bluuebunny/crossref_metadata_2025 huggingface dataset:
A preliminary study on 3D printing feedstock derived from cellulose recovered from cigarette butts
Authors: D’Urso Flavia , Iaccarino Paolo , Giordano Michele , Oliviero Maria , Di Maio Ernesto , Sansone Lucia
Published: April 2024

In this work, we describe the recovery of cellulose acetate (r-CA) polymer from waste cigarette butts (CBs) and their subsequent conversion into feedstock for 3D printing technology. The extraction process for CBs includes two stages: initial washes in water, followed by additional washes in ethanol. A final step involves a dissolution and reprecipitation process, resulting in the creation of a fine powder. The recovery polymer has been analysed and compared to commercial cellulose acetate (p-CA) and unsmoked cigarette filter (u-CA) to assess its purity and examine alterations in its physicochemical properties. The CA powder has also been plasticized with different biocompatible plasticizers to improve the mechanical properties of the CA. We analyze the rheological properties to identify the suitable composition as feedstock for 3D printing.
Environmental regulation, outward foreign direct investment, and China’s green total factor productivity
Authors: Kong Xiangrong , Li Zhezhou , Lei Xiao , Jiang Xiaozhen , Bao Xinzhuo
Published: April 2024

Increasing green total factor productivity (GTFP) is currently the primary goal of sustainable development worldwide. GTFP not only reflects the efficiency of economic expansion but also encompasses resource consumption and pollution. This research enhances the current understanding of GTFP by indicating that aside from reverse technology spillovers, labor mobility, and changes in industrial structure, additional factors, such as environmental regulations, exert a dynamic function in shaping the influence of outward foreign direct investment (OFDI) on the GTFP of the home nation. The empirical findings indicate that OFDI has a single threshold effect on GTFP, and the negative effect increases with the reinforcing of environmental control. The main impact comes from home country’s changes in green technology (GTC) rather than changes in green efficiency. Additionally, environmental regulation has a positive moderating effect on OFDI, the moderating effect of environmental regulation in western regions is more pronounced in promoting the home country’s GTC. It is imperative to take into account regional variations and devise distinct policies for eastern, central, and western regions.
Explainable AI in the military domain
Authors: Wood Nathan Gabriel
Published: April 2024

Artificial intelligence (AI) has become nearly ubiquitous in modern society, from components of mobile applications to medical support systems, and everything in between. In societally impactful systems imbued with AI, there has been increasing concern related to opaque AI, that is, artificial intelligence where it is unclear how or why certain decisions are reached. This has led to a recent boom in research on “explainable AI” (XAI), or approaches to making AI more explainable and understandable to human users. In the military domain, numerous bodies have argued that autonomous and AI-enabled weapon systems ought not incorporate unexplainable AI, with the International Committee of the Red Cross and the United States Department of Defense both explicitly including explainability as a relevant factor in the development and use of such systems. In this article, I present a cautiously critical assessment of this view, arguing that explainability will be irrelevant for many current and near-future autonomous systems in the military (which do not incorporate any AI), that it will be trivially incorporated into most military systems which do possess AI (as these generally possess simpler AI systems), and that for those systems with genuinely opaque AI, explainability will prove to be of more limited value than one might imagine. In particular, I argue that explainability, while indeed a virtue in design, is a virtue aimed primarily at designers and troubleshooters of AI-enabled systems, but is far less relevant for users and handlers actually deploying these systems. I further argue that human–machine teaming is a far more important element of responsibly using AI for military purposes, adding that explainability may undermine efforts to improve human–machine teamings by creating aprima faciesense that the AI, due to its explainability, may be utilized with little (or less) potential for mistakes. I conclude by clarifying that the arguments are not against XAI in the military, but are instead intended as a caution against over-inflating the value of XAI in this domain, or ignoring the limitations and potential pitfalls of this approach.
Integrated Data Modeling and Biological Evaluation of PEGylated Konjac Gum-Rosin Pentaerythritol Polymeric Nanocomposites for Enhanced Antimicrobial Performance
Authors: Yıldız Ahmet , Küçükdeniz Tarık , İlgar Merve , Soomro Razium Ali , Sayed Mohamed E. El , Karakuş Selcan
Published: April 2024

The increasing prevalence of antibiotic-resistant pathogens necessitates the development of novel antimicrobial agents. Herein, PEGylated konjac gum-supported rosin pentaerythritol nanocomposites (KG/PEG/RE PNCs) were synthesized using an environmentally friendly sonochemical method, aiming to explore their potential antibacterial and antifungal properties against a range of pathogens, including Candida albicans, Escherichia coli, Pseudomonas aeruginosa, Aspergillus brasiliensis, and Staphylococcus aureus. An elaborate investigation into the rheological properties of these PNCs highlighted the dependence of viscosity on synthesis parameters such as RE concentration, sonication time, and KG/RE blend ratio with the Higiro model validated as a suitable mathematical model for defining the intricate relationship between experimental and resulting viscosity of PNCs. The integration of machine learning (ML), particularly polynomial regression, enabled the modeling of the complex dynamics influencing PNC viscosity, thus advancing comprehension of PNCs behavior in relation to the synthesis parameters. The modeling facilitated precise formulation to predict PNC viscosity with high accuracy, as confirmed by a mean squared error (MSE) of 3.81 and an R2 of 0.993. Moreover, the PNCs demonstrated broad-spectrum antimicrobial activity, reaching an inhibition plateau during the first week, confirming its efficacy as a versatile antibacterial and antifungal agent. Combining advanced data modeling techniques with biological assessments, this integrated approach represents a step forward in understanding and optimizing polymeric nanostructures.
The Relationship Between Preparedness for Caregiving and Spiritual Well-Being in the Carers of Stroke Patients: A Case Study in Türkiye
Authors: Özkan Tuncay Fatma
Published: April 2024

This study was conducted to determine the relationship between preparedness for caregiving and spiritual well-being in the carers of stroke patients. This descriptive and cross-sectional study was conducted with 130 primary carers of patients hospitalized with the diagnosis of stroke at the neurology clinic of a hospital. It was determined that the participants had moderate levels of preparedness for caregiving, they had high levels of spiritual well-being, and there was a positive significant relationship between these two variables. According to the regression analysis results, the spiritual well-being level of the participants was a predictor of their preparedness for caregiving (B: 0.144, p < 0.001). The results of this study demonstrated that the spiritual well-being of the carers of stroke patients is important in increasing preparedness for caregiving. In this context, to ensure that the carers of all stroke patients feel prepared for the caregiving process, it is recommended to evaluate their preparedness levels, support them in their preparation for their caregiving roles, and identify their spiritual needs.
Corporate entrepreneurship strategy in universities: emerging leadership in austerity time
Authors: Civera Alice , Meoli Michele
Published: April 2024

Public organizations have widely adopted corporate entrepreneurial strategy. The complex and financially constrained context in which public organizations operate calls for the implementation of entrepreneurial actions. Our study validates the theoretical framework of Kearney and Meynhardt (Int Public Manage J 19(4):543–572, 2016), which recognizes strategic vision and organizational factors as the main components of corporate entrepreneurial strategy and theorize its main antecedents and outcomes. Thus, by analyzing the public University of Bergamo as a single case study, we demonstrate that entrepreneurial orientation is beneficial for public organizations such as universities. Specifically, the entrepreneurial leadership was able to recognize opportunities in the unsupportive political external environment characterizing the entire Italian public sector during the period 2009-2015. The austerity policy known as the Gelmini reform was designed to make public organizations more efficient and transparent, by cutting personnel costs, by explicitly accounting for university budgets, and introducing external controls on university governance and performance. Despite the climate of general austerity, the entrepreneurial leadership succeeded in engaging several stakeholders and grounding an entrepreneurial strategy at the university. This has significantly changed the image of this public organization.
SSGAN: A Semantic Similarity-Based GAN for Small-Sample Image Augmentation
Authors: Ma Congcong , Mi Jiaqi , Gao Wanlin , Tao Sha
Published: April 2024

Image sample augmentation refers to strategies for increasing sample size by modifying current data or synthesizing new data based on existing data. This technique is of vital significance in enhancing the performance of downstream learning tasks in widespread small-sample scenarios. In recent years, GAN-based image augmentation methods have gained significant attention and research focus. They have achieved remarkable generation results on large-scale datasets. However, their performance tends to be unsatisfactory when applied to datasets with limited samples. Therefore, this paper proposes a semantic similarity-based small-sample image augmentation method named SSGAN. Firstly, a relatively shallow pyramid-structured GAN-based backbone network was designed, aiming to enhance the model’s feature extraction capabilities to adapt to small sample sizes. Secondly, a feature selection module based on high-dimensional semantics was designed to optimize the loss function, thereby improving the model’s learning capacity. Lastly, extensive comparative experiments and comprehensive ablation experiments were carried out on the “Flower” and “Animal” datasets. The results indicate that the proposed method outperforms other classical GANs methods in well-established evaluation metrics such as FID and IS, with improvements of 18.6 and 1.4, respectively. The dataset augmented by SSGAN significantly enhances the performance of the classifier, achieving a 2.2% accuracy improvement compared to the best-known method. Furthermore, SSGAN demonstrates excellent generalization and robustness.
Efficient Visual Metaphor Image Generation Based on Metaphor Understanding
Authors: Su Chang , Wang Xingyue , Liu Shupin , Chen Yijiang
Published: April 2024

Metaphor has significant implications for revealing cognitive and thinking mechanisms. Visual metaphor image generation not only presents metaphorical connotations intuitively but also reflects AI’s understanding of metaphor through the generated images. This paper investigates the task of generating images based on text with visual metaphors. We explore metaphor image generation and create a dataset containing sentences with visual metaphors. Then, we propose a visual metaphor generation image framework based on metaphor understanding, which is more tailored to the essence of metaphor, better utilizes visual features, and has stronger interpretability. Specifically, the framework extracts the source domain, target domain, and metaphor interpretation from metaphorical sentences, separating the elements of the metaphor to deepen the understanding of its themes and intentions. Additionally, the framework introduces image data from the source domain to capture visual similarities and generate visual enhancement prompts specific to the domain. Finally, these prompts are combined with metaphorical interpretation sentences to form the final prompt text. Experimental results demonstrate that this approach effectively captures the essence of metaphor and generates metaphorical images consistent with the textual meaning.
Examining pre-service teachers’ feedback on low- and high-quality written assignments
Authors: Máñez Ignacio , Lipnevich Anastasiya A. , Lopera-Oquendo Carolina , Cerdán Raquel
Published: April 2024

Assessing student writing assignments and providing effective feedback are a complex pedagogical skill teacher candidates need to master. Scarce literature has closely examined the type of feedback that pre-service high-school teachers spontaneously deliver when assessing student writings, which is the main goal of our study. In a sample of 255 high school teacher candidates, we examined the type of feedback that they provided when assessing two writing assignments that differed in quality. One thousand eight hundred thirty-five comments were analyzed and coded into 11 sub-categories. Results showed that candidates’ feedback not only focused on task performance but also on the writing process. Although candidates provided critical and past-oriented evaluations frequently, they also crafted feedback in a neutral tone and included future-oriented suggestions. Further, feedback varied as a function of candidates’ gender, academic discipline, and students’ quality of writing. Teacher training programs may use this information to design resources to address nuances of feedback provision.
Inclusion Relations Among Fractional Orlicz-Sobolev Spaces and a Littlewood-Paley Characterization
Authors: Breit Dominic , Cianchi Andrea
Published: April 2024

Embeddings among fractional Orlicz-Sobolev spaces with different smoothness are characterized. In particular, besides recovering standard embeddings for classical fractional Sobolev spaces, novel results are derived in borderline situations where the latter fail. For instance, limiting embeddings of Pohozhaev-Trudinger-Yudovich type into exponential spaces are offered. The equivalence of Gagliardo-Slobodeckij norms in fractional Orlicz-Sobolev spaces to norms defined via Littlewood-Paley decompositions, oscillations, or Besov type difference quotients is established as well. This equivalence, of independent interest, is a key tool in the proof of the relevant embeddings. They also rest upon a new optimal inequality for convolutions in Orlicz spaces.
Logicality and the picture theory of language
Authors: Trinh Tue
Published: April 2024

I argue that there is tension in Wittgenstein’s position on trivialities (i.e. tautologies and contradictions) in the Tractatus, as it contains the following claims: (A) sentences are pictures; (B) trivialties are not pictures; (C) trivialities are sentences. A and B follow from the “picture theory” of language which Wittgenstein proposes, while C contradicts it. I discuss a way to resolve this tension in light of Logicality, a hypothesis recently developed in linguistic research. Logicality states that trivialities are excluded by the grammar, and that under the right analysis sentences which look trivial are in fact contingent. The tools necessary to support Logicality, I submit, were not available to Wittgenstein at the time, which explains his commitment to C. I end the paper by commenting on some points of contact between analytic philosophy and the generative enterprise in linguistics which are brought into relief by the discussion.
Factor XI: structure, function and therapeutic inhibition
Authors: Ali Ahmed E. , Becker Richard C.
Published: April 2024

Arterial and venous thromboembolism is a major medical concern that requires therapeutic anticoagulation in various medical fields to prevent its drastic consequences. Despite significant advances in anticoagulant therapy, thrombosis remains a leading cause of morbidity and mortality worldwide. Traditional anticoagulants like heparin and vitamin K antagonists (VKAs) have shown efficacy in preventing and treating thrombosis but come with an inherent risk of bleeding due to their non-specific inhibition of multiple coagulation factors. Subsequent direct oral anticoagulants (DOACs), targeting specific factors such as Xa or thrombin, demonstrated improved safety profiles compared to VKAs, yet bleeding remains a concern. Accordingly, research is focused on developing anticoagulants with improved safety profiles. A safer class of anticoagulants would have broad appeal. The intrinsic pathway of coagulation, involving factor XI (FXI), has attracted attention as a potential target for safer anticoagulants. Preclinical studies and epidemiological data indicate that FXI deficiency or inhibition protects against thrombosis with minimal bleeding. Current research involves evaluating various FXI-directed strategies, and phase 2 studies have shown promising results in orthopedic surgery, atrial fibrillation, end-stage renal disease (ESRD), myocardial infarction, and ischemic stroke. Several agents, such as antisense oligonucleotides, monoclonal antibodies, small synthetic molecules, natural peptides, and aptamers, have been developed to inhibit FXI at different stages, offering potentially safer alternatives to traditional anticoagulants. However, the optimal balance between preventing thrombosis and the risk of bleeding associated with FXI inhibitors requires validation through extensive phase 3 clinical trials using definite clinical endpoints. Several of such trials are currently underway or planned to define the role of FXI inhibitors in clinical practice and determine the most suitable FXI inhibitor for each specific indication. The current review highlights the rationale behind developing FXI inhibitors, presenting the most advanced agents in development, summarizing completed clinical trials, and discussing ongoing research efforts.
Magnetic Resonance Relaxation in Heterogeneous Materials is Analogous to First-Order Chemical Reaction
Authors: Afrough Armin
Published: April 2024

Biological tissue, pharmaceutical tablets, wood, porous rocks, catalytic reactors, concrete, and foams are examples of heterogeneous systems that may contain one or several fluid phases. Fluids in such systems carry chemical species that may participate in chemical reactions in the bulk of a fluid, as homogeneous reactions, or at the fluid/fluid or fluid/solid interfaces, as heterogeneous reactions. Magnetic resonance relaxation measures the return of 1H nuclear magnetization in chemical species of these fluids to an equilibrium state in a static magnetic field. Despite the perceived difference between reaction–diffusion and relaxation–diffusion in heterogeneous systems, similarities between the two are remarkable. This work draws a close parallel between magnetic resonance relaxation–diffusion and chemical reaction–diffusion for elementary unitary reaction $${\text{A}}\to {\text{B}}$$

A
→
B

in a dilute solution—both in heterogeneous systems. A striking similarity between the dimensionless numbers that characterize their relevant behavior is observed: the Damköhler number of the second kind $${{\text{Da}}}^{{\text{II}}}$$

Da

II

for reaction and the Brownstein–Tarr number $${{\text{BT}}}_{i}$$

BT
i

for relaxation. The new vision of analogy between reaction- and magnetic resonance relaxation–diffusion in heterogeneous systems encourages the exploitation of similarities between reaction and relaxation processes to noninvasively investigate the dynamics of chemical species and reactions. One such example of importance in chemical engineering is provided for solid–fluid reaction in packed beds.
The Tacitly Situated Self: From Narration to Sedimentation and Projection
Authors: Colombetti Giovanna , Bogotá Juan Diego
Published: April 2024

Recent analytic-philosophical works in the field of situated cognition have proposed to conceptualize the self as deeply entwined with the environment, and even as constituted by it. A common move has been to characterize the self in narrative terms, and then to argue that the narrative self is partly constituted by narratives about the past that are scaffolded (shaped and maintained) by, or distributed over, a variety of objects that can rekindle episodic memories. While we are sympathetic to these approaches, here we propose a different strategy to situate the self—one which can be seen as complementing the narrative one, and which draws from concepts and ideas central to the phenomenological-existentialist tradition. We suggest, first, that the self has a sense of its past not just via narratives and episodic memories, but in virtue of being embodied and thus, importantly, sedimented (in other words, it has, or rather is, a body memory). Embodiment and sedimentation, in turn, always necessarily imply an environment or a situation, entailing that the self is also inherently situated. Second, we discuss the future-oriented dimension of selfhood, and argue that we understand ourselves as projected into the future, again not necessarily only narratively and reflectively, but also tacitly, in a bodily and inherently situated way.
Deep learning in two-dimensional materials: Characterization, prediction, and design
Authors: Meng Xinqin , Qin Chengbing , Liang Xilong , Zhang Guofeng , Chen Ruiyun , Hu Jianyong , Yang Zhichun , Huo Jianzhong , Xiao Liantuan , Jia Suotang
Published: April 2024

Since the isolation of graphene, two-dimensional (2D) materials have attracted increasing interest because of their excellent chemical and physical properties, as well as promising applications. Nonetheless, particular challenges persist in their further development, particularly in the effective identification of diverse 2D materials, the domains of large-scale and high-precision characterization, also intelligent function prediction and design. These issues are mainly solved by computational techniques, such as density function theory and molecular dynamic simulation, which require powerful computational resources and high time consumption. The booming deep learning methods in recent years offer innovative insights and tools to address these challenges. This review comprehensively outlines the current progress of deep learning within the realm of 2D materials. Firstly, we will briefly introduce the basic concepts of deep learning and commonly used architectures, including convolutional neural and generative adversarial networks, as well as U-net models. Then, the characterization of 2D materials by deep learning methods will be discussed, including defects and materials identification, as well as automatic thickness characterization. Thirdly, the research progress for predicting the unique properties of 2D materials, involving electronic, mechanical, and thermodynamic features, will be evaluated succinctly. Lately, the current works on the inverse design of functional 2D materials will be presented. At last, we will look forward to the application prospects and opportunities of deep learning in other aspects of 2D materials. This review may offer some guidance to boost the understanding and employing novel 2D materials.
Integrating Diversity, Equity, and Inclusion into Preclinical, Clinical, and Public Health Mathematical Models
Authors: Sheen Justin , Curtin Lee , Finley Stacey , Konstorum Anna , McGee Reginald , Craig Morgan
Published: April 2024

Mathematical modelling applied to preclinical, clinical, and public health research is critical for our understanding of a multitude of biological principles. Biology is fundamentally heterogeneous, and mathematical modelling must meet the challenge of variability head on to ensure the principles of diversity, equity, and inclusion (DEI) are integrated into quantitative analyses. Here we provide a follow-up perspective on the DEI plenary session held at the 2023 Society for Mathematical Biology Annual Meeting to discuss key issues for the increased integration of DEI in mathematical modelling in biology.
Performance of postmortem CT in the diagnosis of natural death from out-of-hospital cardiac arrest
Authors: Nakaki Yu , Fukumoto Wataru , Higashibori Haruka , Kawashita Ikuo , Nakamura Yuko , Awai Kazuo
Published: April 2024

Purpose

Postmortem CT (PMCT) is used widely to identify the cause of death. However, its diagnostic performance in cases of natural death from out-of-hospital cardiac arrest (OHCA) may be unsatisfactory because the cause tends to be cardiogenic and cannot be detected on PMCT images. We retrospectively investigated the diagnostic performance of PMCT in the diagnosis of natural death from OHCA and compared it to that of unnatural death.

Materials and methods

Our series included 450 cases; 336 were natural- and 114 were unnatural death cases. Between 2018 and 2022 all underwent non-contrast PMCT to identify the cause of death. Two radiologists reviewed the PMCT images and categorized them as diagnostic (PMCT alone sufficient to determine the cause of death), suggestive (the cause of death was suggested but additional information was needed), and non-diagnostic (the cause of death could not be determined on PMCT images). The diagnostic performance of PMCT was defined by the percentage of diagnosable and suggestive cases and compared between natural- and unnatural death cases. Interobserver agreement for the cause of death on PMCT images was also assessed with the Cohen kappa coefficient of concordance.

Results

The diagnostic performance of PMCT for the cause of natural- and unnatural deaths from OHCA was 30.3% and 66.6%, respectively (p < 0.01). The interobserver agreement for the cause of natural- and unnatural deaths on PMCT images was very good with kappa value 0.92 and 0.96, respectively.

Conclusion

As PMCT identified the cause of natural death by OHCA in only 30% of cases, its diagnostic performance must be improved.
Möglichkeitsräume für Antisemitismus? Zur Öffnung politisch-kultureller Gelegenheitsstrukturen während der Eskalationsphasen des „Nahostkonflikts“
Authors: Beyer Heiko , Goldkuhle Bjarne
Published: April 2024

Der Beitrag untersucht, inwiefern sich der „Nahostkonflikt“ auf das Sicherheitsgefühl von in Deutschland lebenden Jüdinnen und Juden auswirkt. Ausgehend vom Theorieansatz der „politisch-kulturellen Gelegenheitsstrukturen“ untersuchen wir, ob das „Nahostkonflikt“ bezogene Sicherheitsgefühl davon abhängt, wie offen oder geschlossen diese Strukturen von den (potenziell) Betroffenen wahrgenommen werden. Unsere Befunde zeigen, dass je günstiger die Gelegenheiten für antisemitische Mobilisierung erscheinen, desto negativer wirkt sich der „Nahostkonflikt“ auf die hiesige jüdische Gemeinschaft aus, weil in der Wahrnehmung der Betroffenen antisemitische Übergriffe nicht angemessen thematisiert und juristisch verfolgt werden. Mittels Daten einer Online-Befragung von in Deutschland lebenden Jüdinnen und Juden (n = 295) weisen wir nach, dass vor allem die Befürchtung, die Bevölkerungsmeinung gegenüber Jüdinnen und Juden in Deutschland hinge mit dem sogenannten „Nahostkonflikt“ eng zusammen, zu einem stärkeren „Nahostkonflikt“ bezogenen Bedrohungsgefühl beiträgt. Misstrauen in die Fähigkeit von Gerichten und Medien, auf Antisemitismus angemessen zu reagieren, sind weitere statistisch signifikante Korrelate.
Interdiffusion Coefficients and Strengthening Effects of Nb, Ta, and Zr in the α2-Ti3Al Phase
Authors: Haußmann L. , Bresler J. , Neumeier S. , Pyczak F. , Göken M.
Published: April 2024

The creep properties of fully lamellar γ/α2 titanium aluminides can be significantly improved by alloying with Nb, Ta or Zr. While the influence of these alloying elements on the γ-phase has already been examined, their diffusivity and strengthening properties in the α2-phase are still lacking. In order to study the effect of Nb, Ta and Zr in α2-Ti3Al, the alloys Ti-33Al, Ti-33Al-5Nb, Ti-33Al-5Ta and Ti-33Al-5Zr were investigated using a diffusion couple approach and strain rate jump tests. The results show that Zr diffuses the fastest, followed by Nb and Ta. Furthermore, these alloying elements also increase the strength compared to a binary Ti-33Al alloy, from which Zr leads to the highest strength increase followed by Ta and Nb. The lower diffusivity of Ta becomes increasingly important at higher temperatures and lower strain rates resulting in a higher strengthening potential than Nb and Zr under such conditions.
Biomechanical Characterization of Retinal Pigment Epitheliums Derived from hPSCs Using Atomic Force Microscopy
Authors: Herardot Elise , Liboz Maxime , Lamour Guillaume , Malo Michel , Plancheron Alexandra , Habeler Walter , Geiger Camille , Frank Elie , Campillo Clément , Monville Christelle , Ben M’Barek Karim
Published: April 2024

The retinal pigment epithelium (RPE), a multifunctional cell monolayer located at the back of the eye, plays a crucial role in the survival and homeostasis of photoreceptors. Dysfunction or death of RPE cells leads to retinal degeneration and subsequent vision loss, such as in Age-related macular degeneration and some forms of Retinitis Pigmentosa. Therefore, regenerative medicine that aims to replace RPE cells by new cells obtained from the differentiation of human pluripotent stem cells, is the focus of intensive research. However, despite their critical interest in therapy, there is a lack of biomechanical RPE surface description. Such biomechanical properties are tightly related to their functions. Herein, we used atomic force microscopy (AFM) to analyze both the structural and mechanical properties of RPEs obtained from four cell lines and at different stages of epithelial formation. To characterize epitheliums, we used apical markers in immunofluorescence and showed the increase of transepithelial resistance, as well as the ability to secrete cytokines with an apico-basal polarity. Then, we used AFM to scan the apical surface of living or fixed RPE cells. We show that RPE monolayers underwent softening of apical cell center as well as stiffening of cell borders over epithelial formation. We also observed apical protrusions that depend on actin network, suggesting the formation of microvilli at the surface of RPE epitheliums. These RPE cell characteristics are essential for their functions into the retina and AFM studies may improve the characterization of the RPE epithelium suitable for cell therapy.

Graphical Abstract
Hypertension and diabetes, but not leptin and adiponectin, mediate the relationship between body fat and chronic kidney disease
Authors: Lengton Robin , Dekker Friedo W. , van Rossum Elisabeth F. C. , de Fijter Johan W. , Rosendaal Frits R. , van Dijk Ko Willems , Rabelink Ton J. , Le Cessie Saskia , de Mutsert Renée , Hoogeveen Ellen K.
Published: April 2024

Purpose

Obesity may promote kidney damage through hemodynamic and hormonal effects. We investigated the association between body mass index (BMI), total body fat (TBF) and chronic kidney disease (CKD) and whether hypertension, diabetes, leptin and adiponectin mediated these associations.

Methods

In this cross-sectional analysis of the Netherlands Epidemiology of Obesity study, 6671 participants (45–65 y) were included. We defined CKD as eGFR <60 ml/min/1.73 m2 and/or moderately increased albuminuria. The percentage of mediation was calculated using general structural equation modeling, adjusted for potential confounding factors age, sex, smoking, ethnicity, physical activity and Dutch healthy diet index.

Results

At baseline mean (SD) age was 56 (6), BMI 26.3 (4.4), 44% men, and 4% had CKD. Higher BMI and TBF were associated with 1.08 (95%CI 1.05; 1.11) and 1.05-fold (95%CI 1.02; 1.08) increased odds of CKD, respectively. As adiponectin was not associated with any of the outcomes, it was not studied further as a mediating factor. The association between BMI and CKD was 8.5% (95%CI 0.5; 16.5) mediated by diabetes and 22.3% (95%CI 7.5; 37.2) by hypertension. In addition, the association between TBF and CKD was 9.6% (95%CI −0.4; 19.6) mediated by diabetes and 22.4% (95%CI 4.2; 40.6) by hypertension. We could not confirm mediation by leptin in the association between BMI and CKD (35.6% [95%CI −18.8; 90.3]), nor between TBF and CKD (59.7% [95%CI −7.1; 126.6]).

Conclusion

Our results suggest that the relations between BMI, TBF and CKD are in part mediated by diabetes and hypertension.
PIVKA-II combined with alpha-fetoprotein for the diagnostic value of hepatic tumors in children: a multicenter, prospective observational study
Authors: Gao Hongxiang , Xie Chenjie , Wang Jing , Ma Ji , Liu Shijian , Xie Li , Zheng Yijie , Dong Rui , Wang Shan , Fang Yongjun , Wu Yurui , Zhang Xianwei , Lu Xianying , Li Yang , Li Weisong , Pan Qiuhui , Xu Min , Gu Song
Published: April 2024

Background

To investigate whether protein induced by vitamin K antagonist-II (PIVKA-II) combined with alpha-fetoprotein (AFP) can improve the diagnostic and differential diagnostic accuracy of childhood hepatic tumors.

Methods

A multi-center prospective observational study was performed at nine regional institutions around China. Children with hepatic mass (Group T) were divided into hepatoblastoma group (Group THB) and hemangioendothelioma group (Group THE), children with extrahepatic abdominal mass (Group C). Peripheral blood was collected from each patient prior to surgery or chemotherapy. The area under the curve (AUROC) was used to evaluate the diagnostic efficiency of PIVKA-II and the combined tumor markers with AFP.

Results

The mean levels of PIVKA-II and AFP were both significantly higher in Group T than Group C (p = 0.001, p < 0.001), in Group THB than Group THE (p = 0.018, p = 0.013) and in advanced HB than non-advanced HB (p = 0.001, p = 0.021). For the diagnosis of childhood hepatic tumors, AUROC of PIVKA-II (cut-off value 32.6 mAU/mL) and AFP (cut-off value 120 ng/mL) was 0.867 and 0.857. The differential diagnostic value of PIVKA-II and AFP in hepatoblastoma from hemangioendothelioma was further assessed, AUROC of PIVKA-II (cut-off value 47.1mAU/mL) and AFP (cut-off value 560 ng/mL) was 0.876 and 0.743. The combined markers showed higher AUROC (0.891, 0.895 respectively) than PIVKA-II or AFP alone.

Conclusions

The serum level of PIVKA-II was significantly higher in children with hepatic tumors, especially those with malignant tumors. The combination of PIVKA-II with AFP further increased the diagnostic performance.

Trial registration

Clinical Trials, NCT03645655. Registered 20 August 2018, https://www.clinicaltrials.gov/ct2/show/NCT03645655.
Prediction of prostate cancer aggressiveness using magnetic resonance imaging radiomics: a dual-center study
Authors: Pan Nini , Shi Liuyan , He Diliang , Zhao Jianxin , Xiong Lianqiu , Ma Lili , Li Jing , Ai Kai , Zhao Lianping , Huang Gang
Published: April 2024

Purpose

The Gleason score (GS) and positive needles are crucial aggressive indicators of prostate cancer (PCa). This study aimed to investigate the usefulness of magnetic resonance imaging (MRI) radiomics models in predicting GS and positive needles of systematic biopsy in PCa.

Material and Methods

A total of 218 patients with pathologically proven PCa were retrospectively recruited from 2 centers. Small-field-of-view high-resolution T2-weighted imaging and post-contrast delayed sequences were selected to extract radiomics features. Then, analysis of variance and recursive feature elimination were applied to remove redundant features. Radiomics models for predicting GS and positive needles were constructed based on MRI and various classifiers, including support vector machine, linear discriminant analysis, logistic regression (LR), and LR using the least absolute shrinkage and selection operator. The models were evaluated with the area under the curve (AUC) of the receiver-operating characteristic.

Results

The 11 features were chosen as the primary feature subset for the GS prediction, whereas the 5 features were chosen for positive needle prediction. LR was chosen as classifier to construct the radiomics models. For GS prediction, the AUC of the radiomics models was 0.811, 0.814, and 0.717 in the training, internal validation, and external validation sets, respectively. For positive needle prediction, the AUC was 0.806, 0.811, and 0.791 in the training, internal validation, and external validation sets, respectively.

Conclusions

MRI radiomics models are suitable for predicting GS and positive needles of systematic biopsy in PCa. The models can be used to identify aggressive PCa using a noninvasive, repeatable, and accurate diagnostic method.
Towards a Logical Foundation of Randomized Computation: Doctoral Thesis Abstract
Authors: Antonelli Melissa
Published: April 2024

Interactions between logic and theoretical computer science are multiple and profound. In the last decades, they have been deeply investigated, but, surprisingly, the study of probabilistic computation was only marginally touched by such fruitful interchanges. The overall goal of my doctoral thesis was precisely that of start bridging this gap by developing logical systems corresponding to specific aspects of randomized computation and, due to them, by generalizing standard achievements to the probabilistic realm. To do so, the key ingredient is the introduction of new, measure-sensitive quantifiers associated with quantitative interpretations.
Carbamazepine Adsorption onto Giant Macroporous Silica and Adaptive Neuro-Fuzzy Inference System Modeling
Authors: Alver Alper , Yılmaz Bahar Akyüz , Bilican Behlül Koç , Baştürk Emine , Kaya Murat , Işık Mustafa
Published: April 2024

There is an imperative need to eliminate pharmaceutical residues from aquatic environments due to their hazardous properties, including toxicity, mutagenicity, and carcinogenicity, particularly when present in water sources. Conventional water treatment methods have proven insufficient in addressing nano-pollutants such as pharmaceutical residues. Consequently, the ongoing quest for economically viable, sustainable, and environmentally friendly removal mechanisms persists. In this particular study, we employed Giant Macroporous Silica (GMS) derived from marine sponges as a promising biosorbent. GMS exhibits commendable characteristics, including a high specific surface area, swift mass transfer capabilities, and non-discriminatory adsorption qualities. The efficacy of GMS in adsorbing carbamazepine (CBZ), a common drug residue, was scrutinized under diverse experimental conditions, including a sorbate/sorbent ratio ranging from 0.005 to 1.500 weight ratio, contact times spanning from 0 to 240 min, and initial pH values ranging from 5 to 9. Remarkably, at a concentration of 1000 µg L−1, GMS demonstrated an attractive adsorption rate (98.88%) of carbamazepine at pH 7.07, within 90 min. To enhance our understanding, we developed an ANFIS model utilizing the experimental parameters as inputs. The developed model exhibited a high correlation coefficient of 0.9944% and a root mean square error (RMSE) of 1.6693, indicating its dependability in accurately predicting the adsorption of CBZ on GMS. The results of our study highlight the efficacy of GMS in adsorbing CBZ, suggesting its considerable potential for adsorbing other pharmaceutical residues and nano-pollutants. Furthermore, we propose the possibility of developing a solid-phase extraction cartridge from GMS.
Prediction method of stalk content in strip particles after redrying of tobacco leaves
Authors: Xu Qiang , Hu Zongyu , Xue Dong , Miao Chenlin , Xiong Dangan , Zhang Yifan , Mao Wenlong , Yang Yue , Sun Jinghao , Su Xuemiao , Yin Yudong , Liang Yan , Chen Simeng , Yao Zheng
Published: April 2024

Four tobacco leaf modules processed by Yunnan Tobacco Redrying Co., Ltd. during the 2022 roasting season were used to investigate the method of stalk content in strip particles after redrying of tobacco leaves, effectively reducing the loss of strip particles. A total of 151 sets of experimental data were used to construct the prediction model for the stalk content in strip particles after redrying using the BP artificial neural network method, the linear regression method, and the support vector machine method. The results show that the prediction model constructed by the BP artificial neural network method has high accuracy and stability, with a relatively small absolute error of prediction (e = 0.0195%) and the root-mean-square error of interactive verification (RMSECV = 0.0227%), as well as a relatively small mean absolute error of production data validation (e = 0.0675%), while the prediction deviation ratio (RPD = 2.2435) is relatively large. Overall, the prediction model established by BP artificial neural network could provide new insight into the non-destructive detection of stalk content in strip particles of redried tobacco leaves after threshing and redrying and potentially leading to a reduction in tobacco leaf crushing by more than 112,500 kg per year.
The distributed no-idle permutation flowshop scheduling problem with due windows
Authors: Mousighichi Kasra , Avci Mualla Gonca
Published: April 2024

The distributed no-idle permutation flowshop scheduling problem has gained significant attention as a prominent area of research in recent years, particularly in industries where setup operations are so expensive that reactivating the machines is not cost-effective. This study addresses an extension of the distributed permutation flowshop scheduling problem with no-idle and due window constraints. The aim is to determine the job assignments to the factories and their sequences in each factory that provide the minimum total weighted earliness and tardiness (TWET) penalties considering due windows. This study is the first to formulate this problem, offering four different mathematical models, and presents a benchmark to examine different problem cases that may arise in practical applications. Furthermore, to effectively solve the diverse problem instances, two hybrid metaheuristic algorithms based on the Iterated Greedy are proposed. These metaheuristics exhibit promising capabilities, enabling the solution of problem instances involving up to 500 jobs. To assess the effectiveness of the proposed models and algorithms, extensive numerical experiments are conducted, facilitating a thorough evaluation and comparison of their performances.
Towards sustainable injection moulding using 3D printed conformal cooling channels: a comparative simulation study
Authors: Clark Rebecca , Rochman Arif , Refalo Paul , Farrugia Philip , Vella Pierre
Published: April 2024

In recent years, studies have proven that conformal cooling channels (CCC) in an additively manufactured mould result in a more efficient and effective injection moulding process. This can be achieved since CCCs are designed to follow the contour of the part being moulded so that the surface of the part is equidistant from the channel at all points. However, no studies were found which explored the combined effect of mould material thermal conductivity and varying cooling channel designs on the cooling performance of the mould from a sustainability point of view. Within this context, a study was carried out to explore the effect of the tool material’s thermal conductivity on the performance of various CCC designs in comparison with conventional, straight drilled cooling channels. The performance of the cooling channels was analysed from a sustainability point of view by comparing the channel performances in terms of energy consumption, financial implications, and the resulting quality of the part. The results of this study showed that the higher conductivity alloys were especially effective at reducing the cycle time and improving the energy performance of the process in the conventional cooling channel designs. These materials were also capable of reducing the overall cost of the process which was calculated in terms of material costs and electricity consumption. For the CCC designs, however, the high conductivity alloys were less effective in all aspects of this analysis, namely cooling time, energy efficiency, and overall costs. However, it is worth noting that based on the melt flow simulations, the alternative materials had little to no effect on the resulting quality of the part.
Gaussian-discrete restricted Boltzmann machine with sparse-regularized hidden layer
Authors: Yasuda Muneki , Sekimoto Kaiji
Published: April 2024

Overfitting is a critical concern in machine learning, particularly when the representation capabilities of learning models surpass the complexities present in the training datasets. To mitigate overfitting, curtailing the representation power of the model through suitable techniques such as regularization is necessary. In this study, a sparse-regularization method for Gaussian–Discrete restricted Boltzmann machines (GDRBMs) is considered. A GDRBM is a variant of restricted Boltzmann machines that comprises a continuous visible layer and discrete hidden layer. In the proposed model, sparse GDRBM (S-GDRBM), a sparse prior that encourages sparse representations of the hidden layer is employed. The strength of the prior (i.e., the sparse-regularization strength) can be tuned within the standard scenario of maximum likelihood learning; that is, the strength can be adaptively tuned based on the complexities of the datasets during training. We validated the proposed S-GDRBM using numerical experiments.
Green buffers near industrial plants, examples in Jefferson County TX
Authors: Haselbach Liv , Niroula Riwaz , Feizbahr Mahdi
Published: April 2024

Green buffers between industrial plants and residential areas have multiple purposes in mitigating air pollution and protecting the environment. This study focuses on providing information on examples of green space development by industries near residential areas in Jefferson County, Texas. One represents a full buffer where residences were totally removed, another is a partial buffer where some residences were removed in a neighborhood and the third represents vegetation of a formerly industrial facility. Additional estimates on benefits of buffer spaces with respect to a pollutant plume are also considered. The study used geospatial and property data from 1966 to 2020. The transition from a residential area to a full or partial buffers highlights possible mitigation of air pollution impacts while serving additional functions, such as boosting stormwater infiltration and improving the aesthetic attractiveness of the area. The transformation from oil tanks to forested space may display the potential for environmental remediation, but not all spaces are suitable for fully forested buffers, as safety and other limitations should be considered. With the analysis of the classic Plume Equation insights can be provided regarding the possible impact of green buffers on ground-level pollutant concentrations. The calculations for atmospheric stability classes A-F and for various stack heights provide an understanding of how different conditions may affect the pollutant concentrations. The presence of a green buffer can play a crucial role in controlling ground-level pollution concentrations and reduce a community’s perception of risk.