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In this paper, we presented a financial analysis of the values of financial losses for real data in light of a set of indicators for measuring financial risks. The value-at-risk (VAR), tail mean–variance (TMV), tail-VAR (TVAR), tail variance (TV), Peaks Over a Random Threshold Value-at-Risk (PORT-VAR) and the mean of order P (MOO P ) indicators are used in identifying and describing important events or outliers within US financial indemnity loss data. Some extreme financial value theory (EFVT) models are compared in view of financial indemnity loss data and according to some confidence levels. The paper provided a clear financial framework for financial institutions to help them avoid large, sudden losses. Therefore, financial data with a long tail to the right were chosen, and several financial risk measures were used that study and analyze the behavior of the long tail. [ABSTRACT FROM AUTHOR]

We provide an axiomatic foundation for a choice model with two periods between which preferences are updated, but the second period choices are positively correlated with past choices in a manner that is unrelated to the agent's preferences. Specifically, in our model, the agent chooses alternative x over alternative y in contrast to his past choice if and only if the difference between the utility of x and that of y is higher than some fixed cost. While restrictive in its nature, this representation captures several related but distinctive phenomena: a taste for consistency, cognitive dissonance, the escalation of commitment, passive choice, and habit formation. We also provide a representation that allows for a more general form of cost and the revealed preference implications of our models. Finally, we connect our representation to the theories of imperfect discrimination. • This paper provides a general treatment for the "positively correlated choice" phenomena. • We provide the first axiomatic foundation for the cognitive dissonance phenomenon over easily observable primitives. • The model's high explanatory and predictive power is demonstrated, and it revealed preference provided. • The paper generalized existing models of guilt and imperfect discrimination. [ABSTRACT FROM AUTHOR]

This review article on switching overvoltage modelling in UHV AC Transmission lines, provides a state-of-the-art analysis of switching overvoltages and their impact on insulation coordination in UHV AC transmission lines. Switching overvoltages, insulation coordination, switching transient modelling tools, and switching surge flashover rate mathematical models are examined in the paper. It also examines modelling concepts, transmission models, insulation coordination for Ultra High Voltage (UHV) versus Extra High Voltage (EHV) lines, simulation scenarios and platforms, and best practices for insulation coordination limited to transmission lines. The methodology involved extensive research, consulting academic journals and conference papers, and modelling switching overvoltages with a case study. Key findings show that transmission tower insulation decreases because actual switching overvoltages take longer time to reach their peak than the critical wave. Every switching action generates a switching overvoltage with a characteristic waveform, which impacts insulation coordination. The work reveals a gap in existing research, which often focuses on overvoltages generated by a few switching events. This review suggests considering overvoltages from primary switching operations for better assessment of insulation coordination. These aspects must be considered in insulation coordination to improve UHV AC transmission line reliability and security. • This review article presents an overview of the available literature on insulation coordination for UHV AC lines covering the various types of switching overvoltages that can occur in UHV AC lines. • This review article also provides a comprehensive overview of the current state of insulation coordination for UHV AC lines and highlights the importance of considering the switching overvoltages and four significant switching operations in the insulation coordination process. • This article also presents a comprehensive view of the modelling guidelines for modelling the UHV AC transmission system and a helps in choosing the right model for the transmission line. • A brief summary of simulation cases and simulation Platforms used for Switching Transients Studies is also presented based on the literature from 1962 to 2024. [ABSTRACT FROM AUTHOR]

Mobile forensics, particularly in the Android ecosystem, is a rapidly evolving field that demands continuous advancements to address the growing complexity and diversity of mobile devices. This article emphasizes the importance of developing techniques for digitally analyzing Android smartphones, which dominate the smartphone market. The primary objective of this research is to contribute to the development of effective forensic investigation strategies tailored specifically for Android mobile devices, providing insights into the tools and methods used for this purpose. The objective of this study is to improve the precision and effectiveness of forensic examinations pertaining to Android mobile phones. It discusses the fundamental functionality of mobile devices as a source of digital evidence and provides an overview of tools and methodologies for collecting and analyzing such evidence. The importance of comprehending the hardware and software architecture of Android handsets in order to choose the right forensic tools is also highlighted in the article. Furthermore, it proposes future enhancements for Andriller, a popular digital forensic tool, to improve its effectiveness in Android forensic investigations. These enhancements include advancements in data extraction techniques, compatibility with new Android versions, support for additional data types, integration with advanced analysis methods, and addressing identified limitations. Additionally, the paper stresses the need for robust methodologies for conducting cloud forensics on Android devices, particularly in the context of data stored in cloud storage services. The proposed work aims to enhance the capabilities of Andriller and improve the efficiency of digital forensic investigations on Android devices. [ABSTRACT FROM AUTHOR]

For over a decade, mobile applications are frequently used to make people's daily lives more accessible in all areas, such as commerce, tourism, education, medical care, and others. For implementing these applications, several frameworks exist that can accomplish this task, such as Google's Flutter, Microsoft's Xamarin, Facebook's React Native, Apple's Swift UI, Ionic, Django..etc. However, choosing the best framework to develop an application is not a simple enough decision for developers. This paper presents a detailed analysis of the four most widely used frameworks for implementing mobile applications: React Native, Swift UI, Flutter, and Xamarin. Furthermore, this study also aims to assist developers in choosing the most suitable framework by proposing an application that can recommend the best framework according to the needs entered by answering the questions, which are generally related to the relevant criteria resulting from the analysis of these frameworks. [ABSTRACT FROM AUTHOR]

Tools for programmers are constantly evolving, and with that evolution comes tools that support their work, such as those that enable testing and analysis of program code. It is expected that tools integrated with programming environments will analyze code on the fly, and the results of the analysis will be available as soon as the program is compiled. This paper attempts to compare solutions for extracting object-oriented structure that begin the process of static source code analysis. Choosing the right solutions already at the beginning of static analysis, will affect the speed of the whole process as well as accuracy. And this will then affect the cost of programmers' work and the quality of the software tests performed. [ABSTRACT FROM AUTHOR]

Schizophrenia is a major psychiatric disorder with unknown aetiology. Recent evidence suggests a potential role for cytokines in its pathophysiology and that antipsychotic medication may alter this. While the aetiology of schizophrenia remains only partly understood, an altered immune function representing an important avenue of further discovery. In this systematic review and meta-analysis we focus on the specific effects of second generation antipsychotics risperidone and clozapine on inflammatory cytokines. A defined systematic search of PubMed and Web of Science databases was performed to identify relevant studies published between Jan 1900 and May 2022. After screening of 2969 papers, 43 studies (27 single-arm and 8 dual-arm) were included that consisted of a total of 1421 patients with schizophrenia in the systematic review. From these, twenty studies (4 dual-arm; 678 patients) had data available on which a meta-analysis could be carried out. Our meta-analysis showed a significant reduction of pro-inflammatory cytokines post-risperidone treatment in the absence of a similar association with clozapine. Subgroup analyses (First episode v chronic) demonstrated that duration of illness influenced the extent of cytokine alteration; risperidone treatment produced significant cytokine changes (lowered IL-6 and TNF-α) in chronic patients but not in first-episode psychosis (FEP) patients. Varying treatment effects on cytokines can be observed by the use of different antipsychotic drugs. The cytokine alterations post-treatment are influenced by the specific antipsychotic drugs and patient status. This may explain disease progression in certain patient groups and influence therapeutic choices in the future. [ABSTRACT FROM AUTHOR]

Urban energy systems are pivotal in the global shift towards a climate-neutral future. Given the need for these systems to adapt to local conditions, designing them remains complex without standardized solutions. To address this, numerous software tools for energy system planning have been developed. Despite many scientific reviews on these tools' characteristics, none comprehensively assess their suitability for different planning phases and tasks in urban energy or district heating planning. This meta-review evaluates the current review literature on UES, offering a more nuanced analysis of the tool landscape. It identifies both common and unique tool characteristics highlighted across studies, including features such as accessibility, temporal and spatial resolutions, and energy sector coverage. To enhance this study's utility, a clear depiction of each tool's specific functionalities and characteristics is emphasized. Our findings show that most tools are open source, utilize Python or Modelica libraries, and support a wide range of tasks across all identified planning phases. In renewable energy integration, the need for multi-energy system planning tools was observed. Some tools meet this need, but many focus only on a single sector, like electricity or heat. Additionally, a gap in environmental assessment methods suggests potential for future development. • Meta-review of urban energy software tools helps users choose the right tool. • Review papers reveal common and unique tool traits. • Many tools support urban energy and district heating; new ones keep emerging. • Many tools focus on simulation; few offer environmental assessment or pipe design. [ABSTRACT FROM AUTHOR]

The recent surge in scale development studies within tourism research necessitates a critical reevaluation of our approach to construct operationalization. This paper addresses a gap in the literature by posing a fundamental question: what is the intended purpose of the scale we are developing or using? By explicitly considering this question, researchers can make informed decisions about the most appropriate measurement model option. This article presents three common research objectives, each guiding researchers towards the most suitable modelling approach. Additionally, the article offers practical guidance on avoiding common pitfalls in scale development. This framework benefits both early-career researchers new to latent variable modelling and experienced researchers seeking to refine their approach to scale development and construct operationalization. [ABSTRACT FROM AUTHOR]

Despite accelerated developments in agricultural innovations, there has been a limited progress in their adoption by smallholder farmers, especially in the Global South. Innovations are not adequately reaching farmers, and new technologies are often unsuitable for farmers, or the overall innovation landscape is discouraging the deployment of innovations at scale. The methodological approaches to designing, testing and scaling of innovations play an important role. Involving farmers in the innovation process from the start increases the likelihood of them being more relevant and having a higher chance of adoption. Whereas there is much literature on participatory approaches, there is a limited guidance on choosing an appropriate level of farmer participation across the innovation process. This study aims to develop practical guidance for researchers, development agents and project managers on how to choose an appropriate methodological approach with a suitable level of farmer participation for designing, testing and scaling of agricultural innovations, according to levels of innovation readiness and innovation use. Relevant scientific literature on participatory design, research and scaling of agricultural innovations was reviewed, followed by compiling lessons learned from selected vegetable innovation projects implemented by the World Vegetable Center (WorldVeg) in Asia and Africa. A list of methodological approaches for participatory design, testing and scaling of innovations was generated, with various levels of farmer participation. A simple guiding framework was then developed for choosing an appropriate approach with a suitable level of farmer participation, according to the readiness and use of the innovations. Examples of selected approaches and application of the proposed guidance are given in project case studies. Based on the literature review and experience at WorldVeg, we believe it is critical to choose the right level of participation based on how ready are the innovations for application in the local setting. This dimension of how ready the innovations are has been largely overlooked in earlier frameworks on farmer participation. The developed guidance generally recommends increasing participation with higher levels of technology readiness and use. In addition, farmer consultation in early research design should also be strengthened, to co-design locally relevant innovations. The overall innovation systems' landscape must be considered when researching and scaling new innovations. The guidance, experiences and lessons provided in this paper will be useful to various scholars, researchers, development agents, funders and project managers who aim to design, test and scale agricultural innovations in a more inclusive and responsible way. [Display omitted] • Despite rapid developments in agricultural innovations, there has been limited adoption by smallholder farmers. • There is limited guidance on choosing an appropriate level of farmer participation in the innovation processes. • A list of participatory approaches for design, testing and scaling of innovations was created. • A guidance for choosing a suitable level of participation based on 'innovation readiness and use' was developed. [ABSTRACT FROM AUTHOR]

Predicting the difficulty of playing a musical score is essential for structuring and exploring score collections. Despite its importance for music education, the automatic difficulty classification of piano scores is not yet solved, mainly due to the lack of annotated data and the subjectiveness of the annotations. This paper aims to advance the state-of-the-art in score difficulty classification with two major contributions. To address the lack of data, we present Can I Play It? (CIPI) dataset, a machine-readable piano score dataset with difficulty annotations obtained from the renowned classical music publisher Henle Verlag. The dataset is created by matching public domain scores with difficulty labels from Henle Verlag, then reviewed and corrected by an expert pianist. As a second contribution, we explore various input representations from score information to pre-trained ML models for piano fingering and expressiveness inspired by the musicology definition of performance. We show that combining the outputs of multiple classifiers performs better than the classifiers on their own, pointing to the fact that the representations capture different aspects of difficulty. In addition, we conduct numerous experiments that lay a foundation for score difficulty classification and create a basis for future research. Our best-performing model reports a 39.5% balanced accuracy and 1.1 median square error across the nine difficulty levels proposed in this study. Code, dataset, and models are made available for reproducibility. • Introducing CIPI, the most comprehensive piano difficulty dataset, with 652 pieces across 9 levels. • We design models using various music performance aspects, guided by musicology insights. • Using multi-view music performance model predictions boosts accuracy. • Choosing the right loss function is vital for ordinal regression tasks as difficulty prediction. • We carry out extensive experiments helpful for further research. [ABSTRACT FROM AUTHOR]

The European Commission and EU Member States have recently promoted legislation (under the REPower EU Plan) to accelerate renewable energy projects by allowing the elimination or reduction of environmental assessment and public participation in the approval procedure. To avoid adverse effects on the environment, these regulations propose the exclusion of certain areas (identified through available information on protected and/or environmentally sensitive areas) from the procedure. This paper aims to evaluate whether such measures are effective to avoid damage to biodiversity from the implementation of photovoltaic energy. To this end, we studied the distribution and abundance of a threatened bird species, the Black-bellied Sandgrouse, at two geographic scales: i) regional: Andalusia (southern Spain); ii) local: Campo de Tabernas (an excellent example of the semi-arid ecosystems of southeastern Spain) and evaluated the quality of the information that Administrations have on the species by comparing it with own censuses. We also assessed the effectiveness of the environmental impact procedure for photovoltaic plants planned in Campo de Tabernas. Data from the regional Administration reveal that Andalusian protected areas cover a minimal part (17.7%) of the species' range. Moreover, neither the regional nor the national Administration have reliable and updated information on the distribution of the species in Campo de Tabernas (only 17% of the current distribution area is known). The wrong choices made by developers about where to locate the photovoltaic plants together with wrong decisions of the regional Administration (with land-use planning competences, including the planning of power plant locations) have led to the loss of 630 ha of the species' range in Campo de Tabernas. Public participation of experts prevents, through allegations to the projects, an important part of the impact on the species. Policy implications: the information that the Administrations have on this species does not allow them to make proper decisions on where to apply renewable acceleration. Obtaining complete and updated information on the distribution of endangered species to elaborate detailed wildlife sensitivity areas is essential and urgent before implementing measures to accelerate renewables. • The new EU renewable acceleration directives are analysed here for the first time. • A threatened species becomes locally extinct due to solar power plants' poor planning. • Involving experts in EIA procedure greatly reduces the impact on a threatened species. • The design of renewable go-to areas demands specific research on threatened species. • Current EU legislation to accelerate renewables will negatively affect biodiversity. [ABSTRACT FROM AUTHOR]

Decision fatigue is the phenomenon in which a large number of decisions or choices made over a period of time hinders the ability to competently make further decisions. While addressed in the literature of other professions, the concept has not been included in the inventory of phenomena that potentially affects librarians. This paper presents the results of a survey which draws attention to decision fatigue and allows academic librarians to recognize the symptoms while suggesting ways to overcome its effects. • Academic librarians are susceptible to decision fatigue. • Internal and external factors influence librarians' susceptibility and avoidance. • Recognizing symptoms can help librarians control and cope with of decision fatigue. • There are remedies and preventative strategies for dealing with decision fatigue. [ABSTRACT FROM AUTHOR]

In order to understand the spatial variations of students' active school travel (AST) across different regions in mountain cities, this paper examines the spatial variations in the influence of socio-economic, topographic, and built environment characteristics on the active mode choice of students in Guiyang, a typical mountainous city in China. Using the data collected from 2021 Guiyang household travel survey, the travel characteristics of 2585 primary and secondary school students distributed in different regions within the city are analyzed. In addition, a semiparametric geographically weighted logistic regression model is employed to examine whether the determinants of students' AST vary across the studied areas. The results show that noticeable spatial variations exist in the relationship between the choice of AST and the selected explanatory variables, with different student groups exhibit sensitivity to these variables in different geographic areas. For example, among students aged 6–12 living in suburban areas, there is a higher likelihood of choosing AST as the school travel distance increases. However, students with long-distance travel in suburban areas are less inclined to choose AST as the terrain slope increases. Conversely, students aged 13–18 in urban areas are more prone to choose AST as the slope increases. To promote equitable access to AST among urban and rural students, several policy recommendations are proposed after considering their socio-economic attributes and travel environment. These recommendations primarily focus on improving school bus services, creating an active travel-friendly environment, enhancing public transit accessibility, and implementing staggered shifts for parents and students. [ABSTRACT FROM AUTHOR]

In a microgrid, an efficient energy storage system is necessary to maintain a balance between uncertain supply and demand. Distributed energy storage system (DESS) technology is a good choice for future microgrids. However, it is a challenge in determining the optimal capacity, location, and allocation of storage devices (SDs) for a DESS. This paper proposes a two-stage approach to solve these SD decision-making problems in a microgrid. In the first stage, a continuous approximation approach was used to formulate the SD location, capacity, and renewable energy investment problems from a long-term perspective. In the second stage, the dispatch quantity problems are addressed to minimize the operational cost based on the results of Stage 1. Some examples and real data are used to illustrate the model and solution approaches. The results show that our approach can achieve the cost minimization. The optimal SD location and capacity strategy and the renewable energy investment level are close to the real data obtained from Liu et al. (2018) after 20 runs. • Designing a two-stage approach to solve these SD decision-making problems in a microgrid. • A continuous approximation approach is used to formulate the SD location, capacity, and renewable energy investment problems from a long-term perspective at the first stage. • The dispatch quantity problems are addressed to minimize the operational cost based on the results of Stage 1 at Stage 2. [ABSTRACT FROM AUTHOR]

Choice data appear together with drop out data indicating if respondents completed the exercise. In case of non-completion, the choice sequences end at the tasks where the respondents exited the study. In the analysis of choice data, the focus is always on choices made while the drop out behavior is completely ignored. However, the choice making and the drop out process could be latently related. For instance, respondents who are more likely to drop out of the exercise could give less consistent choices throughout or just before they exit. In such cases, ignoring the drop out dimension could lead to biased or inefficient results. In this paper, we use shared random effects and covariate effects to model the association between a scaled multinomial logit model for the choices and two different models for the drop out component. Through simulations, we show that a joint model provides less biased and more precise estimates and its 95% credible intervals have better coverage for true parameter values. • The paper proposes a framework within which information from dropouts in discrete choice experiments can be used in the analyses of the choice making behavior. • The paper shows, through two sets of joint model simulations, the importance of utilizing dropout information in choice analysis • The paper proposes models for both discrete and continous time aspects [ABSTRACT FROM AUTHOR]

In this paper, a numerical analysis has been conducted to predict the fracture response of a novel type of fiber-reinforced concrete blocks, called "multi-functional fiber reinforced concretes" (MFRCs). In MFRCs, fibers have been coated with a shell. This will allow the structure to be used for multiple purposes, including concrete self-healing. This study is conducted utilizing phase-field fracture framework. The shell thickness and the ratio of fiber length to diameter are the geometrical parameters whose effects on the fracture resistance of the MFRCs have been analyzed. As choosing the right shell material is under investigation, in the next step of the study, in addition to the geometrical factors, different material mismatch cases for the critical energy release rate of the shell has been analyzed. Moreover, the application of two different fibers, polyester fiber and polypropylene fiber (with almost 10 times higher critical energy release rate), are looked into. All the structures undergo three loading conditions: tensile loading, compressive loading, and three-point bending. In order to judge what configuration performs best, the values of peak force and absorbed energy of each structure in each case study have been taken into consideration and compared with those of other structures. It was seen that the most favorable performance and configuration depend on the loading condition and also the material set. Under tension, MFRCs with the lowest fiber length to diameter ratio exhibit the highest peak force and absorbed energy in the case of polyester fiber. The same observation was made for all models and material sets under compressive loading. Under three-point bending loading condition, for the cases of polyester fiber, similar results were obtained as the lowest fiber length to diameter ratio showed the best mechanical response. Having said that, it must be mentioned that shell material had a dominant effect on the fracture response of the structure under this loading condition. Polypropylene fibers also managed to increase the peak forces in different loading conditions. • A novel multi-functional coated fiber-reinforced concrete (MFRC) is proposed. • Parametric study on fracture of MFRCs is conducted through phase-field framework. • For polyester fiber, lower lengths of fiber show higher toughness. • Polypropylene fiber increases peak forces while fail at lower displacement loads. • Less tough coating increases energy absorption under flexural loading. [ABSTRACT FROM AUTHOR]

• We present ChampKit, a Python-based software package that enables rapid exploration and evaluation of deep learning models for patch-level classification of histopathology data. It is open source and available at https://github.com/SBU-BMI/champkit. ChampKit is designed to be highly reproducible and enables systematic, unbiased evaluation of patch-level histopathology classification. It incorporates public datasets for six clinically important tasks and access to hundreds of (pre-trained) deep learning models. It can easily be extended to custom patch classification datasets and custom deep learning architectures. • The users are intended to be (1) biomedical research groups interested in finding and fine-tuning the best models to analyze a broad collection of whole slide images, and (2) deep learning methods research groups interested in systematically and quickly evaluating their methods against a set of state-of-the-art methods with different pretraining and transfer learning configurations. • We demonstrate the utility of ChampKit by evaluating two ResNet models and one vision transformer on the six diverse classification tasks for patch-level histopathology datasets. We did not find consistent benefits from pretrained models versus random initialization across the different datasets, which suggests that a thorough exploration of model architectures is important to identify optimal models for a given dataset. Histopathology is the gold standard for diagnosis of many cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopathology images for many tasks, including the detection of immune cells and microsatellite instability. However, it remains difficult to identify optimal models and training configurations for different histopathology classification tasks due to the abundance of available architectures and the lack of systematic evaluations. Our objective in this work is to present a software tool that addresses this need and enables robust, systematic evaluation of neural network models for patch classification in histology in a light-weight, easy-to-use package for both algorithm developers and biomedical researchers. Here we present ChampKit (Comprehensive Histopathology Assessment of Model Predictions toolKit): an extensible, fully reproducible evaluation toolkit that is a one-stop-shop to train and evaluate deep neural networks for patch classification. ChampKit curates a broad range of public datasets. It enables training and evaluation of models supported by timm directly from the command line, without the need for users to write any code. External models are enabled through a straightforward API and minimal coding. As a result, Champkit facilitates the evaluation of existing and new models and deep learning architectures on pathology datasets, making it more accessible to the broader scientific community. To demonstrate the utility of ChampKit, we establish baseline performance for a subset of possible models that could be employed with ChampKit, focusing on several popular deep learning models, namely ResNet18, ResNet50, and R26-ViT, a hybrid vision transformer. In addition, we compare each model trained either from random weight initialization or with transfer learning from ImageNet pretrained models. For ResNet18, we also consider transfer learning from a self-supervised pretrained model. The main result of this paper is the ChampKit software. Using ChampKit, we were able to systemically evaluate multiple neural networks across six datasets. We observed mixed results when evaluating the benefits of pretraining versus random intialization, with no clear benefit except in the low data regime, where transfer learning was found to be beneficial. Surprisingly, we found that transfer learning from self-supervised weights rarely improved performance, which is counter to other areas of computer vision. Choosing the right model for a given digital pathology dataset is nontrivial. ChampKit provides a valuable tool to fill this gap by enabling the evaluation of hundreds of existing (or user-defined) deep learning models across a variety of pathology tasks. Source code and data for the tool are freely accessible at https://github.com/SBU-BMI/champkit. [ABSTRACT FROM AUTHOR]

It is generally assumed that the rankings provided by Multi-Attribute Decision-Making (MADM) techniques are definitive. Once the ranking is delivered, decision makers (DMs) are expected to choose the first alternative and dismiss the remaining ones, concluding the application of the corresponding model. The MADM literature has incorporated fuzziness and imprecision to its models to deal with evaluation uncertainties but has not accounted for its consequences defined in terms of regrettable choices. That is, MADM models do not consider the possible consequences of having chosen an alternative whose actual characteristics do not correspond to those expected by the DM. This paper aims at designing an integrated MADM framework with interval variables where the DM is allowed to modify the initial alternative chosen after observing the realizations of its characteristics. In order to do so, sequences of alternatives including the initial choice as well as subsequent alternate choices should be ranked in place of single alternatives. We analyze the combinatorial decision environment that arises from defining and evaluating sequences of choices by accounting for the whole set of potential realizations and any subsequent change in the alternatives selected. The TOPSIS method is used to design the integrate evaluation framework producing the final ranking. A case study analyzing the entry decision of a firm within a group of European countries based on their levels of ICT development is presented. We illustrate how the countries selected and their order may differ substantially when accounting for the complementarities existing among them. Moreover, the selection process and any subsequent decision vary with the number of modifications considered relative to the initial country selected. The results obtained are of interest not only to firms facing a similar problem, but also to DMs or managers dealing with strategic selection processes where the wrong choice of alternatives may lead to increasingly complex sequential disruptions. • We analyze a Multi-Attribute Decision-Making setting with interval evaluations. • We formalize the regrettable choices inherent to the uncertain evaluations received. • The resulting choice sequences account for combinations of all potential realizations. • Sequences are determined by the number of modifications in the alternatives selected. • We study the entry decision of firms based on the ICT development level of countries. [ABSTRACT FROM AUTHOR]

Rapidly increasing demand for electricity, rising costs of fossil fuels, and growing environmental concerns are driving research into alternative energy sources. Wind energy has experienced significant growth in recent years. Extensive research has been conducted to efficiently generate electricity from wind energy. However, due to limitations such as space and maintenance, it is very difficult and unsafe to install wind turbines for research and educational purposes. A lab-scale wind turbine emulator has many advantages, including independence from prevailing wind conditions, a greater degree of control, and freedom from space constraints. In this article, authors have reviewed and analyzed over 140 technical articles related to wind turbine emulators. It provides comprehensive information on prime movers, generators, and controls used in wind turbine emulator (WTE) research. Additionally, this paper discusses the strengths and weaknesses of various WTE prime movers, generators, and control algorithms used in the literature review. This article will serve as a good reference for the researchers to choose the right technique for WTE according to their needs. [Display omitted] • Overview of the importance of Wind Turbine Emulators (WTE) in wind energy research. • Comparison of different types of wind turbine emulators. • Discussion of recent advances in wind turbine emulation technology. • Analysis of the current state of wind turbine emulation research. [ABSTRACT FROM AUTHOR]

This paper explores the homecoming experiences of 34 Chinese teacher educators (CTEs) for insights into transnational educational transfer after studying in a key Canadian university for three months in 2019. It analyses the interactions between the contextual elements in the home institutions and the key endeavours of the CTEs in the choices they made in effecting change, the strategies to apply new knowledge, and the lessons they learned. Their attempts in educational transfer were affected by the capability to change mindset for problem-solving; understanding of contexts; contextual compatibility between foreign knowledge and local issues; and the accessibility to positional power. [ABSTRACT FROM AUTHOR]

This paper investigates how carriers adapt to the sulphur regulation by choosing between sulphur scrubber (SS) and fuel switching (FS). A cost minimization model is proposed to determine the optimal compliance choices, considering factors such as fuel prices, capital cost difference between SS and FS, and shipping service profile. We find that the optimal choice depends on the tradeoff between the capital cost advantage of FS and the fuel cost advantage of SS. And such a tradeoff is moderated by the vessel's annual fuel consumption. Ignoring the speed differentiation behavior of FS vessels would exaggerate the benefits of SS. But whether it can cause a wrong compliance choice depends on the extent of overestimation, which depends on the presence in emission control areas and fuel prices. When fuel price volatility is factored in, a portfolio of FS and SS might be better than simply choosing either one for all vessels. [ABSTRACT FROM AUTHOR]

Due to anthropological climate change, climate scientists project a significant change in climate in the coming years. Despite the advances in climate modeling, future climate is still subject to large uncertainties. To mitigate greenhouse gas emissions and slow down climate change, the energy system must undergo a fundamental transition. Thereby, energy system modeling tools are used to optimize the design of future energy systems. As many components of an energy system depend on climate variables, changing climate and its uncertainties influence the outcome of energy system models. Energy systems with high shares of renewable energies are thereby especially vulnerable to changing climate conditions. To find how the changing climate and its uncertainties are assessed in energy system modeling, a systematic literature review was conducted. Thereby we address three major issues: First, we depict the most common techniques to convert climate data into energy system input data. As large amounts of climate data are necessary to depict future climate developments, we secondly review data reduction techniques which can be used to restrain computational time. Around 46% of the studies in this review use those techniques with scenario building being the most common one. Finally, we review methods to explicitly assess climate uncertainty in energy system models. The majority of energy system optimization models searches for individual solutions for the scenarios, while only 22% use methods that include the uncertainty explicitly in the optimization process. We see the need for further research especially in the use of more advanced optimization methods and the combination of those with well known data reduction techniques. The review provides a wide synopsis of different methods for the three major issues and can therefore enable energy system modelers to choose the right methods for the specific challenges. • Systematic review including 160 research papers. • Review of methods for assessing climate uncertainty in energy system optimization. • Presentation of synergies between methods and identification of research needs. [ABSTRACT FROM AUTHOR]

In the latent bandit problem, the learner has access to reward distributions and – for the non-stationary variant – transition models of the environment. The reward distributions are conditioned on the arm and unknown latent states. The goal is to use the reward history to identify the latent state, allowing for the optimal choice of arms in the future. The latent bandit setting lends itself to many practical applications, such as recommender and decision support systems, where rich data allows the offline estimation of environment models with online learning remaining a critical component. Previous solutions in this setting always choose the highest reward arm according to the agent's beliefs about the state, not explicitly considering the value of information-gathering arms. Such information-gathering arms do not necessarily provide the highest reward, thus may never be chosen by an agent that chooses the highest reward arms at all times. In this paper, we present a method for information-gathering in latent bandits. Given particular reward structures and transition matrices, we show that choosing the best arm given the agent's beliefs about the states incurs higher regret. Furthermore, we show that by choosing arms carefully, we obtain an improved estimation of the state distribution, and thus lower the cumulative regret through better arm choices in the future. Through theoretical analysis we show that the proposed method retains the sub-linear regret rate of previous methods while having much better problem dependent constants. We evaluate our method on both synthetic and real-world data sets, showing significant improvement in regret over state-of-the-art methods. • We investigate the use of information gathering in latent bandits. • We develop a method that uses information gathering to reduce state uncertainty, while retaining sub-linear regret. • We highlight environment conditions that benefit from information gathering. • Evaluation on synthetic and real-world data show significant improvement over state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

• In this paper, the effect of the fork grating resolution on the orbital angular momentum (OAM) of vortex beams is investigated. • Switching of topological charge (TC) sign is demonstrated at certain resolutions. • Petal-like beams are produced by single fork gratings with a certain resolution at which the magnitude of TC tends toward 0. • Three criteria are introduced and studied to quantify the quality of the vortex beam. • It is shown theoretically and experimentally that the sign and magnitude of TC depend on the resolution of the forked grating. • Based on these results, choosing the right fork grating resolution is a key factor, especially in optical trapping or tweezer applications where the sign of the vortex beam strongly affects the results. We demonstrate the effect of the resolution of forked-diffractive optical elements (DOEs) on the helicity of the topological charge (TC) of the orbital angular momentum (OAM) beams. To aim that, the forked-DOEs are designed with the same TC while they are different in resolution. Changing the resolution allows the fork direction to be switched. Interestingly, switching the sign of the TC is the consequential achievement for the OAM beams, generated by the fork grating elements with specific resolutions. Furthermore, changing the resolution make the fork gratings to be designed for generating petal-like beams in which the magnitude of the TC tends to zero. Finally, three criteria are calculated for all intensity distributions to quantify the generated beams' quality and investigate where the helicity of the TC changes. Consequently, we demonstrate the emphasis on selecting the resolution of DOEs which plays a pivotal role in optical systems, for instance in optical tweezers where the OAM beams need to be generated with a desired sign of TC. [ABSTRACT FROM AUTHOR]

The potential of marginal lands to improve food security, support bioenergy production or ecosystem services has globally got a lot of attention. Defining agricultural marginal land is a task that involves more than just considering the land's quality, its definition changed a lot during the last two centuries. Development of new technologies and policy trends require the concepts of prime land and marginal land to be renewed from time to time. Although much research has been done on the concept of marginal land, it is currently limited by the lack of a clear, globally accepted definition. There are four major sources of criteria of marginal lands: economic (e.g., rent cost, land value), geographical (e.g., temperature, slope, precipitation), ecosystem-based (e.g., protected areas, recreation, ecosystem services), soil suitability (e.g., yield capability, physical and chemical soil properties). The categorisation of agricultural land into groups like productive, marginal or unproductive often depends on the cultivation or management type. Since conceptions of marginal land are dynamic both in time and space, flexible policy and practical solutions are needed for their non-degrading use, which in any case shall support nature-based socioeconomic development. To maintain the socioeconomic value of these areas, it is crucial to develop rural areas that are economically or biophysically marginalised. High nature value farming, bioenergy crops by sustainable land management and afforestation are highly recommended. Choosing the right management can transform marginal land into an optimal soil condition or incorrect management can degrade prime land into marginal land (unproductive land). This paper provides a review and categorisation of the historical and new developments of marginal land concepts especially those which are working with agricultural aspects, including land management and reclamation. It could give a strong basis for further research in topic of marginal land. [Display omitted] • Marginal lands are depending on the spatial scale and the regional conditions. • Marginal land can be turned suitable for the objective of the use. • Marginal lands are dynamic both in time and space. • Flexible policy and practical solutions are needed. [ABSTRACT FROM AUTHOR]