bluuebunny/crossref_metadata_2025_split huggingface dataset:
Leveraging limited data from wildlife monitoring in a conflict affected region in Venezuela
Authors: Stachowicz Izabela , Ferrer-Paris José Rafael , Sánchez-Mercado Ada 
Published: January 2024

AbstractEfficient monitoring of biodiversity-rich areas in conflict-affected areas with poor rule of law requires a combination of different analytical approaches to account for data biases and incompleteness. In the upland Amazon region of Venezuela, in Canaima National Park, we initiated biodiversity monitoring in 2015, but it was interrupted by the establishment of a large-scale mining development plan in 2016, compromising the temporal and geographical extent of monitoring and the security of researchers. We used a resource selection function model framework that considers imperfect detectability and supplemented detections from camera trap surveys with opportunistic off-camera records (including animal tracks and direct sighting) to (1) gain insight into the value of additional occurrence records to accurately predict wildlife resource use in the perturbated area (deforestation, fire, swidden agriculture, and human settlements vicinity), (2) when faced with security and budget constraints. Our approach maximized the use of available data and accounted for biases and data gaps. Adding data from poorly sampled areas had mixed results on estimates of resource use for restricted species, but improved predictions for widespread species. If budget or resources are limited, we recommend focusing on one location with both on-camera and off-camera records over two with cameras. Combining camera trap records with other field observations (28 mammals and 16 birds) allowed us to understand responses of 17 species to deforestation, 15 to fire, and 13 to swidden agriculture. Our study encourages the use of combinations of methods to support conservation in high-biodiversity sites, where access is restricted, researchers are vulnerable, and unequal sampling efforts exist.
“KAIZEN” method realizing implementation of deep-learning models for COVID-19 CT diagnosis in real world hospitals
Authors: Okada Naoki , Umemura Yutaka , Shi Shoi , Inoue Shusuke , Honda Shun , Matsuzawa Yohsuke , Hirano Yuichiro , Kikuyama Ayano , Yamakawa Miho , Gyobu Tomoko , Hosomi Naohiro , Minami Kensuke , Morita Natsushiro , Watanabe Atsushi , Yamasaki Hiroyuki , Fukaguchi Kiyomitsu , Maeyama Hiroki , Ito Kaori , Okamoto Ken , Harano Kouhei , Meguro Naohito , Unita Ryo , Koshiba Shinichi , Endo Takuro , Yamamoto Tomonori , Yamashita Tomoya , Shinba Toshikazu , Fujimi Satoshi 
Published: January 2024

AbstractNumerous COVID-19 diagnostic imaging Artificial Intelligence (AI) studies exist. However, none of their models were of potential clinical use, primarily owing to methodological defects and the lack of implementation considerations for inference. In this study, all development processes of the deep-learning models are performed based on strict criteria of the “KAIZEN checklist”, which is proposed based on previous AI development guidelines to overcome the deficiencies mentioned above. We develop and evaluate two binary-classification deep-learning models to triage COVID-19: a slice model examining a Computed Tomography (CT) slice to find COVID-19 lesions; a series model examining a series of CT images to find an infected patient. We collected 2,400,200 CT slices from twelve emergency centers in Japan. Area Under Curve (AUC) and accuracy were calculated for classification performance. The inference time of the system that includes these two models were measured. For validation data, the slice and series models recognized COVID-19 with AUCs and accuracies of 0.989 and 0.982, 95.9% and 93.0% respectively. For test data, the models’ AUCs and accuracies were 0.958 and 0.953, 90.0% and 91.4% respectively. The average inference time per case was 2.83 s. Our deep-learning system realizes accuracy and inference speed high enough for practical use. The systems have already been implemented in four hospitals and eight are under progression. We released an application software and implementation code for free in a highly usable state to allow its use in Japan and globally.
Impact of glucose metabolism on PD-L1 expression in sorafenib-resistant hepatocellular carcinoma cells
Authors: Cho Sua , Kim Wonjin , Yoo Dayoung , Han Yeonju , Hwang Hyemin , Kim Seunghwan , Kim Jimin , Park Sanghee , Park Yusun , Jo HanHee , Pyun Jae-chul , Lee Misu 
Published: January 2024

AbstractHepatocellular carcinoma (HCC) is the fifth leading cause of cancer-related mortality worldwide. Programmed cell death ligand-1 (PD-L1) is an immune checkpoint protein that binds to programmed cell death-1 (PD-1), which is expressed in activated T cells and other immune cells and has been employed in cancer therapy, including HCC. Recently, PD-L1 overexpression has been documented in treatment-resistant cancer cells. Sorafenib is a multikinase inhibitor and the only FDA-approved treatment for advanced HCC. However, several patients exhibit resistance to sorafenib during treatment. This study aimed to assess the effect of glucose deprivation on PD-L1 expression in HCC cells. We used PD-L1-overexpressing HepG2 cells and IFN-γ-treated SK-Hep1 cells to explore the impact of glycolysis on PD-L1 expression. To validate the correlation between PD-L1 expression and glycolysis, we analyzed data from The Cancer Genome Atlas (TCGA) and used immunostaining for HCC tissue analysis. Furthermore, to modulate PD-L1 expression, we treated HepG2, SK-Hep1, and sorafenib-resistant SK-Hep1R cells with rapamycin. Here, we found that glucose deprivation reduced PD-L1 expression in HCC cells. Additionally, TCGA data and immunostaining analyses confirmed a positive correlation between the expression of hexokinase II (HK2), which plays a key role in glucose metabolism, and PD-L1. Notably, rapamycin treatment decreased the expression of PD-L1 and HK2 in both high PD-L1-expressing HCC cells and sorafenib-resistant cells. Our results suggest that the modulation of PD-L1 expression by glucose deprivation may represent a strategy to overcome PD-L1 upregulation in patients with sorafenib-resistant HCC.
Application of electro oxidation process for treating wastewater from petrochemical with mixed metal oxide electrode
Authors: Mirshafiee Amir , Nourollahi Mohammad , Shahriary Alireza 
Published: January 2024

AbstractPetrochemicals require a large volume of water for their operation, which results in the production of a large volume of wastewater. Treatment of petrochemical wastewater is an important process before discharging it into the environment. This research examines the treatment of real petrochemical wastewater using the electrochemical oxidation process. Direct anodic oxidation is an effective advanced electrochemical oxidation process (AEOP), with different electrodes using a parallel plate electrochemical reactor. Four types of real wastewater were received from different petrochemical units were treated by AEOP. Real wastewater samples with chemical oxygen demand (COD) concentrations ranging from 20,450 to 52,300 mg/l. The main goal of this research is to make electrodes of Mixed Metal Oxide (MMO), which can reduce the treatment time and electricity consumption for oxidation, greater stability of the surface of the electrodes. Investigation of the rate constant kinetics shows that high COD removal efficiency can be achieved following the pseudo-second order reaction rate (R2 > 98%). When the wastewater pH is less than 5, COD removal efficiency is higher and the treatment process will be successful, which succeeded in removing 79% COD, but in alkaline wastewater, COD reduction efficiency was not satisfactory. The electricity consumption for 79% removal during 6 min was 117 kWh/m3. As a result, due to the very short time of the process (6 min), it can be used as one of the pre-treatment steps of petrochemical wastewater with acidic pH.
Predicting CKD progression using time-series clustering and light gradient boosting machines
Authors: Saito Hirotaka , Yoshimura Hiroki , Tanaka Kenichi , Kimura Hiroshi , Watanabe Kimio , Tsubokura Masaharu , Ejiri Hiroki , Zhao Tianchen , Ozaki Akihiko , Kazama Sakumi , Shimabukuro Michio , Asahi Koichi , Watanabe Tsuyoshi , Kazama Junichiro J. 
Published: January 2024

AbstractPredicting the transition of kidney function in chronic kidney disease is difficult as specific symptoms are lacking and often overlooked, and progress occurs due to complicating factors. In this study, we applied time-series cluster analysis and a light gradient boosting machine to predict the trajectories of kidney function in non-dialysis dependent chronic kidney disease patients with baseline estimated glomerular filtration rate (GFR) ≥ 45 mL/min/1.73 m2. Based on 5-year changes in estimated GFR, participants were stratified into groups with similar trajectories by cluster analysis. Next, we applied the light gradient boosting machine algorithm and Shapley addictive explanation to develop a prediction model for clusters and identify important parameters for prediction. Data from 780 participants were available for analysis. Participants were classified into five classes (Class 1: n = 78, mean [± standard deviation] estimated GFR 100 ± 19.3 mL/min/1.73 m2; Class 2: n = 176, 76.0 ± 9.3 mL/min/1.73 m2; Class 3: n = 191, 59.8 ± 5.9 mL/min/1.73 m2; Class 4: n = 261, 52.7 ± 4.6 mL/min/1.73 m2; and Class 5: n = 74, 53.5 ± 12.0 mL/min/1.73 m2). Declines in estimated GFR were 8.9% in Class 1, 12.2% in Class 2, 4.9% in Class 3, 12.0% in Class 4, and 45.1% in Class 5 during the 5-year period. The accuracy of prediction was 0.675, and the top three most important Shapley addictive explanation values were 1.61 for baseline estimated GFR, 0.12 for hemoglobin, and 0.11 for body mass index. The estimated GFR transition of patients with preserved chronic kidney disease mostly depended on baseline estimated GFR, and the borderline for estimated GFR trajectory was nearly 50 mL/min/1.73 m2.
Global species delimitation of the cosmopolitan marine littoral earthworm Pontodrilus litoralis (Grube, 1855)
Authors: Seesamut Teerapong , Oba Yuichi , Jirapatrasilp Parin , Martinsson Svante , Lindström Maria , Erséus Christer , Panha Somsak 
Published: January 2024

AbstractThe marine littoral earthworm Pontodrilus litoralis (Grube, 1855) is widely distributed and is reported as a single species. This study utilized an integrative taxonomic approach based upon morphological examination, phylogenetic reconstruction, and molecular species delimitation, to test whether the taxon is a single species or a species complex. For this, a total of 114 P. litoralis specimens collected from North America, Africa, Australia and Oceania, Europe and Asia were used. The phylogenetic analyses revealed deeply divergent mitochondrial lineages and a high level of genetic diversity among P. litoralis populations. Both single and multi-locus species delimitation analyses yielded several molecular operational taxonomic units. Therefore, due to the homogeneity of morphological characteristics, it is likely that the morphospecies P. litoralis is a complex of four or more cryptic species, suggesting that more sampling is required and that the population structure genetic data and gene flow need to be investigated.
Extremely thin intermetallic layer in dissimilar AA6061-T6 and mild steel friction stir lap welding using a hemispherical tool
Authors: Ambrosio Danilo , Morisada Yoshiaki , Ushioda Kohsaku , Fujii Hidetoshi 
Published: January 2024

AbstractThe dissimilar friction stir lap welding of AA6061-T6 and mild steel using the hemispherical tool tilted towards the retreating side is investigated. Critical defects such as hook features and internal voids are avoided by limiting the plunge depth in the lower plate to a tenth of a millimeter. The low heat generation guaranteed by the hemispherical tool produces a nanoscale intermetallic compound layer alternatively composed of an Al-rich and a ternary Al–Fe–Mg phases. The complex and extremely thin interlayers strengthen the Al–Fe mechanical bonding, guaranteeing high mechanical properties and rupture within the Al-stirred zone. Thermomechanical phenomena governing friction stir lap welding with the hemispherical tool drastically limit the growth of intermetallics, leading to the high mechanical strength of the lap joint.
Heterogeneous integration of high-k complex-oxide gate dielectrics on wide band-gap high-electron-mobility transistors
Authors: Ji Jongho , Yang Jeong Yong , Lee Sangho , Kim Seokgi , Yeom Min Jae , Lee Gyuhyung , Shin Heechang , Bae Sang-Hoon , Ahn Jong-Hyun , Kim Sungkyu , Kim Jeehwan , Yoo Geonwook , Kum Hyun S. 
Published: January 2024

AbstractHeterogeneous integration of dissimilar crystalline materials has recently attracted considerable attention due to its potential for high-performance multifunctional electronic and photonic devices. The conventional method for fabricating heterostructures is by heteroepitaxy, in which epitaxy is performed on crystallographically different materials. However, epitaxial limitations in monolithic growth of dissimilar materials prevent implementation of high quality heterostructures, such as complex-oxides on conventional semiconductor platforms (Si, III-V and III-N). In this work, we demonstrate gallium nitride (GaN) high-electron-mobility transistors with crystalline complex-oxide material enabled by heterogeneous integration through epitaxial lift-off and direct stacking. We successfully integrate high-κ complex-oxide SrTiO3 in freestanding membrane form with GaN heterostructure via a simple transfer process as the gate oxide. The fabricated device shows steep subthreshold swing close to the Boltzmann limit, along with negligible hysteresis and low dynamic on-resistance, indicating very low defect density between the SrTiO3 gate oxide and GaN heterostructure. Our results show that heterogeneous integration through direct material stacking is a promising route towards fabricating functional heterostructures not possible by conventional epitaxy.
Maximising the benefits of calcium carbonate in sustainable cements: opportunities and challenges associated with alkaline waste carbonation
Authors: Jin Fei , Zhao Mingshan , Xu Maochun , Mo Liwu 
Published: January 2024

AbstractCement production significantly contributes to global climate change and implementation of carbon capture, utilisation and storage (CCUS) in construction materials is considered a crucial step toward achieving net-zero emissions. Substituting Portland cement (PC) clinker with limestone has been demonstrated to effectively reduce CO2 emissions while enhancing the properties of cement and concrete. Beyond limestone extraction, quarrying, and crushing, various types of alkaline waste materials generated from industrial processes can serve as valuable resources for producing diverse forms of calcium carbonate, simultaneously capturing a substantial amount of CO2. In this context, we contend that due to the distinct characteristics of various calcium carbonate forms, there exists the substantial potential to maximise their technical, economic, and environmental advantages in the production of sustainable cements. We reviewed existing studies of the effects of different calcium carbonate forms on cement properties and underscored the viability of utilising various alkaline wastes to produce different calcium carbonate products. As a promising approach for CO2 reduction, waste management, and resource recovery, we addressed the opportunities and challenges associated with advancing CCUS through the utilisation of carbonated alkaline wastes in sustainable cements. To achieve real-world impacts, we emphasised the necessity for interdisciplinary research collaborations, active involvement from industry stakeholders, regulatory bodies, and governmental support to facilitate the large-scale adoption of these innovative practices.
Breathing and Speech Adaptation: Do Speakers Adapt Toward a Confederate Talking Under Physical Effort?
Authors: Offrede Tom , Mooshammer Christine , Fuchs Susanne 
Published: January 2024

Purpose:

This study investigated whether speakers adapt their breathing and speech (fundamental frequency [
f
o
]) to a prerecorded confederate who is sitting or moving under different levels of physical effort and who is either speaking or not. Following Paccalin and Jeannerod (2000), we would expect breathing rate to change in the direction of the confederate's, even if the participant is physically inactive. This might in turn affect their speech acoustics.

Method:
We recorded the speech and respiration of 22 native German speakers. They produced solo and synchronous read speech in interaction with a confederate who appeared on a prerecorded video. There were three within-subject experimental conditions: the confederate (a) sitting, (b) biking with light effort, or (c) biking with heavier effort.

Results:

During speech, the confederate's inhalation amplitude and
f
o
increased with physical effort, as expected. Her breath cycle duration changed differently, probably because of read speech constraints. Overall, the only adaptation the participants showed was higher
f
o
with increase in the confederate's physical effort during synchronous, but not solo, speech.

Additionally, they produced shallower inhalations when observing the confederate biking in silence, as compared to the condition without movement. Crucially, the participants' acoustic and breathing data showed large interindividual variability.

Conclusions:

Our findings indicate that, in this paradigm, convergence only took place on
f
o
during synchronous speech and that this phonetic adaptation happened independently from any speech breathing adaptation. It also suggests that participants may adapt their quiet breathing while watching a person performing physical exercise but that the mechanism is more complex than that explained previously.
Induction motor bearing fault classification using deep neural network with particle swarm optimization‐extreme gradient boosting
Authors: Lee Chun‐Yao , Maceren Edu Daryl C. 
Published: January 2024

AbstractIntelligent motor fault diagnosis in industrial applications requires identifying key characteristics to differentiate various fault types effectively. Solely relying on statistical features cannot guarantee high classification accuracy, while complex feature extraction techniques can pose challenges for industry practitioners. Conversely, advanced feature extraction may not ensure that the model effectively learns these features for classification. A feature fusion approach that combines statistical and deep learning features to address these challenges is proposed. Since statistical features form the foundation for general feature extraction, statistical and deep learning features are combined using Extreme Gradient Boosting (XGBoost) algorithm with Particle Swarm Optimization (PSO). The PSO algorithm automates parameter tuning for XGBoost. A deep neural network (DNN) adaptively extracts hidden features, improving bearing fault classification precision using t‐SNE representation. Results successfully prove the DNN's ability to classify diverse motor faults using deep learning features. Thus, integrating statistical features with XGBoost further enhances DNN's performance. To ensure robustness, the proposed method has been compared with different motor fault classification methods and validated across different motor fault datasets, showcasing improved classification accuracy and robust performance, even amidst varying noise levels. This approach represents a promising advancement in intelligent fault diagnosis within industrial contexts.
Politiques de soutien à la parentalité en France
Authors: Sitruk Patricia 
Published: January 2024

Au sein des politiques publiques françaises, celle concernant le soutien à la parentalité est officiellement née en 1998 lors de la Conférence de la famille. Sa création a été motivée par des facteurs tels que l’inquiétude croissante quant aux compétences parentales, les besoins exprimés par les familles en difficulté et la responsabilité perçue de la puissance publique face aux évolutions sociétales. Depuis, elle s’est développée en s’appuyant sur l’accompagnement des parents dans leur rôle éducatif, s’attachant à répondre à l’évolution des structures familiales, de moins en moins traditionnelles et plus diversifiées. Néanmoins, sa mise en place a suscité des questionnements : doit-elle être universelle ou ciblée ? Quel équilibre trouver entre la place des parents et celle de l’enfant ? Des débats politiques ont également émergé concernant les risques de contrôle social ou de dérive vers des préoccupations de prévention de la délinquance.
Les dispositifs de soutien à la parentalité sont variés, allant des lieux d’accueil enfants-parents aux services d’information et d’orientation. Depuis 2021, cette politique cherche à s’affirmer davantage en se dotant d’une Charte nationale et d’un programme prioritaire axé sur les 1 000 premiers jours de l’enfant.
L’intérêt économique de cette politique est de plus en plus souligné, des économistes mettant en avant les bénéfices à investir dans le soutien à la parentalité sur le long terme. Cette politique reste en évolution, composée de dispositifs diversifiés et parfois complexes, mais témoigne d’une vitalité dans sa mise en œuvre et de sa place désormais ancrée au sein des politiques publiques.
Transparenz und Effizienz durch digitale Gesundheitsversorgung?
Ergebnisse eines Surveys der Generation Y
Authors: Kreyenschulte Thea , Bohnet-Joschko Sabine 
Published: January 2024

Zusammenfassung
Hintergrund Generation Y stellt eine besondere Zielgruppe in der
Gesundheitsversorgung dar. Jung, gesund und digital sozialisiert durch
Technologien im Alltag, weist sie spezifische Bedarfe auf. Im Rahmen
gesellschaftlicher Herausforderungen wie dem demographischen Wandel und der
Fachkräftesicherung, sollte eine Berücksichtigung dieser
Bedarfe, insbesondere zur Gesunderhaltung, das Ziel von Public Health sowie
spezifischen Akteuren wie Krankenversicherungen und Arbeitgebern sein.
Ziel der Arbeit Die Untersuchung sollte die Erkenntnisse aus zwei
vorausgegangenen Arbeitsphasen, speziell einer qualitativen Erhebung,
prüfen und vertiefen. So sollten zwei Forschungsfragen beantwortet
werden: I: Welche Bedeutung haben Transparenz und Effizienz für die
Nutzung digitaler Gesundheitsinnovationen durch die Generation Y? und II:
Welchen Einfluss haben neben den Bedarfen darüber hinaus
soziodemographische und technikassoziierte Faktoren auf die Nutzung digitaler
Gesundheitsinnovationen durch die Generation Y?
Material und Methoden Als Abschluss eines sequenziellen Verfahrens wurden
in einer quantitativen Online-Befragung Einschätzungen zu transparenz-
und effizienzassoziierten Bedürfnissen im Rahmen der Nutzung digitaler
Gesundheitsinnovationen von Personen der Generation Y erhoben. 355
vollständig ausgefüllte Datensätze wurden mithilfe der
Software SPSS ausgewertet. Anhand einer ordinalen Regressionsanalyse wurden
Einflüsse von Prädiktoren aus sechs vorab formulierten
Hypothesen geprüft.
Ergebnisse Die Untersuchung konnte Einflüsse der Konstrukte
Transparenz und Effizienz auf die Nutzung von Apps und Smart-Devices
beschreiben. Das Bedürfnis nach Kontrollempfinden zeigte eine
signifikant negative, Bedürfnisse nach Strukturierung und nahtloser
Integration in den Alltag signifikant positive Wirkung. Das Geschlecht und die
Technikbereitschaft nahmen ebenfalls Einfluss. Der Datenauswertung zufolge
scheint für Generation Y Effizienz und somit ein konkreter Alltagsnutzen
durch digitale Gesundheitsinnovationen relevanter zu sein, als eine autonome
Handlungsbefähigung und somit Transparenz.
Schlussfolgerung Indem die beschriebenen Bedürfnisse sowie
entsprechende Präferenzen berücksichtigt würden,
könnte die Zielgruppe angesprochen und die Nutzung potenziell gesteigert
werden. Prävention und Gesundheitsförderung, ebenso wie die
Organisation dieser, sollten sich niedrigschwellig in den Alltag einblenden
lassen sowie der Alltag positiv ergänzt und effizient
unterstützt werden.