bluuebunny/crossref_metadata_2025 huggingface dataset:
Towards population-independent, multi-disease detection in fundus photographs
Authors: Matta Sarah , Lamard Mathieu , Conze Pierre-Henri , Le Guilcher Alexandre , Lecat Clément , Carette Romuald , Basset Fabien , Massin Pascale , Rottier Jean-Bernard , Cochener Béatrice , Quellec Gwenolé
Published: July 2023

Independent validation studies of automatic diabetic retinopathy screening systems have recently shown a drop of screening performance on external data. Beyond diabetic retinopathy, this study investigates the generalizability of deep learning (DL) algorithms for screening various ocular anomalies in fundus photographs, across heterogeneous populations and imaging protocols.The following datasets are considered: OPHDIAT (France, diabetic population), OphtaMaine (France, general population), RIADD (India, general population) and ODIR (China, general population). Two multi-disease DL algorithms were developed: a Single-Dataset (SD) network, trained on the largest dataset (OPHDIAT), and a Multiple-Dataset (MD) network, trained on multiple datasets simultaneously. To assess their generalizability, both algorithms were evaluated whenever training and test data originate from overlapping datasets or from disjoint datasets. The SD network achieved a mean per-disease area under the receiver operating characteristic curve (mAUC) of 0.9571 on OPHDIAT. However, it generalized poorly to the other three datasets (mAUC < 0.9). When all four datasets were involved in training, the MD network significantly outperformed the SD network (p = 0.0058), indicating improved generality. However, in leave-one-dataset-out experiments, performance of the MD network was significantly lower on populations unseen during training than on populations involved in training (p < 0.0001), indicating imperfect generalizability.
Important roles of Hif1a in maternal or adult BPA exposure induced pancreatic injuries
Authors: Liu Huiping , Zhou Yongnian , Li Yike , Gong Zhihua
Published: July 2023

Bisphenol A (BPA) is a monomer to produce polycarbonate plastics and can be released into the environment through human activities, leading to its accumulation in animals, plants and humans through direct contact or environmental exposure. Epidemiological studies have reported that BPA exposure is associated with metabolic disorders. The pancreas is an important endocrine organ and plays an important role in metabolic disorders. To explore the possible long-term effects of BPA exposure on neonatal health, bioinformatic methods were used to identify differentially expressed genes (DEGs) by comparing the neonatal pancreas after maternal exposure to BPA with the adult pancreas after direct exposure to BPA. Two datasets about BPA exposure and pancreatic abnormality, GSE82175 and GSE126297 in Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) were collected. Control (or BPA-exposed) offspring (maternal exposure) and Control (or BPA-exposed) adults (direct exposure) were defined as Control (or BPA) groups. The results showed that BPA disturbed the normal function of the pancreas in both offspring and adults, with offspring showing higher susceptibility to BPA than adults. Seventeen insulin secretion-related DEGs (Stxbp5l, Fam3d, Mia3, Igf1, Hif1a, Aqp1, Kif5b, Tiam1, Map4k4, Cyp51, Pde1c, Rab3c, Arntl, Clock, Edn3, Kcnb1, and Krt20) in the BPA group were identified, and 15 regulator DEGs (Zfp830, 4931431B13Rik, Egr1, Ddit4l, Cep55, G530011O06Rik, Hspa1b, Hspa1a, Cox6a2, Ibtk, Banf1, Slc35b2, Golt1b, Lrp8, and Pttg1) with opposite expression trends and a regulator gene Cerkl with the similar expression trend in the Control and BPA groups were identified. Hif1α might be an important molecular target for pancreatic cancer caused by BPA exposure, and pregnancy is a critical window of susceptibility to BPA exposure.
Sleep disorders among patients suffering from road traffic injuries in an urban setting of Vietnam: an exploratory study
Authors: Vu Hai Minh , Tran Tung Hoang , Dang Anh Kim , Hoang Trong Nang , Nguyen Cuong Tat , Nguyen Huong Lan Thi , Latkin Carl A. , Ho Cyrus S. H. , Ho Roger C. M.
Published: July 2023

Sleep quality is an important indicator of treatment outcome for patients with traffic accident injuries. In Vietnam, the impacts of injury on sleep status are usually amplified in urban areas due to disproportionate distribution of mental care services between the city and less developed settings. Our study investigated deterioration in sleep quality and identified associated demographic factors among traffic injury patients in an small urban setting of Vietnam. A cross-sectional study was conducted among 408 patients in one provincial hospital and five district hospitals in Thai Binh, Vietnam from October to December 2018. A structured questionnaire was designed based on 3 standardized scales: Health-related Quality of Life, the Pittsburgh Sleep Quality Index and the Kessler Scale. Face-to-face interviews and medical records were conducted by trained health professionals on patients hospitalized in the Trauma—Orthopedic/Burn Department and Surgery and General Department. About 16.9% of respondents had sleep disturbances, and there was a statistically significant difference between age group (p < 0.01), education level (p < 0.01), and monthly household income (p < 0.01) between participants who with and without sleep disturbances. Furthermore, more than half (50.7%) of respondents sleep less than 5 h per day, while 18.7% of the sampled also reported that the habitual sleep efficiency was below 85%. Current results indicated that people being female, suffering from traumatic brain injury, being comatose at hospitalization, and having higher psychological distress scores were more likely to suffer from sleep problems. Our study is one of the first evidence in Vietnam to assess sleep disturbances in road traffic injury patients and their correlated factors. It is important to identify patients who are at risk of sleep disturbances based on socio-demographic and clinical characteristics, as well as psychological distress status. Therefore, a holistic approach should be taken to include sleep quality and psychological state in the treatment process and outcome assessment for road traffic injury patients.
Glutathione trisulfide prevents lipopolysaccharide-induced retinal inflammation via inhibition of proinflammatory cytokine production in glial cells
Authors: Tawarayama Hiroshi , Umeki Kota , Inoue-Yanagimachi Maki , Takahashi Naoki , Hasegawa Hirokazu , Himori Noriko , Tsuda Satoru , Kunikata Hiroshi , Akaike Takaaki , Nakazawa Toru
Published: July 2023

We aimed to investigate the impact of glutathione trisulfide (GSSSG) on lipopolysaccharide (LPS)-induced inflammation in retinal glia. Inflammatory responses in mouse-derived glial cells and Wistar rat retinas were stimulated with administration of LPS. Cell survival and proinflammatory cytokine production were examined using the Calcein-AM assay, and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and enzyme-linked immunosorbent assay (ELISA), respectively. Retinal microglia were visualized with immunohistochemistry for Iba1. Administration of LPS (10 µg/mL) or GSSSG (less than 100 µM) did not affect survival of cultured primary Müller cells and established microglial cells (BV-2). RT-qPCR and ELISA indicated that GSSSG inhibited LPS-induced gene upregulation and protein secretion of proinflammatory cytokines in these glial cells and rat retinas. GSSSG inhibited LPS-induced activation of TGF-β-activated kinase 1 (TAK1), which is an upstream kinase of NF-κB, in BV-2 cells. Finally, in vivo experiments indicated that intravitreal administration of GSSSG but not its relative glutathione disulfide (GSSG) inhibited LPS (500 ng)-induced accumulation of Iba1-immunopositive microglia in rat retinas. Taken together, GSSSG has the potential to prevent pathogenesis of inflammation-associated ocular diseases by inhibiting proinflammatory cytokine expression in retinal glial cells.
Exploring biomarkers of premature ovarian insufficiency based on oxford nanopore transcriptional profile and machine learning
Authors: Yu Zhaoyang , Li Mujun , Peng Weilong
Published: July 2023

Premature ovarian insufficiency (POI) is a reproductive endocrine disorder characterized by infertility and perimenopausal syndrome, with a highly heterogeneous genetic etiology and its mechanism is not fully understood. Therefore, we utilized Oxford Nanopore Technology (ONT) for the first time to characterize the full-length transcript profile, and revealed biomarkers, pathway and molecular mechanisms for POI by bioinformatics analysis and machine learning. Ultimately, we identified 272 differentially expressed genes, 858 core genes, and 25 hub genes by analysis of differential expression, gene set enrichment, and protein–protein interactions. Seven candidate genes were identified based on the intersection features of the random forest and Boruta algorithm. qRT-PCR results indicated that COX5A, UQCRFS1, LCK, RPS2 and EIF5A exhibited consistent expression trends with sequencing data and have potential as biomarkers. Additionally, GSEA analysis revealed that the pathophysiology of POI is closely associated with inhibition of the PI3K-AKT pathway, oxidative phosphorylation and DNA damage repair, as well as activation of inflammatory and apoptotic pathways. Furthermore, we emphasize that downregulation of respiratory chain enzyme complex subunits and inhibition of oxidative phosphorylation pathways play crucial roles in the pathophysiology of POI. In conclusion, our utilization of long-read sequencing has refined the annotation information within the POI transcriptional profile. This valuable data provides novel insights for further exploration into molecular regulatory networks and potential biomarkers associated with POI.
Fatty acid metabolism changes in association with neurobehavioral deficits in animal models of fetal alcohol spectrum disorders
Authors: Hwang Hye Mee , Kawasawa Yuka Imamura , Basha Aiesha , Mohammad Shahid , Ito Mariko , Hashimoto-Torii Kazue
Published: July 2023

Fetal alcohol spectrum disorders (FASD) show behavioral problems due to prenatal alcohol exposure (PAE). A previous study reports changes in gene expressions linked to fatty acid (FA) metabolism in the cerebral cortex of the PAE mouse model. We find an increase of palmitic acid and arachidonic acid in phospholipid in the cerebral cortex of PAE at postnatal day 30. The increase of palmitic acid is consistent with increase of the producing enzyme, Fasn (fatty acid synthase). Decrease of 26:6 FA is also consistent with the increase of the enzyme which uses 26:6 as a substrate for making very long chain FAs, Elovl4 (elongation of very long chain fatty acids protein 4). However, there is no increase in the elongated products. Rather, lipid droplets (LDs) accumulated in the brain. Although FA-associated metabolic measurements are not affected by PAE, the abundance of FA-related gut microbiota is altered. This suggests that the gut microbiome could serve as a tool to facilitate uncovering the brain pathophysiology of FASD and a potential target to mitigate neurobehavioral problems.
Disruption prediction for future tokamaks using parameter-based transfer learning
Authors: Zheng Wei , Xue Fengming , Chen Zhongyong , Chen Dalong , Guo Bihao , Shen Chengshuo , Ai Xinkun , Wang Nengchao , Zhang Ming , Ding Yonghua , Chen Zhipeng , Yang Zhoujun , Shen Biao , Xiao Bingjia , Pan Yuan
Published: July 2023

Tokamaks are the most promising way for nuclear fusion reactors. Disruption in tokamaks is a violent event that terminates a confined plasma and causes unacceptable damage to the device. Machine learning models have been widely used to predict incoming disruptions. However, future reactors, with much higher stored energy, cannot provide enough unmitigated disruption data at high performance to train the predictor before damaging themselves. Here we apply a deep parameter-based transfer learning method in disruption prediction. We train a model on the J-TEXT tokamak and transfer it, with only 20 discharges, to EAST, which has a large difference in size, operation regime, and configuration with respect to J-TEXT. Results demonstrate that the transfer learning method reaches a similar performance to the model trained directly with EAST using about 1900 discharge. Our results suggest that the proposed method can tackle the challenge in predicting disruptions for future tokamaks like ITER with knowledge learned from existing tokamaks.
Multi-model assessment identifies livestock grazing as a major contributor to variation in European Union land and water footprints
Authors: Vanham Davy , Bruckner Martin , Schwarzmueller Florian , Schyns Joep , Kastner Thomas
Published: July 2023

Food systems are the largest users of land and water resources worldwide. Using a multi-model approach to track food through the global trade network, we calculated the land footprint (LF) and water footprint (WF) of food consumption in the European Union (EU). We estimated the EU LF as 140–222 Mha yr−1 and WF as 569–918 km3 yr−1. These amounts are 5–7% of the global LF and 6–10% of the global WF of agriculture, with the EU representing 6% of the global population. We also calculated the global LF of livestock grazing, accounting only for grass eaten, to be 1,411–1,657 Mha yr−1, and the global LF of agriculture to be 2,809–3,014 Mha yr−1, which is about two-thirds of what the Food and Agriculture Organization Statistics (FAOSTAT) database reports. We discuss here the different methods for calculating the LF for livestock grazing, underscoring the need for a consistent methodology when monitoring the food LF and WF reduction goals set by the EU’s Farm To Fork Strategy.
Development of a kit for urine collection on filter paper as an alternative for Pompe disease screening and monitoring by LC-HRMS
Authors: de Souza Hygor M. R. , Scalco Fernanda B. , Garrett Rafael , de C. Marques Flávia F.
Published: July 2023

This work reports the validation and application of a method for determination of glucose tetrasaccharide (Glc4) in urine collected on filter paper for monitoring Pompe disease patients.
Biomaterials evolution: from inert to instructive
Authors: Iqbal Sajid , Sohail Muhammad , Fang Shiji , Ding Jiayi , Shen Lin , Chen Minjiang , Shu Gaofeng , Du Yong-Zhong , Ji Jiansong
Published: July 2023

The Evolution of Biomaterials: A Remarkable Journey Fueled by Materials Science and Engineering Advancements, redefining the landscape of the biomaterials field.
Optimized electronic properties and nano-structural features for securing high thermoelectric performance in doped GeTe
Authors: Yang Zan , Tseng Yu-Chih , Meledath Valiyaveettil Suneesh , Yuan Hui , Smith Evan , Chen Kuei-Hsien , Huang Yuyang , Zou Tianze , Kycia Jan , Mozharivskyj Yurij
Published: July 2023

Bi, Zn and In co-doping optimized electrical properties of GeTe via band modification and carrier concentration optimization. Lattice thermal conductivity was also suppressed by various nano structures.
Monitoring the adsorption of per- and polyfluoroalkyl substances on carbon black by LDI-MS capable of simultaneous analysis of elemental and organic carbon
Authors: Min Ke , Deng Shenxi , Shu Zhao , Li Yong , Chen Bo , Ma Ming , Liu Qian , Jiang Guibin
Published: July 2023

Here, we developed a LDI-MS method with a unique capability of simultaneous analysis of EC and OC. By using PFASs as a typical set of OC and CB as a model EC, we successfully monitored the adsorption process of PFASs on CB enabled by LDI-MS.
Temperature-modulated solution-based synthesis of copper oxide nanostructures for glucose sensing
Authors: Zhu Yujiang , Vigil-Hernandez Carolina , Kalha Curran , Fernando Nathalie Kanchena , Firth Steve , Davies Gemma-Louise , Bialas Katarzyna , Moschou Despina , Regoutz Anna
Published: July 2023

Glucose sensors are widely applied in society as an effective way to diagnose and control diabetes by monitoring the blood glucose level.
A study of the size and clustering effects of DyF3 samples
Authors: Garaeva Adeliya , Romanova Irina , Boltenkova Ekaterina , Likholetova Marina , Tagirov Murat , Alakshin Egor
Published: July 2023

The magnetic properties of DyF3 powders with a particle size of 16 nm–7 μm were studied.
Study on the incorporation of various elements into the Keggin lacunary-type phosphomolybdate [PMo9O34]9− and subsequent purification of the polyoxometalates by nanofiltration
Authors: Raabe Jan-Christian , Esser Tobias , Jameel Froze , Stein Matthias , Albert Jakob , Poller Maximilian J.
Published: July 2023

In this work we explore a novel synthetic approach for the design of framework-element substituted Keggin-type phosphomolybdates.
Dry-gel conversion synthesis of SAPO-14 zeolites for the selective conversion of methanol to propylene
Authors: Han Daizong , Yang Dongyuan , Bi Chenyao , Zhang Guoqing , Yang Fei , Hao Qingqing , Zhang Jianbo , Chen Huiyong , Ma Xiaoxun
Published: July 2023

High-crystalline SAPO-14 zeolites were efficiently synthesized through dry-gel conversion (DGC) for the first time. Further using as a catalyst, the optimal SAPO-14 with proper acidity showed superior MTP performance with not only a very high propylene selectivity (76.2%) but also a large P/E ratio (7.19).
Formulating a heterolytic cleavage process of water on Ni3N nanosheets through single transition metal doping for ultra-efficient alkaline hydrogen evolution
Authors: Ma Wansen , Wang Meng , Tan Chaowen , Wang Jiancheng , Dai Yanan , Hu Liwen , Lv Xuewei , Li Qian , Dang Jie
Published: July 2023

The effect of transition metal doping on the HER performance of Ni3N was evaluated by experiments and DFT.
Photochromic porous organic crystals constructed by the self-assembly of triarylethylene derivatives
Authors: Zhang Xiayu , Sun Haodong , Low Kam-Hung , Yu Tao , Au Vonika Ka-Man
Published: July 2023

A new porous organic crystal with reversible photochromic behaviour has been synthesized and utilized in photoswitchable patterning and optical storage.
RSC Advances Outstanding Student Paper Awards 2022
Authors: Fisher Laura
Published: July 2023

We are delighted to announce the winners for the RSC Advances Outstanding Student Paper Awards 2022. These awards recognise outstanding work published in the journal, for which a substantial component of the research was conducted by a student.
Feasible bottom-up development of conjugated microporous polymers (CMPs) for boosting the deep removal of sulfur dioxide
Authors: Li He , Pan Hanqian , Li Yijian , Shang Shuaishuai , Huang Shihui , Cui Xili , Hu Jun , Liu Honglai
Published: July 2023

A pain-point for material development is that computer-screened structures are usually difficult to realize in experiments.
Electrodeposited CuSbTe thin films with enhanced thermoelectric performance
Authors: Tanwar Amit , Kaur Rajvinder , Padmanathan N. , Razeeb Kafil M.
Published: July 2023

HRTEM image of Cu0.4SbTe thin film showing the presence of amorphous CuSbTe and crystallite of Sb2Te3, the Cu0.4SbTe is grown on flexible Nylon fiber which provides output voltage of 18.4 mV at a temperature gradient of 30 K.
Factors Impacting Implementation of Universal Screening of Developmental Language Disorder in Public Schools
Authors: Christopulos Tyler T. , Redmond Sean M.
Published: July 2023

Purpose:

Developmental language disorder (DLD) is an underidentified neurodevelopmental disorder that affects, on average, one out of 11 kindergarten-age children (ages 4–6 years). Children with DLD can face academic, behavioral, psychiatric, emotional, and social challenges. Universal screening is seen as an effective way for public school districts to increase DLD identification rates. However, little is known about factors impacting implementation of school-based universal screenings for DLD. We partnered with a large suburban school district in the Intermountain West region of the United States to gather detailed perspectives from school personnel regarding the barriers and facilitators to the implementation of universal screening in their district.

Method:

Using a two-phase mixed-methods design, we first conducted focus groups to identify potential barriers and facilitators to universal screening. We then used the qualitative data from the first phase to develop a 20-item survey to assess agreement with the focus group results among a wider group of district speech-language pathologists and kindergarten teachers from the school district.

Results:

Our survey showed moderate levels of agreement with our focus group results. In particular, school personnel showed high levels of support for universal screening for DLD, with interesting interplay across various factors: (a) the negative impact of unmanageable workload on personnel under both referral- and universal-based identification formats, (b) the preference for paraprofessionals to administer screenings, (c) the role that Response to Intervention programs may play in offsetting workloads associated with universal screenings, and (d) the need for increased awareness and education about child language development and impairment among general education teachers and the public.

Conclusion:

Recommendations for incorporating these factors into more useful and applicable collaborative research-based efforts are presented.

Supplemental Material:

https://doi.org/10.23641/asha.23661876
Neural dynamics for improving optimiser in deep learning with noise considered
Authors: Su Dan , Stanimirović Predrag S. , Han Ling Bo , Jin Long
Published: July 2023

As deep learning evolves, neural network structures become increasingly sophisticated, bringing a series of new optimisation challenges. For example, deep neural networks (DNNs) are vulnerable to a variety of attacks. Training neural networks under privacy constraints is a method to alleviate privacy leakage, and one way to do this is to add noise to the gradient. However, the existing optimisers suffer from weak convergence in the presence of increased noise during training, which leads to a low robustness of the optimiser. To stabilise and improve the convergence of DNNs, the authors propose a neural dynamics (ND) optimiser, which is inspired by the zeroing neural dynamics originated from zeroing neural networks. The authors first analyse the relationship between DNNs and control systems. Then, the authors construct the ND optimiser to update network parameters. Moreover, the proposed ND optimiser alleviates the non‐convergence problem that may be suffered by adding noise to the gradient from different scenarios. Furthermore, experiments are conducted on different neural network structures, including ResNet18, ResNet34, Inception‐v3, MobileNet, and long and short‐term memory network. Comparative results using CIFAR, YouTube Faces, and R8 datasets demonstrate that the ND optimiser improves the accuracy and stability of DNNs under noise‐free and noise‐polluted conditions. The source code is publicly available at https://github.com/LongJin‐lab/ND.
Photovoltaic hosting capacity improvement based on the economic comparison between curtailment and network upgrade
Authors: Fatima Samar , Püvi Verner , Pourakbari‐Kasmaei Mahdi , Lehtonen Matti
Published: July 2023

The upcoming network investment decisions and regulatory framework adopted by distribution system operators (DSOs) are most likely to be impacted by the integration of fluctuating distributed generation. The economical hosting capacity (HC) improvement method is investigated in this paper as a trade‐off between curtailment and upgrade using a Monte Carlo simulation procedure. The associated costs of both methods are vital indicators for network operators that are trying to maximize the HC and minimize cost. In addition, the breakeven point where curtailment and upgrade costs intersect is the decisive point at which network upgrade becomes sensible as marginal curtailment cost exceeds upgrade cost. A shift in global climate conditions can impact the photovoltaic (PV) levels that motivate network operators to investigate PV penetration, especially in colder climate regions. Thus, the primary objective of this paper is to investigate the shift of the breakeven point to guide DSOs to either adopt PV curtailment as a temporary measure or grid upgrade as a risk aversion strategy considering Finnish climate and load patterns. The real‐time load and PV generation data are utilized for the simulations to consider the dynamic performance indication of three Finnish distribution networks. Curtailment remains a low‐cost option to obtain a percentage HC rise of 13%, 7%, and 8% for rural, suburban, and urban regions, respectively, beyond which curtailment compensation cost surpasses upgrade cost. In essence, PV curtailment serves as an immediate and least‐cost solution to relieve network violations and defer network investment until the HC level (118%, 106%, 97%) making the upgrade a practical option afterwards.
A physical model for the calculation of the corona onset voltage gradient of AC spacers
Authors: Huang Shilong , Mao Jie , Yan Xiaoliang , Xue Weiye , Zhang Zhongyuan , Xie Liang , Liu Yunpeng
Published: July 2023

By correcting the photon area factor (PAF) to suit the structure of the spacer and redefining the effective ionization region of the process of negative corona onset, a physical model for the calculation of COG of AC spacers is established. The validity and practicability of the established physical model are validated by the corona onset test results of spacers in both low‐altitude and high‐altitude areas. The numerical results pertaining to PAF and effective ionization region are consistent with the theoretical analysis. With the established physical model as a basis, the COG of the spacer is calculated under different geometrical structure parameters and ambient conditions. The results imply that changing the curvature of the spacer edge has a more significant impact on the COG of the AC spacer than changing the curvature of the spacer end and thickness H. The decrease in the length of the ionization region and the rapid attenuation of spatial electric field intensity of the spacer with smaller geometric parameters lead to an increase in the COG.
The stellar halo in Local Group Hestia simulations
Authors: Khoperskov Sergey , Minchev Ivan , Libeskind Noam , Haywood Misha , Di Matteo Paola , Belokurov Vasily , Steinmetz Matthias , Gomez Facundo A. , Grand Robert J. J. , Hoffman Yehuda , Knebe Alexander , Sorce Jenny G. , Spaare Martin , Tempel Elmo , Vogelsberger Mark
Published: July 2023

Recent progress in understanding the assembly history of the Milky Way (MW) is driven by the tremendous amount of high-quality data delivered byGaia(ESA), revealing a number of substructures potentially linked to several ancient accretion events. In this work we aim to explore the phase-space structure of accreted stars by analysing six M31/MW analogues from the HESTIA suite of cosmological hydrodynamics zoom-in simulations of the Local Group. We find that all HESTIA galaxies experience a few dozen mergers but only between one and four of those have stellar mass ratios > 0.2, relative to the host at the time of the merger. Depending on the halo definition, the most massive merger contributes from 20% to 70% of the total stellar halo mass. Individual merger remnants show diverse density distributions atz = 0, significantly overlapping with each other and with the in situ stars in theLz − E, (VR, Vϕ) and (R, vϕ) coordinates. Moreover, merger debris often shifts position in theLz − Espace with cosmic time due to the galactic mass growth and the non-axisymmetry of the potential. In agreement with previous works, we show that even individual merger debris exhibit a number of distinctLz − Efeatures. In the (VR, Vϕ) plane, all HESTIA galaxies reveal radially hot, non-rotating or weakly counter-rotating, Gaia-Sausage-like features, which are the remnants of the most recent significant mergers. We find an age gradient inLz − Espace for individual debris, where the youngest stars, formed in the inner regions of accreting systems, deposit to the innermost regions of the host galaxies. The bulk of these stars formed during the last stages of accretion, making it possible to use the stellar ages of the remnants to date the merger event. In action space (Jr, Jz, Jϕ), merger debris do not appear as isolated substructures, but are instead scattered over a large parameter area and overlap with the in situ stars. We suggest that accreted stars can be best identified usingJr > 0.2−0.3(104 kpc km s−1)0.5. We also introduce a new, purely kinematic space (Jz/Jr-orbital eccentricity), where different merger debris can be disentangled better from each other and from the in situ stars. Accreted stars have a broad distribution of eccentricities, peaking atϵ ≈ 0.6 − 0.9, and their mean eccentricity tends to be smaller for systems accreted more recently.
GA-NIFS: A massive black hole in a low-metallicity AGN atz ∼ 5.55 revealed by JWST/NIRSpec IFS
Authors: Übler Hannah , Maiolino Roberto , Curtis-Lake Emma , Pérez-González Pablo G. , Curti Mirko , Perna Michele , Arribas Santiago , Charlot Stéphane , Marshall Madeline A. , D’Eugenio Francesco , Scholtz Jan , Bunker Andrew , Carniani Stefano , Ferruit Pierre , Jakobsen Peter , Rix Hans-Walter , Rodríguez Del Pino Bruno , Willott Chris J. , Boeker Torsten , Cresci Giovanni , Jones Gareth C. , Kumari Nimisha , Rawle Tim
Published: July 2023

We present rest-frame optical data of the compactz = 5.55 galaxy GS_3073 obtained using the integral field spectroscopy mode of the Near-InfraRed Spectrograph on board theJames WebbSpace Telescope. The galaxy’s prominent broad components in several hydrogen and helium lines (though absent in the forbidden lines) and v detection of a large equivalent width of He IIλ4686, EW(He II) ∼20 Å, unambiguously identify it as an active galactic nucleus (AGN). We measured a gas phase metallicity ofZgas/Z⊙∼0.21−0.04+0.08, which is lower than what has been inferred for both more luminous AGN at a similar redshift and lower redshift AGN. We empirically show that classical emission line ratio diagnostic diagrams cannot be used to distinguish between the primary ionisation source (AGN or star formation) for systems with such low metallicity, though different diagnostic diagrams involving He IIλ4686 prove very useful, independent of metallicity. We measured the central black hole mass to be log(MBH/M⊙)∼8.2 ± 0.4 based on the luminosity and width of the broad line region of the Hαemission. While this places GS_3073 at the lower end of known high-redshift black hole masses, it still appears to be overly massive when compared to its host galaxy’s mass properties. We detected an outflow with a projected velocity ≳700 km s−1and inferred an ionised gas mass outflow rate of about 100 M⊙yr−1, suggesting that one billion years after the Big Bang, GS_3073 is able to enrich the intergalactic medium with metals.
Star-formation-driven outflows in local dwarf galaxies as revealed from [CII] observations byHerschel
Authors: Romano M. , Nanni A. , Donevski D. , Ginolfi M. , Jones G. C. , Shivaei I. , Junais  , Salak D. , Sawant P.
Published: July 2023

We characterize the physical properties of star-formation-driven outflows in a sample of 29 local dwarf galaxies drawn from the Dwarf Galaxy Survey. We made use ofHerschel/PACS archival data to search for atomic outflow signatures in the wings of individual [CII] 158 μm spectra and in their stacked line profile. We find a clear excess of emission in the high-velocity tails of 11 sources, which can be explained with an additional broad component (tracing the outflowing gas) in the modeling of their spectra. The remaining objects are likely hosts of weaker outflows that can still be detected in the average stacked spectrum. In both cases, we estimate the atomic mass outflow rates which result to be comparable with the star-formation rates of the galaxies, implying mass-loading factors (i.e., outflow efficiencies) of the order of unity. Outflow velocities in all the 11 galaxies with individual detections are larger than (or compatible with) the escape velocities of their dark matter halos, with an average fraction of 40% of gas escaping into the intergalactic medium (IGM). Depletion timescales due to outflows are lower than those due to gas consumption by star formation in most of our sources, ranging from one hundred million to a few billion years. From the energetic point of view, our outflows are mostly consistent with momentum-driven winds generated by the radiation pressure of young stellar populations on dust grains, although the energy-driven scenario is not excluded if considering a coupling efficiency up to 20% between the energy injected by supernovae and the interstellar medium. Overall, our results suggest that, despite their low efficiencies, galactic outflows can regulate the star-formation history of dwarf galaxies. Specifically, they are able to enrich with metals the circumgalactic medium of these sources, bringing on average a non-negligible amount of gas into the IGM, where it will no longer be available for new star formation. Our findings are suitable for tuning chemical evolution models attempting to describe the physical processes shaping the evolution of dwarf galaxies.
MOKA3D: An innovative approach to 3D gas kinematic modelling
Authors: Marconcini C. , Marconi A. , Cresci G. , Venturi G. , Ulivi L. , Mannucci F. , Belfiore F. , Tozzi G. , Ginolfi M. , Marasco A. , Carniani S. , Amiri A. , Di Teodoro E. , Scialpi M. , Tomicic N. , Mingozzi M. , Brazzini M. , Moreschini B.
Published: July 2023

Studying the feedback process of active galactic nuclei (AGN) requires the characterisation of multiple kinematical components, such as rotating gas and stellar discs, outflows, inflows, and jets. The usual approach to compare the observed galaxy properties with feedback theoretical predictions relies on simplified kinematic models. This allows us to assess the mutual interaction between the galaxy components and determine the energy injection rate into the interstellar medium. However, these models have several limitations, as they often do not take into account projection effects, beam smearing, or the surface brightness distribution of the emitting medium. Here, we present MOKA3D, an innovative approach to modelling the 3D gas kinematics from integral field spectroscopy observations. In this first paper, we discuss its application to the case of AGN ionised outflows, whose observed clumpy emission and apparently irregular kinematics are only marginally accounted for by the existing kinematical models. Unlike previous works, our model does not assume the surface brightness distribution of the gas, but exploits a novel procedure to derive it from observations by reconstructing the 3D distribution of emitting clouds and providing accurate estimates of the physical properties of spatially resolved outflow (e.g., mass rate, kinetic energy). We demonstrate the capabilities of our method by applying it to three nearby Seyfert-II galaxies observed with the Multi Unit Spectroscopic Explorer (MUSE) at the VLT and selected from the Measuring Active Galactic Nuclei Under MUSE Microscope (MAGNUM) survey, showing that the complex kinematic features observed can be described by a conical outflow with a constant radial velocity field and a clumpy distribution of clouds.
Intelligent Medicine Box
Authors: M Shalini , S Rithika , S Nivetha , Mohamed Aezeden , Feruza Yuldasheva , kumar R. Ram
Published: July 2023

The fundamental reason for the venture “INTELLIGENT MEDICINE BOX” is to propose the essential thought of programmed medication update, which will help patients take their recommended medication suitably with appropriate dosages. It is a clever plan to help the patient take as much time as is needed and, thus, lessen an opportunity to recuperate from their infection. Specifically, the matured patient takes some unacceptable medication and some unacceptable measurements mistakenly, causing a serious issue. This framework isn’t only useful for an individual but can likewise make a significant commitment to medical clinics. In the present occupied, pushed, and booked life, individuals are experiencing loads of illnesses but can’t recall their medication and timing of it, and here this framework can be of genuine use. This project utilizes an LCD, ARDUINO module, RTC module. AS per the odder gadgets, this smart medicine box is planned in light of a lower cost. Thus, this convenient and monetarily cheaper framework would be useful to each age group
Medibot for Emergency Vehicle
Authors: Aravind A.R. , S Rithubharathan , P.V.M Sanjaikumar , Rathore Sharad Kumar , Durdona Chorieva , Vasudevan N. , Avudaiappan T.
Published: July 2023

To create a medical robot that would be installed in an ambulance and use IoT to observe and communicate so that the patient might receive care before being brought to the hospital. In the case of a mishap, installing a finger print sensor will enable the hospital emergency room, police station, and the patient’s guardian to be informed of the unfamiliar patient’s bio-data. There are still significant problems with overpopulation and health-related illiteracy in India, and an accident-related mortality happens every minute. To build a clever smart health system, a MediBoT made up of sensors and microcontrollers is intended. It will assess the body’s condition and send information to the IoT.
Evaluation of Groundwater Quality for Drinking and Agricultural Purpose
Authors: S Ramkumar , D Gayathri , G Madhumitha , S Nila Dharshini , V Swetha
Published: July 2023

This study examines a variety of chosen groundwater samples from Mohanur, which has issues with its quality for drinking and agricultural use. Reading the study's findings will give you crucial information status of the ground water in Mohanur and can assist in locating the domestic and industrial sources of contamination. This is very helpful in guiding future research to pinpoint and reduce the issue of ground water pollution and helps to take action to address the problem. When a body of water is harmed by anthropogenic toxins, it is often considered to as polluted when it either cannot be used for human purposes, such as drinking water, or when its ability to support its biotic populations significantly changes. Despite their interdependence, surface water and groundwater have frequently been researched and managed as separate resources. In this study, 14 significant parameters were picked to calculate the water quality index. The World Health Organization (WHO) and Indian Council for Medical Research's criteria (ICMR) for the quality of drinking water were used to create the Water Quality Index (WQI). In this study, the weighted arithmetic index approach was employed to calculate WQI.
Smart Agriculture Land Crop Protection Intrusion Detection Using Artificial Intelligence
Authors: S Kiruthika , P Sakthi , K Sanjay , N Vikraman , T Premkumar , R Yoganantham , M Raja
Published: July 2023

Human-wildlife conflict is the term used to describe when human activity results in a negative outcome for people, their resources, wild animals, or their habitat. Human population growth encroaches on wildlife habitat, resulting in a decrease in resources. In particular habitats, there are numerous forms of human and domesticated animal death or injury as a result of conflict. Farmers and the animals that invade farmland suffer greatly as a result. Our project’s primary objective is to lessen human-animal conflict and loss. The embedded system and image processing technique are utilized in the project. Python is used to perform image processing techniques like segmentation, statistical and feature extraction using expectation maximization, and classification using CNN. The classification is used to determine whether the land is empty or if animals are present. A buzzer sound is produced, a light electric current is passed to the fence, and a message alerting the farmer to the animal’s entry into the farmland is transmitted. This prevents the animal from entering the field and enables the landowner to take the necessary steps to get the animal back to the forest. The result is serially sent to the controller broad from the control board.
IoT Based Patient’s Smart Healthcare Monitoring and Recording Using GSM Module
Authors: M Yuvaraj , R Naveen , N Gokul , S Gowtham , S Ramkumar
Published: July 2023

An IoT based smart healthcare monitoring system is presented in this study. A local database, sensors, a microcontroller, and a GSM module make up the suggested system. Here, we’re using an Arduino Uno to attach sensors, which then sense the environment and record data that is sent over the internet using the GSM protocol. The health of patients in isolated or rural locations without access to medical services can be tracked using this project. The importance of offering patients who were anticipated to decrease travel and direct contact with others remote patient monitoring services became even more clear when the epidemic struck. When there is an abnormal activity identified, healthcare team will get a message and they would make a call and prescribe the activities which need to be do for emergency and even it doesn’t work ambulance is immediately depart to that particular patient’s location since we have the patient’s personal info about his location and his/her relative emergency mobile number.
Semantic-Based Classification of Toxic Comments Using Ensemble Learning
Authors: A Thilagavathy , R Deepa , S.D Lalitha , D Naveen Raju , R Ramya , M Ramya , L Rithika , K Karpoora Sundari
Published: July 2023

A social media is rapidly expanding, and its anonymity feature completely supports free speech. Hate speech directed at anyone or any group because of their ethnicity, clan, religion, national or cultural their heritage, sex, disability, gender orientation, or other characteristics is a violation of their authority. Seriously encourages violence or hate crimes and causes social unrest by undermining peace, trustworthiness, and human rights, among other things. Identifying toxic remarks in social media conversation is a critical but difficult job. There are several difficulties in detecting toxic text remarks using a suitable and particular social media dataset and its high-performance, selected classifier. People nowadays share messages not only in person, but also in online settings such as social networking sites and online groups. As a result, all social media sites and apps, as well as all current communities in the digital world, require an identification and prevention system. Finding toxic social media remarks has proven critical for content screening. The identifying blocker in such a system would need to notice any bad online behavior and alert the prophylactic blocker to take appropriate action. The purpose of this research was to assess each text and find various kinds of toxicities such as profanity, threats, name-calling, and identity-based hatred. Jigsaw's designed Wikipedia remark collection is used for this.
Crop protection from animals based on machine learning
Authors: P Marichamy , P Karuppasamy , B Heera , R Leena Keresi , M Kanagalakshmi
Published: July 2023

This paper will discuss the present level of research and development on human-wildlife conflicts, there are bad interactions between humans and wild animals that are bad for both the resources of the human population as well as the ecosystems of wildlife. Animal detection has an impact on both human food security and animal welfare because it arises from the conflict between humans and wildlife over natural resources. As the population has grown and many communities' land use patterns have changed, these disputes have become more prevalent in recent years. The Mobile Net SSD type is used in this automatic intrusion and deterrent system for enhanced performance. When a dangerous animal is discovered, the system emits an alarm sound and notifies the relevant authorities, alerting them to the discovery. It is more human- friendly due to the quick detection process, and it is more animal-friendly due to the gentle repulsive process.
Regression Test List Sharding in a Distributed Test Environment
Authors: Gonsalves Michelle , Mandala Jyothi
Published: July 2023

One of the major issues during the regression test of the new version of Real Time Operating System (RTOS) is the time involved in test case execution. The main reason being a single embedded system device under test (DUT) is used to execute the test list containing several test cases. This traditional method of regression test also leads to wasted productivity of the other devices at hand that could be otherwise used during this regression test. Hence, in this paper, we propose a technique that aims at reducing the overall regression test cycle time of a newer version of a Real Time Operating System (RTOS) by employing a method known as “test-list sharding” in a distributed test environment. In the proposed work, multiple DUTs are connected to the test server via a communication network. The test server executes the test list containing several test cases and performs the test-list sharding, that is, distributing test cases to different DUTs and executing them in parallel. After the test is executed on the DUT, the test results are sent back to the test server which will summarize all the results. In the proposed work, the sharding is done by distributing the test cases without overloading or under loading any of the DUTs. Test list is sharded in such a way that the same tests are not sent to multiple DUTs. The main advantage of the proposed method is that the test sharding can be easily scalable to accommodate any number of devices that can be connected to the test server. Also, the test list sharding is done in a dynamic way so that the tests are distributed to an idle DUT that has completed a test execution and ready for another test to execute. The comparison study of executing a sample test list sequentially on a single DUT and distributed test system with multiple DUTs is performed. Results obtained showed the performance gain in terms of test cycle time reduction, scalability, equal load distribution and effective resource utilization.
IoT Enabled Smart Garbage Monitoring System
Authors: Verma Jyoti , Goel Pallavi , Manhas Pratima , Thakral Shaveta , Passi Abhiruchi
Published: July 2023

The Internet of Things (IoT) is a commonly used technology because of its various advantages. Building an overall architecture for the IoT is subsequently an unpredictable task, essentially due to the very large variety of gadgets and interface layer advancements. These days many steps are made to provide cleanliness in the country and to make the climate ecofriendly. One of the principle worries with our current scenario has been solid waste management which not only impacts the wellbeing as well as climate. The indication, observation and management of garbage dustbins are one of the fundamental issues of the ongoing time frame.. The traditionally used techniques of physically monitoring the garbage in bins is an awkward cycle and uses more human exertion, time and cost which can undoubtedly be stayed away from with our current innovations. Therefore objective of this paper is to design automated garbage Monitoring System. By using this proposed model people don’t have to screen all of the systems actually and warning will be issued if the container will get filled.