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Paper surfaces (Whatman filter papers, Kimwipes®, and Japanese Washi papers) were employed as support for gold nanoparticles (AuNPs) deposited by the seed-mediated growth method. The AuNP-modified paper or textile was brought into “weak” (or non-permanent) contact with a glassy carbon electrode and immersed into aqueous electrolyte media. Electrochemical responses for the Fe(CN)64−/Fe(CN)63− redox system in 0.1 M phosphate buffer (pH 7.0) and 1.0 M KCl solutions were investigated by voltammetry and impedance methods. Even for weakly contacted AuNP catalysts of relatively low density faster electron transfer was observed. Particle sizes of at least 20 nm diameter were required. There was no permanent contact from AuNP catalysts to the electrode and the process was therefore termed “weak contact catalysis”. The method is proposed as a rapid and novel catalyst screening tool with potential applications in fuel cell and sensor technologies. Keywords: Modified electrodes, Gold nanoparticles, Electron-transfer reactions, Textile, Paper, Catalyst support, Seed-mediated growth method, Voltammetry, Impedance, Contact-electrocatalysis

COVID-19 was first reported in Wuhan, China, in December 2019. It is widely accepted that the world will not return to its prepandemic normality until safe and effective vaccines are available and a global vaccination program has been successfully implemented. Antisense RNAs are single-stranded RNAs that occur naturally or are synthetic and enable hybridizing and protein-blocking translation. Therefore, the main objective of this study was to identify target markers in the RNA of the severe acute respiratory syndrome coronavirus, or SARS-CoV-2, with a length between 21 and 28 bases that could enable the development of vaccines and therapies based on antisense RNA. We used a search algorithm in C language to compare 3159 complete nucleotide sequences from SARS-CoV-2 downloaded from the repository of the National Center for Biotechnology Information. The objective was to verify whether any common sequences were present in all 3159 strains of SARS-CoV-2. In the first of three datasets (SARS-CoV-2), the algorithm found two sequences each of 21 nucleotides (Sequence 1: CTACTGAAGCCTTTGAAAAAA; Sequence 2: TGTGGTTATACCTACTAAAAA). In the second dataset (SARS-CoV) and third dataset (MERS-CoV), no sequences of size N between 21 and 28 were found. Sequence 1 and Sequence 2 were input into BLAST® ≫ blastn and recognized by the platform. The gene identified by the sequences found by the algorithm was the ORF1ab region of SARS-CoV-2. Considerable progress in antisense RNA research has been made in recent years, and great achievements in the application of antisense RNA have been observed. However, many mechanisms of antisense RNA are not yet understood. Thus, more time and money must be invested into the development of therapies for gene regulation mediated by antisense RNA to treat COVID-19 as no effective therapy for this disease has yet been found.

After the breakout of the disease caused by the new virus COVID-19, the mitigation stage has been reached in most of the countries in the world. During this stage, a more accurate data analysis of the daily reported cases and other parameters became possible for the European countries and has been performed in this work. Based on a proposed parametrization model appropriate for implementation to an epidemic in a large population, we focused on the disease spread and we studied the obtained curves, as well as, we investigated probable correlations between the country's characteristics and the parameters of the parametrization. We have also developed a methodology for coupling our model to the SIR-based models determining the basic and the effective reproductive number referring to the parameter space. The obtained results and conclusions could be useful in the case of a recurrence of this repulsive disease in the future., 8 pages, 6 figures and 2 tables

An immunoinformatics-based approach was used to identify potential multivalent subunit CTL vaccine candidates for SARS-CoV-2. Criteria for computational screening included antigen processing, antigenicity, allergenicity, and toxicity. A total of 2604 epitopes were found to be strong binders to MHC class I molecules when analyzed using IEDB tools. Further testing for antigen processing yielded 826 peptides of which 451 were 9-mers that were analyzed for potential antigenicity. Antigenic properties were predicted for 102 of the 451 peptides. Further assessment for potential allergenicity and toxicity narrowed the number of candidate CTL epitopes to 50 peptide sequences, 45 of which were present in all strains of SARS-CoV-2 that were tested. The predicted CTL epitopes were then tested to eliminate those with MHC class II binding potential, a property that could induce hyperinflammatory responses mediated by TH2 cells in immunized hosts. Eighteen of the 50 epitopes did not show class II binding potential. To our knowledge this is the first comprehensive analysis on the proteome of SARS-CoV-2 for prediction of CTL epitopes lacking binding properties that could stimulate unwanted TH2 responses. Future studies will be needed to assess these epitopes as multivalent subunit vaccine candidates which stimulate protective CTL responses against SARS-COV-2., Highlights • Immunoinformatics-based approach to identify potential CTL vaccine candidates for SARS-CoV-2 • 50 peptides predicted as CTL epitopes and to our knowledge, first comprehensive analysis of SARS-CoV-2 for CTL prediction • 18 of the 50 peptides lack predicted binding to class II MHC, property that could otherwise stimulate unwanted Th2 response

Haemorrhagic fever viruses cause emerging infections worldwide, and blood or serum is the main sample used for diagnosis. However, storage and transportation of such samples from remote areas to regional laboratories may be complicated and expensive. In this study, a novel approach was evaluated for the detection of Puumala hantavirus (PUUV) RNA and Rift Valley fever virus (RVFV) RNA. Whole-blood samples spiked with viable virus particles were tested in parallel with clinical samples from patients with acute haemorrhagic fever with renal syndrome (nephropathia epidemica). Individual blood samples were spotted on filter paper, dried, and used for RNA extraction at later time points. PUUV RNA was detected by RT-PCR after storage at room temperature for up to six weeks. In contrast, only low copy numbers of RVFV RNA were detected after 1–2 days even though viable RVFV was eluted from the dried filter papers after the same time. The use of filter paper to collect and store blood samples for PUUV RNA detection is therefore a simple and reliable procedure. This approach might facilitate sampling and analysis of other RNA viruses from human or animal sources and could be used for field studies in remote areas or in developing countries.

Aspect-Oriented Software Development (AOSD) and Model-Driven Development (MDD) are often said to be alike since both approaches are based on the selection of elements (i.e. join points in AOSD and model elements in MDD) and their subsequent adaptation (i.e. weaving in AOSD and transformation in MDD). But does this mean that AOSD and MDD are in fact two words for pretty much the same thing? In this position paper, we argue that there are essential differences between the aspect-oriented and the model-driven approach.

Today the reigning opinion about computer proof assistants based on constructive logic (even from some of the developers of these tools!) is that, while they are very helpful for doing math, they are an absurdly heavy-weight solution to use for practical programming. Yet the Curry-Howard isomorphism foundation of proof assistants like Coq [Yves Bertot and Pierre Castéran. Interactive Theorem Proving and Program Development. Coq'Art: The Calculus of Inductive Constructions. Texts in Theoretical Computer Science. Springer Verlag, 2004] gives them clear interpretations as programming environments.My purpose in this position paper is to make the general claim that Coq is already quite useful today for non-trivial certified programming tasks, as well as to highlight some reasons why you might want to consider using it as a base for your next project in dependently-typed programming.

This work presents a proof of convergence of a discrete solution to a continuous one. At first, the continuous problem is stated as a system of equations which describe the filtration process in the pressing section of a paper machine. Two flow regimes appear in the modeling of this problem. The model for the saturated flow is presented by the Darcy’s law and the mass conservation. The second regime is described by the Richards’ approach together with a dynamic capillary pressure model. The finite volume method is used to approximate the system of PDEs. Then, the existence of a discrete solution to the proposed finite difference scheme is proven. Compactness of the set of all discrete solutions for different mesh sizes is proven. The main theorem shows that the discrete solution converges to the solution of the continuous problem. At the end we present numerical studies for the rate of convergence.

It is shown that the main theorem of Arslan’s paper (Theorem 2, 2011), as stated, is incorrect. Under additional conditions, we present a short proof of the corrected version of the theorem. We also give a proof of a theorem of Rao and Shanbhag (1991) [2], employed by Arslan, without the use of the Kolmogorov Consistency Theorem.

An integrated model for ranking scientific publications together with authors and journals recently presented in [Bini, Del Corso, Romani, ETNA 2008] is closely analyzed. The model, which relies on certain adjacency matrices H,K and F obtained from the relations of citation, authorship and publication, provides the ranking by means of the Perron vector of a stochastic matrix obtained by combining H,K and F. Some perturbation theorems concerning the Perron vector previously introduced by the authors are extended to more general cases and a counterexample to a property previously addressed by the authors is presented. The theoretical results confirm the consistency and effectiveness of our model. Some paradigmatic examples are reported together with some results obtained on a real set of data.

We describe the experience of implementing a Domain-Specific Language using transformation to a General Purpose Language. The domain of application is image processing and low-level computer vision. The transformation is accomplished using the Stratego/XT language transformation toolset. The implementation presented here is contrasted with the original implementation carried out many years ago using standard compiler implementation tools of the day. We highlight some of the unexpected advantages afforded to us, as language designers and implementers, by the source-to-source transformation technique. We also present some of the practical challenges faced in the implementation and show how these issues were addressed.

Currently, security appears to be one of the strongest sales arguments for software vendors all over the world. No other sector of the software industry has undergone a similar wave of mergers and acquisitions recently as the producers of security software. Market analyses from all leading business consultants predict heavy growth in the field, and the annual figures of the major players such as Checkpoint or Symantec back up these statements. However, the main mechanisms of the industry still apply: Innovations have to be created and presented where demand is predicted and the pressure to come up with new solutions is at an all time high. Consequently, the products are pushed out the door as quickly as possible—and all too often before quality control has been dedicated the amount of time and effort that would have been implied by due diligence. While any customer of security software expects the product to enhance the security of their environment, it might actual pose new risks. Blind faith in these solutions can further contribute to compromising the overall security of a network where a substantial advantage had been expected. The purpose of this article is to shed a little light on the situation as it really is.

This paper uses the exogenous shock generated by the COVID-19 pandemic and the relative government response as an informative case in investigating the factors able to affect policy efficacy. Lockdown measures have been widely adopted to limit the diffusion of COVID-19, indirectly supporting the capacity of the hospital system to face the pandemic. Lockdown obliges people to change their social behaviour significantly, and consequently is a matter of serious concern amongst the population. For this reason, how people react to lockdown is of the utmost importance, since failure to observe it properly will be of little benefit in reducing contagion. In this rationale, factors correlated with individuals’ behaviour could affect the efficacy of such measures. The aim of this paper is to investigate whether differences in institutional quality and social capital are correlated with the efficacy of lockdown measures, taking the Italian provinces as a case study. Using a quantitative analysis employing F-GLS estimators, our results suggest that both local social capital and institutional quality have affected the efficiency of lockdown measures in Italian provinces. In general terms these factors contribute to forming the set of incentives able to promote individual behaviour that is in closer compliance with government choices. In this perspective, institutional quality and social capital can be considered factors able to influence the efficacy of policies.

This paper reviews minimax best equivariant estimation in these invariant estimation problems: a location parameter, a scale parameter and a (Wishart) covariance matrix. We briefly review development of the best equivariant estimator as a generalized Bayes estimator relative to right invariant Haar measure in each case. Then we prove minimaxity of the best equivariant procedure by giving a least favorable prior sequence based on non-truncated Gaussian distributions. The results in this paper are all known, but we bring a fresh and somewhat unified approach by using, in contrast to most proofs in the literature, a smooth sequence of non truncated priors. This approach leads to some simplifications in the minimaxity proofs., 21 pages

The input parameters for the hardening law for the elasto-plastic constitutive model of unsaturated soils are generally fitting parameters that control the degree of hardening response and cannot easily be determined without the results of experimental tests in which the changes in suction, water content, and volume are measured or controlled. However, it is next to impossible to complete such precise experimental tests on all the geomaterials comprising the earth structure to be analysed, such as an embankment. It is important, therefore, to establish a method for determining these parameters from more easily attainable data on the material. This paper presents a practical method for estimating the input parameters related to hardening/softening due to desaturation/saturation in the elasto-plastic constitutive model proposed by Ohno et al. (2007a) from a compaction curve, which is frequently obtained and widely used in engineering practice. In the proposed method, the input parameters are estimated by simultaneously solving three theoretical equations, each of which holds for a different point, indicating the statically compacted state on the dry side of the compaction curve, while the approximately represented compaction curve is used instead of a hand-drawn one. The paper demonstrates that the proposed method enables the estimation of the input parameter values within a range small enough so as not to affect the results of the simulation, irrespective of the way in which the points on the represented compaction curve are selected. The validity of the estimated parameters is verified and then discussed through a series of static compaction test simulations.

Highlights • This paper studies the relationship between FDI and carbon emissions. • It considers the innovation, regulation, and efficiency levels of the host countries. • A Panel Autoregressive Distributed Lag was applied on 21 countries divided by income level, for a period from 2001 to 2017. • FDI decreases emissions in high-income countries, while increasing them in the short run in middle-income countries, which supports the Pollution Haven Hypothesis. • This paper reveals that regulatory measures seem to be less effective in reducing emissions in high-income countries., Besides bringing countries closer, the effects of globalization can help increase the production of goods and services, and foster economic growth. Foreign direct investment (FDI) is one of the processes of globalization. One aspect of globalization that has piqued the interest of economists, is the transfer of polluting industries between countries. A principal factor in this are discrepancies of environmental regulations, and these have also been instrumental in a failure to control pollution worldwide. With this impasse in mind, a Panel Autoregressive Distributed Lag was applied to evaluate the impacts of FDI on the carbon dioxide emissions of 21 countries divided by income level, for a period from 2001 to 2017. This methodology allowed the analysis of the resulting dynamics of pollution into the short-run and long-run. The characteristics of efficiency, innovation, and regulation are crucial to better understand the consequences of flows in FDI. Regulation seems to increase pollution in high-income countries, which merits further discussion. FDI decreases emissions in high-income countries, while increasing them in the short-run in middle-income countries, which supports the Pollution Haven Hypothesis. Nonetheless, the capacity of middle-income countries to absorb technology is crucial for them to benefit in the long-run. Trade openness is also highly influenced by environmental regulation in middle-income countries. Since our aim is to understand the transfer of polluting industries, an analysis of emissions from the industrial sector provided a robustness check. It also revealed that policymakers do not seem to be paying sufficient attention to innovation and controlling the environmental degradation that this sector causes.

In this paper, we investigate the problem of whether a retailer should bargain over the wholesale price of a product with a manufacturer or accept the price unilaterally determined by the manufacturer. To investigate the problem, we develop a game-theoretic model describing a supply chain organized by one manufacturer and one retailer. Intuitively, if there is a bargaining opportunity, a retailer is better off bargaining over the wholesale price rather than simply accepting the price determined by the manufacturer. Consistent with this intuition, we first show the benchmark outcome that the retailer achieves a higher profit through bargaining over the price when the manufacturer uses only a single-channel supply chain, in which the manufacturer sells only through the retailer. Conversely, however, we demonstrate that this intuitive result can be completely reversed in a dual-channel supply chain environment, in which the manufacturer is able to sell products not only through the retailer but also directly to end-consumers. Specifically, we find that if a retailer's bargaining power is sufficiently strong and if consumers' substitutability between channels is substantially high, full acceptance of the wholesale price dictated by the manufacturer earns the retailer a higher profit than bargaining. This counterintuitive result is a warning to retailers in dual-channel environments because if a retailer does not accept the wholesale price dictated by a manufacturer, but heedlessly exploits its strong power and bargains with the manufacturer, it may ultimately harm itself and reduce its own profits.

In 2018, the ISC conducted a benchmark stock assessment with a future projection and concluded that the latest stock status of North Pacific shortfin mako was healthy and that its stock abundance would gradually increase within 10 years. Then in 2019, this species was categorized in the North Pacific Ocean as Vulnerable on the IUCN Red List and listed in Appendix II of CITES. The inconsistent outcomes are a controversial issue and raise a fundamental question on why different international organizations had different views on the stock’s current and future declines. To clarify the reasons, this paper reviews the risk assessment conducted by the IUCN and the process of CITES listing, and then, based on the assessment results in 2018, conducts a future projection that incorporates uncertainties in the population trajectory. This projection indicates that the population level does not meet the criteria for the IUCN category of Vulnerable and listing in Appendix II . The results suggest that the IUCN’s simplified methodology of assessment is inappropriate for long-lived, sexually dimorphic species, and that the mechanism of CITES listing is inappropriate from the scientific view of stock assessment because all global stocks, each with a different status, have been treated as one stock. This paper therefore concludes that the IUCN category and CITES listing are both inappropriate for the conservation and management of North Pacific shortfin mako and that such efforts should be implemented by tuna RFMOs based on stock assessment and future projection results derived from a suitable assessment model.

We recall some bibliography on fast polynomial multiplication related to the recent progress in the paper by Cenk and Ozbudak of 2011.

Iiboshi Y., Iwata A., Hayashi T., et al. Energy trapping of circumferential shear horizontal wave in a hollow cylinder. Ultrasonics 133, 107044 (2023); https://doi.org/10.1016/j.ultras.2023.107044., This paper describes energy trapping of circumferential shear horizontal wave (C-SH wave) at a circumferential inner groove of a hollow cylinder. We first derive exact solutions of resonant frequencies of the C-SH wave from the classical theory of guided waves propagating in a hollow cylinder and approximated solutions from the relationship between wavelength of the C-SH wave and length of circumferential path of the hollow cylinder. Next, we examined the energy trapping conditions using the dispersion curves of a guided wave propagating in the longitudinal direction of a hollow cylinder, and showed that C-SH waves trap energy when the hollow cylinder has a circumferential groove on the inner surface, rather than on the outer surface. The energy trapping at an inner groove was confirmed for the C-SH wave with the circumferential order of n = 6 using eigen frequency analysis of finite element method and experiments with electromagnetic transducers. Moreover, when the energy trap mode was used to determine the change in resonance frequency for glycerin solution of different concentrations, it was confirmed that the resonance frequency decreased monotonically as the concentration increased, indicating the possibility of using the energy trap mode as a QCM-like sensor.

The paper examines the global research output on suicidal behavior during the COVID-19 pandemic. The Scopus database was used to identify the publications on suicidal behavior during the COVID-19 pandemic from the beginning of the pandemic to up to 17th May 2021. The 686 publications emerging from 78 countries were found on the topic. These publications received 7970 citations, with an average of 11.62 citations per paper. About one-sixth (16.33%) of the total publications were funded, and these publications averaged 17.24 citations per paper. The publications from the top 10 most productive countries accounted for 92.71% of all publications. The highest number of publications emerged from United States, United Kingdom, and India. However, the relative citation index was highest for publications arising from France, Bangladesh, and Germany. The total number of organizations and authors involved in these publications were 286 and 290, respectively. The top 20 most productive organizations and authors contributed to 35.13% and 17.64% of publications and 79.15% and 58.61% global citations share, respectively. The maximum number of papers were published in the Asian Journal of Psychiatry, followed by Psychiatry Research and Lancet Psychiatry. This study suggests that suicidal behavior has received considerable attention during the ongoing COIVD-19 pandemic.

The COVID-19 epidemic has had a major impact on people's normal travel. Optimizing the control of the number of passengers boarding and deboarding the customized bus (CB) at CB stops can reduce the contact between passengers in the course of travel, which is meaningful for COVID-19 epidemic prevention and control. In this paper, a dynamic programming model based on nonlinear integer programming (NIP) is established to study the problem of boarding and alighting planning at various CB stops under the influence of COVID-19. Using Gurobi 9.1.1 solver, the optimal plan for passengers boarding and deboarding CB buses could be obtained. Besides, the mathematical model established in this paper can obtain the minimum value of the total number of contacts between passengers during travel under different CB numbers. It is found that the model solution results eventually form a Pareto frontier. When the number of CB buses increases, the total number of contacts between passengers will decrease This study has positive significance for ensuring the normal travel of passengers during the COVID-19 epidemic, and provides useful references for the studies about the planning of the customized bus.

Raven appears as an important character in many Alaskan Athabascan myths. He is depicted as a powerful shaman who transforms the world through magic. In this paper, I analyze an often-neglected motif of Alaskan Athabascan Raven stories: Raven as Dog-eaters. I use mythological texts and ethnographic data collected in prior studies and from my fieldwork in the community of Nikolai, Alaska. In some Alaskan Athabascan myths, Raven has a strong appetite for dog meat and assists his neighbors who slaughter their dogs for him. This motif seems to describe a sacrificial practice whereby Raven, as a transformer, receives gifts of food in exchange for providing spiritual services and protection. However, in real life, Northern Athabascans do not practice typical animal sacrifice involving domestic animals. In addition, they do not regard dogs as being suitable for human consumption. This raises several questions. What is the significance of dogs in Alaskan Athabascan societies? Why is Raven thought to be fond of dog meat in traditional Alaskan Athabascan stories? How can we explain this motif of “Dog as Raven's Delicacy” through an ethnography of human–dog–raven relations in Alaskan Athabascan societies? This paper addresses these questions from the perspectives of comparative mythology, ethology and the storytelling strategy.

Using the method and theoretical perspective of language socialization, this paper examines the pragmatic reframing of infant behavior from distress to playfulness among the !Xun San of north–central Namibia. Reframing mobilizes a constellation of multiple semiotic resources. For the !Xun, central among these resources is “gymnastic behavior,” namely, holding infants upright or moving them up and down (bouncing). The analysis shows that !Xun caregivers frequently engage infants in gymnastic behavior in response to infant crying and other displays of distress. They thereby collaboratively create rhythms with infants and encourage them to display joyful reactions. Consequently, infants grow accustomed to pragmatic reframing and rekeying long before they can do so using language. Along with that, caregivers often involve other people in the changing participation framework of family interactions. These findings suggest that pragmatic reframing and rekeying assist infants and caregivers in establishing an intimate intercorporeality. Moreover, by reframing infant behavior in a particular manner, the caregivers display their alignment with !Xun knowledge and value system. Involvement of people around infants in caregiving behavior also enhances the (re)socialization of each interactant into the family and other social identities. It is argued that these features forge the development of !Xun distinct intersubjectivity.

This paper proposes a new approach to estimating investor sentiments and their implications for the global financial markets. Contextualising the COVID-19 pandemic, we draw on the six behavioural indicators (media coverage, fake news, panic, sentiment, media hype and infodemic) of the 17 largest economies and data from 1 st January 2020 to 3 rd February 2021. Our key findings, obtained using a time-varying parameter-vector auto-regression (TVP-VAR) model, indicate the total and net connectedness for the new index, entitled ‘feverish sentiment’. This index provides us insight into economies that send or receive the sentiment shocks. The construction of the network structures indicates that the United Kingdom, China, the United States and Germany became the epicentres of the sentimental shocks that were transmitted to other economies. Furthermore, we also explore the predictive power of the newly constructed index on stock returns and volatility. It turns out that investor sentiment positively (negatively) predicts the stock volatility (return) at the onset of COVID-19. This is the first study of its kind to assess international feverish sentiments by proposing a novel approach and its impacts on the equity market. Based on empirical findings, the study also offers some policy directions to mitigate the fear and panic during the pandemic.

Internet of Things (IoT) has recently brought an influential research and analysis platform in a broad diversity of academic and industrial disciplines, particularly in healthcare. The IoT revolution is reshaping current healthcare practices by consolidating technological, economic, and social views. Since December 2019, the spreading of COVID-19 across the world has impacted the world’s economy. IoT technology integrated with Artificial Intelligence (AI) can help to address COVID-19. UAVs equipped with IoT devices can collect raw data that demands computing and analysis to make intelligent decision without human intervention. To mitigate the effect of COVID-19, in this paper, we propose an IoT-UAV-based scheme to collect raw data using onboard thermal sensors. The thermal image captured from the thermal camera is used to determine the potential people in the image (of the massive crowd in a city), which may have COVID-19, based on the temperature recorded. An efficient hybrid approach for a face recognition system is proposed to detect the people in the image having high body temperature from infrared images captured in a real-time scenario. Also, a face mask detection scheme is introduced, which detects whether a person has a mask on the face or not. The schemes’ performance evaluation is done using various machine learning and deep learning classifiers. We use the edge computing infrastructure (onboard sensors and actuators) for data processing to reduce the response time for real-time analytics and prediction. The proposed scheme has an average accuracy of 99.5% using various performance evaluation metrics indicating its practical applicability in real-time scenarios.

In this paper, blunt slogans used in China's health campaign against coronavirus are closely examined and the public's conflicting comments on them are analyzed. These slogans, due to their extreme effectiveness in making the public comply with the health preventive measures suggested by the government, are called Yinghe or “hardcore” slogans by the Chinese people. Containing harsh and taboo language, they convey threats of death and disease, insults or negative evaluation, and harsh demands. Despite their impolite nature, “hardcore” slogans have received significantly more positive judgment than negative judgment, especially when they made their debut in rural areas in Henan, an agricultural province in China. Criticism towards these blunt slogans then gradually increased after their initial appearance. Plausible factors contributing to the change of judgment are analyzed. The public's conflicting judgment regarding the impoliteness of the slogans may be related to the public's different positioning across time and space. This study shows that impoliteness is a practice situated not only in discourse, genres, and institutions, but also in social, cultural, and political contexts. More attention should be paid to impoliteness in special social configurations (e.g., rural areas) and social emergencies, which not only contextualize a discourse event but also define it.

This paper provides a preliminary study of framework development to manage co-creation process in order to improve customer satisfaction and environmental performance. The study clarified the importance to manage co-creation process for PSS. The discussion on PSS and Co-creation process reveals similarity regarding network and technology requirement to establish the system implies that co-creation process naturally embedded in PSS. Nevertheless, managing the co-creation process become another challenge. To manage co-creation process for PSS, this paper identified the influential factors of co-creation process considering the network and technology. The factors that included in the networks are number of actors, degree of centrality, diversity of actors and network density. The influential factors of co-creation process from technology perspective are modularity and system interoperability. The expected benefits for the company will be in terms of knowledge of co-creation management under product-service system., 9th CIRP IPSS Conference: Circular Perspectives on PSS.

As suggested by the title of Shoham, Powers, and Grenager's position paper [Y. Shoham, R. Powers, T. Grenager, If multi-agent learning is the answer, what is the question? Artificial Intelligence 171 (7) (2007) 365–377, this issue], the ultimate lens through which the multi-agent learning framework should be assessed is “what is the question?”. In this paper, we address this question by presenting challenges motivated by engineering applications and discussing the potential appeal of multi-agent learning to meet these challenges. Moreover, we highlight various differences in the underlying assumptions and issues of concern that generally distinguish engineering applications from models that are typically considered in the economic game theory literature.

New technologies based on artificial agents promise to change the next generation of autonomous systems and therefore our interaction with them. Systems based on artificial agents such as self-driving cars and social robots are examples of this technology that is seeking to improve the quality of people’s life. Cognitive architectures aim to create some of the most challenging artificial agents commonly known as bio-inspired cognitive agents. This type of artificial agent seeks to embody human-like intelligence in order to operate and solve problems in the real world as humans do. Moreover, some cognitive architectures such as Soar, LIDA, ACT-R, and iCub try to be fundamental architectures for the Artificial General Intelligence model of human cognition. Therefore, researchers in the machine ethics field face ethical questions related to what mechanisms an artificial agent must have for making moral decisions in order to ensure that their actions are always ethically right. This paper aims to identify some challenges that researchers need to solve in order to create ethical cognitive architectures. These cognitive architectures are characterized by the capacity to endow artificial agents with appropriate mechanisms to exhibit explicit ethical behavior. Additionally, we offer some reasons to develop ethical cognitive architectures. We hope that this study can be useful to guide future research on ethical cognitive architectures.

For secure transmission of digital images, existing cryptographic algorithms transform coherent visual information into a noise-like appearance prompting an adversary of the presence of a possible cipher. This paper proposes an algorithm that produces a visually coherent and meaningful cipher image. The proposed algorithm consists of a permutation-substitution subroutine to obtain a partial cipher. The Arnold-3D map does the permutation, and a delayed logistic map performs the substitution in this subroutine. The hiding of this partial cipher is done in the reference image using an integer wavelet transform. The pixels of the partial cipher are embedded in the four sub-bands of the decomposed reference image as 4 to 1-pixel encoding using Cantor-like pairing function. In addition to the lossless encryption scheme, the integer nature of all the sub-bands in the wavelet decomposition and the invertible pairing function facilitates the perfect reconstruction of the reference image. One of the significant novelty of this work lies in the subtle use of simple pairing functions, which prohibits the unnecessary increase in the size of the cipher, thereby reducing the storage and transmission costs., Highlights • A novel compression and embedding scheme for generating VMCI. • Subtle use of pairing functions and Integer Wavelet transform in the scheme. • Facilitates perfect decryption/reconstruction of both the original/reference image. • The VMCI transmission cost is reduced by four times as compared to earlier schemes. • Robust and secure against traditional cryptanalysis and steganalysis.

Highlights • A hierarchical temporal attention based model is proposed to support short-term and long-term human mobility sequence prediction. • The proposed hierarchical temporal attention incorporates individual mobility patterns into the model architecture. • The model is compared with four baseline methods on individual trajectory datasets with varying degree of traveling uncertainty. • Experiments demonstrate the outperformance of the proposed method using three evaluation metrics. • The proposed model uncovers individual frequential and periodical mobility patterns in an interpretable manner., Prediction of individual mobility is crucial in human mobility related applications. Whereas, existing research on individual mobility prediction mainly focuses on next location prediction and short-term dependencies between traveling locations. Long-term location sequence prediction is of great importance for long-time traffic planning and location advertising, and long-term dependencies exist as individual mobility regularity typically occurs daily and weekly. This paper proposes a novel hierarchical temporal attention-based LSTM encoder-decoder model for individual location sequence prediction. The proposed hierarchical attention mechanism captures both long-term and short-term dependencies underlying in individual longitudinal trajectories, and uncovers frequential and periodical mobility patterns in an interpretable manner by incorporating the calendar cycle of individual travel regularities into location prediction. More specifically, the hierarchical attention consists of local temporal attention to identify highly related locations in each day, and global temporal attention to discern important travel regularities over a week. Experiments on individual trajectory datasets with varying degree of traveling uncertainty demonstrate that our method outperforms four baseline methods on three evaluation metrics. In addition, we explore the interpretability of the proposed model in understanding individual daily, and weekly mobility patterns by visualizing the temporal attention weights and frequent traveling patterns associated with locations.

This study examines the applicability of an elasto-plastic constitutive model with the concept of plastic rebound to describe the mechanical behaviour of expansive soils. The constitutive model can be characterized by an elliptical yield locus which is analogous to the modified Cam-clay model, but does not pass the origin of the effective stress space. In addition to the intrinsic normal consolidation line, the plastic rebound line is newly defined in order to describe a hardening law which allows the prediction of plastic swelling upon unloading. Through analytical investigations, the paper shows that the constitutive model can effectively predict not only the volume changes, but also the effective stress paths, i.e., K0-values, for both consolidation and swelling in axial and radial problems, despite it being a simple modification. Also presented here are comparisons between the results of a simulation and laboratory tests on expansive soils in order to verify the applicability of the developed constitutive model.

Lubricating oils are crucial in the operation of automotive engines because they both reduce friction between moving parts and protect against corrosion. However, the performance of lubricant oil may be affected by contaminants, such as gasoline, diesel, ethanol, water and ethylene glycol. Although there are many standard methods and studies related to the quantification of contaminants in lubricant oil, such as gasoline and diesel oil, to the best of our knowledge, no methods have been reported for the quantification of ethanol in used Otto cycle engine lubrication oils. Therefore, this work aimed at the development and validation of a routine method based on partial least-squares multivariate analysis combined with attenuated total reflectance in the mid-infrared region to quantify ethanol content in used lubrication oil. The method was validated based on its figures of merit (using the net analyte signal) as follows: limit of detection (0.049%), limit of quantification (0.16%), accuracy (root mean square error of prediction=0.089% w/w), repeatability (0.05% w/w), fit (R2=0.9997), mean selectivity (0.047), sensitivity (0.011), inverse analytical sensitivity (0.016% w/w−1) and signal-to-noise ratio (max: 812.4 and min: 200.9). The results show that the proposed method can be routinely implemented for the quality control of lubricant oils.

Nerve growth factor (NGF), a member of the neurotrophin family, is responsible for the maintenance and survival of cholinergic neurons in the basal forebrain. The degeneration of cholinergic neurons and reduced acetycholine levels are hallmarks of Alzheimer's disease (AD) as well as associated with learning and memory deficits. Thus far, NGF has proven the most potent neuroprotective molecule against cholinergic neurodegeneration. However, delivery of this factor into the brain remains difficult. Recent studies have begun to elucidate the potential use of monocytes as vehicles for therapeutic delivery into the brain. In this study, we employed different transfection and transduction methods to generate NGF-secreting primary rat monocytes. Specifically, we compared five methods for generating NGF-secreting monocytes: (1) cationic lipid-mediated transfection (Effectene and FuGene), (2) classical electroporation, (3) nucleofection, (4) protein delivery (Bioporter) and (5) lentiviral vectors. Here, we report that classical transfection methods (lipid-mediated transfection, electroporation, nucleofection) are inefficient tools for proper gene transfer into primary rat monocytes. We demonstrate that lentiviral infection and Bioporter can successfully transduce/load primary rat monocytes and produce effective NGF secretion. Furthermore, our results indicate that NGF is bioactive and that Bioporter-loaded monocytes do not appear to exhibit any functional disruptions (i.e. in their ability to differentiate and phagocytose beta-amyloid). Taken together, our results show that primary monocytes can be effectively loaded or transduced with NGF and provides information on the most effective method for generating NGF-secreting primary rat monocytes. This study also provides a basis for further development of primary monocytes as therapeutic delivery vehicles to the diseased AD brain., Highlights • Monocytes can be easily transduced using Bioporter protein reagent • Bioporter-loaded monocytes exhibit no functional disruptions • Lentiviral vectors are by far the most potent tool for monocyte transduction • Classical transfection methods are not sufficient for primary monocyte gene transfer • NGF-secreting monocytes may serve as potential therapeutic vehicles in Alzheimer`s disease

The objective of our study is to recycle the industrial waste water of a industrial Complex after treatment by the bioprocess MBR (membrane bioreactor). In order to apply this bioprocess, the water quality in question was first of all studied. To characterize this industrial waste water, a series of physicochemical analysis was carried out according to standardized directives and methods. Following-up the water quality to meet the regulatory requirements with rejection of this industrial waste water, a study was done thanks to the permanently monitoring of the following relevant parameters(P): the flow, the potential of hydrogen (pH), the total suspended solids(TSS), the turbidity (Turb), the chemical oxygen demand (COD),the biochemical oxygen demand (BOD), the Kjeldahl total nitrogen (KTN) and ammonia (NH4+), the total phosphorus (Ptot), the fluorine (F), the oils (O), the fats (F) and the phenols (Ph). According to collected information, it was established the sampling rates to which the quality control was done, the selected analytical methods were validated by the control charts and the analysis test number was determined by the Cochran test. The results of the quality control show that some rejected water contents are not in the Algerian standards, but, in our case, the objective is the preoccupation for a standard setting of these industrial water parameters so as to recycle it. The process adopted by MBR for waste water treatment is being studied, first in the development of the experimental characterizing of the reactor and the selected membrane.

During the last decades for several species – e.g. swine – many mAb to leukocyte-specific molecules have been developed and clusters of differentiation corresponding to human CD could be established. However, for a significant amount of the raised mAb the corresponding antigens were not characterized on the molecular level and therefore preliminary clusters – in swine so-called Swine workshop clusters (SWC) – were established. These clusters contain antigens with currently no obvious orthologs to human leukocyte differentiation antigens. In this study, we describe the generation of a eukaryotic cDNA expression library from in vitro activated porcine peripheral blood mononuclear cells. Screening of this library with an antibody recognizing SWC1 enabled isolation and sequencing of cDNAs coding for the porcine SWC1 molecule. A BLAST search of the obtained sequence revealed that SWC1 is the orthologous molecule of human CD52. Therefore, our study provides the basis for comparative studies on the role of CD52 in different mammalian species. In addition, the established cDNA library can be used for investigation of additional SWC-defined molecules.

The human pentraxin proteins, serum amyloid P component (SAP) and C‐reactive protein (CRP) are important in routine clinical diagnosis, SAP for systemic amyloidosis and CRP for monitoring the non‐specific acute phase response. They are also targets for novel therapies currently in development but their roles in health and disease are controversial. Thus, both for clinical use and to rigorously elucidate their functions, structurally and functionally intact, pharmaceutical grade preparations of the natural, authentic proteins are required. We report here the production from normal human donor plasma and the characterization of the first such preparations. Importantly, we demonstrate that, contrary to reports using recombinant proteins and less well characterized preparations, neither CRP nor SAP stimulate the release by human peripheral blood mononuclear cells in vitro of any TNFα, IL‐6 or IL‐8, nor does SAP cause release of IL‐1β or IL‐10. Furthermore neither of our preparations was pro‐inflammatory in mice in vivo.

Antigen-specific proliferation is a critical function of memory T cells that is often utilised to measure vaccine immunogenicity and T cell function. We proposed that measurement of intracellular expression of the nuclear protein, Ki67, could reliably assess specific T cell proliferation in vitro. Ki67 was expressed in CD4+ and CD8+ T cells that had undergone in vitro proliferation after 6-day culture of human whole blood or PBMC with antigens. T cells cultured with no antigen did not express Ki67. When compared to current flow cytometry based proliferation assays, Ki67 detected proliferating cells with greater sensitivity than BrdU incorporation, whereas its sensitivity was similar to dye dilution of Oregon Green (OG), a CFSE derivative. Overall, the magnitude and cytokine expression profile of proliferating T cells detected by Ki67 expression correlated strongly with T cells detected with BrdU or OG. The intra-assay variability of Ki67 proliferation was 2–3% for CD4+ T cells, and 10–16% for CD8+ T cells. Finally, we demonstrate that the Ki67 assay detects tetanus toxoid-specific CD4+ T cell proliferation after infant vaccination with tetanus toxoid (TT). Overall our data suggest that intracellular Ki67 expression provides a specific, quantitative and reproducible measure of antigen-specific T cell proliferation in vitro.

Cave fish have rarely been investigated with regard to their inner ear morphology, hearing abilities, and acoustic communication. Based on a previous study that revealed morphological differences in the saccular otolith between a cave and two surface populations of Poecilia mexicana, we checked for additional differences in utricular and lagenar otoliths and tested whether different populations have similar hearing sensitivities. We found pronounced differences in the shape of all three otoliths. Otoliths of the saccule and lagena from cave fish differed from those of surface fish in the features of the face oriented towards the sensory epithelium. In addition, otoliths of the utricle and lagena were significantly heavier in cave fish. Auditory sensitivities were measured between 100 and 1500Hz, utilizing the auditory evoked potential recording technique. We found similar hearing abilities in cave and surface fish, with greatest sensitivity between 200 and 300Hz. An acoustic survey revealed that neither ecotype produced species-specific sounds. Our data indicate that cave dwelling altered the otolith morphology in Atlantic mollies, probably due to metabolic differences. Different otolith morphology, however, did not affect general auditory sensitivity or acoustic behavior.

Cervical cytobrushing, biopsy, or lavages have previously been used to collect mononuclear cells from the female genital tract. Compared with blood, obtaining cells from the female genital tract is more invasive and generally yields few cells for subsequent immune studies. Because of the value of including mucosal sampling in HIV vaccine trials, standardisation of methods for collection, processing, and analysis of immunity from cells derived from the female genital tract is important. The aim of this study was to assess the effect of transport conditions on the viability, recovery and antigenic responsiveness of cervical T cells. This was investigated in cervical cytobrush specimens collected from 215 chronically HIV-infected women. Cytobrushes were either processed immediately, after cryopreservation, or after 24h at 37°C, 4°C or room temperature. CD3+ T cell numbers were quantified using Guava automated cell counting. Viability was assessed using Trypan and Annexin/PI staining. Intracellular cytokine staining was used to evaluate IFN-γ responses to PMA, PHA and CEF peptides in cytobrush-derived T cells ex vivo and after delayed processing. In vitro polyclonal expansion of thawed cervical lymphocytes was conducted for 14days in the presence of anti-CD3 and IL-2. We found that CD3+ T cell recovery and viability was similar in cytobrushes processed immediately or after 24h irrespective of the conditions at which they were maintained. Fifty percent of the CD3+ T cells could be recovered after cryopreservation of cytobrushes and these could be polyclonally expanded in half of the cryopreserved samples. IFN-γ production following mitogenic stimulation was similar in ex vivo and delayed processing cytobrushes. Maintaining cytobrushes at 37°C prior to processing significantly improved the detection of CEF-specific T cell responses compared to ex vivo. We conclude that cervical cytobrush-derived T cells are robust and can preserve their viability, phenotype and function over 24h of mock transport.

Mobile cloud computing (MCC) is aimed at integrating mobile devices with cloud computing. It is one of the most important concepts that have emerged in the last few years. Mobile devices, in the traditional agent-client architecture of MCC, only utilize resources in the cloud to enhance their functionalities. However, modern mobile devices have many more resources than before. As a result, researchers have begun to consider the possibility of mobile devices themselves sharing resources. This is called the cooperation-based architecture of MCC. Resource discovery is one of the most important issues that need to be solved to achieve this goal. Most of the existing work on resource discovery has adopted a fixed choice of centralized or flooding strategies. Many improved versions of energy-efficient methods based on both strategies have been proposed by researchers due to the limited battery life of mobile devices. This paper proposes a novel adaptive method of resource discovery from a different point of view to distinguish it from existing work. The proposed method automatically transforms between centralized and flooding strategies to save energy according to different network environments. Theoretical models of both energy consumption and the quality of response information are presented in this paper. A heuristic algorithm was also designed to implement the new adaptive method of resource discovery. The results from simulations demonstrated the effectiveness of the strategy and the efficiency of the proposed heuristic method.

COVID-19 has severely disrupted every aspect of society and left negative impact on our life. Resisting the temptation in engaging face-to-face social connection is not as easy as we imagine. Breaking ties within social circle makes us lonely and isolated, that in turns increase the likelihood of depression related disease and even can leads to death by increasing the chance of heart disease. Not only adults, children's are equally impacted where the contribution of emotional competence to social competence has long term implications. Early identification skill for facial behaviour emotions, deficits, and expression may help to prevent the low social functioning. Deficits in young children's ability to differentiate human emotions can leads to social functioning impairment. However, the existing work focus on adult emotions recognition mostly and ignores emotion recognition in children. By considering the working of pyramidal cells in the cerebral cortex, in this paper, we present progressive lightweight shallow learning for the classification by efficiently utilizing the skip-connection for spontaneous facial behaviour recognition in children. Unlike earlier deep neural networks, we limit the alternative path for the gradient at the earlier part of the network by increase gradually with the depth of the network. Progressive ShallowNet is not only able to explore more feature space but also resolve the over-fitting issue for smaller data, due to limiting the residual path locally, making the network vulnerable to perturbations. We have conducted extensive experiments on benchmark facial behaviour analysis in children that showed significant performance gain comparatively.

The COVID-19 pandemic caused significant disruptions to global operations and supply chains. While the huge impact of the pandemic has nurtured important literature over the last couple of years, little is being said about the role of resource orchestration in supporting resilience in highly disruptive contexts. Thus, this study aims to this knowledge gap by proposing an original model to explore supply chain resilience (SCRE) antecedents, considering supply chain alertness (SCAL) as a central point to support resilience. This study focuses on the resource orchestration theory (ROT) to design a conceptual model. The partial least squares structural equation modeling (PLS-SEM) served to validate the model, exploring data from the UK supply chain decision-makers. The study reveals a number of both expected and unexpected findings. These include the evidence that supply chain disruption orientation (SCDO) has a strong positive effect on the SCAL. In addition, SCAL plays a strong positive effect in resource reconfiguration (RREC), supply chain efficiency (SCEF) and SCRE. We further identified a partial mediation effect of RREC on the relationship between SCAL and SCRE. Surprisingly, it appeared that SCAL strongly influences SCEF, while SCEF itself does not create any significant effect on SCRE. For managers and practitioners, the importance of resource orchestration as a decisive approach to adequately respond to huge disruptions is clearly highlighted by our results. Finally, this paper helps to grasp better how important resource orchestration in operations and supply chains remains for appropriate responses to high disruptions such as the COVID-19 impacts.

Matsubara–Usuba [13] introduced the notion of skinniness and its variants for subsets of P κ λ and showed that the existence of skinny stationary subsets of P κ λ is related to large cardinal properties of ideals over P κ λ and to Jensen's diamond principle on λ. In this paper, we study the existence of skinny stationary sets in more detail.