bluuebunny/crossref_metadata_2025_split huggingface dataset:
PROTOTIPE SISTEM INFORMASI PEMESANAN VALET PARKING BERBASIS INTERNET OF THINGS
Authors: Alfian Ady , Fauzi Lutfi , Firmansyah Heri 
Published: October 2022

This final project aims to create a prototype of information system for parking valet reservation based on Internet of Things, which functions to monitor and reserve a parking space remotely in order to make it easier for vehicle users to get a parking space. Making this prototype begins with collecting needs, build prototyping, evaluation, prototyping, encodes the system and evaluates the system. This project Using codular android application as a monitoring and parking space reservation media, MCU node as a control center and data receiver, red LED indicates the parking slot is occupied, LCD to display parking customer profile data, sensor line proximity to detect vehicles and as a parking fee calculation trigger, firebase realtime database as a temporary storage medium. Based on the results of testing this project that the researchers have done, it has been successfully built and has been tested to function properly. The application can send vehicle profile data, display parking availability and display parking fee calculations. Nodes MCU can retrieve data from the database to be implemented in turning on the led and displaying parking customer profile data on the LCD.
Penelitian ini bertujuan untuk membuat prototipe sistem informasi pemesanan valet parking berbasis Internet of Things, yang berfungsi untuk memonitoring dan memesan tempat parkir dari jarak jauh dalam rangka mempermudah pengguna kendaraan mendapatkan tempat parkir. Pembuatan prototipe ini di awali dengan melakukan pengumpulan kebutuhan, membangun prototyping, evaluasi prototyping, mengkodekan sistem dan mengevaluasi sistem. Alat ini menggunakan aplikasi android kodular sebagai media monitoring dan pemesanan tempat parkir, Node MCU sebagai pusat kendali dan penerima data , lampu LED merah menandakan slot parkir sudah di tempati, LCD untuk menampilkan data profil pemesan parkir, sensor line proximity untuk mendeteksi kendaraan dan sebagai pemicu penghitungan biaya parkir, firebase database realtime sebagai media penyimpanan sementara. Berdasarkan hasil pengujian alat ini yang telah peneliti lakukan sudah berhasil dibuat dan sudah diuji dapat berfungsi dengan baik. Aplikasi dapat mengirim data profil kendaraan, menampilkan ketersediaan parkir dan menampilkan penghitungan biaya parkir. Node MCU dapat mengambil data dari database untuk di implementasikan dalam menyalakan led dan menampilkan data profil pemesan parkir pada LCD.
PENILAIAN BAHAN AJAR MODUL MATERI “PENERAPAN DESAIN BUSANA UNTUK BERBAGAI BENTUK TUBUH WANITA”
Authors: Syela Yosia , Melly  Prabawati  
Published: October 2022

This study aims was to created and to know Assestment of “”design implementation for various female body shape module”. The method used in this research was a pre-experimental design with one-shut case study and used quantitative descriptive to collected the data. The research used a single variable, design implementation for various female body shape module. The indicators of this research is aspects of material, aspects of presentation and aspects of language, self instruction, self contained, stand alone, adaptive, and user friendly, used a questionnaire instrument closed-open with panelists to supported a data as of the collected technique. The results of the research showed that the assessment of design implementation for various female body shape module based on the evaluation of the material and media experts is in the Very Good category with the percentage is 87,5%. The percentage of material propelness is 85% with the highest poin is the material aspect with the percentage is 87.5% and media propelness is 90% with the highest poin is Self Instructional aspects is 92.5%. The module is in the category very good because design implementation for various female body shape matched the criteria based on material experts and media experts. Self Instructional Aspects is the highest aspect that affects the results of the assessment of this study.
Comparison of second-look arthroscopic evaluations between two femoral tunnel locations in anatomical single-bundle ACL reconstruction
Authors: Zhang Lixuan , Huo Jiabang , Guo Hanming , Li Yingshan 
Published: October 2022

Abstract
Background
Anterior cruciate ligament reconstruction (ACL-R) is the best treatment for anterior cruciate ligament (ACL) rupture, and the most widely used reconstruction technique is single beam reconstruction. One of the most important factors affecting the knee function after ACL-R is the location of the femoral tunnel. Therefore, how to accurately select the femoral tunnel during operation, and how to directly evaluate the integration, tension and synovial coverage of the graft after operation are all difficult problems. The purposes of this study were to determine the best femur tunnel for anterior cruciate ligament reconstruction based on a second-look arthroscopic study.
Methods
A total of 67 patients who were treated with single-beam ACL-R using allografts in our department between January 2014 and February 2020 ultimately met the inclusion criteria and were contacted for the study. A second-look arthroscopic evaluation was carried out one year after anatomical single-bundle ACL reconstruction. According to the postoperative 3D-CT, the patients were divided into the anatomic anteromedial (AM) group and the anatomic mid-bundle (center-center) group based on the femoral tunnel location.
Results
In terms of the Lysholm score, a significant difference existed between the groups at the last follow-up evaluation (p < 0.05). The tension of the graft involved was flabby in 2 patients (5.7%) in the center-AM group and 5 patients (15.6%) in the center-center group. Type I (tight cruciate ligament-like structured graft with complete synovial coating) and type II (tight, bundle-like structured graft with incomplete or lacking synovial coating) were found in 33 patients (94.29%) in the center-AM group and 27 patients (84.38%) in the center-center group. The statistical analysis showed a significant correlation between the femoral tunnel position and the arthroscopic evaluation of the grafts according to the Marburger Arthroscopy Score (MAS) (p = 0.02 < 0.05; Χ2 = 7.865).
Conclusions
Our data demonstrated a significant correlation between the femoral tunnel position, clinical condition, and arthroscopic graft constitution. We found that the anatomic AM bundle position of the graft was better than the anatomic mid-bundle placement after ACL reconstruction.
The effect of APN, hs-CRP and APN/hs-CRP in periodontitis with DAA
Authors: Cheng Rui , Xu Xiaojiang , Yang Shurong , Mi Zhongqian , Zhao Yong , Wang Chong , Shi Xuexue , gao Jinhua , Yu Feiyan , Ren Xiuyun 
Published: October 2022

Abstract
Background: Common chronic infections induced low-grade inflammation has been correlated with atherosclerosis as supported by strong evidence. The balance between pro-and anti-inflammatory factors was exploited to elucidate the effects of chronic periodontitis on diabetes-associated atherosclerosis.
Methods: Study subjects encompassed 30 SPF male rats randomly divided into four groups: A group,normal control(NC), B group, type 2 diabetes mellitus (T2DM), C group, chronic periodontitis (CP), D group (DM+CP). After developing the model, blood samples were collected from the angular vein analyze serum adiponectin (APN), high sense- c reactive protein (hs-CRP), and blood lipid. the carotid artery was isolated for HE staining.
Result: Compared with group A, the serum APN in group B, C and D decreased gradually with the progression of the disease. Serum hS-CRP in group B, C and D was significantly increased. At T3, T4 and T5 in group B, C and D, APN/hs-CRP significantly decreased .TC, LDL and TG significantly increased in group B, D; HDL significantly decreased in group C. Carotid artery HE staining showed: compared with group A, different degrees of endothelial defect, destruction of elastic fibers in the middle membrane, disorder of smooth muscle arrangement, and partial dissolution 、 fragmentation and Calcium salt deposition necrosis occurred in group B, C and D.
Conclusion: Enhanced systemic inflammation, decreased adiponectin level, and disorganized lipid metabolism with or without type 2 diabetes attributed to local inflammation of periodontitis can result in an imbalance of pro-inflammatory and anti-inflammatory effects. Therefore, it’s more meaningful to predict the progression of DAA with anti-inflammatory/pro-inflammatory variation.
Natural Language Processing (NLP) to Facilitate Abstract Review in Medical Research: The Application of BioBERT to exploring the 20-years use of NLP in medical research
Authors: Masoumi Safoora , Amirkhani Hossein , Sadeghian Najmeh , Shahraz Saeid 
Published: October 2022

AbstractBackgroundAbstract review is a time and labor-consuming step in the systematic and scoping literature review in medicine. Automation methods, typically natural language processing (NLP), may efficiently replace manual abstract screening. This study applies NLP to a deliberately selected literature review problem, the trend of using NLP in medical research, to demonstrate the performance of this automated abstract review model.MethodsScanning PubMed, Embase, PsycINFO, and CINAHL databases, we identified 22,294 with a final selection of 12,817 English abstracts published between 2000 to 2021. We invented a manual classification of medical fields, three variables, i.e., the context of use (COU), text source (TS), and primary research field (PRF). A training dataset was developed after reviewing 485 abstracts. We used a language model called Bidirectional Encoder Representations from Transformers to classify the abstracts. To evaluate the performance of the trained models, we report a micro f1-score and accuracy.ResultsThe trained models' micro f1-score in classifying abstracts the three variables were 77.35 % for COU, 76.24% for TS, and 85.64 % for PRF. The average annual growth rate (AAGR) of the publications was 20.99% between 2000 and 2020 (72.01 articles (95%CI: 56.80-78.30) yearly increase), with 81.76% of the abstracts published between 2010 and 2020. Studies on neoplasms constituted 27.66% of the entire corpus with an AAGR of 42.41%, followed by studies on mental conditions (AAGR=39.28%). While electronic health or medical records comprised the highest proportion of text sources (57.12%), omics databases had the highest growth among all text sources with an AAGR of 65.08%. The most common NLP application was clinical decision support (25.45%).ConclusionsBioBERT showed an acceptable performance in the abstract review. If future research shows the high performance of this language model, it can reliably replace manual abstract reviews.
Language Models Learn Sentiment and Substance from 11,000 Psychoactive Experiences
Authors: Friedman Sam Freesun , Ballentine Galen 
Published: October 2022

Abstract
With novel hallucinogens poised to enter psychiatry, a unified framework for quantifying which changes in consciousness are optimal for treatment is needed. Using transformers (i.e. BERT) and 11,816 publicly-available drug testimonials, we first predicted 28-dimensions of sentiment across each narrative, and then validated these predictions with adjudication by a clinical psychiatrist. Secondly, we fine-tuned BERT to predict biochemical and demographic information from natural language testimonials of drug experiences. Thirdly, canonical correlation analysis (CCA) linked 52 drugs' receptor affinities with word usage, revealing 11 statistically-significant latent receptor-experience factors, each mapped to a 3D cortical atlas. Together, these machine learning methods elucidate a neurobiologically-informed, temporally-sensitive portrait of drug-induced subjective experiences. The models’ results converged, revealing a pervasive distinction between the universal psychedelic heights of feeling in contrast to the grim, mundane, and personal experiences with addiction and mental illness. MDMA was linked to "Love", DMT and 5-MeO-DMT to "Mystical Experiences" and “Entities and Beings”, and other tryptamines to "Surprise", "Curiosity" and "Realization". Applying these models to real-time biofeedback, practitioners could delicately calibrate the course of therapeutic sessions.
A phase-separated transcription factor regulates an interspecific barrier
Authors: Fujii Sota , Yamamoto Eri , Tangpranomkorn Surachat , Kimura Yuka , Miura Hiroki , Yamaguchi Nobutoshi , Niidome Maki , Shimosato-Asano Hiroko , Kato Yoshinobu , Wada Yuko , Ito Toshiro , Takayama Seiji 
Published: October 2022

Abstract
Pre-zygotic interspecies incompatibility in angiosperms is an important mechanism to prevent unfavorable hybrids between species. We used a genome-wide association study to identify Stigmatic privacy 2 (SPRI2), a transcription factor with a zinc-finger domain, which regulates interspecies barriers in Arabidopsis thaliana. CRISPR/Cas9-mediated genome-editing analyses indicated that SPRI2 and its SPRI2-like paralog are required for rejection of the male pollen grains of other species in the female pistils. We also show that SPRI2 and SPRI2-like express interspecific incompatibility by directly regulating the mRNA transcription of two xylan O-acetyltransferases (TBL45 and TBL40) related to cell wall modification. In addition, they control the mRNA accumulation level of SPRI1, a key factor required for interspecific pollen rejection found in our previous study. SPRI2 is localized as condensed structures in the nucleus formed via liquid-liquid phase separation (LLPS). A prion-like sequence found in the N-terminal region of SPRI2 is responsible for the formation of the condensates. Our discovery of the LLPS-regulated SPRI2 has revealed the connections between transcriptional regulation, cell wall modification, and SPRI1 in interspecific pollen rejection.
Optimization of composting methods for efficient use of cassava waste, using microbial degradation
Authors: He Xiangning , Cong Riyao , Gao Wei , Duan Xueying , Gao Yi , Li Hong , Li Zepu , Diao Hailin , Luo Jianju 
Published: October 2022

Abstract
With the recent revolution in the green economy, agricultural solid waste resource utilization has become an important project. A small-scale laboratory orthogonal experiment was set up to investigate the effects of C/N ratio, initial moisture content and fill ratio (v cassava residue: v gravel) on the maturity of cassava residue compost by adding Bacillus subtilis and Azotobacter chroococcum. The highest temperature in the thermophilic phase of the low C/N ratio treatment is significantly lower than the medium and high C/N ratios. The C/N ratio and moisture content have a significant impact on the results of cassava residue composting, while the filling ratio only has a significant impact on the pH value and phosphorus content. Based on comprehensive analysis, the recommended process parameters for pure cassava residue composting are a C/N ratio of 25, an initial moisture content of 60%, and a filling ratio of 5. Under these conditions, the high-temperature conditions can be reached and maintained quickly, the organic matter has been degraded by 36.1%, the pH value has dropped to 7.36, the E4/E6 ratio is 1.61, the conductivity value has dropped to 2.52 mS/cm, and the final germination index increased to 88%. The thermogravimetry, scanning electron microscope, and energy spectrum analysis also showed that the cassava residue was effectively biodegraded. Cassava residue composting with this process parameter has great reference significance for the actual production and application of agriculture.
Artificial-intelligence-based antivirus specialized in Citadel malware pattern recognition
Authors: Santos Carlos Henrique Macedo dos , de Lima Sidney Marlon Lopes 
Published: October 2022

Abstract
Background and Objective:
The constant growth of invasions and information theft by using infected software has always been
a problem. According to McAfee labs in 2020, on average, 480 new viruses are created each hour.
The means of identifying such threats, categorizing and creating vaccines may not be that fast. Thanks
to the increasing processing power and the popularity of artificial intelligence, it is now possible to
integrate intelligence on an antivirus engine to enhance its protecting capabilities. And doing so with
good algorithms and parameterization can be a key asset in securing one’s environment. In this work
we analyze the overall performance of our authorial antivirus and compare it with other state-of-art
antiviruses.
The means of identifying such threats, categorizing and creating vaccines may not be that fast.
Thanks to the increasing processing power of computers and the popularity of artificial intelligence,
it is now possible to integrate intelligence on an antivirus engine to enhance its protecting capabilities.
And doing so with good algorithms and parameterization can be a key asset in securing one’s
environment. In this work we are analyzing the overall performance of our authorial antivirus and
comparing it with other state-of-art antiviruses.
Methods: In this work we are using mELM, a deep neural network which can perform quick
training sections and have a satisfactory accuracy when classifying unknown files that may or may
not be infected with Citadel. Our virus database is built with many examples of well-known infected
files and our results are compared with 7 other intelligent antiviruses created by other authors.
Results: Our antivirus achieves an overall performance of 91.33% when classifying benign and
malware PE (portable executable) programs. Different initial conditions, learning functions and architectures
of our antivirus are investigated in order to maximize their accuracy.
Conclusions: In this work we have found that mELMs implementations are feasible and its performance
can match state-of-art implementations. Its training and classification time is among the
smallest found and the accuracy when detecting citadel-infected PEs is acceptable.
Design, Implementation, and Evaluation of the Computer-aided Clinical Decision Support System based on Learning-to-Rank: Collaboration between physicians and machine learning in the differential diagnosis process
Authors: Miyachi Yasuhiko , Ishii Osamu , Torigoe Keijiro 
Published: October 2022

Abstract
Background
We are researching, developing, and publishing the Clinical Decision Support System based on Learning-to-Rank. The main objectives are 1) Supporting differential diagnoses by internists and general practitioners and 2) Preventing diagnostic errors by physicians. The main features are that "A physician inputs a patient's symptoms, findings, and test results to the system, and the system outputs a ranking list of possible diseases."
Method
The software libraries for machine learning and artificial intelligence are TensorFlow and TensorFlow Ranking. The prediction algorithm is Learning-to-Rank., with a listwise approach. The ranking metric is Normalized Discounted Cumulative Gain (NDCG). The loss functions are Approximate NDCG (A-NDCG) and Gumbel Approximate NDCG (G-A-NDCG). We evaluated the machine learning performance on k-fold cross-validation. We evaluated the differential diagnosis performance with actual cases.
Results
The machine learning performance of our system was much higher than that of the conventional system. The machine learning performance using G-A-NDCG was slightly higher than that of A-NDCG. The differential diagnosis performance of our system was much higher than that of the conventional system. We have shown that the Clinical Decision Support System prevents physicians' diagnostic errors due to confirmation bias.
Conclusions
We have demonstrated that the Clinical Decision Support System is useful for supporting differential diagnoses and preventing diagnostic errors. We believe that Differential diagnosis by physicians and Learning-to-Rank by machine has a high affinity. We found that Information Retrieval and Clinical Decision Support Systems have much in common (Target data, Learning-to-Rank, etc.). We believe that Clinical Decision Support Systems have the potential to support 1) recall of rare diseases, 2) differential diagnoses for difficult-to-diagnose diseases, and 3) prevention of diagnostic errors. We also believe that our system has the potential to evolve into an Explainable Clinical Decision Support System.
The applications of geomatics to predicate forest fire based on earlier pre-fire conditions. A case study: Belezma national park -Algeria
Authors: AKAKBA Ahmed , Lahmar Belkacem 
Published: October 2022

Abstract
Remote sensing has become a primary data source for fire hazard prediction, including other techniques, but not limited to geographic information systems (GIS), artificial intelligence (AI), and machine learning. This research paper aims to measure the vulnerability to forest fires and define pre-forest fire criteria in semi-dry climates. The study area is "Belezma" national park; which represents one of the most vital national parks in Algeria since it contains numerous rarest plants in the world; and over 20% of reserved animals; as a result, the park represents an example of fire prevention in areas of high environmental values. The vulnerability is assessed by using various pre-fire factors: Normalized Difference Vegetation Index (NDVI), Normal Difference Water Index (NDWI), and land surface temperature (LST). Once the variables are generated based on Landsat 8 satellite imagery collected between May and August of the same year, the proposed fire model is implemented into a GIS environment using the analytical hierarchy process (AHP). The results demonstrated that elevation had a significant impact compared to other factors; as a result, most fires are triggered during the dry period where the vulnerability is low in the highest elevation area; vice versa, the vulnerability values are higher in the louder elevation zones. Finally, the validation was performed by using two approaches: firstly, we checked the results on the ground, where we detected 150–300 years old non-burned trees in the low vulnerability area using the dendrochronology method; secondly, we geolocated every local wildfire between 2018 and 2020; then measuring the spatial correlation between recorded wildfires and the vulnerability using Moran index method.
Association of the Pretreatment Lung Immune Prognostic Index with Immune Checkpoint Inhibitor Outcomes in Patients with Advanced Hepatocellular carcinoma
Authors: Sun Tao , Guo Yusheng , Sun Bo , Chen Lei , Ren Yanqiao , Zhu Licheng , Zhang Lijie , Liu Yiming , Zheng Chuansheng 
Published: October 2022

Abstract
Objective
To evaluate whether the pretreatment Lung Immune Prognostic Index (LIPI) is associated with outcomes in advanced hepatocellular carcinoma (HCC) patients under ICI.
Methods
A two-center retrospective study of patients with HCC treated with immune checkpoint inhibitors (ICIs) between January 2018 and January 2021 was performed. The patients were stratified into three groups (good LIPI, intermediate LIPI, and poor LIPI) according to pretreatment LIPI based on dNLR (neutrophil count/ [white blood cell count - neutrophil count]) greater than 3 and LDH greater than LDH normal value. The primary endpoints were overall survival (OS) and progression-free survival (PFS). The second endpoints were disease control rate (DCR) and objective response rate (ORR).
Results
In the pooled cohort (n = 224), 80 (35.7%) had a good LIPI (zero factor), 91 (40.6%) had intermediate LIPI (one factor), and 53 (23.7%) had poor LIPI (two factors). The median follow-up was 25.1 months. Median OS was 16.8 months, 12.5 months, and 9.5 months for the good, intermediate, and poor LIPI groups, respectively (P < 0.0001). Median PFS was 11.8 months, 7.8 months, and 4.0 months for the good, intermediate, and poor LIPI groups, respectively (P < 0.0001) The intermediate LIPI and poor LIPI were independently associated with shorter OS, PFS, and lower ORR, DCR (P༜0.05).
Conclusion
Pretreatment LIPI was correlated with worse outcomes for ICIs suggesting that LIPI could be promising biomarker for advanced HCC patients under ICIs.