bluuebunny/crossref_metadata_2025_split huggingface dataset:
Acid‐Responsive Anticorrosion Microcapsules for Self‐Protecting Coatings
Authors: Thakare Dhawal R. , Xiong Thao M. , Flueck Ian L. , Morado Ephraim G. , Zimmerman Steven C. , Sottos Nancy R. 
Published: January 2022

AbstractEncapsulated anticorrosion agents provide a suitable alternative to dispersion of metal‐based compounds in protective polymeric coatings on metal substrates. Stimuli‐responsive microcapsules enhance protection abilities by autonomously responding to corrosion‐induced environmental changes, rather than relying on damage‐induced mechanical stimuli. This work reports pH‐responsive microcapsules with triggered release over a wide range of acidic pH values (pH < 6) and robust enough to be incorporated in commercial solvent‐based epoxy coatings. The pH responsiveness is achieved by integrating acid labile ketals that undergo rapid hydrolysis within the cross‐linked polyamide shell and readily release acetylenic diol or jojoba oil as the anticorrosive agent. The microcapsules are stable up to a temperature of 150 °C and provide long‐term room temperature stability up to 3 months. Degradation and release kinetics of the microcapsules are quantified at various pH levels (1 ≤ pH ≤ 9) using 1H NMR and gas chromatography, respectively. Microcapsules exhibit complete release in under 5 min at pH 1 and ≈2 hours at pH 5, respectively. Coating performance is evaluated by electrochemical corrosion tests conducted in 5 wt% salt solutions with varying pH and concentration of the microcapsules in the coating. Inhibition efficiencies up to 70% are achieved in acidic saltwater solutions.
Hybrid‐attention densely connected U‐Net with GAP for extracting livers from CT volumes
Authors: Chen Ying , Hu Fei , Wang Yerong , Zheng Cheng 
Published: January 2022

AbstractPurposeLiver segmentation is an important step in the clinical treatment of liver cancer, and accurate and automatic liver segmentation methods are extremely important. U‐Net has been used as the benchmark for many medical segmentation networks, but it cannot fully utilize low‐resolution information and global contextual information. To solve these problems, we propose a new network architecture named the hybrid‐attention densely connected U‐Net (HDU‐Net).MethodsThe proposed HDU‐Net has three main changes relative to U‐Net, as follows: (1) It uses a densely connected structure and dilated convolution to achieve feature reuse and avoid information loss. (2) A global average pooling block is proposed to further augment the receptive field and improve the segmentation accuracy of the network for small or disconnected liver regions. (3) By combining the spatial attention and channel attention mechanisms, a hybrid attention structure is proposed to replace the skip connection component to filter and integrate low‐resolution information.ResultsExperiments conducted on the LITS2017, 3Dircadb and Sliver07 datasets show that the proposed model can segment the liver accurately and effectively. Dice scores reach 96.5%, 96.18%, and 97.57% on these datasets, respectively, constituting results that are superior to many previously proposed methods.ConclusionsThe experimental liver segmentation results have demonstrated that our proposed network provides improved segmentation performance in comparison with other networks. The experimental results without postprocessing confirmed that our network solves the oversegmentation and undersegmentation problems to some extent. The proposed model is effective, robust, and efficient in terms of liver segmentation without requiring extensive training time or a very large dataset.
Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors
Authors: Kumar Neeraj , Verma Ruchika , Chen Chuheng , Lu Cheng , Fu Pingfu , Willis Joseph , Madabhushi Anant 
Published: January 2022

AbstractWe assessed the utility of quantitative features of colon cancer nuclei, extracted from digitized hematoxylin and eosin‐stained whole slide images (WSIs), to distinguish between stage II and stage IV colon cancers. Our discovery cohort comprised 100 stage II and stage IV colon cancer cases sourced from the University Hospitals Cleveland Medical Center (UHCMC). We performed initial (independent) model validation on 51 (143) stage II and 79 (54) stage IV colon cancer cases from UHCMC (The Cancer Genome Atlas's Colon Adenocarcinoma, TCGA‐COAD, cohort). Our approach comprised the following steps: (1) a fully convolutional deep neural network with VGG‐18 architecture was trained to locate cancer on WSIs; (2) another deep‐learning model based on Mask‐RCNN with Resnet‐50 architecture was used to segment all nuclei from within the identified cancer region; (3) a total of 26 641 quantitative morphometric features pertaining to nuclear shape, size, and texture were extracted from within and outside tumor nuclei; (4) a random forest classifier was trained to distinguish between stage II and stage IV colon cancers using the five most discriminatory features selected by the Wilcoxon rank‐sum test. Our trained classifier using these top five features yielded an AUC of 0.81 and 0.78, respectively, on the held‐out cases in the UHCMC and TCGA validation sets. For 197 TCGA‐COAD cases, the Cox proportional hazards model yielded a hazard ratio of 2.20 (95% CI 1.24–3.88) with a concordance index of 0.71, using only the top five features for risk stratification of overall survival. The Kaplan–Meier estimate also showed statistically significant separation between the low‐risk and high‐risk patients, with a log‐rank P value of 0.0097. Finally, unsupervised clustering of the top five features revealed that stage IV colon cancers with peritoneal spread were morphologically more similar to stage II colon cancers with no long‐term metastases than to stage IV colon cancers with hematogenous spread. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Prediction of curing process for thermosetting prepreg compression molding process based on machine learning
Authors: Hou Jiatong , You Bo , Xu Jiazhong , Wang Tao 
Published: January 2022

AbstractIn the curing process of thermosetting prepreg compression molding (PCM), the distribution of the temperature field and the curing degree field have an important influence on the performance of composites. Therefore, the establishment of method to accurately predict the temperature difference and the degree of cure (DoC) difference during the curing process is significance for improving the performance of composites. In this paper, three kinds of machine learning models are studied: back propagation (BP) neural network, genetic algorithm‐back propagation (GA‐BP) neural network, radial basis function (RBF) neural network, then predictive models based on finite element method (FEM) and machine learning models are proposed. In the double‐dwell curing curve, six typical parameters are selected as inputs; the maximum value of temperature, the maximum value of temperature overshoot, the maximum DoC difference, the curing time, these four parameters during the curing process are selected as outputs, then the rapid predictive model is established. Within the value range of the process parameters, the Latin hypercube sampling (LHS) method is used to select 100 sets of sample points, and after training on three predictive models, comparison, and verification are carried out. The results show that the predictive effect of the RBF model is the best. In these three models, the RBF model is more suitable for the performance prediction of composites PCM. In this article, the research provides the basis for the performance prediction of composites and the multiobjective optimization of the curing process.
Experimental and modeling investigation of ultrafine polyvinylidene fluoride hollow fiber membrane module for recovery of lactic acid from aqueous solutions
Authors: Madupathi Madhumala , Nandala Shivaprasad , Sundergopal Sridhar 
Published: January 2022

AbstractThis study investigated the aqueous lactic acid extraction process using indigenous ultrafine polyvinylidene fluoride (PVDF) hollow fiber membrane contactor. Ultrafine PVDF hollow fibers with tighter pore structure were produced by wet spinning technique and fabricated in‐house with an epoxy resin. A kinetic study was performed by varying acid concentrations ranging from 60 to 1320 mol/m3 in an aqueous phase and 210–620 mol/m3 tri‐n‐octylamine (TOA) concentration in 1‐octanol diluent. The solutions were allowed to flow countercurrently using peristaltic pumps at constant aqueous and organic flow velocity of 0.006 and 0.005 m/s, respectively. A PRO/II simulation of the membrane contactor system was carried out to validate experimental results. The study showed a maximum acid extraction of 88 ± 0.1% for initial acid and amine concentrations of 250 and 620 mol/m3, respectively, which corresponds to a high KD value of 11.5 which is larger than the value found from PRO/II simulation for direct mixing of the aqueous and organic phases which demonstrates the merit of the membrane contactor. The exploratory study on kinetics has helped in understanding the mass transfer phenomena of the solute across the membrane. In addition, membrane characterization was done to investigate the surface and cross‐sectional morphologies and mechanical strength properties.
Optical Characterization of Large Icosahedral Colloidal Clusters
Authors: Ohnuki Ryosuke , Kunimoto Naoki , Takeoka Yukikazu , Yoshioka Shinya 
Published: January 2022

AbstractColloidal particles form a face‐centered‐cubic (FCC) lattice on crystallization; however, recent studies have indicated that the cluster with the icosahedral symmetry can be formed under spherical confinement. The icosahedral cluster appears only when the number of constituent particles is small because the FCC lattice is favored thermodynamically for a bulk colloidal crystal. This paper reports the observation of a large icosahedral colloidal cluster comprising ≈4.9 × 105 colloidal particles, which is considerably larger than the previously reported number. This large icosahedral cluster clearly exhibits the triangular reflection pattern that corresponds to one face of the icosahedron. Further, an oblique illumination with sample rotation is found to be an effective method to characterize the icosahedral cluster optically. In this study, larger colloidal particles are used than those in previous studies. It is hypothesized that slow reconfiguration during the evaporation process can result in the formation of a large icosahedral cluster due to the large particle size.
Assessing farmer perceptions on livestock intensification and associated trade‐offs using fuzzy cognitive maps; a study in mixed farming systems in the mid‐hills of Nepal
Authors: Alomia‐Hinojosa Victoria , Groot Jeroen C. J. , Andersson Jens A. , Speelman Erika N. , McDonald Andrew J. , Tittonell Pablo 
Published: January 2022

AbstractIntensified livestock production is considered as a promising pathway for smallholder farmers. Nevertheless, this pathway may entail prohibitive investment requirements of labour, capital or trade‐offs at farm level that preclude sustainable intensification. We used fuzzy cognitive mapping (FCM) to assess farmers' perceptions of changes in the farm household system resulting from adding livestock to their mixed farms. Farmers identified trade‐offs between the increased income and farmyard manure production versus increases in labour requirements for fodder imports. Furthermore, a sensitivity analysis performed on the FCMs showed that an increase in milk market demand could have strong positive effects on livestock production and income. We conclude that FCM is a good tool to rapidly identify trade‐offs and analyse perceptions of farmers which revealed that although they consider intensification a promising strategy, the perceived deepening of labour constraints and increasing dependency on fodder import makes a concurrent (sustainable) intensification of these farm systems unlikely.
The Capability Approach in Social Work with Older People
Authors: Kjellberg Inger 
Published: January 2022

AbstractThis chapter reports the findings from a scoping review of the use of the capability approach in social work with older people. Four peer-reviewed articles using the capability approach and addressing social work practice with older people are discussed. The purpose was to examine how the use of the capability approach is linked to policies, practices and social justice approaches in social work research. Two main applications of the capability approach were found: to explore the subjective sense of well-being, and to address social inequalities at a structural level. The capability approach was framed as congruent with the aims of social work, and as a call for action for social workers to promote social justice, human dignity and well-being. Only a few studies were found in this scoping review; however, more seemed to have been done within the fields of healthcare and developmental studies. Despite these limitations, the conclusion is that the capability approach has attracted some attention in social work with older people, and the number of studies elaborating on the capability approach in social work in general is growing.
Sustainable artificial intelligence: A corporate culture perspective
Authors: Isensee Carmen , Griese Kai-Michael , Teuteberg Frank 
Published: January 2022

AbstractIn recent years, various studies have highlighted the opportunities of artificial intelligence (AI) for our society. For example, AI solutions can help reduce pollution, waste, or carbon footprints. On the other hand, there are also risks associated with the use of AI, such as increasing inequality in society or high resource consumption for computing power. This paper explores the question how corporate culture influences the use of artificial intelligence in terms of sustainable development. This type of use includes a normative element and is referred to in the paper as sustainable artificial intelligence (SAI). Based on a bibliometric literature analysis, we identify features of a sustainability-oriented corporate culture. We offer six propositions examining the influence of specific manifestations on the handling of AI in the sense of SAI. Thus, if companies want to ensure that SAI is realized, corporate culture appears as an important indicator and influencing factor at the same time.
Partnerschaftliche Gewalt während der COVID-19-Pandemie in der Schweiz
Authors: Baier Dirk , Biberstein Lorenz , Kliem Sören 
Published: January 2022

ZusammenfassungDer Beitrag berichtet Ergebnisse zu 2 schweizweit repräsentativen Befragungsstudien. Im Jahr 2018 und erneut im Jahr 2021 wurden Erwachsene, die zum Befragungszeitpunkt mit einem Partner bzw. einer Partnerin zusammenlebten, nach dem Erleben verschiedener partnerschaftlicher Übergriffe in den letzten 12 Monaten gefragt. Die Ergebnisse zeigen, dass es im Zeitvergleich nicht zu einem Anstieg partnerschaftlicher Gewalt gekommen ist. Der Anteil an Befragten, die psychische Gewalt erlebt haben, beträgt 13,8 (Befragung 2018) bzw. 11,7 % (Befragung 2021), der Anteil an Befragten, die physische Gewalt erlebt haben, 2,9 bzw. 3,1 %. Die Stabilität der Prävalenzraten zeigt sich allerdings nicht für alle demografischen Gruppen: Bei jüngeren Befragten findet sich ein Anstieg physischer Gewalterfahrungen.
Graph coarsening: from scientific computing to machine learning
Authors: Chen Jie , Saad Yousef , Zhang Zechen 
Published: January 2022

AbstractThe general method of graph coarsening or graph reduction has been a remarkably useful and ubiquitous tool in scientific computing and it is now just starting to have a similar impact in machine learning. The goal of this paper is to take a broad look into coarsening techniques that have been successfully deployed in scientific computing and see how similar principles are finding their way in more recent applications related to machine learning. In scientific computing, coarsening plays a central role in algebraic multigrid methods as well as the related class of multilevel incomplete LU factorizations. In machine learning, graph coarsening goes under various names, e.g., graph downsampling or graph reduction. Its goal in most cases is to replace some original graph by one which has fewer nodes, but whose structure and characteristics are similar to those of the original graph. As will be seen, a common strategy in these methods is to rely on spectral properties to define the coarse graph.
Women-led Firms and Credit Access. A Gendered Story?
Authors: Basiglio Stefania , De Vincentiis Paola , Isaia Eleonora , Rossi Mariacristina 
Published: January 2022

AbstractThis work focuses on credit access and demand in Italian firms using the RIL dataset, a sample representative of Italian firms, for the year 2015. We investigate whether the gender of the firm’s decision-maker plays a role in requesting and obtaining a loan. Our results suggest that women are significantly less likely to ask for credit, while no significant differences in credit approval are found between the two genders. Moreover, the gender gap disappears for more educated women, as well as for firms in the north of the country.
Untersuchung von Aufmerksamkeitseffekten für die gezielte Gestaltung von Visualisierungsoberflächen für zeitgemäße Mensch-Maschine-Interaktion
Authors: Hoppe Annette , Ganßauge Rico , Geißler Uwe , Henke Anna-Sophia , Reßut Norman 
Published: January 2022

ZusammenfassungIn der modernen Arbeitswelt besteht eine wachsende Bedeutung von Steuer- und Überwachungstätigkeiten, die heute in Leitwarten und in Zukunft möglicherweise mittels neuer Interaktionsformen ausgeführt werden. Bei diesen Tätigkeiten ist verantwortungsvolles Handeln gefordert, da eine Verpflichtung für die Aufrechterhaltung der Prozesse besteht. Die Mensch-Maschine-Interaktion findet häufig mittels einer komplexen Visualisierung sowie einer Vielzahl an Bildschirmen statt. Dies kann bedeuten, dass sich relevante Information außerhalb der direkten visuellen Wahrnehmung eines Bedieners befinden. Es besteht die Herausforderung, die Aufmerksamkeit durch Signale gezielt zu lenken, damit handlungsleitende Informationen wahrnehmbar werden. Ziel der durchgeführten Untersuchung war es deshalb, Mindestanforderungen für die Erkennung von Signalen im peripheren Blickfeld zu untersuchen. Ausgehend von Vorerkenntnissen wurden in einem Laborexperiment Signale in verschiedenen Leuchtdichtekontrasten und Frequenzen in festgelegten Winkeln des peripheren Blickfeldes untersucht. Diese zeigen die Überlegenheit hoher Leuchtdichtekontraste und Frequenzen für die Erkennung, steigende Winkel verringerten diese. Die aus den Daten abgeleiteten Regressionen gestatten die Abschätzung von Erkennungswahrscheinlichkeit und Reaktionszeiten.Praktische Relevanz: Die erreichten Ergebnisse geben Entwickler*innen und Anwender*innen von Technik ein Werkzeug für die gezielte Gestaltung von Signalen in der Mensch-Maschine-Interaktion bei komplexen Visualisierungshintergründen an die Hand. Aus den Erkenntnissen können konkrete Maßnahmen abgeleitet werden, die helfen, Sicherheit und Zuverlässigkeit durch eine verbesserte Gestaltung der Mensch-Maschine-Interaktion zu unterstützen.
Maximizing Regulatory Review Efficiency: The Evolution of the FDA OCE RTOR Pilot
Authors: Gao Ya Grace , Roberts Samantha , Guy Allison 
Published: January 2022

AbstractTo promote the efficient review of oncology drug applications, the US Food and Drug Administration (FDA) Oncology Center of Excellence (OCE) launched the Real-Time Oncology Review (RTOR) pilot program in 2018. RTOR allows FDA to review individual sections of eCTD modules of a drug application for oncology drugs in contrast to requiring the applicant to submit complete modules or the complete application before review is initiated. Initially, the program accepted only supplemental applications with simple study designs and easily interpretable endpoints, but the scope has since been expanded to include applications for New Molecular Entities (NME), and other applications with more complex features. Though many applicants experience faster approvals under RTOR, it is difficult to isolate the effect of the RTOR program on review timelines as its contribution is masked by other expedited programs like priority review and breakthrough therapy designation (BTD). This article discusses the expanded scope of RTOR, its interplay with other OCE initiatives to modernize regulatory review, summarizes Genentech’s experiences in planning RTOR submissions from February 2019 to July 2021, and provides considerations for the future of the program.
Disagreement on expectations: firms versus consumers
Authors: Claveria Oscar 
Published: January 2022

AbstractThis paper evaluates the dynamic response of economic activity to shocks in agents’ perception of uncertainty. The study focuses on the comparison between manufacturers’ and consumers’ perception of economic uncertainty, gauged by a geometric discrepancy indicator to quantify the proportion of disagreement in eleven European countries and the Euro Area. A vector autoregressive framework is used to estimate the impulse response functions to innovations in disagreement, both for manufacturers and consumers. The effect on economic activity of shocks to the perception of uncertainty is found to differ markedly between both types of agents. On the one hand, shocks to consumer discrepancy tend to be of greater magnitude and duration than those to manufacturer discrepancy. On the other hand, innovations in disagreement between the two collectives have an opposite effect on economic activity: shocks to manufacturer discrepancy lead to a decrease in economic activity, as opposed to shocks to consumer discrepancy. This finding is of particular relevance to researchers when using cross-sectional dispersion of survey-based expectations for approximating and assessing economic uncertainty, since the effect on economic growth of shocks to disagreement may be dependent on the type of agent and the way in which expectations have been elicited.
„Lügenpresse“, „alternative Fakten“ und die epistemologische Naivität des sozialen Konstruktivismus
Authors: Kempf Wilhelm 
Published: January 2022

ZusammenfassungAufbauend auf dem methodischen Konstruktivismus und gestützt auf die Unterscheidung zwischen (1) einer transsubjektiv begründeten (objektiven) Wirklichkeit, einer bloß intersubjektiv geteilten (sozialen) Wirklichkeit und einer subjektiven Wirklichkeit i.e. S. sowie (2) zwischen (gesicherten) Tatsachen, möglichen Sachverhalten, fingierten Sachverhalten und simulierten Sachverhalten wird die Haltbarkeit der sozial-konstruktivistischen Auffassung untersucht, wonach jede Repräsentation der Wirklichkeit nur ein verzerrtes Abbild der Realität biete, weshalb alle Repräsentation der Wirklichkeit gleichrangig nebeneinanderstehen.Dass die sozial konstruierte Wirklichkeit notgedrungen selektiv und daher eine interpretierte (bedeutungshaltige) Wirklichkeit ist, wird nicht in Frage gestellt, wohl jedoch die Schlussfolgerungen, welche der soziale Konstruktivismus daraus zieht: (1) Die Rede von einer „äußeren Realität“ und deren „Repräsentation“ kann nicht aufrechterhalten werden. (2) Im Unterschied zu Tatsachen, die transsubjektiv nachgeprüft werden können, entbehren Bedeutungen einer transsubjektiven Basis, weshalb Aussagen über die Bedeutung eines Sachverhalts weder wahr noch falsch sind. (3) Trotzdem kann nicht jede bedeutungshaltige Wirklichkeitskonstruktion denselben Geltungsanspruch erheben. (4) Zumindest „alternative Fakten“ sind als solche identifizierbar. (5) Wahrheit vs. Falschheit behaupteter Sachverhalte ist nicht das einzige Kriterium, an dem eine Wirklichkeitskonstruktion gemessen werden kann. (6) Obwohl der Diskurs über Bedeutungen kein Diskurs darüber sein kann, ob sie wahr oder falsch sind, können Wirklichkeitskonstruktionen dennoch auf ihre Angemessenheit hin befragt werden. Und je nachdem, worauf wir unser Handeln orientieren, können sie sich als angemessen oder unangemessen erweisen.