finding suitable candidates for an open job position would be a repetitive and time-consuming task, especially from a large pool of candidates. The same task could be an actual fair screening and shortlisting though. Indeed, it is not acceptable to lose the chance to employ top skilled candidates because of the tardiness of the whole census or the poor selection caused by human fault. In order to help automating this cycle, this study introduces a new CV recognition system that combines advanced technologies: You Only Look Once (YOLO) for detecting important sections in the CV files, Tesseract-OCR for extracting text in each section, and an efficient post processing steps for correcting possible faults in recognized text. We also propose to automatically organize the data within the CV into a database to facilitate performing any data analytics or searching processes. To evaluate the system, we used a dataset of 1,300 resumes in JPEG, PNG, and JPG formats from various sources, showcasing different formats, languages, and quality levels. Preprocessing steps were taken to ensure the data is high quality and consistent. This approach saves time and improves the efficiency and accuracy of sorting resumes, helping HR teams focus on more strategic tasks and making the hiring process easier and less stressful. Results support the validity of the proposed system through the experiments with this diverse dataset. The system’s effectiveness was confirmed through experiments with this diverse dataset, achieving a mean Average Precision (mAP) of 92.1%, a precision rate of 92.2%, and a recall rate of 86.0%.
Methods for verifying the relationship between weak uranium information and uranium-rich geological bodies in the covered areas of Erlian Basin, Inner Mongolia
Authors: Shi Liancheng , Li Huaiyuan , Wang Nanping , Han Penghui , Shen Zhengxin , Yu Cong , ZHANG Xiang , Meng Xiangbao 
Published: September 2024

Abstract

The Erlian Basin, being an inportant base for sandstone-type uranium deposit exploration in Chin, is affected by overburden layers, resulting in indistinct characteristics of uranium anomalies in airborne gamma-ray spectrometry(AGS). To harness the potential of AGS, it is imperative to develop effective verification methods that can identify the spatial relationship between weak uranium information and deep uranium-rich geological bodies. This study presents a comprehensive investigation of geophysical and geochemical measurements conducted in four distinct areas. There is a significant positive correlation between the ground gamma spectrometry equivalent uranium(eUGGS) content, soil radon concentration(CRn), geoelectrochemical uranium(UGEC) and metal activity state uranium(UMAS) content directly above and at the edges of the uranium-rich geological bodies. When the buried depth of the uranium-rich geological body is more than 100 m, the eUGGS content and CRn above it mainly show locally weak increases with low values, while sharp anomalies and high-value zones are more likely to appear at its edges. Meanwhile, UGEC and UMAS content show sawtooth-like uranium peak anomalies on the profile. The verification methods and corresponding interpretation indicators, namely GGS, CRn, GEC or MAS measurements, can quickly reveal the spatial relationship and provide reliable basis for concealed uranium deposits exploration.