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This paper assumes that tourism educated and trained students play different roles (in driving future tourist demands and in meeting current tourist preferences, respectively), and it states that the main features characterising the four stakeholders involved in the design, development and implementation of tourism programmes (firms, students, educational and governmental institutions), together with the main facts they face in taking their decisions, lead to a non-optimal strategic long-run equilibrium, where tourism non-graduated or differently-from-tourism graduated employees prevail. The development of an evolutionary model allows to identify the main features characterising firms and students, to be focused on by educational and governmental institutions, in order to move towards the optimal equilibrium, where tourism graduated employees prevail, while the development of a dynamic model allows to show that this equilibrium is not detrimental to tourism trained employees. This work also suggests a possible educational strategy that could allow to move away from the non-optimal equilibrium, by achieving public objectives (such as environmental or ethical tourism), by relying on feasible educational approaches (about what and how to teach), and by taking into account the private characteristics (of firms and students). Therefore, balancing tourism education and training is both possible and beneficial to all stakeholders involved.

The new identifiable case appeared in \cite{AGMO}, together with the analysis on simultaneous identifiability of pairs of ternary forms recently developed in \cite{BG}, suggested the following conjecture towards a complete classification of all simultaneous Waring identifiable cases: for any $ d \geq 2 $, the general polynomial vectors consisting of $ d-1 $ ternary forms of degree $ d $ and a ternary form of degree $ d+1 $, with rank $ \frac{d^2+d+2}{2} $, are identifiable over $ \mathbf{C} $. In this paper, by means of a computer-aided procedure inspired to the one described in \cite{AGMO}, we obtain that the case $ d = 4 $ contradicts the previous conjecture, admitting at least $ 36 $ complex simultaneous Waring decompositions (of length $ 11 $) instead of $ 1 $., Comment: 10 pages, accepted for publication on Journal of Symbolic Computation

Use of pharmaceutical products has seen a tremendous increase in the recent decades. It has been observed that more than thirty million tons of pharmaceuticals are consumed worldwide. The used pharmaceutical products are not completely metabolized in human and animal body. Therefore, they are excreted to the environment and remain there as persistent organic chemicals. These compounds emerge as toxic contaminants in water and affect the human metabolism directly or indirectly. This literature review is an endeavour to understand the origin, applications and current advancement in the removal of pharmaceuticals from the environment. It discusses about the pharmaceuticals used in medical applications such diagnosis and disease treatment. In addition, it discusses about the recent approaches applied in pharmaceutical removal including microbial fuel cells, biofiltration, and bio nanotechnology approaches. Moreover, the challenges associated with pharmaceutical removal are presented considering biological and environmental factors. The review suggest the potential recommendations on pharmaceutical removal., The corresponding author Prof. Vinay Kumar is thankful to all the co-authors for their collaborative efforts in writing this paper. This work was supported by Department of Community Medicine, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India.

Conversations on how to assess, innovate, and develop policies for carbon removal are for now largely confined to the Global North – reflecting a concentration of academic interest (and concern), innovation capacity, early funding initiatives, and policy path-dependence in climate, energy, and land-use. However, future population growth, emissions trajectories, and even concentrations of economic (and technological power) are shifting to the Global South. Here, after explaining the positionality of the author, this paper summarizes the perspectives and concerns of 90 key academics, technologists, and policy entrepreneurs on expanding carbon removal assessment, innovation, and policy beyond early foci within (northern) Europe, the US, Japan, and Australia. It explores how concerns about systems (coupling and infrastructure deployment), justice (equity and inclusion), and governance (including pledges, funding, and offsets) markedly differ across Global North and Global South dynamics. It discusses how such issues intersect with each other, and concludes with insights for research and policy.

The irradiation variations caused by cloud changes can cause rapid power fluctuations in large photovoltaic (PV) plants. The increased PV power share of the grid adversely affects the quality of power and the reliability of the power supply. Energy storage systems (ESSs)are often used to mitigate power fluctuations in the grid through various control algorithms. These algorithms create an ESS power reference that opposes the variations of the PV and reduces them to an acceptable value. Despite their everyday use, there have been few performance comparisons among the various methods, especially battery status, and the battery can operate in several systems with various smoothing methods. The paper aims to analyze the ramp-rate and step-rate control methods for smoothing solar PV fluctuations based on the irradiation profiles in a DC microgrid (MG) environment. The battery in the system operates as a continuous energy application, and a supercapacitor (SC) is used to remove transients in the battery system. The combined battery-SC coordinated system improves the performance of the DC microgrid system. The battery cycle analysis and depth are conducted using the well-established Coulomb counting technique. MATLAB/Simulink validates the simulation results, and the OP-5700 HIL test bench conducts real-time results. 2022 The Author(s) This work is supported by the Department of Science and Technology (DST), Government of India (GOI), with the project grant SR/FST/ETI-420/2016(C) under the FIST scheme. Open Access funding provided by the Qatar National Library. Scopus

The 2018/844 Energy Performance of Buildings Directive (EU) has widened the scope of appropriate design of buildings from a pure energy performance and carbon emissions perspective to a wider scope that includes indoor comfort, and indoor air quality among others. To this effect, external parameters, especially solar energy, have a strong impact on the energy performance of buildings in Mediterranean regions, which requires careful consideration when it comes to benefiting from natural lighting while avoiding solar overheating. This paper addresses the considerations of natural lighting in the deep renovation of a housing block in the Mediterranean climate of the Republic of Malta, comparing some of the usual illuminance ranges to achieve optimal conditions based on international recommendations. DesignBuilder v7.0.0.102 has been the selected software to model the building that has been calibrated through experimental measurements. The model enabled the natural lighting conditions in the building evaluated and the effectiveness of suggested improvements to be determined. Results pointed out that the building under study satisfies the international standards about the prevention of visual discomfort only. Increasing the size of windows in identified zones, especially the first floor, was found to help improve other natural lighting characteristics. One of the proposed designs (Model 6) that replaces single-glazed with double-glazed windows that include an external spectrally-selective coating would significantly improve access to natural light bringing the building closer to the recommended levels of Annual Sunlight Exposure and reducing artificial lighting usage by up to five times. The relocation of room spaces could also reduce the use of artificial lighting., peer-reviewed

The voltage source converter (VSC) based HVDC (high voltage direct current system) offers the possibility to integrate other renewable energy sources (RES) into the electrical grid, and allows power flow reversal capability. These appealing features of VSC technology led to the further development of multi-terminal direct current (MTDC) systems. MTDC grids provide the possibility of interconnection between conventional power systems and other large-scale offshore sources like wind and solar systems. The modular multilevel converter (MMC) has become a popular technology in the development of the VSC-MTDC system due to its salient features such as modularity and scalability. Although, the employment of MMC converter in the MTDC system improves the overall system performance. However, there are some technical challenges related to its operation, control, modeling and protection that need to be addressed. This paper mainly provides a comprehensive review and investigation of the control and protection of the MMC-based MTDC system. In addition, the issues and challenges associated with the development of the MMC-MTDC system have been discussed in this paper. It majorly covers the control schemes that provide the AC system support and state-of-the-art relaying algorithm/ dc fault detection and location algorithms. Different types of dc fault detection and location algorithms presented in the literature have been reviewed, such as local measurement-based, communication-based, traveling wave-based and artificial intelligence-based. Characteristics of the protection techniques are compared and analyzed in terms of various scenarios such as implementation in CBs, system configuration, selectivity, and robustness. Finally, future challenges and issues regarding the development of the MTDC system have been discussed in detail.

Refereed/Peer-reviewed Utilization of bottom-of-the-barrel petroleum products like heavy fuel oils (HFO) derived from vacuum residue oils (VRO) is essential for the sustainable use of crude oil. The high sulfur content and asphaltenes with metallic impurities of these products, such as vanadium and nickel, lead to the production of criteria pollutants and inorganic ash upon combustion, which has limited their widespread use in many industries and jurisdictions. Industrially mature thermo-chemical conversion technologies (e.g., cracking, pyrolysis, partial oxidation (POx), and gasification) have been utilized to upgrade and convert oil residues like HFO into cleaner fuels such as syngas and hydrogen. Among these processes, commercial POx and gasification typically proceed via catalyst free-operations. However, the novel variants of gasification processes that can improve the utilization of heavy oil residues can be benefited from using catalysts. That is because not only can catalysts facilitate the reactions within a smaller reactor, but also they can provide better control over the product composition. Nevertheless, the typical characteristics of high carbon, impurities, and sulfur content lead to the deactivation of the solid-state catalysts mainly due to the coking and poisoning. The use of molten salts, metal/metal oxide as catalysts or oxygen carriers, and hydrogen peroxide as a gasifying agent in the gasification of heavy hydrocarbons are identified to offer significant potential to overcome these challenges, albeit introducing alternative difficulties. This paper surveys and briefly discusses the state-of-the-art thermo- and catalytic chemical conversion technologies for petroleum residue derived fuels, focusing on the use of molten catalysts, oxygen carriers, and hydrogen peroxide as oxidizing agents. The paper also briefly reviews methane pyrolysis, dry reforming with molten metal catalysts, and liquid chemical looping gasification to find similarities and know-how that can be used for gasifying heavy oil residues with the molten metal/metal oxides being used as either catalyst or reaction medium.

© 2023 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ In the ongoing energy transition, small and medium-sized industrial companies are making energy equipment investments due to the obsolescence of their current equipment as well as social, political and market pressures. These firms typically choose investments with low risk exposure based on a combination of criteria that are not always quantifiable. However, published studies on energy investment to date have not been suitable for industrial SMEs because they do not assess the value of the investment over time, ignore the qualitative aspects of decision-making, and do not consider uncertainties. To fill this gap in the literature, this paper proposes a methodology that considers both quantitative and qualitative parameters and risks over time through an extended two-stage risk-informed approach. The proposed methodology includes fuzzy and statistical techniques for evaluating both qualitative and quantitative parameters, as well as their uncertainties, at the time of decision-making and over the investment lifetime. Fuzzy logic is used in the first stage of the optimisation process to measure qualitative parameters and their uncertainty, while quantitative parameters are expressed using probability density functions to account for their uncertainty and measure the quantitative risk assumed by the investor. This methodology is applied to a case study involving a real industrial SME, and the results show that considering both quantitative and qualitative parameters and uncertainties in the optimisation process leads to a more balanced consideration of economic, environmental and social criteria and reduces the variability of the outcome compared to economic-only approaches that do not account for risks. Specifically, the case study shows that considering these parameters and uncertainties resulted in a 15.7% reduction in the size of the cogeneration system due to its environmental and social impacts, and 4.2% reduction in the variability of the economic result.

Sweden has an ambitious plan to fully decarbonise district heating by 2030 and to contribute with negative emissions of greenhouse gases in 2050. The vagaries of the energy market associated with climate, political, and social changes entail cross-sectoral integration that can fulfill these national targets. Fifth-generation district heating and cooling (5GDHC) is a relatively new concept of district energy systems that features a simultaneous supply of heating and cooling using power-to-heat technologies. This paper presents best practices for developing 5GDHC systems in Sweden to reach a consensus view on these systems among all stakeholders. A mixed-method combining best practice and roadmapping workshops has been used to disseminate mixed knowledge and experience from middle agents representing industry professionals and practitioners. Four successful implementations of 5GDHC systems are demonstrated and the important learned lessons are shared. The best practices are outlined for system planning, system modeling and simulation, prevailing business models for energy communities, and system monitoring. A roadmap from the middle agents’ point of view is composed and can be utilised to establish industry standards and common regulatory frameworks.

Research on the order Saprolegniales (Oomycota) has been an ongoing quest for more than a century. The best studied genera are Saprolegnia and Aphanomyces, known for their pathogenicity on freshwater animals. In this study, we reviewed 1073 papers and 2803 ITS sequences of Saprolegniales to investigate their taxonomy, diversity and potential roles in mainly freshwater ecosytems. We found that, in general, our knowledge on diversity and ecology of Saprolegniales is limited. Neither classic taxonomy nor available molecular techniques have been sufficient to delineate genera and species and show their relative distribution in freshwater-associated habitats. Also, we currently lack a comprehensive understanding of their involvement in carbon turnover and food web dynamics. Finally, due to lack of using high-throughput sequencing techniques, it is not clear how and to what extent communities of Saprolegniales might differ in freshwater econiches. Therefore, we provide a historical perspective on the establishment of Saprolegniales, explain improvements, highlight deficiencies, and finally propose new research avenues for more systematic studies. We conclude that challenges in studying Saprolegniales can be removed by increasing the practicality of classic taxonomy and applying available molecular toolboxes (multi-gene phylogeny and high-throughput sequencing). Additionally, inclusion of Saprolegniales in freshwater carbon cycling should be addressed for their better ecological resolution.

By analogy with complex $K$--theory and $K$--theory with reality, there are theories of unitary functor calculus and unitary functor calculus with reality, both of which are generalisations of Weiss' orthogonal calculus. In this paper we show that unitary functor calculus can be completely recovered from the unitary functor calculus with reality, in analogy to how complex topological $K$--theory is completely recovered from $K$--theory with reality via forgetting the $C_2$--action., v2: Updated title and references. v3: accepted version

Objective: Shared decision making (SDM) and use of patient decision aids (PtDAs) are key components in patient-centered care in relapsed ovarian cancer. This paper describes the development and implementation process of PtDAs into a clinical routine in three departments. Methods: Two PtDAs were developed in collaboration between patients and clinicians. Acceptability and usability of the PtDAs were tested on clinicians and patients using items from the internationally validated questionnaire “Preparation for Decision Making Scale”. Results: Ten patients and 15 clinicians participated in the study. Most patients indicated that PtDAs would be helpful as preparation for the decision-making process with the clinicians. Ten (75%) of the clinicians responded that the PtDAs helped the patients to understand the benefits and disadvantages of each treatment option. Generally, the clinicians indicated that they would use SDM if they had a PtDA tailored to the clinical situation. Conclusions: Two PtDAs were systematically developed, tested, and implemented thereby supporting an SDM intervention. The PtDAs are still in use at the participating departments. Innovation: This study was successful in reusing a generic template for a patient decision aid (PtDA) developed at one institution and implemented in two other institutions. This was guided by a well-described systematic development process for PtDAs.

Secondary plant cell walls are composed of carbohydrate and lignin polymers, and collectively represent a significant renewable resource. Leveraging these resources depends in part on a mechanistic understanding for diffusive processes within plant cell walls. Common wood protection treatments and biomass conversion processes to create biorefinery feedstocks feature ion or solvent diffusion within the cell wall. X-ray fluorescence microscopy experiments have determined that ionic diffusion rates are dependent on cell wall hydration as well as the ionic species through non-linear relationships. In this work, we use classical molecular dynamics simulations to map the diffusion behavior of different plant cell wall components (cellulose, hemicellulose, lignin), ions (Na+ , K+ , Cu2+ , Cl- ) and water within a model for an intact plant cell wallat various hydration states (3 - 30 wt.% water). From these simulations, we analyze the contacts between different plant cell wall components with each other and their interaction with the ions. Generally, diffusion increases with increasing hydration, with lignin and hemicellulose components increasing diffusion by an order of magnitude over the tested hydration range. Ion diffusion depends on charge. Positively charged cations preferentially interact with hemicellulose components, which include negatively charged carboxylates. As a result, positive ions diffuse more slowly than negatively charged ions. Measured diffusion coefficients are largely observed to best fit piecewise linear trends, with an inflection point between 10-15% hydration. These observations shed light onto the molecular mechanisms for diffusive processes within secondary plant cell walls at atomic resolution.

The existing supervisory control and data acquisition (SCADA) system continuously collects data from wind turbines (WTs), which provides a basis for condition monitoring (CM) of WTs. However, due to the complexity and high dimension and nonlinearity of data, effective modeling of spatial-temporal correlations among the data still becomes a primary challenge. In this paper, we propose a novel CM approach based on the multidirectional spatial-temporal feature aggregation networks (MSTFAN) to accurately evaluate the performance and hence diagnose the faults of the turbines. Firstly, the data collected from various sensors are formulated into graph-structured data at each timestamp. Spatial-temporal network constructed by combing a graph attention network (GAT) and a temporal convolutional network (TCN) is used to extract spatial-temporal features of the data. Then, a bi-directional long short-term memory (BiLSTM) neural network is adopted to further study long-term spatial-temporal dependency of the extracted features. Finally, the condition score is obtained and the streaming peaks over threshold (SPOT) is applied to determine the abnormal threshold for early warning of the fault occurrence. Experiments on datasets from real-world wind farms demonstrate that the proposed approach can detect the early abnormal situation of the WTs, and outperform other established methods.

This paper investigates the distribution problem of the COVID-19 vaccine at the provincial level in Turkey and the management of medical waste, considering the cold chain requirements and the perishable nature of vac-cines. In this context, a novel multi-period multi-objective mixed-integer linear programming model is initially presented over a 12-month planning horizon for solving the deterministic distribution problem. The model in-cludes newly structured constraints due to the feature of COVID-19 vaccines, which must be administered in two doses at specified intervals. Then, the presented model is tested for the province of Izmir with deterministic data, and the results show that the demand can be satisfied and community immunity can be achieved in the specified planning horizon. Moreover, for the first time, a robust model is created using polyhedral uncertainty sets to manage uncertainties related to supply and demand quantities, storage capacity, and deterioration rate, and it has been analyzed under different uncertainty levels. Accordingly, as the level of uncertainty increases, the percentage of meeting the demand gradually decreases. It is observed that the biggest effect here is the uncer-tainty in supply, and in the worst case, approximately 30% of the demand cannot be met.

In this study, the dynamic stability of plate with the active-damping layer under a thrust is investigated. The structure is actively damped by piezoelectric layers, and adopted a control algorithm for vibration suppression. Thin piezoelectric layers are assumed to be embedded on the top and the bottom surfaces of the structure. The top and the bottom layers are taken as the actuator and the sensor, respectively. The structure model is based on the first-order shear deformation plate theory, and the finite element method is applied in the numerical analysis. In addition, the method of multiple scales is adopted to analyze the parametrically excited system. This analysis is specifically focused on the mutual interaction of the thrust and the piezoelectric active-damping layer for the vibration suppression. In this paper, the effects of the active-damping layer with piezoelectric sensors/actuators are discussed in detail.

In this paper, we propose the tensor Noda iteration (NI) and its inexact version for solving the eigenvalue problem of a particular class of tensor pairs called generalized $\mathcal{M}$-tensor pairs. A generalized $\mathcal{M}$-tensor pair consists of a weakly irreducible nonnegative tensor and a nonsingular $\mathcal{M}$-tensor within a linear combination. It is shown that any generalized $\mathcal{M}$-tensor pair admits a unique positive generalized eigenvalue with a positive eigenvector. A modified tensor Noda iteration(MTNI) is developed for extending the Noda iteration for nonnegative matrix eigenproblems. In addition, the inexact generalized tensor Noda iteration method (IGTNI) and the generalized Newton-Noda iteration method (GNNI) are also introduced for more efficient implementations and faster convergence. Under a mild assumption on the initial values, the convergence of these algorithms is guaranteed. The efficiency of these algorithms is illustrated by numerical experiments., 45 pages, 6 figures

Given access to the hypergraph through a subset query oracle in the query model, we give sublinear time algorithms for Hitting-Set with almost tight parameterized query complexity. In parameterized query complexity, we estimate the number of queries to the oracle based on the parameter $k$, the size of the Hitting-Set. The subset query oracle we use in this paper is called Generalized $d$-partite Independent Set query oracle (GPIS) and it was introduced by Bishnu et al. (ISAAC'18). GPIS is a generalization to hypergraphs of the Bipartite Independent Set query oracle (BIS) introduced by Beame et al. (ITCS'18 and TALG'20) for estimating the number of edges in graphs. Formally, GPIS is defined as follows: GPIS oracle for a $d$-uniform hypergraph $\mathcal{H}$ takes as input $d$ pairwise disjoint non-empty subsets $A_1, \ldots, A_d$ of vertices in $\cal H$ and answers whether there is a hyperedge in $\mathcal{H}$ that intersects each set $A_i$, where $i \in \{1, \, 2, \, \ldots, d\}$. } For $d=2$, the GPIS oracle is nothing but BIS oracle. We show that $d$-Hitting-Set, the hitting set problem for $d$-uniform hypergraphs, can be solved using $\widetilde{\mathcal{O}}_d(k^{d} \log n)$ GPIS queries. Additionally, we also showed that $d$-Decesion-Hitting-Set, the decision version of $d$-Hitting-Set can be solved with $\widetilde{\mathcal{O}}_d\left( \min \left\{ k^d\log n, k^{2d^2} \right\} \right)$ {\sc GPIS} queries. We complement these parameterized upper bounds with an almost matching parameterized lower bound that states that any algorithm that solves $d$-Decesion-Hitting-Set requires $\Omega \left( \binom{k+d}{d} \right)$ GPIS queries., Comment: 22 pages. A preliminary version has appeared in ISAAC'19 and the full version has been accepted in JCSS

The $q$-Onsager algebra $O_q$ is defined by two generators and two relations, called the $q$-Dolan/Grady relations. We investigate the alternating central extension $\mathcal O_q$ of $O_q$. The algebra $\mathcal O_q$ was introduced by Baseilhac and Koizumi, who called it the current algebra of $O_q$. Recently Baseilhac and Shigechi gave a presentation of $\mathcal O_q$ by generators and relations. The presentation is attractive, but the multitude of generators and relations makes the presentation unwieldy. In this paper we obtain a presentation of $\mathcal O_q$ that involves a subset of the original set of generators and a very manageable set of relations. We call this presentation the compact presentation of $\mathcal O_q$. This presentation resembles the compact presentation of the alternating central extension for the positive part of $U_q(\widehat{\mathfrak{sl}}_2)$., Comment: 26 pages. arXiv admin note: text overlap with arXiv:2103.03028

The Auslander-Reiten conjecture is a notorious open problem about the vanishing of Ext modules. In a Cohen-Macaulay complete local ring $R$ with a parameter ideal $Q$, the Auslander-Reiten conjecture holds for $R$ if and only if it holds for the residue ring $R/Q$. In the former part of this paper, we study the Auslander-Reiten conjecture for the ring $R/Q^\ell$ in connection with that for $R$, and prove the equivalence of them for the case where $R$ is Gorenstein and $\ell\le \dim R$. In the latter part, we generalize the result of the minimal multiplicity by J. Sally. Due to these two of our results, we see that the Auslander-Reiten conjecture holds if there exists an Ulrich ideal whose residue ring is a complete intersection. We also explore the Auslander-Reiten conjecture for determinantal rings., Comment: 19 pages. Errors are corrected

Objective: Mobile applications have been used frequently in post-operative orthopedic rehabilitation in recent years. However, no systematic review has emphasized the importance of mobile applications in the rehabilitation process after total knee arthroplasty (TKA). This systematic review and meta-analysis aimed to evaluate the effectiveness of mobile application-based rehabilitation practices in patients with TKA. Material and Methods: PubMed, Web-of-Science, Scopus, ScienceDirect and Cochrane databases were searched. The Physiotherapy Evidence Database (PEDro) and the Revised Cochrane risk-of-bias tool randomized trials 2 (RoB2) tools were used to demonstrate the methodological quality and risk of bias. Results: A total of 584 articles were screened. Finally, six papers were included in the systematic review. PEDro scores ranged from 4 to 7 (median: 5.5), indicating fair to good methodological quality. All studies were classified as \"some concerns\" in RoB2. Mobile application-based rehabilitation demonstrated better scores on pain, range of motion (ROM), objective and subjective function, satisfaction and compliance in general. Meta-analysis proved that mobile application-based telerehabilitation demonstrated better results on subjective function (ES:0.57, 95% CI: 0.11–1.02). Conclusion: Compared to conventional rehabilitation, application-based telerehabilitation provides more effective results in function, pain and ROM. Furthermore, mobile application-based rehabilitation should also be considered regarding patient satisfaction and compliance.

In this paper, we derive a recurrence relation of anti-forcing polynomial for catacondensed hexagonal systems.

With the growing concern about the security and privacy of smart grid systems, cyberattacks on critical power grid components, such as state estimation, have proven to be one of the top-priority cyber-related issues and have received significant attention in recent years. However, cyberattack detection in smart grids now faces new challenges, including privacy preservation and decentralized power zones with strategic data owners. To address these technical bottlenecks, this paper proposes a novel Federated Learning-based privacy-preserving and communication-efficient attack detection framework, known as FedDiSC, that enables Discrimination between power System disturbances and Cyberattacks. Specifically, we first propose a Federated Learning approach to enable Supervisory Control and Data Acquisition subsystems of decentralized power grid zones to collaboratively train an attack detection model without sharing sensitive power related data. Secondly, we put forward a representation learning-based Deep Auto-Encoder network to accurately detect power system and cybersecurity anomalies. Lastly, to adapt our proposed framework to the timeliness of real-world cyberattack detection in SGs, we leverage the use of a gradient privacy-preserving quantization scheme known as DP-SIGNSGD to improve its communication efficiency. Extensive simulations of the proposed framework on publicly available Industrial Control Systems datasets demonstrate that the proposed framework can achieve superior detection accuracy while preserving the privacy of sensitive power grid related information. Furthermore, we find that the gradient quantization scheme utilized improves communication efficiency by 40% when compared to a traditional federated learning approach without gradient quantization which suggests suitability in a real-world scenario.

This paper considers the problem of finding near-optimal Markovian randomized (MR) policies for finite-state-action, infinite-horizon, constrained risk-sensitive Markov decision processes (CRSMDPs). Constraints are in the form of standard expected discounted cost functions as well as expected risk-sensitive discounted cost functions over finite and infinite horizons. The main contribution is to show that the problem possesses a solution if it is feasible, and to provide two methods for finding an approximate solution in the form of an ultimately stationary (US) MR policy. The latter is achieved through two approximating finite-horizon CRSMDPs which are constructed from the original CRSMDP by time-truncating the original objective and constraint cost functions, and suitably perturbing the constraint upper bounds. The first approximation gives a US policy which is $\epsilon$-optimal and feasible for the original problem, while the second approximation gives a near-optimal US policy whose violation of the original constraints is bounded above by a specified $\epsilon$. A key step in the proofs is an appropriate choice of a metric that makes the set of infinite-horizon MR policies and the feasible regions of the three CRSMDPs compact, and the objective and constraint functions continuous. A linear-programming-based formulation for solving the approximating finite-horizon CRSMDPs is also given., Comment: 38 pages

In dial-a-ride systems involving autonomous vehicles circulating along a circuit, a fleet of vehicles must serve clients who request rides between stations of the circuit such that the total number of pickup and drop-off operations is minimized. In this paper, we focus on a unitary variant where each client requests a single place in the vehicles and all the clients must be served within a single tour of the circuit. Such unitary variant induces a combinatorial optimization problem for which we introduce a nontrivial special case that is polynomially solvable when the capacity of each vehicle is at most 2 but it is NP-Hard otherwise. We also study the polytope associated with the solutions to this problem. We introduce new families of valid inequalities and give necessary and sufficient conditions under which they are facet-defining. Based on these inequalities, we devise an efficient branch-and-cut algorithm that outperforms the state-of-the-art commercial solvers.

In this paper, we consider the contextual variant of the MNL-Bandit problem. More specifically, we consider a dynamic set optimization problem, where a decision-maker offers a subset (assortment) of products to a consumer and observes the response in every round. Consumers purchase products to maximize their utility. We assume that a set of attributes describe the products, and the mean utility of a product is linear in the values of these attributes. We model consumer choice behavior using the widely used Multinomial Logit (MNL) model and consider the decision maker problem of dynamically learning the model parameters while optimizing cumulative revenue over the selling horizon $T$. Though this problem has attracted considerable attention in recent times, many existing methods often involve solving an intractable non-convex optimization problem. Their theoretical performance guarantees depend on a problem-dependent parameter which could be prohibitively large. In particular, existing algorithms for this problem have regret bounded by $O(\sqrt{\kappa d T})$, where $\kappa$ is a problem-dependent constant that can have an exponential dependency on the number of attributes. In this paper, we propose an optimistic algorithm and show that the regret is bounded by $O(\sqrt{dT} + \kappa)$, significantly improving the performance over existing methods. Further, we propose a convex relaxation of the optimization step, which allows for tractable decision-making while retaining the favourable regret guarantee., Comment: Accepted to be published at Elsevier European Journal of Operational Research (EJOR)

Machine learning (ML) models have been quite successful in predicting outcomes in many applications. However, in some cases, domain experts might have a judgment about the expected outcome that might conflict with the prediction of ML models. One main reason for this is that the training data might not be totally representative of the population. In this paper, we present a novel framework that aims at leveraging experts' judgment to mitigate the conflict. The underlying idea behind our framework is that we first determine, using a generative adversarial network, the degree of representation of an unlabeled data point in the training data. Then, based on such degree, we correct the \textcolor{black}{machine learning} model's prediction by incorporating the experts' judgment into it, where the higher that aforementioned degree of representation, the less the weight we put on the expert intuition that we add to our corrected output, and vice-versa. We perform multiple numerical experiments on synthetic data as well as two real-world case studies (one from the IT services industry and the other from the financial industry). All results show the effectiveness of our framework; it yields much higher closeness to the experts' judgment with minimal sacrifice in the prediction accuracy, when compared to multiple baseline methods. We also develop a new evaluation metric that combines prediction accuracy with the closeness to experts' judgment. Our framework yields statistically significant results when evaluated on that metric., Comment: Accepted to Expert Systems with Applications Journal, 2023

The impedance-based analysis has been extensively used for frequency domain stability analysis in power systems with power electronics devices. Also, such analysis is used to determine the oscillatory mode frequencies. The impedance ratio analysis is a conventional approach employed to determine both stability conditions and oscillatory mode frequencies. In particular, oscillatory mode frequencies can be approximated from the gain and phase crossover of the impedance ratio. Such an assumption has been considered in the harmonic frequency range, but in the low frequency range, approximately under 100 Hz, this might not be valid. Low frequency interactions are mainly caused by voltage source converter outer loops and synchronous generator circuits and controls. This paper presents an alternative impedance-based analysis to identify mode frequencies and the participation of impedance components to the interactions. This method is compared to the approximation from the gain and phase crossover of the impedance ratio. Also, the simplification of synchronous generators as Thévenin equivalents is explored for low frequencies based on the impedance frequency response. An essential power system model with a voltage source converter, a synchronous generator, and a load is presented to show the advantages of this alternative impedance-based analysis compared to the gain and phase crossover of the impedance

International audience; The benefits of small-scale renewable energy resources are restricted by a feed-in-tariff strategy that works with distribution grids only. Virtual Power Plant (VPP) aggregates diverse distributed energy resources on the demand side for efficient control, which can improve their revenue and encourage the growth of renewable energy. Based on the general Nash bargaining theory, this paper proposes a multi-VPP energy-sharing mechanism. This approach enables VPPs to increase their revenue by engaging in peer-topeer energy transactions. First, a general Nash bargaining-based model is proposed for multi-VPPs energy sharing considering peer-to-peer energy transactions. The model is then decomposed into two subproblems: minimizing the total cost of the VPP energy-sharing alliance, and the cost allocation of peer-to-peer energy transactions. Considering the privacy protection of VPPs, the first subproblem is solved by a distributed algorithm based on the alternating direction multiplier method. In the second subproblem, due to the introduction of a bargaining factor that is positively related to each VPP's contribution to energy sharing alliance, the cost allocation better reflects their contribution. The numerical test demonstrates the proposed energy-sharing mechanism, with little computational complexity, delivers a cost distribution result that is comparable to the classical Shapley value while promoting the development of renewable energy resources.

Set-theoretic solutions to the Yang-Baxter equation have been studied extensively by means of related algebraic systems such as cycle sets and braces, dynamical versions of which have also been developed. No work focuses on set-theoretic solutions to the Hom-Yang-Baxter equation (HYBE for short). This paper investigates set-theoretic solutions to HYBE and associated algebraic system, called Hom-cycle sets. We characterize left non-degenerate involutive set-theoretic solutions to HYBE and Hom-cycle sets, and establish their relations. We discuss connections among Hom-cycle sets, cycle sets, left non-degenerate involutive set-theoretic solutions to HYBE and the Yang-Baxter equation.

Let $p$ be a polynomial in non-commutative variables $x_1,x_2,\ldots,x_n$ with constant term zero over an algebraically closed field $K$. The object of study in this paper is the image of this kind of polynomial over the algebra of upper triangular matrices $T_m(K)$. We introduce a family of polynomials called multi-index $p$-inductive polynomials for a given polynomial $p$. Using this family we will show that, if $p$ is a polynomial identity of $T_t(K)$ but not of $T_{t+1}(K)$, then $p \left(T_m(K)\right)\subseteq T_m(K)^{(t-1)}$. Equality is achieved in the case $t=1,~m-1$ and an example has been provided to show that equality does not hold in general. We further prove existence of $d$ such that each element of $T_m(K)^{(t-1)}$ can be written as sum of $d$ many elements of $p\left( T_m(K) \right)$. It has also been shown that the image of $T_m(K)^\times$ under a word map is Zariski dense in $T_m(K)^\times$., Comment: 39 pages, 6 figures, and comments are welcome; Waring type problem is modified

\emph{Proto-exact categories}, introduced by Dyckerhoff and Kapranov, are a generalization of Quillen exact categories which provide a framework for defining algebraic K-theory and Hall algebras in a \emph{non-additive} setting. This formalism is well-suited to the study of categories whose objects have strong combinatorial flavor. In this paper, we show that the categories of modules over semirings and hyperrings - algebraic structures which have gained prominence in tropical geometry - carry proto-exact structures. In the first part, we prove that the category of modules over a semiring is equipped with a proto-exact structure; modules over an idempotent semiring have a strong connection to matroids. We also prove that the category of algebraic lattices $\mathcal{L}$ has a proto-exact structure, and furthermore that the subcategory of $\mathcal{L}$ consisting of finite lattices is equivalent to the category of finite $\mathbb{B}$-modules as proto-exact categories, where $\mathbb{B}$ is the \emph{Boolean semifield}. We also discuss some relations between $\mathcal{L}$ and geometric lattices (simple matroids) from this perspective. In the second part, we prove that the category of modules over a hyperring has a proto-exact structure. In the case of finite modules over the \emph{Krasner hyperfield} $\mathbb{K}$, a well-known relation between finite $\mathbb{K}$-modules and finite incidence geometries yields a combinatorial interpretation of exact sequences.

In 1976 S. Eilenberg and M.-P. Schützenberger posed the following diabolical question: if $\mathbf{A}$ is a finite algebraic structure, $Σ$ is the set of all identities true in $\mathbf{A}$, and there exists a finite subset $F$ of $Σ$ such that $F$ and $Σ$ have exactly the same finite models, must there also exist a finite subset $F'$ of $Σ$ such that $F'$ and $Σ$ have exactly the same finite and infinite models? (That is, must the identities of $\mathbf{A}$ be "finitely based"?) It is known that any counter-example to their question (if one exists) must fail to be finitely based in a particularly strange way. In this paper we show that the "inherently nonfinitely based" algebras constructed by Lawrence and Willard from group actions do not fail to be finitely based in this particularly strange way, and so do not provide a counter-example to the question of Eilenberg and Schützenberger. As a corollary, we give the first known examples of inherently nonfinitely based "automatic algebras" constructed from group actions., 15 pages

In this paper, we have given a new definition of continuous fractional wavelet transform in $\mathbb{R}^N$, namely the multidimensional fractional wavelet transform (MFrWT) and studied some of the basic properties along with the inner product relation and the reconstruction formula. We have also shown that the range of the proposed transform is a reproducing kernel Hilbert space and obtain the associated kernel. We have obtained the uncertainty principle like Heisenberg's uncertainty principle, logarithmic uncertainty principle and local uncertainty principle of the multidimensional fractional Fourier transform (MFrFT). Based on these uncertainty principles of the MFrFT we have obtained the corresponding uncertainty principles i.e., Heisenberg's, logarithmic and local uncertainty principles for the proposed MFrWT.

In this paper, we define and study Loewner chains and evolution families on finitely multiply-connected domains in the complex plane. These chains and families consist of conformal mappings on parallel slit half-planes and have one and two "time" parameters, respectively. By analogy with the case of simply connected domains, we develop a general theory of Loewner chains and evolution families on multiply connected domains and, in particular, prove that they obey the chordal Komatu-Loewner differential equations driven by measure-valued processes. Our method involves Brownian motion with darning, as do some recent studies., 64 pages; (v3)Theorems 2.2 and 2.5 are improved, and Appendix D is added for the explanation on weak and vague convergences of measures (v4)minor modification (v5)typos are collected in the proof of Proposition C.6; to appear in J. Math. Anal. Appl

In this paper we investigate the massive Sine-Gordon model in the ultraviolet finite regime in thermal states over the two-dimensional Minkowski spacetime. We combine recently developed methods of perturbative algebraic quantum field theory with techniques developed in the realm of constructive quantum field theory over Euclidean spacetimes to construct the correlation functions of the equilibrium state of the Sine-Gordon theory in the adiabatic limit. First of all, the observables of the Sine-Gordon theory are seen as functionals over the free configurations and are obtained as a suitable combination of the S-matrices of the interaction Lagrangian restricted to compact spacetime regions over the free massive theory. These S-matrices are given as power series in the coupling constant with values in the algebra of fields over the free massive theory. Adapting techniques like conditioning and inverse conditioning to spacetimes with Lorentzian signature, we prove that these power series converge when evaluated on a generic field configuration. The latter observation implies convergence in the strong operator topology in the GNS representations of the considered states. In the second part of the paper, adapting the cluster expansion technique to the Lorentzian case, we prove that the correlation functions of the interacting equilibrium state at finite temperature (KMS state) can be constructed also in the adiabatic limit, where the interaction Lagrangian is supported everywhere in space., Comment: 55 pages

In this paper we study quasilinear elliptic equations driven by the double phase operator involving a Choquard term of the form \begin{align*} -\mathcal{L}_{p,q}^{a}(u) + |u|^{p-2}u+ a(x) |u|^{q-2}u = \left( \int_{\mathbb{R}^N} \frac{F(y, u)}{|x-y|^\mu}\,\mathrm{d} y\right)f(x,u) \quad\text{in } \mathbb{R}^N, \end{align*} where $\mathcal{L}_{p,q}^{a}$ is the double phase operator given by \begin{align*} \mathcal{L}_{p,q}^{a}(u):= \operatorname{div}\big(|\nabla u|^{p-2}\nabla u + a(x) |\nabla u|^{q-2}\nabla u \big), \quad u\in W^{1,\mathcal{H}}(\mathbb{R}^N), \end{align*} $0

Person re-identification (Re-ID) is one of the primary components of an automated visual surveillance system. It aims to automatically identify/search persons in a multi-camera network having non-overlapping field-of-views. Owing to its potential in various applications and research significance, a plethora of deep learning based re-Id approaches have been proposed in the recent years. However, there exist several vision related challenges, e.g., occlusion, pose scale \& viewpoint variance, background clutter, person misalignment and cross-domain generalization across camera modalities, which makes the problem of re-Id still far from being solved. Majority of the proposed approaches directly or indirectly aim to solve one or multiple of these existing challenges. In this context, a comprehensive review of current re-ID approaches in solving theses challenges is needed to analyze and focus on particular aspects for further advancements. At present, such a focused review does not exist and henceforth in this paper, we have presented a systematic challenge-specific literature survey of 230+ papers between the years of 2015-21. For the first time a survey of this type have been presented where the person re-Id approaches are reviewed in such solution-oriented perspective. Moreover, we have presented several diversified prominent developing trends in the respective research domain which will provide a visionary perspective regarding ongoing person re-Id research and eventually help to develop practical real world solutions.

Deep learning technology has made great progress in multi-view 3D reconstruction tasks. At present, most mainstream solutions establish the mapping between views and shape of an object by assembling the networks of 2D encoder and 3D decoder as the basic structure while they adopt different approaches to obtain aggregation of features from several views. Among them, the methods using attention-based fusion perform better and more stable than the others, however, they still have an obvious shortcoming -- the strong independence of each view during predicting the weights for merging leads to a lack of adaption of the global state. In this paper, we propose a global-aware attention-based fusion approach that builds the correlation between each branch and the global to provide a comprehensive foundation for weights inference. In order to enhance the ability of the network, we introduce a novel loss function to supervise the shape overall and propose a dynamic two-stage training strategy that can effectively adapt to all reconstructors with attention-based fusion. Experiments on ShapeNet verify that our method outperforms existing SOTA methods while the amount of parameters is far less than the same type of algorithm, Pix2Vox++. Furthermore, we propose a view-reduction method based on maximizing diversity and discuss the cost-performance tradeoff of our model to achieve a better performance when facing heavy input amount and limited computational cost.

Lagrangian data assimilation exploits the trajectories of moving tracers as observations to recover the underlying flow field. One major challenge in Lagrangian data assimilation is the intrinsic nonlinearity that impedes using exact Bayesian formulae for the state estimation of high-dimensional systems. In this paper, an analytically tractable mathematical framework for continuous-in-time Lagrangian data assimilation is developed. It preserves the nonlinearity in the observational processes while approximating the forecast model of the underlying flow field using linear stochastic models (LSMs). A critical feature of the framework is that closed analytic formulae are available for solving the posterior distribution, which facilitates mathematical analysis and numerical simulations. First, an efficient iterative algorithm is developed in light of the analytically tractable statistics. It accurately estimates the parameters in the LSMs using only a small number of the observed tracer trajectories. Next, the framework facilitates the development of several computationally efficient approximate filters and the quantification of the associated uncertainties. A cheap approximate filter with a diagonal posterior covariance derived from the asymptotic analysis of the posterior estimate is shown to be skillful in recovering incompressible flows. It is also demonstrated that randomly selecting a small number of tracers at each time step as observations can reduce the computational cost while retaining the data assimilation accuracy. Finally, based on a prototype model in geophysics, the framework with LSMs is shown to be skillful in filtering nonlinear turbulent flow fields with strong non-Gaussian features.

Recycling is a common policy response to problems of waste, particularly in developed countries. Understanding what influences whether and how well people recycle is important for optimising recycling outcomes. Behaviour is complex, and understanding it fully requires drawing on insights from many disciplines. However past reviews of recycling behaviour research have revealed that the field is currently fragmented into single discipline endeavours, and highly concentrated around psychological approaches. The objective of this paper was to conduct a systematic review of current published knowledge from a wide range of disciplines on what influences households recycling behaviour. The resulting factors were systematically synthesised to produce a comprehensive multi-level hierarchy of potentially influencing factors. The process also revealed potential directions for future research to improve the field’s understanding of the influences on recycling behaviour, and the ability to develop more effective interventions that address waste and recycling issues.

In this paper, we employ partial- and multiple-wavelet coherence analyses to examine co-movement between international stock markets by considering the influence of crude oil in a time domain perspective. Overall, we find that crude oil is a major factor driving co-movement between international stock markets in the median and long term. However, when considering the oil-importing and oil-exporting countries differently, we still find that crude oil is a driver for interdependence between oil-importing and oil-exporting countries. In contrast, the crude oil has relative lower impact on the co-movement in oil-importing or in oil-exporting countries, which indicates its co-movement is caused by other factors. In addition, Gulf Cooperation Council stock market may lead the stock markets of oil-importing countries in the long term. Our empirical results provide meaningful information for investors and policymakers.