Miracle-dz/UniFAR-FTUs huggingface dataset:
Current Status of Oxide Dielectric Materials for Terahertz Applications–An Overview

Terahertz electromagnetic radiation, its generation, detection and material interaction is an emerging field of study. Diverse range of researches is going on to explore and understand this frequency range and its potential applications. Situated between microwave and optical frequency domain, THz radiation is non-responding to the techniques commonly used for these two domains, thus insufficiency of supporting technology, high atmospheric absorption, lack of high power source have kept this spectrum away from technical and commercial interest for a long time. Advancements in Photonics like high power ultrafast lasing devices, high-power THz generation by means of nonlinear effects, band-engineered hetero-structures have greatly extended its potential. In order to explore all its merits and realize it as a full-fledged functional frontier, more high power sources, more sensitive sensors and efficient waveguides need to be developed. In order to achieve that, the main focus of the research world has moved towards the study of various classes of materials so that they can be categorized based on the applications they can be used for. In this overview article, recent studies on THz dielectric material interaction, their observations, outcomes and their prospect have been presented in a condensed manner so that the information can be used for further developments in this frequency regime.
Peridomestic conditions affect La Crosse virus entomological risk by modifying the habitat use patterns of its mosquito vectors

ABSTRACT: Anthropogenic land-use change may affect the transmission risk for endemic vector-borne diseases such as La Crosse encephalitis. In this study, we applied a comparative ecological approach to evaluate differences in vector species abundance, gonotrophic status, and environmental variables among six residential habitats (historical case houses) and six paired adjacent forest patches in a La Crosse virus endemic area of North Carolina. A total of 93,158 container Aedes spp. eggs were obtained by ovistrips and 1,040 resting mosquito adults were collected by large-bore aspiration from 10 June through 8 October, 2010. At sites characterized by high densities of artificial containers, the totals of eggs and adult mosquitoes were higher in the peridomestic plots. At sites characterized by lower densities of artificial containers, the totals of eggs and adult mosquitoes were higher in the forested plots. Although Aedes triseriatus, the primary La Crosse virus vector, was more commonly found in forested habitat overall, in sites characterized by high density of artificial containers, it was found in higher abundance in the peridomestic habitat. Similarly, the proportion of gonotrophically active (parous and gravid) mosquitoes was higher in the peridomestic habitat for sites with highartificial container density. Our study suggests that La Crosse virus transmission risk may be higher at peridomestic habitats with higher densities of artificial containers and thus reinforces the importance of public health measures to improve source reduction efforts.
Life cycle of Triatoma huehuetenanguensis Lima-Cordón, Monroy, Stevens, Rodas, Rodas, Dorn, Justi 2019 (Hemiptera: Reduviidae: Triatominae) from Mexico

ABSTRACT: Several important biological parameters for estimating the vectorial capacity of a triatomine species were determined on a Mexican population of the recently described Triatoma huehuetenanguensis Lima-Cordón, Monroy, Stevens, Rodas, Rodas, Dorn, Justi (Hemiptera: Reduviidae: Triatominae). The biological parameters were the egg-to-adult development time, number of blood meals required for molting, cumulative mortality, onset time for feeding, feeding and defecation behaviors, and fertility and fecundity of T. huehuetenanguensis. The median egg-to-adult development time was 253 days, with 11 blood meals required for molting. A cumulative mortality rate of 47.41% was observed. The time to begin feeding was between 2.5 and 3.5 min. Feeding times were longer than 15 min. The highest percentages of defecation delays were 1 to 10 min in all nymphal instars and also in males. In contrast, significantly (P < 0.01) more females defecated while eating. At the end of the cycle, the percentage of the obtained females was 54.9%. The mean number of eggs laid per day per female for a month was 2.08. These eggs had an eclosion rate of 98.08%, with an incubation period of 19 days. Our results contribute to estimations of the potential role of T. huehuetenanguensis in the transmission of Trypanosoma cruzi in its distribution area.
The effects of particle clustering on hindered settling in high-concentration particle suspensions

Abstract We study the effects of Archimedes number $Ar$ and volume fraction $\phi$ in three-dimensional, high concentration and monodispersed particle suspensions. Simulations were conducted using the immersed boundary method with direct forcing for triply periodic cases and with $Ar = 21 - 23\,600$ and $\phi = 0.22 - 0.43$. We find that cluster formation is strongly dependent on the Archimedes number but weakly dependent on the volume fraction for concentrated suspensions. Particles in low $Ar$ cases are characterized by less frequent but long-lived clusters, resulting in higher hindered settling, while high $Ar$ cases consist of more frequent but short-lived clusters, leading to reduced hindered settling. By quantifying the effects of collisions on the hydrodynamic fluctuations, we show that the lifespan of clusters for the low $Ar$ cases is longer because particles are subject to appreciable wake interactions without collisions. On the other hand, clusters for high $Ar$ cases are broken before being subject to appreciable wake interactions due to frequent collisions, leading to a shorter cluster lifespan. The results imply that there exists an $Ar$ for particles in fluidized bed reactors that can reduce short circuiting due to clustering and enhance performance by maximizing flow–particle interactions. This result is consistent with existing reactor studies demonstrating that optimal particle diameters and $Ar$ values correspond to cases with short-lived clusters, although more thorough experimental studies are needed.
The origin of asexual brine shrimps

Determining how and how often asexual lineages emerge within sexual species is central to our understanding of sex-asex transitions and the long-term maintenance of sex. Asexuality can arise “by transmission” from an existing asexual lineage to a new one, through different types of crosses. The occurrence of these crosses, cryptic sex, variation in ploidy and recombination within asexuals greatly complicates the study of sex-asex transitions, as they preclude the use of standard phylogenetic methods and genetic distance metrics. In this study we show how to overcome these challenges by developing new approaches to investigate the origin of the various asexual lineages of the brine shrimp Artemia parthenogenetica. We use a large sample of asexuals, including all known polyploids, and their sexual relatives. We combine flow cytometry with mitochondrial and nuclear DNA data. We develop new genetic distance measures and methods to compare various scenarios describing the origin of the different lineages. We find that all diploid and polyploid A. parthenogenetica likely arose within the last 80,000 years through successive and nested hybridization events that involved backcrosses with different sexual species. All A. parthenogenetica have the same common ancestor and therefore likely carry the same asexuality gene(s) and reproduce by automixis. These findings radically change our view of sex-asex transitions in this group, and show the importance of considering asexuality “by transmission” scenarios. The methods developed are applicable to many other asexual taxa.
Explaining Path Plan Optimality: Fast Explanation Methods for Navigation Meshes Using Full and Incremental Inverse Optimization

Path planners are important components of various products from video games to robotics, but their output can be counter-intuitive due to problem complexity. As a step towards improving the understanding of path plans by various users, here we propose methods that generate explanations for the optimality of paths. Given the question "why is path A optimal, rather than B which I expected? ", our methods generate an explanation based on the changes to the graph that make B the optimal path. We focus on the case of path planning on navigation meshes, which are heavily used in the computer game industry and robotics. We propose two methods - one based on a single inverse-shortest-paths optimization problem, the other incrementally solving complex optimization problems. We show that these methods offer computation time improvements of up to 3 orders of magnitude relative to domain-independent search-based methods, as well as scaling better with the length of explanations. Finally, we show through a user study that, when compared to baseline cost-based explanations, our explanations are more satisfactory and effective at increasing users' understanding of problems.
Information Overload in Crisis Management: Bilingual Evaluation of Embedding Models for Clustering Social Media Posts in Emergencies

Past studies in the domains of information systems have analysed the potentials and barriers of social media in emergencies. While information disseminated in social media can lead to valuable insights, emergency services and researchers face the challenge of information overload as data quickly exceeds the manageable amount. We propose an embedding-based clustering approach and a method for the automated labelling of clusters. Given that the clustering quality is highly dependent on embeddings, we evaluate 19 embedding models with respect to time, internal cluster quality, and language invariance. The results show that it may be sensible to use embedding models that were already trained on other crisis datasets. However, one must ensure that the training data generalizes enough, so that the clustering can adapt to new situations. Confirming this, we found out that some embeddings were not able to perform as well on a German dataset as on an English dataset.
Exploring the adoption of mixed-reality sport platforms: A qualitative study on ZWIFT

A novel type of application is presently advancing the digitalisation of sports by transferring physical performance into a virtual world, enabling users to compete and exercise together online. Nevertheless, mass adoption remains a significant challenge. We focus on the running and cycling application ZWIFT, which has received considerable attention as an exemplary mixed-reality sport (MRS) platform. To illuminate adoption factors, we conducted interviews with 22 (non-)users of ZWIFT (including professional cyclists), and apply qualitative content analysis based on the multi-level framework of technology acceptance and use. We refine the UTAUT2 model and define individual-level (e.g., realism, sports identification, task purpose) and higher-level (e.g., weather, values of sport, cheating) context factors and new outcome phenomena (e.g., social network size). Our findings advance the literature on the acceptance of mixed-reality applications in the context of sports, and provide a foundation for further research. Moreover, guidelines for fostering MRS adoption are provided.
Using Deep Learning for Extracting User-Generated Knowledge from Web Communities

The interest in user-generated content (UGC) is steadily growing, as it provides a valuable data source for companies to extract information that can be used for competitive advantage. However, mining UGC and extracting knowledge is a costly and labor-intense endeavour. Against this backdrop, the steep advancements in deep learning (DL) during the last years offer the potential to counteract this. However, DL is still in its infancy in the realm of UGC. Thus, we aim at contributing to the field of knowledge extraction of UGC by comparing traditional machine learning (ML) approaches with state-of-the-art DL models (e.g., BERT) for mining user-generated instructions. We follow the knowledge discovery process to construct a novel corpus of user-generated instructions and develop a best-practice approach to mine knowledge from UGC. Thereby, we intend to foster a better understanding of knowledge extraction systems for UGC and provide a valuable solution to extend existing information systems.
Implementing Modular Design In Digitized Products: Contingencies of Environmental Turbulence And Customer Involvement

Firms today operate in an environment that is characterized by increasing digitization and rapid transformation. Implementing modular architectural design in software incorporated in digitized products thereby provides them with the flexibility needed to successfully adapt their offering to changing environmental conditions. However, this is not a straightforward task. Factors influencing the implementation of modular design are thus of great importance to system engineers. While existing research highlights the relevance of customer involvement as well as environmental factors individually, their contingent effects remain unexplored. Our paper addresses this gap in the literature by empirically investigating the interaction of customer knowledge integration mechanisms with market and technological turbulence, to determine their effect on the implementation of modularity in products’ software architecture. We thereby draw upon theories of modularity as well as insights on customer involvement from information systems (IS) and new product development (NPD) literature.
Explainable AI for tailored electricity consumption feedback-An experimental evaluation of visualizations

Machine learning (ML) methods can effectively analyse data, recognize patterns in them, and make high-quality predictions. Good predictions usually come along with “black-box” models that are unable to present the detected patterns in a human-readable way. Technical developments recently led to eXplainable Artificial Intelligence (XAI) techniques that aim to open such black-boxes and enable humans to gain new insights from detected patterns. We investigated the application of XAI in an area where specific insights can have a significant effect on consumer behaviour, namely electricity use. Knowing that specific feedback on individuals’ electricity consumption triggers resource conservation, we created five visualizations with ML and XAI methods from electricity consumption time series for highly personalized feedback, considering existing domain-specific design knowledge. Our experimental evaluation with 152 participants showed that humans can assimilate the pattern displayed by XAI visualizations, but such visualizations should follow known visualization patterns to be well-understood by users.
Connecting the Dots of Digital Platform Ecosystem Research: Constructs, Causal Links and Future Research

Digital platform ecosystems are at the core of several of the world’s most valuable companies and constitute a strongly growing but fragmented research area at the intersection of multiple research streams such as IS, economics, marketing, strategy and technology management. To date, prior research mainly examines individual constructs and their interrelations in an isolated fashion, with no holistic synthesis of the field’s empirical evidence. Addressing this gap, we surveyed 97 empirical studies in top IS and management journals, extracting all variables and causal links between them. Variables were then aggregated to 51 recurring constructs on seven micro (individual entity) and macro (ecosystem and market) levels of analysis and causal links between them were summarised. We contribute a nomo-logical network of DPE research and present three future research avenues: an emergent multi-level perspective, complex dynamics, and studying the heterogeneity of the field to further bridge its isolated insights.
Approximate Distributionally Robust Nonlinear Optimization with Application to Model Predictive Control: A Functional Approach

We provide a functional view of distributional robustness motivated by robust statistics and functional analysis. This results in two practical computational approaches for approximate distribution-ally robust nonlinear optimization based on gradient norms and reproducing kernel Hilbert spaces. Our method can be applied to the settings of statistical learning with small sample size and test distribution shift. As a case study, we robustify scenario-based stochastic model predictive control with general nonlinear constraints. In particular, we demonstrate constraint satisfaction with only a small number of scenarios under distribution shift
Amazon Prime Video Yesterday, Netflix Today: Explaining Subscribers' Switching Behavior from a Retrospective

Video-on-demand (VoD) services attract millions of subscribers around the globe. Despite their popularity, practice shows the interesting behavior of subscribers of VoD services switching regularly between different providers, such as Netflix or Amazon Prime Video. To sustain their revenues due to subscriptions, providers need to understand the reasons why subscribers switched to other VoD services. While existing research with a prospective point of view explains that users develop switching intentions between different services because of, for instance, dissatisfaction, there is scant research on their actual switching behavior from a retrospective. By analyzing interviews with 23 subscribers that switched VoD services, findings reveal five switching causes and three switching barriers that together explain switching behavior between VoD services. With that, the findings contribute to switching research by identifying switching causes and switching barriers, zooming in on causes of subscribers’ dissatisfaction with VoD services, and studying switching behavior from a retrospective.
Accessibility and availability: A cross-cultural study of shopper responses to online retail stock-outs

ABSTRACT Accessibility of products in online retail is an expected part of the shopper experience journey. Frequent products are not accessible due to non-availability. Introduced by Sheth and Sisodia, the 4A’s framework articulates how success in any marketing program depends on four dimensions: Awareness, Acceptability, Affordability, and Accessibility. This article demonstrates how, like dominos fall, marketing investments can fail when the final 4A’s stage, Accessibility, is not adequately addressed in online retailing. Surveying more than 2,000 shoppers across five European and Asian countries that encountered a non-available item while shopping online for one of six fast-moving consumer goods categories, the research study examines shoppers’ switching behavior when Accessibility has been interrupted in the purchasing stage of the customer journey. The overall goal is to better understand how shoppers change their behavior, and it examines a variety of causes that drive switching behavior, whether it be to switch stores, switch brands, or switch intentions when the item they desired is unavailable. Switching behavior was found to vary greatly among the five countries, but less between categories, and switching was greatly affected by the way shoppers encountered the non-available item. The study concludes with recommendations to address Accessibility both in product availability and shoppers’ transaction costs.
Business transparency and willingness to act environmentally conscious behavior: Applying the sustainable fashion evaluation system “Higg Index”

ABSTRACT The authors conducted a survey of 120 consumers in South Korea to study how fashion brands can use “Higg Index” to increase information transparency, which then enhances environmental concerns, brand trust, willingness to act environmentally conscious behavior, and purchase intentions. Furthermore, consumer sustainable knowledge moderates the effects of environmental concerns and brand trust. The analysis indicates that business transparency positively affects environmental concerns, brand trust, and willingness to act environmentally conscious behavior. Consumers’ sustainable knowledge moderates the effects of business transparency on environmental concerns and brand trust. The findings have meaningful implications for marketer by suggesting the need for transparency, which can be indicated by using an effective transparent evaluation system.
How customer experience management reconciles strategy differences between East and West

ABSTRACT This paper studies how customers of a global firm evaluate their experiences within and across 44 countries. It focuses on customers’ emotional, cognitive, sensory and behavioral responses to the catalog experience. It develops a theory-based model of satisfaction with the catalog experience as a function of experiential attributes and control variables. A second model captures how each experiential attribute’s contribution to the customer experience is influenced by market and customer characteristics. The models were operationalized using survey data from 366,185 customers who used the firm’s catalog across different trade areas in 44 countries, yielding 571 equations that describe satisfaction with the customer experience. Consistent with theoretical work on context-dependent judgments, nine contingency factors explain significant and substantial amounts of variation (30% on average) in the elasticities of the 12 experiential attributes. East and West can appear similar when market characteristics are similar – or when they are different. Emotional, cognitive, sensory, and behavioral responses to the customer experience systematically differ due to economic, demographic, technological, cultural and consumer characteristics. East and West especially differ in terms of responses to emotional and sensory experiences. Customer experience management can help to shape a strategy that resolves strategy differences between East and West.
A new type of multivariate records: depth-based records

There are different definitions for multivariate records which are based on component-wise approach. In this paper, a new approach based on the notion of depth is employed for introducing a depth-based record as a different multivariate record. This record is more in line with univariate records, has Markovian property, ascertained marginal and joint distributions, and some other exclusive features compared to other records. The depth-based procedure can recognize records satisfactorily. The performance of which is evaluated through studying the drought in Kermanshah, Iran, and the air pollution in Leeds, England. The outputs confirm the suitable potency of depth-based record procedure.
Digital service innovation, customer engagement, and customer equity in AR marketing

ABSTRACT Service innovation with digital technology such as augmented reality allows brands to deliver additional values to customers, as interactions between brand and customer improve a brand’s performance. However, there is little research on digital service innovation and its impact on customer behavior. Thus, the authors focus on digital service innovation and aim to answer whether it plays a role in positive customer response. This study examines the relationship between digital service innovation, customer engagement, and customer equity. The findings of this study show that digital service innovation helps improve customer engagement, which also leads to customer equity. Finally, the authors discuss academic and managerial implications.