bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development of high-resolution 72 h precipitation and hillslope flood maps over a tropical transboundary region by physically based numerical atmospheric–hydrologic modeling
Authors: Trinh T. , Ho C. , Do N. , Ercan A. , Kavvas M. L. 
Published: July 2020

Abstract
Long-term, high spatial and temporal resolution atmospheric and hydrologic data are crucial for water resource management. However, reliable high-quality precipitation and hydrologic data are not available in various regions around the world. This is, in particular, the case in transboundary regions, which have no formal data sharing agreement among countries. This study introduces an approach to construct long-term high-resolution extreme 72 h precipitation and hillslope flood maps over a tropical transboundary region by the coupled physical hydroclimate WEHY-WRF model. For the case study, Da and Thao River watersheds (D-TRW), within Vietnam and China, were selected. The WEHY-WRF model was set up over the target region based on ERA-20C reanalysis data and was calibrated based on existing ground observation data. After successfully configuring, WEHY-WRF is able to produce hourly atmospheric and hydrologic conditions at fine resolution over the target watersheds during 1900–2010. From the modeled 72 h precipitation and flood events, it can be seen that the main precipitation mechanism of DRW and TRW are both the summer monsoon and tropical cyclone. In addition, it can be concluded that heavy precipitation may not be the only reason to create an extreme flood event. The effects of topography, soil, and land use/cover also need to be considered in such nonlinear atmospheric and hydrologic processes. Last but not least, the long-term high-resolution extreme 72 h precipitation and hillslope flood maps over a tropical transboundary region, D-TRW, were constructed based on 111 largest annual historical events during 1900–2010.
Hydro-meteorological characteristics and occurrence probability of extreme flood events in Moroccan High Atlas
Authors: Saidi Mohamed El Mehdi , Saouabe Tarik , El Alaoui El Fels Abdelhafid , El Khalki El Mahdi , Hadri Abdessamad 
Published: July 2020

Abstract
Flood frequency analysis could be a tool to help decision-makers to size hydraulic structures. To this end, this article aims to compare two analysis methods to see how rare an extreme hydrometeorological event is, and what could be its return period. This event caused many deadly floods in southwestern Morocco. It was the result of unusual atmospheric conditions, characterized by a very low atmospheric pressure off the Moroccan coast and the passage of the jet stream further south. Assessment of frequency and return period of this extreme event is performed in a High Atlas watershed (the Ghdat Wadi) using historical floods. We took into account, on the one hand, flood peak flows and, on the other hand, flood water volumes. Statistically, both parameters are better adjusted respectively to Gamma and Log Normal distributions. However, the peak flow approach underestimates the return period of long-duration hydrographs that do not have a high peak flow, like the 2014 event. The latter is indeed better evaluated, as a rare event, by taking into account the flood water volumes. Therefore, this parameter should not be omitted in the calculation of flood probabilities for watershed management and the sizing of flood protection infrastructure.
Effect of liquid digestate recirculation on biogas production and enzyme activities for anaerobic digestion of corn straw
Authors: Xue Shuaixing , Qiu Ling , Guo Xiaohui , Yao Yiqing 
Published: July 2020

Abstract
To accelerate the degradation of substrate, 50% liquid digestate recirculation (LDR) was used in the anaerobic digestion (AD) of corn straw. The effects of recirculation on the enzyme activities and biogas production were investigated by comparing with control reactor (ReactorCK). During the AD process, the fermentation system with 50% LDR was more stable. The average biogas and methane production in ReactorLDR were 7,891 mL·d−1 and 347 mL CH4·g−1 VSadded·d−1 respectively. The total volatile fatty acids (TVFAs) concentration in the two reactors both increased at first and then decreased with time. The LDR made the VFAs accumulation significant, especially propionic acid accumulation in 4 ∼ 16 days. The maximum peak value of cellulase, xylanase, dehydrogenase and coenzyme F420 activities in ReactorLDR were 0.51 mg·g−1·h−1, 0.29 mg·g−1·h−1, 4.88 mL·g−1·h−1 and 6.69 μmol·L−1, respectively, which were higher than that in ReactorCK. With or without recirculation, the concentration of TVFAs was positively correlated with cellulase, xylanase and dehydrogenase activities, while was negatively correlated with coenzyme F420 activity. Besides, a very significant correlation existed between hydrolase and dehydrogenase activities and daily biogas production in ReactorCK. And the peaks of cellulase, xylanase and dehydrogenase activities appeared ahead of the peak of daily biogas production with the LDR.
Recent Advances in the Cross-Coupling of Arylaluminums with Electrophile Reagents
Authors: Qinghan Li , Xin Jiang , Kun Wu , Ruiqiang Luo , Zhihao Zhang , Meng Liang , Zheyao Huang 
Published: July 2020

:
The arylaluminum reagents are good nucleophiles for organic reactions because of their
high reactivity, high chemoselective, low toxicity, and ease of preparation. Therefore, arylaluminum
reagents are widely applied in many organic reactions. This review summarizes the recent advancement
of the arylaluminum reagents applied in cross-coupling reactions, involving various reaction
systems and properties of reaction.
Synthesis and Apoptotic Activities of New 2(3H)-benzoxazolone Derivatives in Breast Cancer Cells
Authors: Erdag Emine , Becer Eda , Mulazim Yusuf , Vatansever Hafize Seda , Kabadayı Hilal , Kesanli Banu 
Published: July 2020

Background:
2(3H)-Benzoxazolone derivatives are preferential structural blocks in pharmacological
probe designing with the possibility of modifications at various positions on the core structure. Benzoxazolones
showed various biological activities such as analgesics, anti-inflammatory and anti-cancer.

Objective:
In the present work, we have prepared new Mannich bases of 2(3H)-benzoxazolone derivatives and
evaluated their cytotoxicities and proapoptotic properties in MCF-7 breast cancer cell line.

Methods:
The structures of these compounds were characterized by FT-IR, elemental analysis, 1H and 13C
NMR. Cytotoxicities of all the target compounds were investigated by MTT assay. Apoptotic properties of
compounds were evaluated by immunocytochemistry using antibodies against caspase-3, cytochrome-c, FasL,
and also TUNEL assay.

Results:
These two novel compounds, 1 and 2, both have the same piperazine substituent on the nitrogen atom
of benzoxazolone and the main difference in the structures of these compounds is the presence of Cl substituent
at the 5- position of the benzoxazolone ring. MTT results showed that compounds 1 and 2 were effective in
terms of reduction of cell viability at 100μM and 50μM concentration for 48h, respectively. As a result of immunohistochemical
staining, Fas L and caspase-3 immunoreactivities were significantly increased in MCF-7
cells after treatment with compound 1. Additionally, caspase-3 and cytochrome-c immunoreactivities were also
increased significantly in MCF-7 cells after treatment with compound 2. The number of TUNEL positive cells
was significantly higher in MCF-7 cells when compared with the control group after treatment with both compounds
1 and 2.

Conclusion:
It could be concluded that N-substituted benzoxazolone derivatives increase potential anti-cancer
effects and they could be promising novel therapeutic agents for chemotherapy.
The Anti-Breast Cancer Effects of Green-Synthesized Zinc Oxide Nanoparticles Using Carob Extracts
Authors: Pouresmaeil Vahid , Haghighi Shaghayegh , Raeisalsadati Asieh S. , Neamati Ali , Homayouni-Tabrizi Masoud 
Published: July 2020

Background:
The use of nanoparticles synthesized by the green method to treat cancer is fairly recent.
The aim of this study was to evaluate cytotoxicity, apoptotic and anti-angiogenic effects and the expression
of involved genes, of Zinc Oxide Nanoparticles (ZnO-NPs) synthesized with Carob extracts on different human
breast cancer cell lines.

Methods:
ZnO-NPs were synthesized using the extracts of Carob and characterized with various analytical
techniques. The MCF-7 and MDA-MB231 cells were treated at different times and concentrations of ZnO-NPs.
The cytotoxicity, apoptosis, and anti-angiogenic effects were examined using a series of cellular assays. Expression
of apoptotic genes (Bax and Bcl2) and anti-angiogenic genes, Vascular Endothelial Growth Factor (VEGF)
and its Receptor (VEGF-R) in cancer cells treated with ZnO-NPs were examined with Reverse Transcriptionquantitative
Polymerase Chain Reaction (RT-qPCR). The anti-oxidant activities of ZnO-NPs were evaluated by
ABTS and DPPH assay.

Results:
Exposure of cells to ZnO-NPs resulted in a dose-dependent loss of cell viability. The IC50 values at 24,
48, and 72 hours were 125, 62.5, and 31.2μg/ml, respectively (p<0.001). ZnO-NPs treated cells showed, in
fluorescent microscopy, that ZnO-NPs are able to upregulate apoptosis and RT-qPCR revealed the upregulation
of Bax (p<0.001) and downregulation of Bcl-2 (p<0.05). ZnO-NPs increased VEGF gene expression while
decreasing VEGF-R (p<0.001). The anti-oxidant effects of ZnO-NPs were higher than the control group and
were dose-dependent (p<0.001).

Conclusion:
ZnO-NPs synthetized using Carob extract have the ability to eliminate breast cancer cells and
inhibit angiogenesis, therefore, they could be used as an anticancer agent.
Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review
Authors: Choudhury Avishek , Asan Onur 
Published: July 2020

Background
Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes.

Objective
The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes.

Methods
We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review.

Results
We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting.

Conclusions
This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.
Public Disclosure on Social Media of Identifiable Patient Information by Health Professionals: Content Analysis of Twitter Data
Authors: Ahmed Wasim , Jagsi Reshma , Gutheil Thomas G , Katz Matthew S 
Published: July 2020

Background
Respecting patient privacy and confidentiality is critical for doctor-patient relationships and public trust in medical professionals. The frequency of potentially identifiable disclosures online during periods of active engagement is unknown.

Objective
The objective of this study was to quantify potentially identifiable content shared on social media by physicians and other health care providers using the hashtag #ShareAStoryInOneTweet.

Methods
We accessed and searched Twitter’s API using Symplur software for tweets that included the hashtag #ShareAStoryInOneTweet. We identified 1206 tweets by doctors, nurses, and other health professionals out of 43,374 tweets shared in May 2018. Tweet content was evaluated in January 2019 to determine the incidence of instances where names or potentially identifiable information about patients were shared; content analysis of tweets in which information about others had been disclosed was performed. The study also evaluated whether participants raised concerns about privacy breaches and estimated the frequency of deleted tweets. The study used dual, blinded coding for a 10% sample to estimate intercoder reliability using Cohen κ statistic for identifying the potential identifiability of tweet content.

Results
Health care professionals (n=656) disclosing information about others included 486 doctors (74.1%) and 98 nurses (14.9%). Health care professionals sharing stories about patient care disclosed the time frame in 95 tweets (95/754, 12.6%) and included patient names in 15 tweets (15/754, 2.0%). It is estimated that friends or families could likely identify the clinical scenario described in 242 of the 754 tweets (32.1%). Among 348 tweets about potentially living patients, it was estimated that 162 (46.6%) were likely identifiable by patients. Intercoder reliability in rating the potential identifiability demonstrated 86.8% agreement, with a Cohen κ of 0.8 suggesting substantial agreement. We also identified 78 out of 754 tweets (6.5%) that had been deleted on the website but were still viewable in the analytics software data set.

Conclusions
During periods of active sharing online, nurses, physicians, and other health professionals may sometimes share more information than patients or families might expect. More study is needed to determine whether similar events arise frequently and to understand how to best ensure that patients’ rights are adequately respected.
Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks
Authors: Adler Daniel A , Ben-Zeev Dror , Tseng Vincent W-S , Kane John M , Brian Rachel , Campbell Andrew T , Hauser Marta , Scherer Emily A , Choudhury Tanzeem 
Published: July 2020

Background
Schizophrenia spectrum disorders (SSDs) are chronic conditions, but the severity of symptomatic experiences and functional impairments vacillate over the course of illness. Developing unobtrusive remote monitoring systems to detect early warning signs of impending symptomatic relapses would allow clinicians to intervene before the patient’s condition worsens.

Objective
In this study, we aim to create the first models, exclusively using passive sensing data from a smartphone, to predict behavioral anomalies that could indicate early warning signs of a psychotic relapse.

Methods
Data used to train and test the models were collected during the CrossCheck study. Hourly features derived from smartphone passive sensing data were extracted from 60 patients with SSDs (42 nonrelapse and 18 relapse >1 time throughout the study) and used to train models and test performance. We trained 2 types of encoder-decoder neural network models and a clustering-based local outlier factor model to predict behavioral anomalies that occurred within the 30-day period before a participant's date of relapse (the near relapse period). Models were trained to recreate participant behavior on days of relative health (DRH, outside of the near relapse period), following which a threshold to the recreation error was applied to predict anomalies. The neural network model architecture and the percentage of relapse participant data used to train all models were varied.

Results
A total of 20,137 days of collected data were analyzed, with 726 days of data (0.037%) within any 30-day near relapse period. The best performing model used a fully connected neural network autoencoder architecture and achieved a median sensitivity of 0.25 (IQR 0.15-1.00) and specificity of 0.88 (IQR 0.14-0.96; a median 108% increase in behavioral anomalies near relapse). We conducted a post hoc analysis using the best performing model to identify behavioral features that had a medium-to-large effect (Cohen d>0.5) in distinguishing anomalies near relapse from DRH among 4 participants who relapsed multiple times throughout the study. Qualitative validation using clinical notes collected during the original CrossCheck study showed that the identified features from our analysis were presented to clinicians during relapse events.

Conclusions
Our proposed method predicted a higher rate of anomalies in patients with SSDs within the 30-day near relapse period and can be used to uncover individual-level behaviors that change before relapse. This approach will enable technologists and clinicians to build unobtrusive digital mental health tools that can predict incipient relapse in SSDs.
Sistem Pakar Untuk Mengidentifikasi Hama Dan Penyakit Pada Tanaman Apel Menggunakan Metode Dempster Shafer
Authors: Permana Muhammad Rizky Iman , Azhar Yufiz , Endarto Otto 
Published: July 2020

In this study, the identification of pest and disease attack on apple plant on with case study in Balitjestro. The problem faced is the difficulty of determining the information of pests or diseases that attack on apple plants that must meet an expert directly, it takes a long time and cost. The purpose of this study is to accelerate the information obtained from the identification of pests and diseases of the apple plant and its solution without having to meet an expert. Types of pests and diseases that is in pests are Green Lice, Mites, Trips, Leaf Cats, Leaf Sucking Insects, Black Leaf Caterpillars and Fruit Flies. In the disease there are Flour Powder, Leaf Spot, Upas Mushroom, Cancer and Rotten Fruit. From pest and disease data, all symptom data is 38 data. To get the information, made Expert System. Expert system in this study to determine the results of identification using Dempster Shafer calculation method. Demster Shafer is one of the mathematical theories used to perform a proof that is based on the function of trust and also logic makes sense. At the test stage yields an accuracy value of 100%. However there is one test of the expert only producing one and the results of the system show more than one. But one of the results given by the system is the same as the result given by an expert, then it is justified.
Pengembangan Sistem Perpustakaan Universitas Muhammadiyah Malang Berbasis Android Dengan Metode Waterfall
Authors: Ilham Muhammad , Marthasari Gita Indah , Nuryasin Ilyas 
Published: July 2020

AbstractThe library is a source of knowledge and supporting facilities in each educational institution, especially in this discussion the library is at a college or university. With the library, it will facilitate students in finding various information about teaching and learning, and will also improve the teaching and learning process. The Indonesian Internet Service Providers Association (APPJI) in its survey of several years stated that the use of mobile devices has a very large increase. Recorded from 2013 to 2017 has a very large increase of 61.26 million internet users in Indonesia. The behavior of internet users based on the device used to access the internet is 44.16% in the use of mobile devices, mobile devices and computers ortop 4.49%, the use of mobile devices and computers by 39.28%. This means that the behavior of internet users in Indonesia is very dominant in accessing the internet through mobile devices. Moreover statistics show that mobile internet users are also increasing from year to year. Previously there was a library system at the University of Muhammadiyah Malang web-based. But in this case the tendency of the behavior of internet users in the use of mobile devices, the Muhammadiyah University Library of Malang has a huge opportunity in developing an improving services in meeting the needs of students in finding information needs.AbstrakPerpustakaan adalah sumber ilmu pengetahuan dan sarana penunjang pada setiap instansi pendidikan terutama dalam pembahasannya ini perpustakaan yang ada pada perguruan tinggi atau universitas. Dengan adanya perpustakaan maka akan memudahkan mahasiswa dalam mencari berbagai informasi mengenai belajar mengajar, dan juga akan meningkatkan dalam proses belajar mengajar. Asosiasi Penyelenggara Jasa Internet Indonesia (APPJI) pada surveynya dari beberapa tahun ini menyatakan penggunaa pada perangkat mobile memiliki peningkatan yang sangat besar. Tercatat dari tahun 2013 sampai 2017 memiliki peningkatan yang sangat besar yaitu 61,26 juta jiwa pengguna internet di Indonesia. Perilaku pengguna internet berdasarkan perangkat yang digunakan untuk mengakses internet yaitu 44.16% dalam penggunaan perangkat mobile, perangkat mobile dan komputer ataulaptop 4.49 %, pengunaan perangkat mobile dan komputer sebesar 39.28%. Hal ini berarti perilaku pengguna internet di Inonesia sangat dominan mengakses internet melalui perangkat mobile. Apalagi statistic menunjukkan bahwa pengguna mobile internet juga semakin meningkat dari tahun ketahun. Sebelumnya sudah ada sistem perpustakaan pada Universitas Muhammadiyah Malang berbasis web. Tetapi dalam hal ini kecenderungan perilaku pengguna internet dalam penggunaan perangkat mobile, maka Perpustakaan Universitas Muhammadiyah Malang memiliki peluang yang sangat besar dalam mengembangkan dan meningkatkan pelayanan dalam memenuhi kebutuhan mahasiswa dalam mencari kebutuhan informasi.