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Waste paper disposed in landfills notably contributes to greenhouse gas (GHG) emissions and impedes more sustainable, circular alternatives, such as recycling. In Hong Kong, this unsustainable approach is currently dominant as 68% of waste paper products are treated in landfills in 2020. To contextualize the impact of local waste paper management and explore mitigation potentials of circular alternatives, this paper develops a quantitative assessment framework around GHG emissions development trajectories. Combining guidelines of the Intergovernmental Panel on Climate Change (IPCC), national GHG inventories, and local parameters from life cycle analysis, five GHG emissions projections were simulated along the Shared Socioeconomic Pathways (SSPs) until 2060. Most recent baselines indicate that Hong Kong's current waste paper treatment generated 638,360 tons CO2-eq in 2020, comprising 1,821,040 tons CO2-eq from landfill and 671,320 tons CO2-eq from recycling, and −1,854,000 tons CO2-eq from primary material replacement. Proceeding along a Business-as-Usual scenario under SSP5, GHG emissions will dramatically increase to a net 1,072,270 tons CO2-eq by 2060, whereas a recycling-intensive scenario will lead to a net saving of −4,323,190 tons CO2-eq. To complement the quantitative evidence on the benefits of waste paper recycling, field research was conducted to explore the feasibility of circular policy innovation from the perspective of recycling stakeholders. These empirical qualitative and quantitative findings from stakeholders' business routines and material transactions provide crucial indications for policy and institutional innovation: Essentially, for Hong Kong to improve waste paper recycling capacities and facilitate a circular economy (CE), local stakeholders require support via fiscal policy measures (financial subsidies or tax reductions) and infrastructure improvements (delivery access and material storage). In sum, this study employs a novel analytical fr

Pre-compiled guidelines with a static question list can stimulate critical thinking while reading a scientific paper. However, they could be less engaging than taking live question prompts from others. In this paper, we develop CReBot that interactively asks section-level critical thinking questions and customize it for routine paper readers with prior research experience and novices new to research. Our first within-subjects study with 24 routine readers demonstrates CReBot's engagement and usefulness over static guidelines. Then, with more teacher-like question-specific hints prepared for CReBot, we conduct another within-subjects study with 20 novices. The results, however, indicate that CReBot might not be better than static guidelines for beginners. Nevertheless, both user groups favor CReBot's contextualized questions and interaction flexibility. We conclude with design implications for interactive tools to facilitate critical reading. © 2022 Elsevier Ltd

Using waste paper as fuel for domestic heating is a beneficial recycling option for small island developing states where there are lacks of resources for energy and waste treatment. However, there are concerns about the impact of air pollutants emitted from the burning of the self-made paper briquettes as household air pollution is recognised as the greatest environmental risk for human. In this study, combustion tests were carried out for paper briquettes made in one Pacific island and three commercial fuels in Australia including wood briquettes, kindling firewood and coal briquettes in order to: 1) characterise the emissions of three criteria air pollutants including particulate matters, CO and NOx including their emission factors (EF) from the tested fuels; and 2) compare the EFs among the tested fuels and with others reported in the literature. The results showed that waste paper briquettes burned quickly and generated high temperature but the heat value is relatively low. Paper briquettes and coal briquettes produced higher CO concentration than the others while paper briquettes generated the highest NOx level. Only PM2.5 concentration emitted from paper briquettes was similar to kindling firewood and lower than wood briquettes. Burning of paper briquettes and wood briquettes produced particulate matter with large average count median diameter (72 and 68 nm) than coal briquette and kindling firewood (45 and 51 nm). The EFs for CO, NOx and PM2.5 of paper briquettes were within the range of EFs reported in this study as well as in the literature. Overall, the results suggested that using paper briquettes as fuel for domestic heating will not likely to generate higher level of three major air pollutants compared to other traditional fuels.

BACKGROUND: With an increasing prevalence of patients with valvular heart disease (VHD), a dedicated management approach is needed. The challenges encountered are manifold and include appropriate diagnosis and quantification of valve lesion, organization of adequate follow-up, and making the right management decisions, in particular with regard to the timing and choice of interventions. Data from the Euro Heart Survey have shown a substantial discrepancy between guidelines and clinical practice in the field of VHD and many patients are denied surgery despite having clear indications. The concept of heart valve clinics (HVCs) is increasingly recognized as the way to proceed. At the same time, very few centres have developed such expertise, indicating that specific recommendations for the initial development and subsequent operating requirements of an HVC are needed. AIMS: The aim of this position paper is to provide insights into the rationale, organization, structure, and expertise needed to establish and operate an HVC. Although the main goal is to improve the clinical management of patients with VHD, the impact of HVCs on education is of particular importance: larger patient volumes foster the required expertise among more senior physicians but are also fundamental for training new cardiologists, medical students, and nurses. Additional benefits arise from research opportunities resulting from such an organized structure and the delivery of standardized care protocols. CONCLUSION: The growing volume of patients with VHD, their changing characteristics, and the growing technological opportunities of refined diagnosis and treatment in addition to the potential dismal prognosis if overlooked mandate specialized evaluation and care by dedicated physicians working in a specialized environment that is called the HVC

Polyallylamine hydrochloride (PAH) and polyacrylic acid (PAA) have been used to build up polyelectrolyte multilayers (PEM) on wood fibres and on silicon oxide surfaces, under various pH conditions. Consecutive adsorption onto silicon oxide surfaces of PAH and PAA were studied using stagnation point adsorption reflectometry, and the results showed a steady build-up of multilayers. Furthermore, by altering pH, the build-up of the multilayer could be made either linear or exponential in terms of adsorbed amount. Nitrogen analysis of sheets prepared from modified fibres showed that the adsorbed amount of PAH increased throughout PEM build-up, the amount of increase depending on pH during adsorption. Strength measurements of the sheets, i.e., stress at break and strain at break, showed significant improvements ranging from 60 to 200%, depending on both pH during adsorption and type of polyelectrolyte in the outer layer. A good correlation between the adsorbed amount of PAH and the improved strength properties of the paper was also found.

The aim of this study was to assess the agreement between a novel electronic appetite rating system (EARS) and traditional paper and pen visual analog scales (VAS) in a clinical population. 28 hemodialysis patients (mean age 61±17y, 50% male, median dialysis vintage 19.5(4-101) months) were asked to rate their subjective sensations of hunger, fullness and desire to eat on VAS using both methods. The mean (SD) bias ranged from 2.6(16.6)mm to 6.2(15.7)mm which indicated that the two methods did not agree. Patients preferred the paper and pen method compared with the EARS. Either method would be suitable to use in a clinical population; however, it would be inappropriate to use the methods interchangeably.

The aim of this article is to outline the issues involved in the application of machine vision to the automatic extraction and registration of watermarks from continuous web paper. The correct identification and localization of watermarks are key issues in paper manufacturing. As well as requiring the position of the watermark for defect detection and classification, it is necessary to insure its position on the paper prior to the cutting process. Two paper types are discussed, with and without laid and chain lines (these lines appear as a complex periodic background to the watermark and further complicate the segmentation process). We will examine both morphological and Fourier approaches to the watermark segmentation process, concentrating specifically on those images with complex backgrounds. Finally we detail a system design suitable for real-time implementation.

BACKGROUND: With an increasing prevalence of patients with valvular heart disease (VHD), a dedicated management approach is needed. The challenges encountered are manifold and include appropriate diagnosis and quantification of valve lesion, organization of adequate follow-up, and making the right management decisions, in particular with regard to the timing and choice of interventions. Data from the Euro Heart Survey have shown a substantial discrepancy between guidelines and clinical practice in the field of VHD and many patients are denied surgery despite having clear indications. The concept of heart valve clinics (HVCs) is increasingly recognized as the way to proceed. At the same time, very few centres have developed such expertise, indicating that specific recommendations for the initial development and subsequent operating requirements of an HVC are needed. AIMS: The aim of this position paper is to provide insights into the rationale, organization, structure, and expertise needed to establish and operate an HVC. Although the main goal is to improve the clinical management of patients with VHD, the impact of HVCs on education is of particular importance: larger patient volumes foster the required expertise among more senior physicians but are also fundamental for training new cardiologists, medical students, and nurses. Additional benefits arise from research opportunities resulting from such an organized structure and the delivery of standardized care protocols. CONCLUSION: The growing volume of patients with VHD, their changing characteristics, and the growing technological opportunities of refined diagnosis and treatment in addition to the potential dismal prognosis if overlooked mandate specialized evaluation and care by dedicated physicians working in a specialized environment that is called the HVC

Farms face various risks such as uncertainties in the natural growth process, stiffened financing sources, volatile prices, lack of market access, unpredictable changes in farm-related policies and regulations, and farmers’ health problems. Accordingly, farmers have to make decisions to be prepared for such situations or mitigate their impacts and maintain essential functions. Deep learning (DL) has become a state-of-the-art machine learning method for automatic processing of different data types in various science fields, including the agricultural risk management (ARM) domain. DL methods have been employed to support decision making in agriculture through the extraction of effective information from data. The potential of DL in ARM has recently increased with advances in technology and digitalization. This review aims to complement prior reviews by identifying and analyzing state-of-the-art advances in DL-based ARM (DL-ARM), and thus, demonstrate the current achievements and challenges. In particular, a systematic literature review approach is adopted to extract current trends in publications, publishers, used DL methods, applications, lessons learned, weaknesses, and open problems, to formulate recommendations for researchers and field practitioners. The papers were categorized based on the addressed risk types, specific objectives, deep learning algorithms, and data types. In total, 203 papers were retrieved, of which 100 papers were selected based on the defined exclusion criteria for a detailed review. Guidelines are discussed on how DL can further contribute to ARM by shifting focus away from assessing only production risk and leveraging DL to make decisions rather than simply support making them.

In this paper we consider some iterative estimation algorithms, which are valid to analyse the variance of data, which may be either non-grouped or grouped with different classification intervals. This situation appears, for instance, when data is collected from different sources and the grouping intervals differ from one source to another. The analysis of variance is carried out by means of general linear models, whose error terms may be general. An initial procedure in the line of the EM, although it does not necessarily agree with it, opens the paper and gives rise to a simplified version where we avoid the double iteration, which implicitly appears in the EM and, also, in the initial procedure mentioned above. The asymptotic stochastic properties of the resulting estimates have been investigated in depth and used to test ANOVA hypothesis, Depto. de Estadística e Investigación Operativa, Fac. de Ciencias Matemáticas, TRUE, pub

Rail inspection research is a substantive topic for the railway industry. Management of rolling contact fatigue rail defects is crucial for transport safety worldwide because uncontrolled development of these defects could cause railway accidents, including derailments. This paper presents the review of non-destructive inspection methods for surface and subsurface rolling contact fatigue defects, with particular emphasis on squat and head checking rail defects. Main types of standard and novel non-destructive inspection methods are presented and described from the aspect of basic characteristics, advantages and disadvantages for its practical application for rails in the track, and possible improvements of equipment and software. The presented review is based on the relevant international researches and review papers. In addition, a combination of inspection methods are presented, which can improve the reliability of defect detection.

The popular brain monitoring method of electroencephalography (EEG) has seen a surge in commercial attention in recent years, focusing mostly on hardware miniaturization. This has led to a varied landscape of portable EEG devices with wireless capability, allowing them to be used by relatively unconstrained users in real-life conditions outside of the laboratory. The wide availability and relative affordability of these devices provide a low entry threshold for newcomers to the field of EEG research. The large device variety and the at times opaque communication from their manufacturers, however, can make it difficult to obtain an overview of this hardware landscape. Similarly, given the breadth of existing (wireless) EEG knowledge and research, it can be challenging to get started with novel ideas. Therefore, this paper first provides a list of 48 wireless EEG devices along with a number of important—sometimes difficult-to-obtain—features and characteristics to enable their side-by-side comparison, along with a brief introduction to each of these aspects and how they may influence one's decision. Secondly, we have surveyed previous literature and focused on 110 high-impact journal publications making use of wireless EEG, which we categorized by application and analyzed for device used, number of channels, sample size, and participant mobility. Together, these provide a basis for informed decision making with respect to hardware and experimental precedents when considering new, wireless EEG devices and research. At the same time, this paper provides background material and commentary about pitfalls and caveats regarding this increasingly accessible line of research.

Matched-field processing (MFP) has recently been utilized to detect leaks in pipelines. The defect detection functional (DDF) of MFP features a primary lobe linked to the actual leak alongside secondary lobes. Such side-lobes have, until now, been considered as a nuisance and source of false identification of defects in pressurized pipelines. In this study, we turn this nuisance into a defect identification advantage. Specifically, we theoretically and experimentally demonstrate that the spatial positioning of the side-lobes of the DDF of MFP varies with the candidate leak location and the probing wave frequency, and that the paths or “trajectories” of the undesirable side-lobes converge to the accurate leak position as the frequency increases. By leveraging this property of the side-lobes, this paper introduces novel broadband MFP techniques for high-resolution leak detection in the presence of white, colored, and impulse-type noise. Both numerical and experimental data are used to demonstrate the accuracy, stability, and resolution of the proposed defect detection techniques and their ability to suppress the influence of undesirable side-lobes. © 2024 Elsevier Ltd

Marginal cost curves (MCCs) are popular decision-support tools for assessing and ranking the cost-effectiveness of different options in environmental policy and management. However, conventional MCC approaches have been criticized for lack of transparency and disregard for complexity; not accounting for interaction effects between measures; ignoring ancillary benefits and costs; and not considering intertemporal dynamics. In this paper, we present an approach to address these challenges using a system dynamics (SD)-based model for producing dynamic MCCs. We describe the approach by applying it to evaluate efforts to address water scarcity in a hypothetical, but representative, Swedish city. Our results show that the approach effectively addresses all four documented limitations of conventional MCC methods. They also show that combining MCCs with behavior-over-time graphs and causal-loop diagrams can lead to new policy insights and support a more inclusive decision-making process.

In response to global challenges in resource supply, many industries are adopting the principles of the Circular Economy (CE) to improve their resource acquisition strategies. This paper introduces an innovative approach to address the environmental impact of waste Glass Fiber Reinforced-Polymer (GFRP) pipes and panels by repurposing them to manufacture structural components for new bicycle and pedestrian bridges. The study covers the entire process, including conceptualization, analysis, design, and testing of a deck system, with a focus on the manufacturing process for a 7-m-long prototype bridge. The study shows promising results in the concept of a sandwich structure utilizing discarded GFRP pipes and panels, which has the flexibility to account for variabilities in dimensions of incoming products while still meeting mechanical requirements. The LCA analysis shows that the transportation of materials is the governing contributing factor. It was concluded that further development of this concept should be accompanied by a business model that considers the importance of the contributions from the whole value chain., The authors extend their gratitude to the Swedish Energy Agency(Energimyndigheten) for funding the RECINA project (REuse of Composite parts for INfrastructure Applications – Dnr 2019–021576, Projektnr 49763–1). This project has been made possible through asuccessful collaboration with a Swedish Industrial consortium includingComposite Design, Marstrom¨ Composite, Hitachi-ABB Power Grids,Podcomp, GreenPlank, and Eventhotell.

Marginal cost curves (MCCs) are popular decision-support tools for assessing and ranking the cost-effectiveness of different options in environmental policy and management. However, conventional MCC approaches have been criticized for lack of transparency and disregard for complexity; not accounting for interaction effects between measures; ignoring ancillary benefits and costs; and not considering intertemporal dynamics. In this paper, we present an approach to address these challenges using a system dynamics (SD)-based model for producing dynamic MCCs. We describe the approach by applying it to evaluate efforts to address water scarcity in a hypothetical, but representative, Swedish city. Our results show that the approach effectively addresses all four documented limitations of conventional MCC methods. They also show that combining MCCs with behavior-over-time graphs and causal-loop diagrams can lead to new policy insights and support a more inclusive decision-making process.

In response to global challenges in resource supply, many industries are adopting the principles of the Circular Economy (CE) to improve their resource acquisition strategies. This paper introduces an innovative approach to address the environmental impact of waste Glass Fiber Reinforced-Polymer (GFRP) pipes and panels by repurposing them to manufacture structural components for new bicycle and pedestrian bridges. The study covers the entire process, including conceptualization, analysis, design, and testing of a deck system, with a focus on the manufacturing process for a 7-m-long prototype bridge. The study shows promising results in the concept of a sandwich structure utilizing discarded GFRP pipes and panels, which has the flexibility to account for variabilities in dimensions of incoming products while still meeting mechanical requirements. The LCA analysis shows that the transportation of materials is the governing contributing factor. It was concluded that further development of this concept should be accompanied by a business model that considers the importance of the contributions from the whole value chain., The authors extend their gratitude to the Swedish Energy Agency(Energimyndigheten) for funding the RECINA project (REuse of Composite parts for INfrastructure Applications – Dnr 2019–021576, Projektnr 49763–1). This project has been made possible through asuccessful collaboration with a Swedish Industrial consortium includingComposite Design, Marstrom¨ Composite, Hitachi-ABB Power Grids,Podcomp, GreenPlank, and Eventhotell.

In this paper we deal with set-valued maps T:X⇉X⁎ defined on a Banach space X, that are generalized monotone in the sense of Karamardian. Under various continuity assumptions on T, we investigate the regularity of suitable selections of the set-valued map R++T that shares with T the generalized monotonicity properties. In particular, we show that for every quasimonotone set-valued map T satisfying the Aubin property around (y,y⁎)∈gph(T) with y⁎≠0 there exist locally Lipschitz selections of R++T∖{0}. In the last part some notions of Minty points of T are introduced, and their relationship with zeros as well as effective zeros of T is discussed; a correlation is established between these concepts and the broader context of the generalized monotonicity of the map T.

In this paper we establish inclusions and noninclusions between various Hardy type spaces on noncompact Riemannian manifolds M with Ricci curvature bounded from below, positive injectivity radius and spectral gap. Our first main result states that, if L is the positive Laplace–Beltrami operator on M, then the Riesz–Hardy space HR1(M) is the isomorphic image of the Goldberg type space h1(M) via the map L1/2(I+L)−1/2, a fact that is false in Rn. Specifically, HR1(M) agrees with the Hardy type space X1/2(M) recently introduced by the first three authors; as a consequence, we prove that HR1(M) does not admit an atomic characterisation. Noninclusions are mostly proved in the special case where the manifold is a Damek–Ricci space S. Our second main result states that HR1(S), the heat Hardy space HH1(S) and the Poisson–Hardy space HP1(S) are mutually distinct spaces, a fact which is in sharp contrast to the Euclidean case, where these three spaces agree.

In controlled environment agriculture spaces, the conditions fluctuate between photoperiod and dark period, with crops growing continuously. As crops grow, their impact on the energy demand and energy use, often estimated using a building performance simulation tool, becomes more prominent. In this paper, a dynamic crop model integrated into a building performance simulation tool is proposed to estimate the yield and heat gain/loss from crops by combining a growth model and an energy balance model of the crops. The developed growth model is an adjusted version of a greenhouse lettuce growth model modified for high-density controlled environment agriculture applications by calibrating the sensitive parameters for several indoor environment conditions (temperature, lighting, etc.) using an experimental growth dataset. The yield, the energy demand and the energy use were assessed for a case study modelled in TRNSYS. The results obtained using the greenhouse and developed growth models were compared to those generated with the experimental growth dataset. Depending on the indoor environment conditions, the difference in specific energy use estimated using the experimental growth dataset and the developed model varied between 0.1% and 3.5%, indicating that the model led to an acceptable level of accuracy. The dynamic crop model estimates yield and heat gain/loss from crops for various indoor environment conditions, which are essential for carrying out energy, financial, and environmental analyses.

We study linear PDE with constant coefficients. The constant rank condition on a system of linear PDEs with constant coefficients is often used in the theory of compensated compactness. While this is a purely linear algebraic condition, the nonlinear algebra concept of primary decomposition is another important tool for studying such system of PDEs. In this paper we investigate the connection between these two concepts. From the nonlinear analysis point of view, we make some progress in the study of weak lower semicontinuity of integral functionals defined on sequences of PDE constrained fields, when the PDEs do not have constant rank.

Machine Learning (ML) has widely been used for modeling and predicting physical systems. These techniques offer high expressive power and good generalizability for interpolation within observed data sets. However, the disadvantage of black-box models is that they underperform under blind conditions since no physical knowledge is incorporated. Physics-based ML aims to address this problem by retaining the mathematical flexibility of ML techniques while incorporating physics. In accord, this paper proposes to embed mechanics-based models into the mean function of a Gaussian Process (GP) model and characterize potential discrepancies through kernel machines. A specific class of kernel function is promoted, which has a connection with the gradient of the physics-based model with respect to the input and parameters and shares similarity with the exact Auto-covariance function of linear dynamical systems. The spectral properties of the kernel function enable considering dominant periodic processes originating from physics misspecification. Nevertheless, the stationarity of the kernel function is a difficult hurdle in the sequential processing of long data sets, resolved through hierarchical Bayesian techniques. This implementation is also advantageous to mitigate computational costs, alleviating the scalability of GPs when dealing with sequential data. Using numerical and experimental examples, potential applications of the proposed method to structural dynamics inverse problems are demonstrated.

In this paper, we incorporate the effect of nonlinear damping with the concept of locally resonant metamaterials to enable vibration attenuation beyond the conventional bandgap range. The proposed design combines a linear host cantilever beam and periodically distributed inertia amplifiers as nonlinear local resonators. The geometric nonlinearity induced by the inertia amplifiers causes an amplitude-dependent nonlinear damping effect. Through the implementation of both modal superposition and numerical harmonic methods with Alternating Frequency Time and numerical continuation techniques, the finite nonlinear metamaterial is accurately modeled. The resulting nonlinear frequency response reveals the bandgap is both amplitude-dependent and broadened. Furthermore, the nonlinear interaction between the local resonators and the mode shapes of the host beam is discussed, which leads to efficient modal frequency dissipation ability. The theoretical results are validated experimentally. By embedding the nonlinear damping effect into locally resonant metamaterials, wideband and shock wave attenuation of the proposed metamaterial is achieved, which opens new possibilities for versatile metamaterials beyond the conventional bandgap ranges of their linear counterparts. © 2023 The Author(s)

Decentralized finance (DeFi), which executes financial transactions using blockchain without an intermediary, has attracted over US$250 billion in total value locked (TVL) at its peak. However, little is known about how DeFi protocols assure users of the safety of their investments. This paper provides the first empirical evidence on DeFi audit services that check and verify the smart contracts underlying these protocols. Using data on 316 of the largest protocols, we find that those vetted by more smart contract auditors and by higher quality auditors have higher TVL and that these protocols have higher market capitalization (native token values). Using an event study approach, we document that TVL and token values significantly increase after a protocol's first audit, especially those involving a high-quality auditor. We also find that protocols with more auditors and higher audit quality exhibit a smaller drop in TVL and token values after the collapse of the TerraUSD stablecoin, which reduced aggregate DeFi TVL by almost 65%. Overall, our findings suggest that DeFi users and investors perceive audits as providing assurance regarding the safety of their deposits and investments. © 2023 The Authors

Previous research papers on urban water resources accounting were confined to the perspectives of production and consumption, ignoring the perspective of income. This paper proposes a systems framework to analyze the income, production, and consumption-based water uses and underlying driving forces of a city based on the methods of multi-scale input-output analysis and structural decomposition analysis. A case study is performed for Shanghai as a megacity. The results show that the income, production and consumption-based water uses of Shanghai had decreased from 5.70 billion m3, 10.85 billion m3 and 28.45 billion m3 in 2007 to 2.80 billion m3, 6.20 billion m3 and 24.10 billion m3 in 2017, respectively. Domestic imported primary inputs had emerged as an important virtual water supplier of Shanghai and its share of total supply-side water use had increased from 23.92% in 2007 to 42.95% in 2017. Meanwhile, about 46% and 40% of Shanghai's total consumption-based water use had been imported from other Chinese regions and foreign countries in 2017, respectively. It is revealed that trade played an important role in relieving water use pressure in Shanghai. The factors that had increased the uses of water resources in Shanghai include population, per capita value-added, per capita output, final consumption structure, and per capita final consumption. The factors that had reduced the water uses in Shanghai include technology, value added mix, output structure, value added structure, domestic import, commodity mix, and foreign import. It is suggested that in addition to curbing urban water use from the production side, more targeted water-saving measures should be devised from the supply (e.g., restricting loan to heavy water-consuming enterprises) and consumption sides (e.g., encouraging residents to buy low-water products).

The composition and volume of patients treated in a hospital, i.e., the patient case-mix, directly impacts resource utilisation. Despite advances in technology, existing case-mix planning approaches are mostly manual. In this paper, we report on a solution that was developed in collaboration with the Queensland Children’s Hospital for supporting its case-mix planning using process mining. We investigated (1) How can process mining capabilities be used to inform hospital case-mix planning?, and (2) How can process data be used to assess hospital capacity assessment and inform hospital case-mix planning? The major contributions of this paper include (i) an automated workflow to support both process mining analysis, and capacity assessment, (ii) a process mining analysis designed to detect process performance and variations, and (iii) a novel capacity assessment model based on limiting-resource saturation.

A passive detection method has been proposed in a prior paper to extract key parameters and detect faults using the ambient noise present in water pipeline networks. This paper presents field experiments and data processing results to provide systematic experimental validation of this method. Field experiments were carried out in operational water pipeline networks at the University of Canterbury campus and the Waimakariri District, New Zealand, during which ambient noise was measured by pairs of pressure sensors installed at selected hydrants on pipelines of different materials, network topologies and simulated faults. Auto-correlation and cross-correlation analysis of noise at a single sensor and sensor pairs were carried out to estimate the wave speed and to locate faults in the networks. Data processing results indicate that water usage generating pressure transients are the dominant sources of ambient noise in operational water pipeline networks. This type of ambient noise can also be utilized by the passive detection method to achieve similar wave speed estimation accuracy and fault detection performance as the conventional active pressure wave detection methods. © 2022 Elsevier Ltd

Previous research papers on urban water resources accounting were confined to the perspectives of production and consumption, ignoring the perspective of income. This paper proposes a systems framework to analyze the income, production, and consumption-based water uses and underlying driving forces of a city based on the methods of multi-scale input-output analysis and structural decomposition analysis. A case study is performed for Shanghai as a megacity. The results show that the income, production and consumption-based water uses of Shanghai had decreased from 5.70 billion m3, 10.85 billion m3 and 28.45 billion m3 in 2007 to 2.80 billion m3, 6.20 billion m3 and 24.10 billion m3 in 2017, respectively. Domestic imported primary inputs had emerged as an important virtual water supplier of Shanghai and its share of total supply-side water use had increased from 23.92% in 2007 to 42.95% in 2017. Meanwhile, about 46% and 40% of Shanghai's total consumption-based water use had been imported from other Chinese regions and foreign countries in 2017, respectively. It is revealed that trade played an important role in relieving water use pressure in Shanghai. The factors that had increased the uses of water resources in Shanghai include population, per capita value-added, per capita output, final consumption structure, and per capita final consumption. The factors that had reduced the water uses in Shanghai include technology, value added mix, output structure, value added structure, domestic import, commodity mix, and foreign import. It is suggested that in addition to curbing urban water use from the production side, more targeted water-saving measures should be devised from the supply (e.g., restricting loan to heavy water-consuming enterprises) and consumption sides (e.g., encouraging residents to buy low-water products).

The field of phylogenetics has burgeoned into a great diversity of statistical models, providing researchers with a vast amount of analytical tools for investigating the evolutionary theory. This abundance of theoretical work has the merit that many different aspects of evolution can be investigated using various types of data. However, empiricists may sometimes struggle to find the right model for their needs amid such variety. In particular, some computer programs gather the theory of many different models, published in hundreds of different papers, within the same operational framework. This makes it particularly difficult for users to obtain comprehensive information about the assumptions and structure of various models. Yet, a large part of phylogenetic models are structured in individual modules that can be linked together in the same conceptual framework, akin to some sort of phylogenetic supermodel. In this paper, we propose to browse through the network of phylogenetic models, emphasizing their modular structure, with the purpose to outline the commonalities and differences of individual models. Focusing on probabilistic models, we describe how to go from the model assumptions to the corresponding probability distributions as pedagogically as possible. To achieve this task, we resort heavily on graph theory to represent the probabilistic relationships among parameters and data, and present the models in their most elementary form (i.e. including parameters that are generally marginalized out), which simplifies the mathematics considerably. We concentrate on models designed for species trees, but evoke the link with other types of trees (e.g. gene trees).

The paper deals with the existence of nontrivial solutions for (p,N) equations in RN with critical exponential growth. The main features and novelty of the paper are the (p,N) growth of the elliptic operator, combined with the double lack of compactness. In order to state a multiplicity result, we introduce a tricky step analysis based on the application of a completely new Brézis and Lieb type lemma for exponential nonlinearities.

Global Reporting Initiative (GRI) guidelines have become the most relevant institution in sustainability reporting standards, highly adopted by organizations worldwide. However, much criticism has been raised about these standards’ ability to further sustainable development within organizations and render their sustainability performance more accountable and transparent. Using a case study of Hydro-Québec, an important hydro-electricity provider in Quebec, Canada, and its relationship with the Cree, an Indigenous community, the purpose of this study is to provide theoretical and empirical insights on the subject by showing how GRI guidelines, legitimized and reinforced through their institutionalization, tend to create a limited scope and an incomplete picture of the organization’s sustainability performance reporting. More specifically, this paper highlights two main problems arising from the use of these standards. First, drawing on the ideology of numbers of Chelli and Gendron (2013), this paper examines how GRI technocratic guidelines frame the sustainability reporting discourse, thereby contributing to leaving aspects of organizational sustainability performance in the shadows. Second, drawing on the ontological typology of Descola (2013), this study examines how these guidelines are ontologically driven by a Western view of nature. This contributes to silencing alternative ontologies in organizational sustainability performance reporting.

Affective computing is an emerging multidisciplinary research field that is increasingly drawing the attention of researchers and practitioners in various fields, including artificial intelligence, natural language processing, cognitive and social sciences. Research in affective computing includes areas such as sentiment, emotion, and opinion modelling. The internet is an excellent source of data required for sentiment analysis, such as customer reviews of products, social media, forums, blogs, etc. Most of these data, called big data, are unstructured and unorganized. Hence there is a strong demand for developing suitable data processing techniques to process these rich and valuable data to produce useful information. Early surveys on sentiment and emotion recognition in the literature have been limited to discussions using text, audio, and visual modalities. So far, to the author's knowledge, a comprehensive survey combining physiological modalities with these other modalities for affective computing has yet to be reported. The objective of this paper is to fill the gap in this surveyed area. The usage of physiological modalities for affective computing brings several benefits in that the signals can be used in different environmental conditions, more robust systems can be constructed in combination with other modalities, and it has increased anti-spoofing characteristics. The paper includes extensive reviews on different frameworks and categories for state-of-the-art techniques, critical analysis of their performances, and discussions of their applications, trends and future directions to serve as guidelines for readers towards this emerging research area.

Global Reporting Initiative (GRI) guidelines have become the most relevant institution in sustainability reporting standards, highly adopted by organizations worldwide. However, much criticism has been raised about these standards’ ability to further sustainable development within organizations and render their sustainability performance more accountable and transparent. Using a case study of Hydro-Québec, an important hydro-electricity provider in Quebec, Canada, and its relationship with the Cree, an Indigenous community, the purpose of this study is to provide theoretical and empirical insights on the subject by showing how GRI guidelines, legitimized and reinforced through their institutionalization, tend to create a limited scope and an incomplete picture of the organization’s sustainability performance reporting. More specifically, this paper highlights two main problems arising from the use of these standards. First, drawing on the ideology of numbers of Chelli and Gendron (2013), this paper examines how GRI technocratic guidelines frame the sustainability reporting discourse, thereby contributing to leaving aspects of organizational sustainability performance in the shadows. Second, drawing on the ontological typology of Descola (2013), this study examines how these guidelines are ontologically driven by a Western view of nature. This contributes to silencing alternative ontologies in organizational sustainability performance reporting.

Physical protection systems (PPS) play an important role in safeguarding critical infrastructures against malevolent attacks. A PPS is a comprehensive solution that integrates people, procedures, and tools to provide effective protection. Developing an effective PPS is a complex process that requires consideration of various factors. Although PPSs may differ, they all share a similar structure and set of features that can be developed through a systematic approach to large-scale reuse. One such approach is product line engineering (PLE), which has been successfully used in various application domains to shorten the time to market, reduce costs, and enhance overall system quality. This paper provides an overview of the PPS domain, with a specific focus on its importance for critical infrastructures. We describe the three different types of PLE methods, including single, multiple, and feature-based PLE, and present a method for selecting the appropriate PLE method. To illustrate the practical application of the method, we apply it to a real industrial case study for a large-scale systems company.

The paper deals with simple and composite hypotheses in statistical models with i.i.d. observations and with arbitrary families dominated by a finite measures and parametrized by vector-valued variables. It introduces phi-divergence testing statistics as alternatives to the classical ones: the generalized likelihood ratio and the statistics of Wald and Rao. It is shown that, under the assumptions of standard type about hypotheses and model densities, the results about asymptotic distribution of the classical statistics established so far for the counting and Lebesgue dominating measures (discrete and continuous models) remain true also in the general case. Further, these results are extended to the phi-divergence statistics with smooth convex functions phi. The choice of phi-divergence statistics optimal from the point of view of power is discussed and illustrated by several examples., UCM, DGICYT, GA, Depto. de Estadística e Investigación Operativa, Fac. de Ciencias Matemáticas, TRUE, pub

The paper presents asymptotic distributions of phi-disparity goodness-of-fit statistics in product multinomial models, under hypotheses and alternatives assuming sparse and nonsparse cell frequencies. The phi-disparity statistics include the power divergences of Read and Cressie (Goodness-of-fit Statistics for Discrete Multivariate Data, Springer, New York, 1988), the phi-divergences of Ciszar (Studia Sci. Math. Flungar. 2 (1967) 299) and the robust goodness of fit statistics of Lindsay (Ann. Statist. 22 (1994) 1081)., Depto. de Estadística e Investigación Operativa, Fac. de Ciencias Matemáticas, TRUE, pub

Let k be a real closed field. A real AP-curve (over k) is a 1-dimensional, excellent Henselian local real domain with residue field k. A 1-dimensional Noetherian local ring is Arf, if emb dim(B)=mult(B) for every local ring B infinitely near to A [ J. Lipman , Amer. J. Math. 93 (1971), 649–685]. For n≥1, the 2nth Pythagoras number p2n of a commutative ring A is the least p, 1≤p≤+∞, such that any sum of 2nth powers in A is a sum of no more than p2nth powers in A. A main purpose of this paper is to affirm the following conjectures proposed by Ruiz [J. Algebra 94 (1985), no. 1, 126–144]: Let A be a real AP-curve, and let A be Pythagorean (i.e., p2=1). Then (i) A is Arf. (ii) Every local ring infinitely near to A is Pythagorean. Actually, the authors obtain a finer result: For a real AP-curve A, the following assertions are equivalent: (1) A is Arf; (2) A is Pythagorean; (3) p2n=1 for some n; (4) p2n=1 for all n. Here, (2)(1) is exactly Conjecture (i) and (1)(2) reduces Conjecture (ii) to the obvious fact that, if A is Arf, every local ring infinitely near to A is Arf too. Of course, the result contains some additional insight into the study of Pythagoras's numbers, even of higher order, of real curve germs., CICYT, Depto. de Álgebra, Geometría y Topología, Fac. de Ciencias Matemáticas, TRUE, pub

In this paper we extend the classical notion of offset to the concept of generalized offset to hypersurfaces. In addition, we present a complete theoretical analysis of the rationality and unirationality of generalized offsets. Characterizations for deciding whether the generalized offset to a hypersurface is parametric or it has two parametric components are given. As an application, an algorithm to analyse the rationality of the components of the generalized offset to a plane curve or to a surface, and to compute rational parametrizations of its rational components, is outlined., DGICYT, Universidad de Alcalá, DGICYT, Depto. de Álgebra, Geometría y Topología, Fac. de Ciencias Matemáticas, TRUE, pub

In this paper, we propose a compositional approach to constructing correct formal models of information systems from correct models of interacting components. Component behavior is represented using workflow nets — a class of Petri nets. Interactions among components are encoded in an additional interface net. The proposed approach is used to model and compose synchronously and asynchronously interacting workflow nets. Using Petri net morphisms and their properties, we prove that the composition of interacting workflow nets preserves the correctness of components and of an interface.

The vibrational energy, often considered a negative factor, is abundant in everyday life. Especially in railway systems, the negatively impacted track vibrations resulting from moving trains can be captured to provide a practical power supply solution for wireless sensor networks. This paper proposed a variable damping vibration energy harvester with a half-wave flywheel for a freight train-based railway. A double-sided rack as the input member converts the track vibration into the opposite rotation of the two pinions, which are then transmitted to the two parallel shafts respectively. According to the work characteristics of the one-way bearing, the upper and lower vibrations can be collected separately and output a one-way rotation to the generator module. The proposed harvester with a half-wave flywheel features a larger damping force for vibration reduction during the downward track vibration and a smaller damping force conducive to returning the track's original state during the upward track vibration. The experimental results achieve a maximum output power of 10.247 W and a maximum mechanical efficiency of 74.49%. Both simulations and experiments have verified that the proposed system with a half-wave flywheel can increase the damping force in the vibration reduction process and reduce the damping force in the reset process, which is characteristic of improving its power generation performance with a good vibration reduction effect. The VEH with the half-wave flywheel achieved an average power of 5.321 W at the train speed of 90 km/h under random vibration testing, which verifies the feasibility of self-powered wireless sensor networks in railway environments.

The ambient noise in water pipelines are observed as spontaneous signal sources that can be used for pipe fault detection by correlation analysis. However, the limited bandwidth of these noise signal causes strong correlation sidelobes, which introduces significant ambiguities when extracting the system response from correlation results and this increases the risk of false alarms from fault detections. This paper proposes a compressive sensing based method that can extend the noise bandwidth and suppress the correlation sidelobes. Numerical and field experiment results have shown that with the recovered wider bandwidth, the correlation sidelobes can be significantly suppressed and the pipe faults can be identified with greater certainty. The impacts of fault size as well as noise bandwidth, strength and spectrum features on the proposed method are also assessed through numerical experiments. © 2023 Elsevier Ltd