bluuebunny/crossref_metadata_2025_split huggingface dataset:
Social Media Analysis on ASEAN Energy Transition Trends
Authors: Safrina Rika , Yurnaidi Zulfikar 
Published: August 2023

Abstract
This study examines how controversies of the energy transition and climate change in the Southeast Asia region were portrayed in public perspectives using big data from the social media platform. This novel data source can then support mapping the emergence and predicting the acceptance of energy transition, which leads to the following research question, “How can social media inform social controversy and expectations associated with the energy-climate nexus in ASEAN?” As a case study, this paper will be limited to focusing on the ASEAN energy transition and climate change discussion on the Twitter platform between January 2015 and December 2020. This case has been selected for a number of reasons: (i) Climate change is a complex interdisciplinary issue that has gained much global attention recently, (ii) Energy transition is considered an important action by the ASEAN Member States to enhance the regional energy security and sustainability as stated in the ASEAN Plan of Action for Energy Cooperation blueprint, and (iii) Twitter is one of the world’s most popular social media platforms. The study relies on two theoretical frameworks as the base for building its research problem: socio-technical transition multi-level perspective and social construction of technology. A multi-quantitative method was performed for data collection and data processing. The data was analysed using descriptive trend analysis, sentiment analysis, and co-word analysis.
Size Segregated Carbonaceous Aerosol Down to Ultrafine Particle in Medan City, Indonesia
Authors: Putri Rahmi Mulia , Amin Muhammad , Phairuang Worradorn , Furuuchi Masami , Hata Mitsuhiko 
Published: August 2023

Abstract
In this study, the distribution of PMs and their carbonaceous components, including organic (OC) and elemental carbon (EC) was evaluated to determine how much carbon profiles within ambient aerosol differ between four different sites in Medan city or (roadside (RS), school environment (SE), industrial area (IA), and volcano area (VA). Samples were taken by using ambient nano sampler (ANS) from February 19th to March 12th, 2019. Carbon analyser followed by IMPROVE_TOR methods was used to evaluate the carbonous component in all sites. Overall, OC is the primary carbon component, comprising more than 60% of all sites. Regarding particle size, the largest OC content was recorded in PM0.5-1 across all locations, suggesting that this particle size should be the primary focus of future evaluations of the consequences of biomass burning. The concentration of soot-EC dropped with the increase in particle size, since it was commonly influenced by vehicle emissions, which typically generated ultrafine and fine particles. PM0.1 has the greatest concentration of soot-EC. Due to their proximity to the city’s busiest thoroughfare, RS and SE sites had a larger soot-EC proportion than the two remaining sites (IA and VA) (Sisingamangaraja Street).
Ten Years Behavior of Carbonaceous Ultrafine Particulate Matter (PM0.1) in Phnom Penh, Cambodia
Authors: Phairuang Worradorn , Hata Mitsuhiko , Takao Masashi , Taing Chanreaksmey , Ho Sotasing , Aun Srean , Hang Leakhena , Kan Rithy , Or Chanmoly , Hul Seingheng , Ikemori Fumikazu , Wada Masashi , Hongtieab Surapa , Sok Pisith , Amin Muhammad , Furuuchi Masami 
Published: August 2023

Abstract
The long-term behavior of mass concentration of ambient ultrafine particulate matter was discussed on samples collected at a site located in Phnom Penh City, Cambodia, between 2011-2020. A cascade air sampler that can collect PM0.1 (<0.1 μm) has been used at a site located on a roof floor of a 5-story building on the campus of the Institute of Technology of Cambodia (ITC) located near the central part of Phnom Penh City. The main goal was to investigate PM0.1 and carbon compositions, including elemental carbon (EC) and organic carbon (OC). As a result, PM0.1 increased by about 2.3 times, and PM0.1 remarkably increased in the past ten years. EC and soot-EC, which are considered to be road traffic sources, also increase PM0.1 mass concentration. Soot-EC and the number of vehicles per year are correlated. Biomass burning indicators such as OC, char-EC, and hot spots number increase at the same time. Consequently, it is considered that the dry season (January to March) is greatly affected by open fires in Cambodia and neighboring countries.
Fine and nano particles in the school environments and the respiratory deposition doses to schoolchildren in a Middle City of Jambi, Indonesia
Authors: Handika R A , Hata M , Furuuchi M 
Published: August 2023

Abstract
Five public high schools in Jambi, Indonesia, located on the roadside, were chosen as sampling sites, i.e., at the school gate (SG) and schoolyard (SY), to assess the respiratory deposition doses (RDDs) to schoolchildren during the COVID-19 pandemic (December 2021 to February 2022). The fine- and nano-particles information was proven to be critical and limited during the pandemic, but school activities have resumed as usual, even though they only occur for ~4 hours per day at that time. The results show average PM concentrations at the school gate (SG) were higher than those at the schoolyard (SY) regardless of the site location, which was attributed to a difference in influences from road traffic that may be due to the distance from the road. Each PM2.5 concentration, particularly at SY during the school day, exceeded the World Health Organization’s PM2.5 daily standard (15 μg m-3), resulting in higher RDDs compared to other particle sizes, particularly for headways. The deposition of PM2.5 and PM0.1 needs more attention since RDDs have higher values and can infiltrate the alveoli more deeply. Overall, these findings will help to improve health surveillance and guide future efforts in Indonesia to develop air pollution mitigation strategies, particularly for children during the school day.
Scenario of Water Sensitive Urban Design for Cimahi City, Indonesia
Authors: Ari Ismu Rini Dwi , Dein Ratna Shafa Alif , Hariyani Septiana 
Published: August 2023

Abstract
Cimahi City as a part of the Upper Citarum Sub-Watershed has problems with flooding, land subsidence, and clean water crisis caused by urban development. Currently, the multidisciplinary approach to urban water management has been expanded to include the hydrological cycle in it; one of the concepts that has been developed is water-sensitive urban design (WSUD). The purpose of this study is to measure changes in surface runoff as a result of changes in infiltration and runoff processes in the water cycle, calculate planned flood volume, determine the potential location and area of five WSUD elements, and formulate and measure the capability of WSUD scenarios in Cimahi City. This study uses the algebraic average, soil conservation service curve figures (SCS-CN), log-Pearson-III distribution, planned flood volume, and geospatial analysis. Based on the results of the SCS-CN method in Cimahi City in 3 time spans (2013, 2016, and 2019), there was an increase in the CN index and a decrease in the value of surface drift (S) that reflected the potential for runoff water produced. However, at the next stage of analysis, the SCS-CN method showed a decrease in surface runoff; this was due to the decreased rainfall from 2013 to 2016, then from 2016 to 2019 in Cimahi City. The geospatial analysis resulted in the potential location and area of five WSUD elements in Cimahi City. Furthermore, after testing the effectiveness of five WSUD elements and formulating a WSUD scenario, three WSUD elements were obtained as a part of the WSUD scenario in this study, namely sediment basin, retarding basin with, and stormwater harvesting. Last, the measurement of the WSUD scenario capability shows that the WSUD scenario reduces the planned flood volume by 84%.
Recovery of Silicon Wafers from End-of-Life Silicon-Based Solar Cells
Authors: Dum Saravy , Chuangchote Surawut 
Published: August 2023

Abstract
Silicon wafer is the main component of silicon-based solar cells which carries around sixty percent of the cell cost. Silicon wafer manufacturing is the most energy-consumed process compared to other materials, thus, discovering free silicon wafer recourses saves the environment and increases sustainable development. This work extracted the silicon wafers from the end-of-life (EoL) cells in the laboratory which were already forgone after those cells have been studied. The experiment has been started by submerging the full cells into the solution of HNO3, then in the concentrated H3PO4 followed by cleaning in NaOH and DI water. After the etching process, the full pure silicon wafers have been obtained with similar properties to the origin wafers before fabricating. The obtained wafers have been investigated using SEM and EDS characterizations and found that they have potential properties as a semiconductor and are worth trying in other optoelectronic applications.
Learning to see via epiretinal implant stimulation in silico with model-based deep reinforcement learning
Authors: Lavoie Jacob , Besrour Marwan , Lemaire William , Rouat Jean , Fontaine Réjean , Plourde Eric 
Published: August 2023

Abstract
Objective: Diseases such as age-related macular degeneration and retinitis pigmentosa cause the degradation of the photoreceptor layer. One approach to restore vision is to electrically stimulate the surviving retinal ganglion cells with a microelectrode array such as epiretinal implants. Epiretinal implants are known to generate visible anisotropic shapes elongated along the axon fascicles of neighboring retinal ganglion cells. Recent work has demonstrated that to obtain isotropic pixel-like shapes, it is possible to map axon fascicles and avoid stimulating them by inactivating electrodes or lowering stimulation current levels. Avoiding axon fascicule stimulation aims to remove brushstroke-like shapes in favor of a more reduced set of pixel-like shapes. Approach: In this study, we propose the use of isotropic and anisotropic shapes to render intelligible images on the retina of a virtual patient in a reinforcement learning environment named rlretina. The environment formalizes the task as using brushstrokes in a stroke-based rendering task. Main Results: We train a deep reinforcement learning agent that learns to assemble isotropic and anisotropic shapes to form an image. We investigate which error-based or perception-based metrics are adequate to reward the agent. The agent is trained in a model-based data generation fashion using the psychophysically validated axon map model to render images as perceived by different virtual patients. We show that the agent can generate more intelligible images compared to the naive method in different virtual patients. Significance: This work shares a new way to address epiretinal stimulation that constitutes a first step towards improving visual acuity in artificially-restored vision using anisotropic phosphenes.
Quantum atomic matter near two-dimensional materials in microgravity
Authors: Del Maestro Adrian , Wook Kim Sang , Bigelow Nicholas P , Thompson Robert J , Kotov Valeri N 
Published: August 2023

Abstract
Novel two-dimensional atomically flat materials, such as graphene and transition-metal dichalcogenides, exhibit unconventional Dirac electronic spectra. We propose to effectively engineer their interactions with cold atoms in microgravity, leading to a synergy between complex electronic and atomic collective quantum phases and phenomena. Dirac materials are susceptible to manipulation and quantum engineering via changes in their electronic properties by application of strain, doping with carriers, adjustment of their dielectric environment, etc. Consequently the interaction of atoms with such materials, namely the van der Waals/Casimir–Polder interaction, can be effectively manipulated, leading to the potential observation of physical effects such as quantum reflection off atomically thin materials and confined Bose–Einstein condensate frequency shifts.
Relative perversity
Authors: Hansen David , Scholze Peter 
Published: August 2023

We define and study a relative perverse

t
t

-structure associated with any finitely presented morphism of schemes

f
:
X
→
S

f: X\to S

, with relative perversity equivalent to perversity of the restrictions to all geometric fibres of

f
f

. The existence of this

t
t

-structure is closely related to perverse

t
t

-exactness properties of nearby cycles. This

t
t

-structure preserves universally locally acyclic sheaves, and one gets a resulting abelian category

P
e
r
v

U
L
A

(
X

/

S
)

\mathrm {Perv}^{\mathrm {ULA}}(X/S)

with many of the same properties familiar in the absolute setting (e.g., noetherian, artinian, compatible with Verdier duality). For

S
S

connected and geometrically unibranch with generic point

η
\eta

, the functor

P
e
r
v

U
L
A

(
X

/

S
)
→

P
e
r
v

(

X
η

)

\mathrm {Perv}^{\mathrm {ULA}}(X/S)\to \mathrm {Perv}(X_\eta )

is exact and fully faithful, and its essential image is stable under passage to subquotients. This yields a notion of “good reduction” for perverse sheaves.
TMKit: a Python interface for computational analysis of transmembrane proteins
Authors: Sun Jianfeng , Kulandaisamy Arulsamy , Ru Jinlong , Gromiha M Michael , Cribbs Adam P 
Published: August 2023

Abstract
Transmembrane proteins are receptors, enzymes, transporters and ion channels that are instrumental in regulating a variety of cellular activities, such as signal transduction and cell communication. Despite tremendous progress in computational capacities to support protein research, there is still a significant gap in the availability of specialized computational analysis toolkits for transmembrane protein research. Here, we introduce TMKit, an open-source Python programming interface that is modular, scalable and specifically designed for processing transmembrane protein data. TMKit is a one-stop computational analysis tool for transmembrane proteins, enabling users to perform database wrangling, engineer features at the mutational, domain and topological levels, and visualize protein–protein interaction interfaces. In addition, TMKit includes seqNetRR, a high-performance computing library that allows customized construction of a large number of residue connections. This library is particularly well suited for assigning correlation matrix-based features at a fast speed. TMKit should serve as a useful tool for researchers in assisting the study of transmembrane protein sequences and structures. TMKit is publicly available through https://github.com/2003100127/tmkit and https://tmkit-guide.herokuapp.com/doc/overview.
HimGNN: a novel hierarchical molecular graph representation learning framework for property prediction
Authors: Han Shen , Fu Haitao , Wu Yuyang , Zhao Ganglan , Song Zhenyu , Huang Feng , Zhang Zhongfei , Liu Shichao , Zhang Wen 
Published: August 2023

Abstract
Accurate prediction of molecular properties is an important topic in drug discovery. Recent works have developed various representation schemes for molecular structures to capture different chemical information in molecules. The atom and motif can be viewed as hierarchical molecular structures that are widely used for learning molecular representations to predict chemical properties. Previous works have attempted to exploit both atom and motif to address the problem of information loss in single representation learning for various tasks. To further fuse such hierarchical information, the correspondence between learned chemical features from different molecular structures should be considered. Herein, we propose a novel framework for molecular property prediction, called hierarchical molecular graph neural networks (HimGNN). HimGNN learns hierarchical topology representations by applying graph neural networks on atom- and motif-based graphs. In order to boost the representational power of the motif feature, we design a Transformer-based local augmentation module to enrich motif features by introducing heterogeneous atom information in motif representation learning. Besides, we focus on the molecular hierarchical relationship and propose a simple yet effective rescaling module, called contextual self-rescaling, that adaptively recalibrates molecular representations by explicitly modelling interdependencies between atom and motif features. Extensive computational experiments demonstrate that HimGNN can achieve promising performances over state-of-the-art baselines on both classification and regression tasks in molecular property prediction.
Stifled, invisible, and threatened: cultural appropriation in K-pop through the lens of identity-negotiating fans of color
Authors: Kim Kristin April 
Published: August 2023

AbstractThrough a series of in-depth interviews, this article investigates how fans of color negotiate incidents of cultural appropriation in K-pop. Respondents employed various coping mechanisms to navigate the impact of such offenses while simultaneously grappling with the dissonance arising from negotiating alienating rhetoric and their own cultural identities. Criticism of cultural appropriation incidents was met with severe backlash, particularly exacerbating the marginalization faced by Native American fans who encountered underrepresentation and limited support. Drawing attention to the paradoxical interplay of globalization and ethnic nationalism in Korea, this research unveils a troubling cycle in which incidents of cultural appropriation in K-pop silence and render fans of color invisible within hostile environments.
Day-ahead seasonal solar radiation prediction, combining VMD and STACK algorithms
Authors: El Bakali Saida , Hamid Ouadi , Gheouany Saad 
Published: August 2023

Abstract
This article proposes a method for accurately predicting solar irradiance over a 24-hour horizon to forecast photovoltaic energy generation in a positive-energy building. In order to make this prediction, the input data are divided into seasons and preprocessed using the variational mode decomposition (seasonal-VMD) method. The VMD method is used for extracting high-bandwidth features from the input data, decomposing them into a finite number of smooth modes and focusing on specific frequency ranges. Hence, the accuracy of signal extraction using the VMD method can be improved by selecting particular parameters judiciously, which impacts the smoothing and frequency concentration of the extracted signal. In this regard, the salp swarm algorithm (SSA) is employed to identify the optimal VMD parameters that can be used to enhance extraction accuracy. In addition, the obtained residual between the observed solar irradiation data and their decomposed modes is treated to enhance the prediction process. A stacking algorithm (STACK) is used to predict the following 24-hour solar irradiance modes and the residual, which are finally summed to reconstruct the desired signal. The performances of the proposed prediction method are evaluated using two quantitative evaluation indices: the normalized root mean square percentage error (NRMSPE) and normalized mean absolute percentage error (NMAPE). The proposed model is trained on data collected for three years in Rabat (2019–22). The performance of the proposed model is evaluated by predicting the 24-hour solar irradiance for a different season. The proposed approach seasonal-VMD-STACK is compared with two other methods in the case of using VMD-based STACK without season partition and STACK method only. Moreover, the proposed method has exhibited stability and proven good results with an NRMSPE of 3.87% and an NMAPE of 1.58% for cloudy days during the test phase. The results demonstrate that residual preprocessing, seasonal input data partition and appropriate selection of VMD parameters improve the performance and accuracy of the prediction.
A Validated High-Performance Thin-Layer Chromatography Technique for Routine Analysis of Curcumin in Four Different Species of Curcuma Viz. C. amada, C. caesia, C. longa and C. zedoaria
Authors: Gangal Avinash , Duseja Manisha , Sethiya Neeraj K , Bisht Dheeraj , Chaudhary Sushil Kumar , Rana Vijay Singh 
Published: August 2023

Abstract
In this study, we investigated a new, simple, sensitive, selective and precise high-performance thin-layer chromatography (HPTLC) fingerprint and quantitative estimation method for the routine analysis of curcumin in Curcuma species viz. Curcuma amada, Curcuma caesia, Curcuma longa and Curcuma zedoaria. Linear ascending development was carried out in a twin-trough glass chamber saturated with toluene:acetic acid (4:1; v/v with 20 minutes of saturation). The plate was dried and analyzed by CAMAG TLC scanner III at white light and 366 nm. The system was found to give compact spots for curcumin (Rf 0.42). The relationship between the concentration of standard solutions and the peak response is linear within the concentration range of 10–70 ng/spot for curcumin. In result, curcumin was not detected in any of C. caesia extracts. The percentage of curcumin was found between 0.042 and 4.908 (%w/w) in different Curcuma species obtained by two different extraction methods viz. Soxhlet and sonication, respectively. Further, extraction via Soxhlet method is most suitable method to get higher curcumin content from rhizomes. The proposed HPTLC method may be use for routine quality testing and quantification of curcumin in Curcuma samples.
Development of Relatively Simple Sample Pretreatment Strategies to Selectively Remove Chromatographic Interfering Peaks of Polysorbate 80 from Liquid Oral Finished Drug Product
Authors: Adhikari Sarju , Berger Shane N , Rustum Abu M 
Published: August 2023

Abstract
Polysorbate 80 (PS 80) is a nonionic surfactant, used in myriad of pharmaceutical, food and cosmetic formulations. PS 80 components have strong UV absorbance and retain under reversed-phase chromatographic conditions, significantly masking sections of the chromatogram. PS 80-related peaks interferences in a sample are common and can be difficult to separate from the analyte peaks. A liquid oral finished product (LOFP) containing PS 80 and Ivermectin as the active pharmaceutical ingredient (API) was selected for this study. Herein, we report two sample pretreatment strategies focusing on the selective removal of PS 80 from the LOFP. Both methods significantly reduce and/or practically eliminate excipients and PS 80-related peaks interferences from the LOFP without a negative impact on the API and its key-related substances recovery. The solid-phase extraction (SPE) strategy uses a C18 SPE followed by a silica gel SPE, whereas the liquid–liquid extraction strategy uses in situ-generated sodium caprylate for the removal of formulation excipients and PS 80. These methods can significantly increase the reliability of high-performance liquid chromatography methods and decrease false positive out-of-specifications events because of coelution of PS 80-related peaks with peaks of interest.