bluuebunny/crossref_metadata_2025_split huggingface dataset:
A route of polyethylene glycol-based phase change heat storage wood with AlN as the thermal conductive filler
Authors: Li Yanchen , Sun Jingmeng , Zhang Weiye , Fang Xiaoyang , Wang Beibei , Li Fangzhou , Zhang Daihui , Guo Hongwu , Liu Yi 
Published: December 2022

Abstract
Wood is a great potential natural material with good energy storage and temperature adjustment for the energy crisis. In this study, wood was delignified, and the PEG based eutectic polymer (PGMA) was impregnated into the delignified wood, and styrene (SM) grafted AlN was selected as thermal conductive filler to synthesize a stable phase change heat storage wood. The result shows that the styrene (SM) polymerized on the surface of AlN to form polystyrene (PS), the dispersion of AlN was enhanced. The thermal stability and loss resistance of PCES-Wood are improved for the penetration of PS-AlN into the cavities, which is attributed to the PS-AlN playing a "limiting" role in the crystallization of PGMA. When the content of PS-AlN is 6%, the thermal conductivity of PCES-Wood reaches 0.5148 W/(m·K). 4% PS-AlN-PCES@Balsa has the lowest undercooling and heat loss rate, and the melting enthalpy and solidification enthalpy reach 125.60 J/g and 120.30 J/g, respectively. The phase change temperature range is between 19.59 ℃ and 33.87 ℃, which is in the comfortable temperature range for the human. Under the illumination of the simulated solar light source, the surface temperature of PS-AlN-PCES@Balsa rises rapidly and shows a lasting heat storage capacity at low temperatures. After 200 cold and hot cycle tests, the phase change enthalpy of PS-AlN-PCES@Balsa is still high, and there is a slight leakage phenomenon. The phase-change wood prepared in this study shows that it is a potential material to store and release solar energy in practical applications.
The influence of leaf anatomical traits on photosynthetic in coffee leaves in Yunnan of China
Authors: Xiao Ziwei , Ma Guanrun , Bai Xuehui , Li Jinhong , Zhao Mingzhu , Su Linlin , Zhou Hua 
Published: December 2022

Abstract
Leaf photosynthesis is largely determined by anatomical features. This study aimed to reveal the quantitative effects of the anatomical structure of Coffea arabica leaves on photosynthesis. Pearson’s correlation and path analysis were used to explore the correlation between leaf structure and photosynthesis. To calculate the comprehensive evaluation value of the correlation between leaf anatomical traits and photosynthetic parameters, the Criteria Importance Through Intercriteria Correlation (CRITIC) method was used to obtain the objective weight of each photosynthetic parameter. Most of the anatomical traits of the leaf significantly (P<0.05) to highly significantly (P<0.01) correlated with photosynthetic parameters, suggesting that anatomical traits greatly affected the photosynthetic capacity in Coffea arabica leaf. Likewise, path coefficient analysis revealed that the photosynthetic capacity of Arabic coffee leaves is strongly correlated, both directly and indirectly, with the stomatal characteristics of the leaves. The results of the comprehensive evaluation also showed that leaf thickness (LT) and stomatal density (d) were the anatomical characteristics most closely related to photosynthesis. In these results, understanding the effects of the anatomical structure of coffee leaves on photosynthesis, may provide useful information for coffee breeding programs and the management of coffee plantations to increase photosynthesis.
Conditional Inference on the Generalized Gamma Distribution Parameters
Authors: Maswadah M. 
Published: December 2022

AbstractIt is widely known that the Bayes method is usually efficient especially when the sample sizes are small or when the data are heavily censored. However, the subjectivity of the Bayesian method to the prior distribution can take a posterior decision to unwelcome consequences. Therefore, this paper's main objective is to find the conditional point estimates using the pivotal functions. The conditional inference was used to estimate the generalized gamma distribution parameters, based on the generalized progressive hybrid-censoring scheme, and compare it with the Bayesian estimates, via Monte Carlo simulation. The simulation results showed that the conditional inference is highly efficient and provides better estimates than the Bayesian estimates based on different loss functions. Finally, from a future perspective, the proposed model could be important for analysing biostatistics data on COVID-19 deaths in Egypt to demonstrate the efficiencies of the proposed methods.AMS Subject Classification:62F15; 62F40; 62F86; 62N01; 62N02
Is theoretical chemistry entering a new era of big data learning and reasoning: deep electron cloud-activity and field-activity relationships
Authors: Xu Lu , Yang Qin 
Published: December 2022

Abstract
Chemists have been pursuing general mathematical laws to explain the properties of chemical molecules for a long time. However, the traditional quantitative structure-activity relationship (QSAR) models only provide small pieces of knowledge due to their poor generalization performance and limited application domain. This paper attempts to find a path to realize aunified QSAR that can theoretically predict ANY properties of ANY molecules. A framework combining deep learning, accurate quantum chemistry data and a large number of molecularentities is proposed, and its feasibility is studied. The proposed methods are called deep electron cloud-activity relationships (DECAR) and deep field-activity relationships (DFAR), which consist of three essentials: (1) thousands of molecule entities and activities (even reaching the limit of the current computing resources) as input objects and activity responses; (2) three-dimensional electron cloud density or related field data obtained by accurate density functional theory (DFT) methods as input features; and (3) a deep learning model with three-dimensional convolution layers that can learn these large data. The DECAR and DFAR methods are used to distinguish 977 sweet and 1965 nonsweet molecules, and the classification performance of deep learning models is demonstrated to be significantly better than that oftraditional least squares support vector machine (LS-SVM) models. This work will enable the establishment of an interactive international platform for collecting and sharing the accurate electronic cloud and field data of millions of molecules with annotated activities. We envision the development of hundreds of networks trained for various molecular activities using a large number of molecules, which will become open and shared learning and inference tools for chemists all over the world. These networks will make the best use of our existing knowledge of molecular activity to infer the properties of an increasing number ofunknown molecules and will promote our prediction and understanding of molecular properties with unprecedented strength.
Predicting Acute Clinical Deterioration with Interpretable Machine Learning to support Emergency Care Decision Making
Authors: Logothetis Stelios Boulitsakis , Green Darren , Holland Mark , Moubayed Noura Al 
Published: December 2022

Abstract
The emergency department is a fast-paced environment responsible for large volumes of patients with varied acuity. Operationalpressures on emergency departments are increasing, which creates the imperative to efficiently identify patients at risk ofacute deterioration and minimise patients’ risk of morbidity and mortality. We apply state-of-the-art machine learning methodsto predict patient deterioration early, based on their first recorded vital signs, observations, laboratory results, and otherpredictors documented in Electronic Patient Records. We build on prior work by incorporating interpretable machine learningand fairness-aware modelling, and achieve improved classification performance over the current standard, the National EarlyWarning Score 2, measured by average precision and daily alert rate. We use a cross-sectional Electronic Patient Recorddataset comprising 121, 058 unplanned admissions at Salford Royal Hospital, UK, to systematically compare model variationsfor predicting mortality and critical care utilisation within 24 hours of admission. Our models yield up to 0.375 increase inaverage precision, up to 18.5% reduction in daily alert rate, and a median 0.577 reduction in differential bias amplification acrossthe protected demographics of age and sex. We use Shapely Additive exPlanations to justify the models’ outputs, verify thatthe captured data associations align with domain knowledge, and pair predictions with the causal context of each patient’smost influential characteristics. We encourage future research to follow a systematised approach to data-driven risk modellingand help obtain clinically applicable support tools.
Pembuatan Aplikasi Perpustakaan Digital Untuk Anak-Anak Kampung Stamplat, Desa Indragiri, Kabupaten Bandung
Authors: Loekito Jimmy Agustian , Wong Hendry , Setiawan Deni , Pranata Yosafat 
Published: December 2022

Desa Indragiri terletak di Kecamatan Rancabali, Kabupaten Bandung, memiliki 4 dusun yang menyebar, salah satunya adalah Dusun Ciparay yang didalamnya terdapat Kampung Stamplat. Di kampung ini hidup 28 Kepala Keluarga yang pada umumnya bermata pencaharian sebagai petani sayuran, petani kopi, peternak, dan pegawai perkebunan teh. Peran teknologi informasi antara lain sistem informasi manajemen perpustakaan baik itu pada perpustakaan konvensional (pembuatan katalog buku elektronik) maupun perpustakaan digital yang mana berorientasi pada data digital dan jaringan komputer. Koleksi buku di perpustakaan juga dialih mediakan ke bentuk elektronik sebagai contoh koleksi jurnal, prosiding, dan textbook dalam bentuk e-book. Tujuan kegiatan pengabdian kepada masyarakat (PkM) dalam tulisan ini adalah tujuan kegiatan tahap pertama yaitu pembuatan aplikasi (software) perpustakaan digital untuk anak-anak Kampung Stamplat, Dusun Ciparay, Desa Indragiri, Kecamatan Rancabali, Kabupaten Bandung. Ruang lingkup kegiatan PkM tahap pertama berupa hasil produk aplikasi yang di-install pada komputer, pembuatan modul pelatihan penggunaan aplikasi. Adapun tujuan kegiatan tahap kedua (lanjutan) adalah sosialisasi dan pelatihan penggunaan aplikasi bagi anak-anak Kampung Stamplat yang akan direalisasikan pada bulan September s.d. Oktober 2021 melalui 3 kegiatan pelatihan. Hasil kegiatan yang dapat disampaikan di tulisan ilmiah ini adalah produk aplikasi perpustakaan digital akan dapat dimanfaatkan oleh anak-anak Kampung Stamplat, Dusun Ciparay, Desa Indragiri, Kecamatan Rancabali, Kabupaten Bandung sebagai media belajar secara digital. Perpustakaan digital dapat diakses oleh semua anak di Kampung Stamplat sebagai media pendamping belajar khususnya mempelajari materi-materi terkait kurikulum baik Sekolah Dasar maupun Sekolah Menengah Pertama.Kata kunci: Software, Perpustakaan digital, Kampung Stamplat, Pembelajaran.
THE EFFECTS OF INSTRUMENT-ASSISTED SOFT TISSUE MOBILIZATION AND KINESIO TAPING ON PAIN, FUNCTIONAL DISABILITY AND DEPRESSION IN PATIENTS WITH CHRONIC LOW BACK PAIN: A RANDOMIZED TRIAL
Authors: ÇAKMAK Özge , ATICI Emine , GÜLŞEN Mustafa 
Published: December 2022

Purpose: Low back pain is a common condition that can become chronic, which reduces the life quality of the patient by causing functional disability and depression. This study aimed to investigate the effects of instrument-assisted soft tissue mobilization (IASTM) and kinesio taping (KT) along with conservative treatment in patients with chronic nonspecific low back pain (CNLBP).
Methods: A total of 30 patients with chronic low back pain aged between 30-50 years included in the study were randomized to IASTM (n=15) and KT (n=15) groups. Both the groups underwent conservative treatment that comprised of a hot pack, ultrasound, transcutaneous electrical nerve stimulation (TENS) and home exercises. In this study, assessments were made using the Visual Analogue Scale (VAS), Roland Morris Disability Questionnaire (RMDQ), and Beck Depression Inventory (BDI). The measurements were carried out at the beginning and end of the treatment.
Results: As a result of the study, IASTM method improved in parameters such as pain (p
Into the air: a freestanding vertical greenery system (VGS) for evapotranspiration (ET) of roof runoff
Authors: Lausen Emilia Danuta , Jensen Marina Bergen , Randall Mark Taylor 
Published: December 2022

Abstract
In the search for space-efficient nature-based solutions (NBS) for stormwater management, we designed a vertical greenery system (VGS) for enhanced evapotranspiration (ET). After assessing a range of construction options, an 80 m long and 3.2 m high freestanding stormwater-VGS, referred to as the Green Climate Screen (GCS), was constructed in 2019 in Copenhagen. The GCS receives runoff from 240 m2 of roof top and has a high ratio of surface-to-ground area to allow for the clothesline effect to enhance ET. The conveyance of runoff to the top of the GCS is based on gravitational force. It is estimated that 24 h events with return periods up to the 0.1 y (13.9 mm) are managed by ET, from 0.1 to 15 y (48.5 mm) by infiltration beneath the screen, and from 15 to &gt;100 y by overflow to an adjacent area allowing for aboveground storage (167 mm), in total 229 mm. With the fulfillment of most performance criteria and successful inclusion of selected co-benefits ET-based stormwater, NBS could become future standard elements. To reduce CO2 and resource footprint, steel, concrete, and mineral wool must be replaced with renewable materials. Adaptation to more spatial contexts is encouraged.
Probabilistic leak localization in water distribution networks using a hybrid data-driven and model-based approach
Authors: Mazaev Ganjour , Weyns Michael , Vancoillie Filip , Vaes Guido , Ongenae Femke , Van Hoecke Sofie 
Published: December 2022

Abstract
In this paper, a hybrid leak localization approach in WDNs is proposed, combining both model-based and data-driven modeling. Pressure heads of leak scenarios are simulated using a hydraulic model, and then used to train a machine-learning-based leak localization model. A key element of the methodology is that discrepancies between simulated and measured pressures are accounted for using a dynamically calculated bias correction, based on historical pressure measurements. Data of in-field leak experiments in operational water distribution networks were produced to evaluate our approach on realistic test data. The results show that the leak localization model is able to reduce the leak search region in parts of the network where leaks induce detectable drops in pressure. When this is not the case, the model still localizes the leak but is able to indicate a higher level of uncertainty with respect to its leak predictions.
Optical Coherence Tomography Angiography Findings in β-Thalassemia Patients in Two Age Groups
Authors: Mahmoud Hany , Salama Eman H. , Abdel-baset Asmaa A. , Gaber Mahmoud , Fahmy Eman Mohamed , Tohamy Dalia , Anbar Mohamed , Mostafa Engy M. 
Published: December 2022

Purpose:
β- thalassemia major causes hypoxia, which affects the retinal nerve fiber layer (RNFL), causing apoptosis. The frequent blood transfusion in transfusion-dependent thalassemia (TDT) accumulates ferritin, causing more damage. Using Optical Coherence Tomography (OCT) and Optical Coherence Tomography Angiography (OCTA), we aim to compare these changes in Youngs and adults.

Methods:
In this cross-sectional non-randomized comparative study, 50 TDT patients were included: 28 were under 18 years (group1) and 22 were above 18 years (group2). They were subjected to hematological examination and ophthalmological examination, including OCT and OCTA. Then, the data were collected and analyzed.

Results:
There was a statistical difference between the age in both groups (p=0.01). The two groups did not differ in sex distribution as well (p=0.085). All hematological parameters showed fewer values in (group 2) with a statistically significant difference in Serum ferritin, showing a marked increasing difference. There was a statistical difference between all Macular parameters and peripapillary quadrantal RNFL thickness of both groups (except for the C/D ratio), with higher values in (group 1). A moderate or strong positive correlation was found between all retinal parameters except for the C/D ratio and Hb level. A moderate or strong positive correlation was found between all retinal parameters except for the C/D ratio and serum ferritin. Moreover, there was a moderate to strong negative correlation between all retinal parameters except for the C/D ratio and frequency of blood transfusion.

Conclusion:
patients above 18 years (group2) are more affected by more RNFL thinning and vascular density changes.
Post-traumatic Mental and Physical Consequences of Frontline Reporting in the MENA Region
Authors: Chadli Lakhdar , Haq Farooq Muhammad , Okasha Ahmed , Attou Rafik 
Published: December 2022

Background:
A current need in journalistic frontline work is to understand the potential psychological and physical traumatic consequences that may result from on-duty appointments. Journalists are active in frontline zones to report on conflicts, crises, and natural disasters. In the Middle East and North Africa (MENA) region, reporters are not equipped or trained mentally or emotionally to handle stressful events. Most journalists suffer from certain degrees of post-traumatic mental and physical disorders associated with their frontline duties.

Objective:
The objective of this exploratory study is to provide comprehensive insights into challenges faced by journalists reporting in conflict zones in the MENA region.

Methods:
This research study is based on a qualitative research approach where data was collected by directly interviewing eight journalists who have covered frontline conflicts and disasters in the MENA region. The collected qualitative data was analysed by conducting a thematic analysis to appreciate emerging categories. The ontology of critical realism was adopted to recognise the real feelings and experiences of the responding journalists.

Results:
This original study presents six themes emerging from the data and researcher triangulation. The health and social issues in MENA are found to be most critical for high-risk reporting.

Conclusion:
Amidst the psychological and physical problems, all journalists did not give up their jobs or ask for privileges. Media houses could consider a more robust training plan based on health and safety to prepare these journalists.
Índice de sitio para plantaciones forestales de Pinus patula en el Estado de México
Authors: Hernández-Ramos Jonathan , Hernández-Ramos Adrián , Ordaz-Ruiz Gustavo , García-Espinoza Guadalupe Geraldine , García-Magaña J. Jesús , García-Cuevas Xavier 
Published: December 2022

La falta de herramientas silvícolas cuantitativas para clasificar la productividad de áreas arboladas en la planificación, gestión y aprovechamiento de plantaciones forestales comerciales (PFC) es un problema recurrente. El objetivo fue modelar el índice de sitio (IS) en función de la edad (E) mediante la inclusión de covariables para las PFC de Pinus patula en las regiones forestales VI y VII del Estado de México. Se utilizaron datos de 65 sitios de muestreo (250 m2) distribuidos en 90.8 ha de once PFC de entre 4 y 20 años, para ajustar con el software R® mediante efectos fijos y mixtos el modelo de crecimiento de Cieszewski y Bella (1989). La evaluación estadística se realizó mediante el cumplimiento de supuestos de regresión y los valores del sesgo medio ( ), sesgo en porcentaje ( ) y la raíz del error medio cuadrático (REMC). Se generaron curvas de crecimiento en altura dominante (Ad), incremento corriente y medio anual (ICA e IMA). El modelo cumple con los supuestos de regresión. Las diferencias en Ad están determinadas por la exposición topográfica en donde los terrenos con orientación Este y Norte son los más productivos. El , y RCME fueron de -0.091 m, -0.358% y 2.4 m, respectivamente. Las curvas de crecimiento polimórficas a una edad base de 20 años describieron la Ad y se determinó el IS con clases de 19 m, 22 m y 25 m. El ICA máximo y turno técnico fueron a los seis y once años. Clasificar las PFC de acuerdo con su rendimiento y productividad maderable da pautas para su manejo silvícola.
Interpretable Deep-Learning Approaches for Osteoporosis Risk Screening and Individualized Feature Analysis Using Large Population-Based Data: Model Development and Performance Evaluation
Authors: Suh Bogyeong , Yu Heejin , Kim Hyeyeon , Lee Sanghwa , Kong Sunghye , Kim Jin-Woo , Choi Jongeun 
Published: December 2022

Background
Osteoporosis is one of the diseases that requires early screening and detection for its management. Common clinical tools and machine-learning (ML) models for screening osteoporosis have been developed, but they show limitations such as low accuracy. Moreover, these methods are confined to limited risk factors and lack individualized explanation.

Objective
The aim of this study was to develop an interpretable deep-learning (DL) model for osteoporosis risk screening with clinical features. Clinical interpretation with individual explanations of feature contributions is provided using an explainable artificial intelligence (XAI) technique.

Methods
We used two separate data sets: the National Health and Nutrition Examination Survey data sets from the United States (NHANES) and South Korea (KNHANES) with 8274 and 8680 respondents, respectively. The study population was classified according to the T-score of bone mineral density at the femoral neck or total femur. A DL model for osteoporosis diagnosis was trained on the data sets and significant risk factors were investigated with local interpretable model-agnostic explanations (LIME). The performance of the DL model was compared with that of ML models and conventional clinical tools. Additionally, contribution ranking of risk factors and individualized explanation of feature contribution were examined.

Results
Our DL model showed area under the curve (AUC) values of 0.851 (95% CI 0.844-0.858) and 0.922 (95% CI 0.916-0.928) for the femoral neck and total femur bone mineral density, respectively, using the NHANES data set. The corresponding AUC values for the KNHANES data set were 0.827 (95% CI 0.821-0.833) and 0.912 (95% CI 0.898-0.927), respectively. Through the LIME method, significant features were induced, and each feature’s integrated contribution and interpretation for individual risk were determined.

Conclusions
The developed DL model significantly outperforms conventional ML models and clinical tools. Our XAI model produces high-ranked features along with the integrated contributions of each feature, which facilitates the interpretation of individual risk. In summary, our interpretable model for osteoporosis risk screening outperformed state-of-the-art methods.
Carbon/Alginate/Chitosan Composite as a Sorbent for Solid-Phase Extraction and Preconcentration of Cu(II)
Authors: Yaqin Ahmad Akhib Ainul , Suherman Suherman , Mudasir Mudasir , Siswanta Dwi 
Published: December 2022

Carbon/Alginate/Chitosan Composite (CAC) as a sorbent for solid-phase extraction of Cu(II) (pre-concentration) with atomic absorption spectrometry for analysis was successfully prepared. The sorbent was made with a mass ratio of chitosan:alginate:carbon = 1:1:1. CAC sorbents were characterized using FTIR for functional group analysis while SEM-EDX characterization was performed to determine surface morphology and percentage of elements. SPE parameters were optimized for adsorption-desorption efficiency under various conditions, i.e. of pH 5, Cu(II) concentration of 4 mg/L, the flow rate of Cu(II), and Na2EDTA eluent at 5 mL/min, the volume of Cu(II) sample and Na2EDTA eluent 25 mL and 1.0 M of Na2EDTA eluent concentration. Applying CAC-based SPE for Cu(II) analysis in water samples at optimum conditions gave 80.9–83.6% recovery. In bottled drinking water samples, a pre-concentration factor of 2.5–5 times was obtained, and the Cu(II) concentration of 0.017–0.020 mg/L was detected.