bluuebunny/crossref_metadata_2025_split huggingface dataset:
Some mathematical comments about the analytic hierarchy process: Part I – theoretical analysis
Authors: Alvarez Gustavo B. , de Almeida Rafael G. , Hernández Cecilia T. , de Sousa Patrícia A. P. 
Published: August 2021

AbstractThe Analytic Hierarchy Process (AHP) is a decision making method, which has as its greatest criticism the rank reversal effect. Here a new mathematical analysis of this method is performed, and three new results are highlighted. First, the method is formulated as a linear system of equations, where it is possible to assign a geometric interpretation, determine the number of possible solutions, and perform an sensitivity analysis based on the condition number of the matrix. Second, the causes of rank reversal can be encompassed by two mathematical aspects related to the properties of the matrix of AHP: high condition number and deficient rank. When the matrix is deficient rank, it is possible to obtain a condensed formulation of the AHP with a new full rank matrix. This guarantees greater stability to the method. Third, some mathematical results can be used as a robustness test for the matrix of AHP.
Multiple Conformer Descriptors for QSAR Modeling
Authors: Nikonenko Aleksandra , Zankov Dmitry , Baskin Igor , Madzhidov Timur , Polishchuk Pavel 
Published: August 2021

AbstractThe most widely used QSAR approaches are mainly based on 2D molecular representation which ignores stereoconfiguration and conformational flexibility of compounds. 3D QSAR uses a single conformer of each compound which is difficult to choose reasonably. 4D QSAR uses multiple conformers to overcome the issues of 2D and 3D methods. However, many of existing 4D QSAR models suffer from the necessity to pre‐align conformers, while alignment‐independent approaches often ignore stereoconfiguration of compounds. In this study we propose a QSAR modeling approach based on transforming chirality‐aware 3D pharmacophore descriptors of individual conformers into a set of latent variables representing the whole conformer set of a molecule. This is achieved by clustering together all conformers of all training set compounds. The final representation of a compound is a bit string encoding cluster membership of its conformers. In our study we used Random Forest, but this representation can be used in combination with any machine learning method. We compared this approach with conventional 2D and 3D approaches using multiple data sets and investigated the sensitivity of the approach proposed to tuning parameters: number of conformers and clusters.
Understanding relational values in cultural landscapes in Romania and Germany
Authors: Riechers Maraja , Balázsi Ágnes , Engler John‐Oliver , Shumi Girma , Fischer Joern 
Published: August 2021

Abstract

Relational values recently emerged as a concept to comprehensively understand and communicate the many values of nature. Relational values can be defined as preferences and principles about human–nature relationships and focus both on human–nature connections and well as human–human connections.

Here, drawing on 819 face‐to‐face questionnaires, we analysed relational, intrinsic and instrumental values across a total of six agricultural landscapes in Transylvania (Romania) and Lower Saxony (Germany). The landscapes described a gradient of land use intensity, within and across the countries.

Our results suggest a bundling of values into four groups: those concerned with individual cognition (including intrinsic values), those that focus on nature as a place for social interaction and relaxation, those that capture cultural identity and spiritual values and one bundle that only includes instrumental values.

These different values, in turn, were strongly related to (a) respondents’ attitudes towards environmental conservation and the (b) frequency with which respondents used nature as a resource.

Instrumental values have the tendency to be inversely related to relational values and were found to increase with the land use intensity of the focal landscapes.

A free Plain Language Summary can be found within the Supporting Information of this article.
Developing centrifugal spun thermally cross‐linked gelatin based fibrous biomats for antibacterial wound dressing applications
Authors: Gungor Melike , Sagirli Merve Nur , Calisir Mehmet Durmus , Selcuk Sule , Kilic Ali 
Published: August 2021

AbstractFibrous materials obtained from natural polymers, such as gelatin, have been used in medical applications due to their biocompatibility and biodegradability. Herein, free‐standing durable fibrous gelatin biomats with antibacterial activity were developed via a simple, low cost and fast production route, centrifugal spinning, and subsequent thermal crosslinking. After a series of preliminary experiments, droplet−/bead‐free porous biomats with fine fibers, 3.41 ± 1.8 μm in diameter, were fabricated. Subsequently, antimicrobial biomats were produced by adding AgNO3 into the production solution. X‐ray diffractometer and energy dispersive X‐ray results showed Ag NPs existing as AgCl in the biomats, which could be attributed to chemical reaction between the Ag NPs and residual Cl in the impure gelatin. Later, both the neat‐gelatin and Ag‐gelatin biomats were thermally crosslinked at 170°C to gain stability against water. Although the Ag addition reduced ultimate tensile strength by half, from 881 to 495 kPa, the crosslinked biomats were robust enough to be used for wound dressing applications. They were also found to be highly breathable, with the air permeability of 256 and 81.2 mm/s, respectively. The biomats showed antibacterial activity against Escherichia coli and Staphylococcus aureus bacteria. The results show that the free‐standing fibrous‐gelatin‐based biomats produced is applicable for wound dressing applications.
Easy and reliable maximum a posteriori Bayesian estimation of pharmacokinetic parameters with the open‐source R package mapbayr
Authors: Le Louedec Félicien , Puisset Florent , Thomas Fabienne , Chatelut Étienne , White‐Koning Mélanie 
Published: August 2021

AbstractPharmacokinetic (PK) parameter estimation is a critical and complex step in the model‐informed precision dosing (MIPD) approach. The mapbayr package was developed to perform maximum a posteriori Bayesian estimation (MAP‐BE) in R from any population PK model coded in mrgsolve. The performances of mapbayr were assessed using two approaches. First, “test” models with different features were coded, for example, first‐order and zero‐order absorption, lag time, time‐varying covariates, Michaelis–Menten elimination, combined and exponential residual error, parent drug and metabolite, and small or large inter‐individual variability (IIV). A total of 4000 PK profiles (combining single/multiple dosing and rich/sparse sampling) were simulated from each test model, and MAP‐BE of parameters was performed in both mapbayr and NONMEM. Second, a similar procedure was conducted with seven “real” previously published models to compare mapbayr and NONMEM on a PK outcome used in MIPD. For the test models, 98% of mapbayr estimations were identical to those given by NONMEM. Some discordances could be observed when dose‐related parameters were estimated or when models with large IIV were used. The exploration of objective function values suggested that mapbayr might outdo NONMEM in specific cases. For the real models, a concordance close to 100% on PK outcomes was observed. The mapbayr package provides a reliable solution to perform MAP‐BE of PK parameters in R. It also includes functions dedicated to data formatting and reporting and enables the creation of standalone Shiny web applications dedicated to MIPD, whatever the model or the clinical protocol and without additional software other than R.
Political affiliation dissimilarity: Exploring lone affiliate employees' work attitudes and experiences
Authors: Henderson Alexandra A. , Jeong Sophia S. 
Published: August 2021

AbstractPolitical affiliation is an important demographic variable that has been relatively neglected in the organizational literature. At present, it is unclear how political dissimilarity between employees and their coworkers affects employees' attitudes and experiences, and whether traditional theories are applicable to this unique form of diversity. Based on time‐lagged data from a sample of working Americans (N = 360), we found that lone affiliates (employees who work with coworkers who do not share the same political affiliation) experienced lower levels of positive attitudes than majority affiliates (employees who work with coworkers who do share the same political affiliation). Specifically, in Republican majority organizations, Democrats had lower job satisfaction and affective commitment compared to Republicans. This difference was not found in Democrat majority organizations. Interestingly, these trends were not found for negative experiences, such as incivility from coworkers and depletion. Unaffiliated employees had a unique set of attitudes and experiences in that they suffered the most in organizations with no clear affiliation. Theoretical and practical implications, limitations, and future directions are also discussed.
Small‐Molecule Prodrug Nanoassemblies: An Emerging Nanoplatform for Anticancer Drug Delivery
Authors: Li Guanting , Sun Bingjun , Li Yaqiao , Luo Cong , He Zhonggui , Sun Jin 
Published: August 2021

AbstractThe antitumor efficiency and clinical translation of traditional nanomedicines is mainly restricted by low drug loading, complex preparation technology, and potential toxicity caused by the overused carrier materials. In recent decades, small‐molecule prodrug nanoassemblies (SMP‐NAs), which are formed by the self‐assembly of prodrugs themselves, have been widely investigated with distinct advantages of ultrahigh drug‐loading and negligible excipients‐trigged adverse reaction. Benefited from the simple preparation process, SMP‐NAs are widely used for chemotherapy, phototherapy, immunotherapy, and tumor diagnosis. In addition, combination therapy based on the accurate co‐delivery behavior of SMP‐NAs can effectively address the challenges of tumor heterogeneity and multidrug resistance. Recent trends in SMP‐NAs are outlined, and the corresponding self‐assembly mechanisms are discussed in detail. Besides, the smart stimuli‐responsive SMP‐NAs and the combination therapy based on SMP‐NAs are summarized, with special emphasis on the structure–function relationships. Finally, the outlooks and potential challenges of SMP‐NAs in cancer therapy are highlighted.
Engineering Self‐Reconstruction via Flexible Components in Layered Double Hydroxides for Superior‐Evolving Performance
Authors: Liu Jiao , Ding Peng , Zhu Zexuan , Du Wei , Xu Xiaoyong , Hu Jingguo , Zhou Yong , Zeng Haibo 
Published: August 2021

AbstractMost transition metal‐based catalysts for electrocatalytic oxygen evolution reaction (OER) undergo surface reconstruction to generate real active sites favorable for high OER performance. Herein, how to use self‐reconstruction as an efficient strategy to develop novel and robust OER catalysts by designing pre‐catalysts with flexible components susceptible to OER conditions is proposed. The NiFe‐based layered double hydroxides (LDHs) intercalated with resoluble molybdate (MoO42−) anions in interlayers are constructed and then demonstrated to achieve complete electrochemical self‐reconstruction (ECSR) into active NiFe‐oxyhydroxides (NiFeOOH) beneficial to alkaline OER. Various ex situ and in situ techniques are used to capture structural evolution process including fast dissolution of MoO42− and deep reconstruction to NiFeOOH upon simultaneous hydroxyl invasion and electro‐oxidation. The obtained NiFeOOH exhibits an excellent OER performance with an overpotential of only 268 mV at 50 mA cm−1 and robust durability over 45 h, much superior to NiFe‐LDH and commercial IrO2 benchmark. This work suggests that the ECSR engineering in component‐flexible precursors is a promising strategy to develop highly active OER catalysts for energy conversion.
Flower‐Like Interlayer‐Expanded MoS2−x Nanosheets Confined in Hollow Carbon Spheres with High‐Efficiency Electrocatalysis Sites for Advanced Sodium–Sulfur Battery
Authors: Luo Sainan , Ruan Jiafeng , Wang Yan , Hu Jiaming , Song Yun , Chen Min , Wu Limin 
Published: August 2021

AbstractThe room‐temperature sodium–sulfur (RT‐Na/S) battery is one of the most promising technologies for low‐cost energy storage. However, application of RT‐Na/S batteries is currently impeded by severe shuttle effects and volume expansion that limits both energy density and cycling stability. Herein, first, the first‐principal calculation is used to find that the introduction of sulfur vacancies in MoS2 can effectively enhance polysulfide adsorption and catalytic ability as well as both the ion and electron conductivities. Then, unique MoS2−x/C composite spheres are further designed and synthesized with flower‐like few‐layer and interlayer‐enlarged MoS2−x nanosheets space‐confined in hollow carbon nanospheres by a “ship‐in‐a‐bottle” strategy. With this novel design, the mass loading of S in the MoS2−x/C composite can be reached to as high as 75 wt%. Owing to the synergetic effect of interlayer‐expanded and few‐layer MoS2−x nanosheets and hollow carbon spheres matrix with high electronic/Na+ conductivity, the RT‐Na/S batteries deliver highly stable cycle durability (capacity retention of 85.2% after 100 cycles at 0.1 A g−1) and remarkable rate capability (415.7 mAh g−1 at 2 A g−1) along with high energy density. This design strategy of defect‐ and interlayer‐engineering may find wide applications in synthesizing electrode materials for high‐performance RT‐Na/S batteries.
Review on Supervised and Unsupervised Learning Techniques for Electrical Power Systems: Algorithms and Applications
Authors: Chen Songbo 
Published: August 2021

AbstractMachine learning (ML) has become a rising sophisticated technological application trend in the electrical industry in recent years. Such innovation provides optional methodologies for many existing applications, such as power and load profile forecasting, reliability evaluation, substation behavior detection and state observation of electrical equipment, and so on. This paper presents a review of various supervised and unsupervised ML techniques and applications for electrical power systems, including generation, transmission, distribution and micro‐grid. The algorithms and applications are mainly summarized from IEEE journals and the interest of this paper shows the roles and developments of most used algorithms and its corresponding extensions and performance in different applications. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Featurization strategies for protein–ligand interactions and their applications in scoring function development
Authors: Xiong Guoli , Shen Chao , Yang Ziyi , Jiang Dejun , Liu Shao , Lu Aiping , Chen Xiang , Hou Tingjun , Cao Dongsheng 
Published: August 2021

AbstractThe predictive performance of classical scoring functions (SFs) seems to have reached a plateau. Currently, SFs relying on sophisticated machine learning techniques have shown great potential in binding affinity prediction and virtual screening. As one of the most indispensable components in the workflow of training a machine learning scoring function (MLSF), the featurization or representation process enables us to catch certain physical processes that are important for protein–ligand interactions and to obtain machine‐readable descriptors. Currently, according to how they are derived, the descriptors used in MLSFs for both continuous and binary binding affinity estimates can be grouped into two broad categories: handcrafted features and automated‐extraction features. Moreover, the automated‐extraction features emerge as a new featurization trend along with the application of deep learning algorithms. Here, we make a thorough summary of the advances in the featurization strategies for protein–ligand interactions in the context of MLSFs, with emphasis on the recently rising automated‐extraction features. We also discuss the similarity between protein–ligand interaction representations and small‐molecule representations, and the challenges confronted by the scientific community in characterizing protein–ligand interactions. We expect that this review could inspire the development of novel featurization approaches and boosted MLSFs.This article is categorized under:
Data Science > Artificial Intelligence/Machine Learning
Software > Molecular Modeling
Molecular and Statistical Mechanics > Molecular Interactions
Modeling Augmented Humanity
Authors: Bryant Peter T. 
Published: August 2021

AbstractThis multidisciplinary work analyzes the impact of digitalization on civilized humanity, conceived in terms of purposive, goal-directed agency. More particularly, it examines the close collaboration of human and artificial agents as augmented agents, viewing them both as complex, open adaptive systems, which vary dynamically in context. This first chapter explains general metamodels of agentic form and function, where metamodels are defined as families or related sets of models. From this perspective, augmented agency presents a new class of agentic metamodel, for individuals, groups, and collectives. At the same time, however, new risks and dilemmas emerge, which reflect the challenge of combining and supervising different human and artificial capabilities and potentialities. Problems of this kind are a recurrent topic throughout the book.
Agentic Modality
Authors: Bryant Peter T. 
Published: August 2021

AbstractThree organizing agentic modalities consistently occur, namely, individuals, relational groups, and collectives of various kinds. Digital augmentation transforms these modalities. Particularly, the infusion of artificial agency compresses traditional hierarchies and layers. It also strengthens organizing capabilities, enabling new agentic forms and functions. Yet human agency is likely to remain layered and relatively uncompressed. Therefore, augmented agentic modality could be either more fluid and flattened, or internally conflicted. This chapter examines these developments and highlights the changing nature of procedural habit and routine, as key mediators of agentic modality, mindful that habit forms the scaffold for individual personality, and collective routine for collectivity. When these patterns of action are digitalized, they become more dynamic and generative. Although here, too, risks and dilemmas emerge, regarding the potential divergence and convergence of agentic modalities.
Learning
Authors: Bryant Peter T. 
Published: August 2021

AbstractHuman and artificial agents are both committed to learning. Evaluation of performance is a key driver. This is the case for adaptive feedback, which is generated from the evaluation of performance outcomes. It is also true for feedforward guidance, which results from real-time monitoring of ongoing processes. Augmented agents will learn in both ways. However, these developments signal a shift from historic patterns of learning from performance, which mainly focus on slower, inter-cyclical feedback cycles. Indeed, much human learning occurs in simple increments and takes years to complete. By comparison, artificial agents learn complex lessons with extraordinary speed and precision. Therefore, if collaborative supervision is poor, artificial learning will be fast and complex, while human learning is relatively sluggish and incremental. Such learning will be distorted, often ambiguous, ambivalent, and potentially dysfunctional. This chapter examines these dilemmas.
Ursachen visueller Halluzinationen bei der Parkinson-Krankheit
Authors: Diederich Nico J. 
Published: August 2021

Zusammenfassung
Hintergrund
Visuelle Halluzinationen (VH) werden zumeist als Spätsymptome der Parkinson-Krankheit (PK) angesehen. Sie kommen jedoch in leichterer Form auch in Frühstadien der Erkrankung vor. Ursächlich wurden VH anfänglich als Folge einer dopaminergen Überstimulation gesehen, später ebenso im Rahmen einer demenziellen Entwicklung der PK.

Fragestellung
Die vorliegende Arbeit untersucht, ob sich das Entstehungsmodell der VH in den letzten Jahren erweitert hat.

Material und Methodik
Basierend auf klinischen, pharmakologischen und neuropathologischen Arbeiten sowie funktioneller Magnetresonanzgraphie erfolgt eine systematische Aufgliederung in monomodale und multimodale Entstehungsmodelle der VH. Die Anwendbarkeit auf unterschiedliche VH-Formen und -Auslösungsmomente wird jeweils kritisch beleuchtet.

Ergebnisse
Einbußen bei der visuellen Informationsaufnahme und -verarbeitung, Defizite der Aufmerksamkeit und fehlerhafte Konnektivität zwischen kortikalen Netzwerken werden herausgearbeitet. Es bestehen z. T. Überlappungen mit dem Lhermitte-Syndrom und dem Charles-Bonnet-Syndrom. Kein Modell erklärt jedoch befriedigend alle Spielarten der VH. Nicht alle VH weisen die gleiche Pathogenese und stets eine schlechte Prognose auf.

Schlussfolgerung
Die Ursachenkette visueller Halluzinationen ist komplex und individuell unterschiedlich. Inwieweit dies therapeutisch einsetzbar ist, ist bisher wenig erforscht. Es gibt erste Hinweise, dass neben einer Änderung der Medikation auch Visusverbesserung, die Einbindung des Partners/der Partnerin und vielleicht individuell anpassbare Copingstrategien erfolgreich eingesetzt werden könnten.
A constructive approach to accessible group classes
Authors: de Giovanni Francesco , Trombetti Marco 
Published: August 2021

AbstractLet $${\mathfrak {X}}$$
X
be a group class. A group G is an opponent of $${\mathfrak {X}}$$
X
if it is not an $${\mathfrak {X}}$$
X
-group, but all its proper subgroups belong to $${\mathfrak {X}}$$
X
. Of course, every opponent of $${\mathfrak {X}}$$
X
is a cohopfian group and the aim of this paper is to describe the smallest group class containing $${\mathfrak {X}}$$
X
and admitting no such a kind of cohopfian groups.
Metallurgical Analysis as a Useful Method for Fire Investigation: the Case of Galvanized Steel Sheets
Authors: Casaroli A. , Boniardi M. , Gerosa R. , Rivolta B. 
Published: August 2021

AbstractIn this work, fire simulations were carried out on zinc-coated steel sheets usually employed for the structural parts of furniture, transformer rooms and aeration pipes. The thermal alteration induced by the fire was simulated by heating samples at 13 different temperatures (from 20° C to 1050° C) and for different exposure times (1 h and 6 h) in a laboratory furnace. The metallurgical investigation concerned the surface coloring by photographic analysis, the morphology and chemical composition of the surface oxides by SEM and EDS and the microstructure modifications as a function of the temperature by LOM. The influence of the different parameters was discussed by a statistical approach. The obtained results allowed to associate the temperature to one or more alteration of the steel microstructure and of the color, the morphology and the chemical composition of the surface oxides.