bluuebunny/crossref_metadata_2025 huggingface dataset:
Gender Differences in Equity Crowdfunding
Authors: Horvat Emoke-Agnes , Papamarkou Theodore
Published: October 2022

Online peer-to-peer investment platforms are increasingly popular venues for entrepreneurs and investors to engage in financial transactions without the involvement of banks and loan managers. Despite their purported transparency and lack of bias, it is unclear whether social inequalities present in traditional capital markets transfer to these platforms as well, impeding their hoped revolutionary potential. In this paper we analyze nearly four years' worth of data from one of the leading UK-based equity crowdfunding platforms. Specifically, we investigate gender-related differences in patterns of entrepreneurship, investment, and success. In agreement with offline trends, men have more activity on the platform. Yet, women entrepreneurs benefit of higher success rates in fund-raising, a finding that mimics trends seen on some rewards-based crowdfunding platforms. Surprisingly, we also find that female investors tend to choose campaigns that have lower success rates. Our findings contribute to a better understanding of gender-related discrepancies in success on the online capital market and point to differences in activity that are key factors in the apparent patterns of gender inequality.
Capturing Ambiguity in Crowdsourcing Frame Disambiguation
Authors: Dumitrache Anca , Aroyo Lora , Welty Chris
Published: October 2022

FrameNet is a computational linguistics resource composed of semantic frames, high-level concepts that represent the meanings of words. In this paper, we present an approach to gather frame disambiguation annotations in sentences using a crowdsourcing approach with multiple workers per sentence to capture inter-annotator disagreement. We perform an experiment over a set of 433 sentences annotated with frames from the FrameNet corpus, and show that the aggregated crowd annotations achieve an F1 score greater than 0.67 as compared to expert linguists. We highlight cases where the crowd annotation was correct even though the expert is in disagreement, arguing for the need to have multiple annotators per sentence.  Most importantly, we examine cases in which crowd workers could not agree, and demonstrate that these cases exhibit ambiguity, either in the sentence, frame, or the task itself, and argue that collapsing such cases to a single, discrete truth value (i.e. correct or incorrect) is inappropriate, creating arbitrary targets for machine learning.
The Role of Novelty in Securing Investors for Equity Crowdfunding Campaigns
Authors: Horvát Emőke-Ágnes , Wachs Johannes , Wang Rong , Hannák Anikó
Published: October 2022

In recent years crowdfunding has diversified and grown beyond most experts' projections. Originally aiming to serve venture ideas and entrepreneurs outside the focus of traditional capital markets, the crowdfunding marketplace has developed a complicated relationship with novel ideas. Yet, there is little to no research on the relationship between project novelty and success in crowdfunding. This paper measures the novelty of crowdfunding campaigns using the content and language of their pitches, capturing their tendency to combine different venture sectors and topics in distinctive ways. Using a unique data set that covers four years of activity on a leading equity crowdfunding platform, we investigate the link between novelty and success, as well as how novelty appeals to different kinds of investors. We find that novelty derived from campaign pitches is negatively related with fundraising success even when controlling for quality and style of writing. We also find that novel campaigns are more likely to attract less-frequent, large-sum investors. Our findings contribute to the long-standing debate related to the trade-offs between innovativeness and conventionality in maximizing chances of startup survival. Our results also have important implications for entrepreneurs writing fundraising pitches and for platform providers who wish to facilitate successful innovation.
Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure
Authors: Nushi Besmira , Kamar Ece , Horvitz Eric
Published: October 2022

As machine learning systems move from computer-science laboratories into the open world, their accountability becomes a high priority problem. Accountability requires deep understanding of system behavior and its failures. Current evaluation methods such as single-score error metrics and confusion matrices provide aggregate views of system performance that hide important shortcomings. Understanding details about failures is important for identifying pathways for refinement, communicating the reliability of systems in different settings, and for specifying appropriate human oversight and engagement. Characterization of failures and shortcomings is particularly complex for systems composed of multiple machine learned components. For such systems, existing evaluation methods have limited expressiveness in describing and explaining the relationship among input content, the internal states of system components, and final output quality. We present Pandora, a set of hybrid human-machine methods and tools for describing and explaining system failures. Pandora leverages both human and system-generated observations to summarize conditions of system malfunction with respect to the input content and system architecture. We share results of a case study with a machine learning pipeline for image captioning that show how detailed performance views can be beneficial for analysis and debugging.
Going against the (Appropriate) Flow: A Contextual Integrity Approach to Privacy Policy Analysis
Authors: Shvartzshnaider Yan , Apthorpe Noah , Feamster Nick , Nissenbaum Helen
Published: October 2022

We present a method for analyzing privacy policies using the framework of contextual integrity (CI). This method allows for the systematized detection of issues with privacy policy statements that hinder readers’ ability to understand and evaluate company data collection practices. These issues include missing contextual details, vague language, and overwhelming possible interpretations of described information transfers. We demonstrate this method in two different settings. First, we compare versions of Facebook’s privacy policy from before and after the Cambridge Analytica scandal. Our analysis indicates that the updated policy still contains fundamental ambiguities that limit readers’ comprehension of Facebook’s data collection practices. Second, we successfully crowdsourced CI annotations of 48 excerpts of privacy policies from 17 companies with 141 crowdworkers. This indicates that regular users are able to reliably identify contextual information in privacy policy statements and that crowdsourcing can help scale our CI analysis method to a larger number of privacy policy statements.
How Do We Talk about Other People? Group (Un)Fairness in Natural Language Image Descriptions
Authors: Otterbacher Jahna , Barlas Pınar , Kleanthous Styliani , Kyriakou Kyriakos
Published: October 2022

Crowdsourcing plays a key role in developing algorithms for image recognition or captioning. Major datasets, such as MS COCO or Flickr30K, have been built by eliciting natural language descriptions of images from workers. Yet such elicitation tasks are susceptible to human biases, including stereotyping people depicted in images. Given the growing concerns surrounding discrimination in algorithms, as well as in the data used to train them, it is necessary to take a critical look at this practice. We conduct experiments at Figure Eight using a controlled set of people images. Men and women of various races are positioned in the same manner, wearing a grey t-shirt. We prompt workers for 10 descriptive labels, and consider them using the human-centric approach, which assumes reporting bias. We find that “what’s worth saying” about these uniform images often differs as a function of the gender and race of the depicted person, violating the notion of group fairness. Although this diversity in natural language people descriptions is expected and often beneficial, it could result in automated disparate impact if not managed properly.
Not Everyone Writes Good Examples but Good Examples Can Come from Anywhere
Authors: Doroudi Shayan , Kamar Ece , Brunskill Emma
Published: October 2022

In many online environments, such as massive open online courses and crowdsourcing platforms, many people solve similar complex tasks. As a byproduct of solving these tasks, a pool of artifacts are created that may be able to help others perform better on similar tasks. In this paper, we explore whether work that is naturally done by crowdworkers can be used as examples to help future crowdworkers perform better on similar tasks. We explore this in the context of a product comparison review task, where workers must compare and contrast pairs of similar products. We first show that randomly presenting one or two peer-generated examples does not significantly improve performance on future tasks. In a second experiment, we show that presenting examples that are of sufficiently high quality leads to a statistically significant improvement in performance of future workers on a near transfer task. Moreover, our results suggest that even among high quality examples, there are differences in how effective the examples are, indicating that quality is not a perfect proxy for pedagogical value.
Studying the “Wisdom of Crowds” at Scale
Authors: Simoiu Camelia , Sumanth Chiraag , Mysore Alok , Goel Sharad
Published: October 2022

In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been dubbed the “wisdom of the crowd”. However, due to the varying contexts, sample sizes, methodologies, and scope of previous studies, it has been difficult to gauge the extent to which conclusions generalize. To investigate this question, we carried out a large online experiment to systematically evaluate crowd performance on 1,000 questions across 50 topical domains. We further tested the effect of different types of social influence on crowd performance. For example, in one condition, participants could see the cumulative crowd answer before providing their own. In total, we collected more than 500,000 responses from nearly 2,000 participants. We have three main results. First, averaged across all questions, we find that the crowd indeed performs better than the average individual in the crowd—but we also find substantial heterogeneity in performance across questions. Second, we find that crowd performance is generally more consistent than that of individuals; as a result, the crowd does considerably better than individuals when performance is computed on a full set of questions within a domain. Finally, we find that social influence can, in some instances, lead to herding, decreasing crowd performance. Our findings illustrate some of the subtleties of the wisdom-of-crowds phenomenon, and provide insights for the design of social recommendation platforms.
Gamification of Loop-Invariant Discovery from Code
Authors: Walter Andrew T. , Boskin Benjamin , Cooper Seth , Manolios Panagiotis
Published: October 2022

Software verification addresses the important societal problem of software correctness by using tools to mechanically prove that software is free of errors. Since the software verification problem is undecidable, automated tools have limited capabilities; hence, to verify non-trivial software, engineers use human-in-the-loop theorem provers that depend on human-provided insights such as loop invariants. The effective use of modern theorem provers requires significant expertise and recent work has explored the possibility of creating human computation games that enable non-experts to find useful loop invariants. A common feature of these games is that they do not show the code to be verified. We present and evaluate a game which does show players code. Showing code poses a number of design challenges, such as avoiding cognitive overload, but, as our experimental evaluation confirms, also provides an opportunity for richer human-computer interactions that lead to more effective human-in-the-loop systems which augment the ability of programmers who are not verification experts to find loop invariants.
Crowdsourced PAC Learning under Classification Noise
Authors: Heinecke Shelby , Reyzin Lev
Published: October 2022

In this paper, we analyze PAC learnability from labels produced by crowdsourcing. In our setting, unlabeled examples are drawn from a distribution and labels are crowdsourced from workers who operate under classification noise, each with their own noise parameter. We develop an end-to-end crowdsourced PAC learning algorithm that takes unlabeled data points as input and outputs a trained classifier. Our three-step algorithm incorporates majority voting, pure-exploration bandits, and noisy-PAC learning. We prove several guarantees on the number of tasks labeled by workers for PAC learning in this setting and show that our algorithm improves upon the baseline by reducing the total number of tasks given to workers. We demonstrate the robustness of our algorithm by exploring its application to additional realistic crowdsourcing settings.
What You See Is What You Get? The Impact of Representation Criteria on Human Bias in Hiring
Authors: Peng Andi , Nushi Besmira , Kıcıman Emre , Inkpen Kori , Suri Siddharth , Kamar Ece
Published: October 2022

Although systematic biases in decision-making are widely documented, the ways in which they emerge from different sources is less understood. We present a controlled experimental platform to study gender bias in hiring by decoupling the effect of world distribution (the gender breakdown of candidates in a specific profession) from bias in human decision-making. We explore the effectiveness of representation criteria, fixed proportional display of candidates, as an intervention strategy for mitigation of gender bias by conducting experiments measuring human decision-makers’ rankings for who they would recommend as potential hires. Experiments across professions with varying gender proportions show that balancing gender representation in candidate slates can correct biases for some professions where the world distribution is skewed, although doing so has no impact on other professions where human persistent preferences are at play. We show that the gender of the decision-maker, complexity of the decision-making task and over- and under-representation of genders in the candidate slate can all impact the final decision. By decoupling sources of bias, we can better isolate strategies for bias mitigation in human-in-the-loop systems.
Fair Work: Crowd Work Minimum Wage with One Line of Code
Authors: Whiting Mark E. , Hugh Grant , Bernstein Michael S.
Published: October 2022

Accurate task pricing in microtask marketplaces requires substantial effort via trial and error, contributing to a pattern of worker underpayment. In response, we introduce Fair Work, enabling requesters to automatically pay their workers minimum wage by adding a one-line script tag to their task HTML on Amazon Mechanical Turk. Fair Work automatically surveys workers to find out how long the task takes, then aggregates those self-reports and auto-bonuses workers up to a minimum wage if needed. Evaluations demonstrate that the system estimates payments more accurately than requesters and that worker time surveys are close to behaviorally observed time measurements. With this work, we aim to lower the threshold for pro-social work practices in microtask marketplaces.
Privacy-Preserving Face Redaction Using Crowdsourcing
Authors: Alshaibani Abdullah , Carrell Sylvia , Tseng Li-Hsin , Shin Jungmin , Quinn Alexander
Published: October 2022

Redaction of private information from images is the kind of tedious, yet context-independent, task for which crowdsourcing is especially well suited. Despite tremendous progress, machine learning is not keeping pace with the needs of sensitive applications in which inadvertent disclosure could have real-world consequences. Human workers can detect faces that machines cannot; however, an open call to crowds would entail disclosure. We present IntoFocus, a method for engaging crowd workers to redact faces from images without disclosing the facial identities of people depicted. The method works iteratively, starting with a heavily filtered form of the image, and gradually reducing the strength of the filter, with a different set of workers reviewing the image at each step. IntoFocus exploits the gap between the filter level at which a face becomes unidentifiable and the level at which it becomes undetectable. To calibrate the algorithm, we performed a perceptual study of detection and identification of faces in images filtered with the median filter. We present the system design, the results of the perception study, and the results of a summative evaluation of the system
Impact of Algorithmic Decision Making on Human Behavior: Evidence from Ultimatum Bargaining
Authors: Erlei Alexander , Nekdem Franck , Meub Lukas , Anand Avishek , Gadiraju Ujwal
Published: October 2022

Recent advances in machine learning have led to the widespread adoption of ML models for decision support systems. However, little is known about how the introduction of such systems affects the behavior of human stakeholders. This pertains both to the people using the system, as well as those who are affected by its decisions. To address this knowledge gap, we present a series of ultimatum bargaining game experiments comprising 1178 participants. We find that users are willing to use a black-box decision support system and thereby make better decisions. This translates into higher levels of cooperation and better market outcomes. However, because users under-weigh algorithmic advice, market outcomes remain far from optimal. Explanations increase the number of unique system inquiries, but users appear less willing to follow the system’s recommendation. People who negotiate with a user who has a decision support system, but cannot use one themselves, react to its introduction by demanding a better deal for themselves, thereby decreasing overall cooperation levels. This effect is largely driven by the percentage of participants who perceive the system’s availability as unfair. Interpretability mitigates perceptions of unfairness. Our findings highlight the potential for decision support systems to further human cooperation, but also the need for regulators to consider heterogeneous stakeholder reactions. In particular, higher levels of transparency might inadvertently hurt cooperation through changes in fairness perceptions.
How Context Influences Cross-Device Task Acceptance in Crowd Work
Authors: Hettiachchi Danula , Wijenayake Senuri , Hosio Simo , Kostakos Vassilis , Goncalves Jorge
Published: October 2022

Although crowd work is typically completed through desktop or laptop computers by workers at their home, literature has shown that crowdsourcing is feasible through a wide array of computing devices, including smartphones and digital voice assistants. An integrated crowdsourcing platform that operates across multiple devices could provide greater flexibility to workers, but there is little understanding of crowd workers’ perceptions on uptaking crowd tasks across multiple contexts through such devices. Using a crowdsourcing survey task, we investigate workers’ willingness to accept different types of crowd tasks presented on three device types in different scenarios of varying location, time and social context. Through analysis of over 25,000 responses received from 329 crowd workers on Amazon Mechanical Turk, we show that when tasks are presented on different devices, the task acceptance rate is 80.5% on personal computers, 77.3% on smartphones and 70.7% on digital voice assistants. Our results also show how different contextual factors such as location, social context and time influence workers decision to accept a task on a given device. Our findings provide important insights towards the development of effective task assignment mechanisms for cross-device crowd platforms.
A Case for Soft Loss Functions
Authors: Uma Alexandra , Fornaciari Tommaso , Hovy Dirk , Paun Silviu , Plank Barbara , Poesio Massimo
Published: October 2022

Recently, Peterson et al. provided evidence of the benefits of using probabilistic soft labels generated from crowd annotations for training a computer vision model, showing that using such labels maximizes performance of the models over unseen data. In this paper, we generalize these results by showing that training with soft labels is an effective method for using crowd annotations in several other ai tasks besides the one studied by Peterson et al., and also when their performance is compared with that of state-of-the-art methods for learning from crowdsourced data.
Schema and Metadata Guide the Collective Generation of Relevant and Diverse Work
Authors: Xu Xiaotong , Fan Judith , Dow Steven
Published: October 2022

While most crowd work seeks consistent answers, creative domains often seek more diverse input. The typical crowd mechanisms for controlling quality may stifle creativity, yet removing them altogether could just produce noise. Schemas and metadata provide two mechanisms for embedding existing knowledge into task environments. Schemas are expert-derived patterns designed to structure how people think through a problem. Metadata, on the other hand, illustrate a range of creative input that fits within the structure of a schema. To understand the relative effects of schemas and metadata, we conducted a study where crowd workers are asked to generate creative interpretations for a set of placemaking examples. Crowd workers were guided either by schema plus metadata, schema alone, or neither. We found that showing schema along with crowd-produced metadata helped workers contribute interpretations that are both more on-topic and diverse, compared to using the schema alone or no schema. We discuss the implications on how crowds can creatively build on insights shared by others.
From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence
Authors: Alvarez Melis David , Kaur Harmanpreet , Daumé III Hal , Wallach Hanna , Wortman Vaughan Jennifer
Published: October 2022

We take inspiration from the study of human explanation to inform the design and evaluation of interpretability methods in machine learning. First, we survey the literature on human explanation in philosophy, cognitive science, and the social sciences, and propose a list of design principles for machine-generated explanations that are meaningful to humans. Using the concept of weight of evidence from information theory, we develop a method for generating explanations that adhere to these principles. We show that this method can be adapted to handle high-dimensional, multi-class settings, yielding a flexible framework for generating explanations. We demonstrate that these explanations can be estimated accurately from finite samples and are robust to small perturbations of the inputs. We also evaluate our method through a qualitative user study with machine learning practitioners, where we observe that the resulting explanations are usable despite some participants struggling with background concepts like prior class probabilities. Finally, we conclude by surfacing design implications for interpretability tools in general.
Exploring the Music Perception Skills of Crowd Workers
Authors: Samiotis Ioannis Petros , Qiu Sihang , Lofi Christoph , Yang Jie , Gadiraju Ujwal , Bozzon Alessandro
Published: October 2022

Music content annotation campaigns are common on paid crowdsourcing platforms. Crowd workers are expected to annotate complicated music artefacts, which can demand certain skills and expertise. Traditional methods of participant selection are not designed to capture these kind of domain-specific skills and expertise, and often domain-specific questions fall under the general demographics category. Despite the popularity of such tasks, there is a general lack of deeper understanding of the distribution of musical properties - especially auditory perception skills - among workers. To address this knowledge gap, we conducted a user study (N=100) on Prolific. We asked workers to indicate their musical sophistication through a questionnaire and assessed their music perception skills through an audio-based skill test. The goal of this work is to better understand the extent to which crowd workers possess higher perceptions skills, beyond their own musical education level and self reported abilities. Our study shows that untrained crowd workers can possess high perception skills on the music elements of melody, tuning, accent and tempo; skills that can be useful in a plethora of annotation tasks in the music domain.
Enhancing Image Classification Capabilities of Crowdsourcing-Based Methods through Expanded Input Elicitation
Authors: Yasmin Romena , Grassel Joshua T. , Hassan Md Mahmudulla , Fuentes Olac , Escobedo Adolfo R.
Published: October 2022

This study investigates how different forms of input elicitation obtained from crowdsourcing can be utilized to improve the quality of inferred labels for image classification tasks, where an image must be labeled as either positive or negative depending on the presence/absence of a specified object. Three types of input elicitation methods are tested: binary classification (positive or negative); level of confidence in binary response (on a scale from 0-100%); and what participants believe the majority of the other participants' binary classification is. We design a crowdsourcing experiment to test the performance of the proposed input elicitation methods and use data from over 200 participants. Various existing voting and machine learning (ML) methods are applied and others developed to make the best use of these inputs. In an effort to assess their performance on classification tasks of varying difficulty, a systematic synthetic image generation process is developed. Each generated image combines items from the MPEG-7 Core Experiment CE-Shape-1 Test Set into a single image using multiple parameters (e.g., density, transparency, etc.) and may or may not contain a target object. The difficulty of these images is validated by the performance of an automated image classification method. Experimental results suggest that more accurate classifications can be achieved when using the average of the self-reported confidence values as an additional attribute for ML algorithms relative to what is achieved with more traditional approaches. Additionally, they demonstrate that other performance metrics of interest, namely reduced false-negative rates, can be prioritized through special modifications of the proposed aggregation methods that leverage the variety of elicited inputs.
Unraveling Balinese Binding
Authors: Satik Deniz
Published: October 2022

This paper discusses the logophoric properties of Balinese anaphors. This paper first presents evidence showing that the Balinese complex anaphor may be interpreted logophorically even in the presence of a syntactic coargument. This leads to a novel solution of the "Balinese Bind paradox." This proposal is better able to account for the interpretive constraints observed in raising constructions than previous proposals. In addition to this analysis, this paper discusses the implications of the properties of the Balinese simplex anaphor, which cannot be interpreted logophorically; only its complex counterpart can. This presents a pattern opposite to the one reported and predicted by the literature.
Victorian Beauty…1945–1955
Authors: Nead Lynda
Published: October 2022

This article examines the reworking of Victorian beauty in the visual culture of post-war Britain. Although the work of post-war modernization and reconstruction was regarded as a symbolic break with the nineteenth-century past, the Victorian age continued to haunt the nation in the years following the end of the war, generating its own distinctive fascination and beauty. Focusing on an historical melodrama, Pink String and Sealing Wax (UK; dir. Robert Hamer, 1945), the article examines the narrative and its star, Googie Withers, in order to examine the role, meaning, and appeal of Victorianism and Victorian beauty in the post-war years.
The Pinwill Sisters
Authors: Warner Marina
Published: October 2022

This study of the Pinwill Sisters traces their woodcarving designs across Cornwall and Devon, emphasizing their place as part of a wider aesthetic revolution. It addresses in particular the skill and craft of Violet Pinwill (1874–1957), studying her artistry alongside other woodcarvers such as Grinling Gibbons (1648–1721), as well as artists later into the twentieth century (Barbara Hepworth and John Skeaping).
Sparganion hyperborei – new alliance in water-bodies of the Arctic and mountainous regions of Eurasia
Authors:   Institute of Biology Komi SC UB RAS, Teteryuk Boris Yu. , Lavrinenko Olga V. ,   V.L. Komarov Botanical Institute RAS, Kipriyanova Laura M. ,   Central Siberian Botanical Garden SB RAS
Published: October 2022

New associations of aquatic vegetation of the Arctic and mountain regions outside the Arctic, Sparganietum hyperborei ass. nov. with two variants (S. h. inops и S. h. typica) and ass. Warnstorfio exannulatae–Sparganietum hyperborei ass. nov., are described. Associations assigned to the new alliance Sparganion hyperborei all. nov. in the class Littorelletea uniflorae Br.-Bl. et Tx. ex Westhoff et al. 1946.
The first records of Cyanobacteria diversity in the benthos of the Israeli Coast of the Mediterranean Sea
Authors:   Moscow Representative Office of A.O. Kovalevsky Institute of Biology of the Southern Seas RAS, Miroshnichenko Ekaterina S. , Barinova Sophia S. ,   Institute of Evolution, University of Haifa, Ryabushko Larisa I. ,   Moscow Representative Office of A.O. Kovalevsky Institute of Biology of the Southern Seas RAS
Published: October 2022

The work aims the study the species diversity and ecology of Cyanobacteria in the benthos on the Israeli coast of the Mediterranean Sea. As a result, 24 taxa of Cyanobacteria were found. Seven taxa were noted for the first time for this sea: Aphanocapsa incerta, Aphanothece nidulans, Chlorogloea cf. microcystoides, Pseudanabaena mucicola, Chroococcus sp., Gloeocapsopsis sp., Trichocoleus sp. Most Cyanobacteria species were benthic (90 %) and marine (67 %). There were only four indicators of saprobity, and all of them were freshwater species common in oligosaprobic and beta-mesosaprobic waters. Most species had a wide geographic distribution. Species marked of boreal (10 %), boreal-tropical (38 %), arctic-boreal-tropic (29 %) and cosmopolites (24 %) groups. The species richness of Cyanobacteria increased in the sites with the maximal anthropogenic loads from industrial and urban areas of the central Israel, and the minimal ones were in the protected areas.
Quantitative dynamics of the early Pliocene climate and vegetation in the Lena River Delta (northern Yakutia, Eastern Siberia)
Authors:   Federal Scientific Center of the East Asia Terrestrial Biodiversity FEB RAS, Bondarenko Olesia V. , Utescher Torsten ,   Senckenberg Research Institute and Natural History Museum,   Steinmann Institute, University of Bonn
Published: October 2022

Early Pliocene climate and vegetation evolution in the Lena River Delta (northern Yakutia, Eastern Siberia) are quantitatively studied for the first time in de­ tail, based on a palaeobotanical record. Palaeobotanical data for this time-interval were obtained on 28 palynofloras from the Sardakhskaya Formation in the coastal cliffs of the Sardakh-Sisse Island (Lena River Delta). In this first integrative study we apply the Coexistence Approach (CA) for quantification of palaeoclimate, the Plant Functional Types (PFT) approach and Integrated Plant Record (IPR) vegetation analysis for quantification of palaeovegetation. Our investigation documents the persistence of temperate climate with two warmer and colder phases. Moreover, our reconstructions show significantly higher mean values of all temperature parameters in comparison to previous estimations. Overall hu­ mid climate conditions (up to 1500 mm) prevailed throughout the early Pliocene correspond to an oceanic climate. Our reconstruction indicates relatively strong temperature and precipitation seasonality. Our data for vegetation show more open vegetation in comparison to previous studies reconstructing mixed conifer- deciduous forests for the early Pliocene of Northeast of Russia. Based on PFT, the early Pliocene vegetation of northern Yakutia in Lena River Delta mainly was presented by more or less open mixed deciduous woodlands of warmer or colder temperate type. The results obtained from the PFT approach generally are in ac­ cordance with our data obtained from IPR approach. However, based on IPR, periodically forests and grasslands were obtained in addition to open woodlands.
Diversity of filamentous fungi associated with Sargassum miyabei Yendo
Authors:   G.B. Elyakov Pacific Institute of Bioorganic Chemistry FEB RAS, Kirichuk Natalya N. , Chausova Victoriya Ye. ,   G.B. Elyakov Pacific Institute of Bioorganic Chemistry FEB RAS, Pivkin Michael V. ,   G.B. Elyakov Pacific Institute of Bioorganic Chemistry FEB RAS
Published: October 2022

We present data on the diversity of culturable fungi associated with brown algae Sargassum miyabei (the Sea of Japan, Russia). By the plate method, 269 isolates were obtained using solid nutrient media and 31 species from 12 genera were identified based on phenotypic as well as molecular genetic traits (using ITS and beta-tubulin markers) in cases where identification based on morphological features alone was difficult. The structure of the species composition was analyzed and the list of species was given. The analysis showed that facultative marine fungi were the vast majority of the species diversity, namely Penicillium, Cladosporium, Alternaria, Monodictys, Sarocladium, Trichoderma, Fusarium, Arthrinium, Botrytis and Aspergillus species. Among obligate marine fungi, Paradendryphiella and Piricauda species were revealed by the method mentioned above. Also, taxonomic affiliation and phylogenetic position of some Penicillium representatives have been clarified.
Assessment of the mixed coniferousbroadleaved forest canopy disturbance induced by typhoon Maysak (2020) using drone-borne images near Vladivostok, Russia
Authors:   Botanical Garden-Institute FEB RAS, Dziziurova Violetta D. , Korznikov Kirill A. ,   Botanical Garden-Institute FEB RAS, Petrenko Tatiana Y. ,   Botanical Garden-Institute FEB RAS, Dudov Sergey V. ,   Lomonosov Moscow State University, Krestov Pavel V. ,   Botanical Garden-Institute FEB RAS
Published: October 2022

We assess the impact of typhoon Maysak (2020) on the canopy structure of mixed coniferous-broadleaved forests with Abies holophylla, Pinus koraiensis and a complex of deciduous broad-leaved species and secondary oak forests of Quercus mongolica. The study was conducted in the experimental forest of the Botanical Garden-Institute FEB RAS. We classified the natural forest vegetation of the study area, created a high-resolution vegetation map, and assessed the damage from typhoon Maysak remotely using the ultra-high resolution images, obtained with an unmanned aerial vehicle. We found that the largest number and area of canopy gaps are associated with the conditionally primary coniferous-broadleaved forests, where Abies holophylla and Pinus koraiensis form the main canopy layer. The smallest number and area of gaps relate to the secondary oak forests.
Phytoextraction of Chromium VI by Raphanus sativus L. under exogenous application of citric acid
Authors:   Laboratory of Ecology and Environment, Faculty of Sciences Ben M'sik, Hassan II University of Casablanca, Bouhadi Mohammed , Elkouali M’hammed ,   Laboratory of Analytical and Molecular Chemistry, Faculty of Sciences Ben M'sik, Hassan II University of Casablanca, Talbi Mohammed ,   Laboratory of Analytical and Molecular Chemistry, Faculty of Sciences Ben M'sik, Hassan II University of Casablanca, Amegrissi Fatiha ,   Laboratory of Analytical and Molecular Chemistry, Faculty of Sciences Ben M'sik, Hassan II University of Casablanca, Fougrach Hassan ,   Laboratory of Ecology and Environment, Faculty of Sciences Ben M'sik, Hassan II University of Casablanca
Published: October 2022

The pollution of environment by toxic heavy metals has increased dramatically over the past decades due to various sources of contamination, mainly industrial and human activities. Soil is the main recipient of air and water contamination. In this study, we evaluated the ability of radish plants to accumulate chromium VI (Cr(VI)) (150 ppm) under the application of citric acid (CA) (15 mM). The results showed that Cr(VI) reduced shoot dry weight, root dry weight, hypocotyl dry weight, and total chlorophyll content. On the other hand, Cr(VI) increased H2O2, malondialdehyde, and antioxidant activities of the radish plant. However, the application of CA improved the growth and physiological parameters and antioxidant content (superoxide dismutase 5 % and 4 %, peroxidase content 24 % and 4.25 %, catalase 38 % and 28 %, and ascorbate peroxidase 4 % and 35 %) in root and shoot part respectively, and increases the accumulation of Cr(VI) in different parts of the radish plant.
The effects of different typical meteorological year data on the heating and cooling demand of buildings: Case study of Turkiye
Authors: ACAR Uğur , KAŞK Önder , TOKGÖZ Nehir
Published: October 2022

The most important parameter which affects the results of building energy analysis is the weather data and it can be obtained by different methods for the same location. Although lots of studies have been conducted for Türkiye, it was seen that the impact of different weather data for the same location has never been investigated. The aims of this study were to compare the heating and cooling demands of the buildings with respect to different weather files. Building loads were calculated using five different meteorological source data. Calculations are made for eight cities which represent heating and cooling dominated climates of Türkiye. Calculation procedure of internal heat gain was explained in detail. All simulations were performed using Energyplus v9.2. The findings of the comparison showed that although some results are similar to each other for some weather files, they could have great variances in the energy analysis also. A common missing meteorological data-filling algorithm may be developed in order to reduce the deviations in energy analysis results.
Epidemiological study of COVID-19 mortality in a tertiary care center of South India
Authors: Thayyil Jayakrishnan , Padmanabhan Aparna , Gangadharan Alan , Annikady Navya
Published: October 2022

Background: COVID-19 infection have taken a huge death toll across the globe since its emergence in 2019 in Wuhan, China. There have been inconsistencies related to many of the epidemiological features, which vary from country to country or are not known or explained properly. Therefore, there is an urgent need to generate epidemiological evidence of COVID-19 at the state level based on the data. Also, very limited published studies from India have analyzed the factors associated with death due to COVID-19. The objective of this study was to identify the epidemiological factors involved in COVID deaths in a tertiary care center of north Kerala, South India and to compare the trend of these factors affecting COVID-19 mortality in the consecutive years.Methods: Descriptive study was conducted prospectively using secondary data extracted from the electronic surveillance records of the tertiary care center were analyzed using suitable software and presented.Results: During the study period 1747 deaths due to COVID-19 was reported. The duration of hospital stay was 6.5+ 6.3 days. The median age was 67.5 years (IQR14) and males constitute 67.5% (95% CI: 64.8-69.3%). Among them 88.3% (95% CI:86.7-89.7%) have any of reported co morbidities which mainly diabetes and hypertension. Immediate cause of mortality mainly (73.6%) ARDS. Epidemiological factors related to mortality was similar during years.Conclusions: Since COVID-19 deaths affect older age groups, and those with comorbidities, reverse isolation needs to be given more importance wherever possible and its high time to consider targeted COVID-19 vaccination among them.
Ergonomic Working Design Model in Reducing Fatigue due to Air Traffic Control (ATC) at Kuala Namu Airport, Indonesia
Authors: Aisyah Siti , Abbas Aries , Hasibuan Abdurrozzaq , Masri Debi , Frieyadie  , Fudholi Ahmad
Published: October 2022

Generally, humans can work properly and achieve optimal results when supported by good environmental conditions. The effect associated with the inconvenience of the working environment can be experienced over a prolonged period. Therefore, this study aims to determine the ergonomic working design model in reducing fatigue due to Air Traffic Control (ATC) at Kuala Namu Airport, Deli Serdang – Medan, Indonesia. This is because it is necessary to consider the physical condition or health conditions of controllers to enable the company to extend its services' life and profitability. Rapid Upper Limb Assessment (RULA) is the research method used to investigate upper limb disorders. RULA was developed as a method to detect posture, which is a risk factor. This method is designed to assess workers and determine the musculoskeletal loads likely to disrupt upper limbs. Under such conditions, the management is advised to develop 'open management' based on political will, which involves operators in every step of the improvement, because they have adequate ideas of the problems at hand. Participation in ergonomics enables employees with their supervisors and managers to apply adequate knowledge in their workplace to enhance working environment conditions.
Comprehensive Analysis of Privacy Attacks in Online Social Network: Security Issues and Challenges
Authors: Kahate Sandip A. , Raut Atul D.
Published: October 2022

Nowadays, users value their privacy of information is more than money but the Online Social Network is creating new platforms for cybercrime to intimidate innocent users of countries because of the privacy pitfalls in the present traditional centralized architecture like Facebook, WhatsApp, and Twitter, Instagram, and many more. Third-party apps and malicious attackers breach innocent users' private information, especially adolescent users for their personal purpose. Many Asian countries have no special data protection laws like a European. The main objectives of this research paper are to study the different types of privacy attacks for data confidentiality in the Online Social networks with analyzed mitigation techniques and discussed future proposed work on how to control and detect the user’s private information from unreliable people. Even then, few privacy destructions are unresolved!
Risk Management System at an Engineering Enterprise in Conditions of Ensuring Security
Authors: Petryshyn Nataliia , Mykytyn Oleh , Malinovska Olga , Khalina Olena , Kirichenko Olha
Published: October 2022

In modern conditions, the problem of the survival of companies, and the preservation and provision of their further development has become particularly relevant. The crisis has engulfed not only individual enterprises but entire industries. The most affected, in particular, is the engineering industry. The main purpose of the study is the formation of a risk management system at an engineering enterprise in terms of ensuring its security. To do this, we applied the IDEF0 modelling methodology with its main elements. The dynamism of the economic environment and the complexity of the links between its elements necessitate the adoption of informed management decisions in the face of risk and uncertainty of future results. Risk management is becoming an obligatory activity for engineering enterprises, implementation of projects, and operations. Based on the results of the study, a basic IDEF0 model of the risk management system at an engineering enterprise in terms of ensuring its security was formed. The study has limitations and, first of all, they relate to the specifics of the activity of engineering enterprises, other areas of activity are not taken into account. Further research requires expanding the model and taking into account not only risks but also threats and direct dangers.
A method for generating a microwave vortex field
Authors:   V.I. Vernadsky Crimean Federal University, Poletaev D.A. , Sokolenko B.V. ,   V.I. Vernadsky Crimean Federal University
Published: October 2022

In this paper, we propose an on-end structure of a circular waveguide that forms a vortex field of the microwave range. We construct its numerical model. The numerical experiments conducted confirm the possibility of generating a microwave vortex field by means of the proposed structure. The best suitable material for fabricating this structure is also selected in the work.
E2F1-induced long non-coding RNA MCF2L-AS1 modulates Cyclin D1 mRNA stability through ELAVL1 to induce Gefitinib resistance in non-small cell lung cancer
Authors: Shan Kui Zhong , Yang Su Fang , Deng Yu Jiang , Yue Pei Yu , Du Zhi Quan
Published: October 2022

Purpose: Gefitinib is a widely used therapeutic drug for non-small cell lung cancer (NSCLC), and its acquired resistance has become one of the barriers to the successful use of the drugs to treat NSCLC patients. Long non-coding RNA (lncRNA) has an essential role in developing cancer drug resistance. Hence, this study aimed to investigate the effect and modulatory mechanisms of lncRNA MCF2L-AS1 in Gefitinib resistance in NSCLC. Methods: IBEAS-2B and A549 cells and human NSCLC tissues were used. A549/GR cell line was constructed by continuous exposure to Gefitinib. Cell viability, apoptosis, migration, colony formation, and protein expression studies were done in transfected cells. Interactions of MCF2L-AS1, ELAVL1, and Cyclin D1 (CCND1 was also investigated. Results: In patients with Gefitinib-resistant NSCLC, MCF2L-AS1 and CCND1 were both up-regulated. Knockdown of MCF2L-AS1 reduced Gefitinib-resistant NSCLC cell progression, indicating that inhibition of MCF2L-AS1 restrained Gefitinib-resistant NSCLC. Mechanically, MCF2L-AS1 enhanced CCND1 mRNA stability via combining with ELAVL1, thereby elevating the resistance of NSCLC cells to Gefitinib. Moreover, E2F1 could transcriptionally up-regulate MCF2L-AS1. Conclusion: The results manifest that lncRNA MCF2L-AS1, as an oncogene of NSCLC, controls CCDN1 via ELAVL1 to drive the growth of NSCLC cells and Gefitinib resistance.
Interaction of 3H-furan-2-ones and 4-oxobutanoic acids with 2-(aminophenyl)methanol
Authors:   Saratov State University, Amalʼchieva Olga A. , Grinev Vyacheslav S. ,   Saratov State University, Institute of Biochemistry and Physiology of Plants and Microorganisms, Russian Academy of Sciences, Demeshko Ilya A. ,   Saratov State University, Yegorova Alevtina Yu. ,   Saratov State University
Published: October 2022

Reactions of 4-oxobutanoic acids as well as their cyclic analogues 3H-furan-2-ones with 1,3-binucleophilic reagent (2-aminophenyl) methanol lead to the formation of 1-R-5H-benzo[d]pyrrolo[2,1-b][1,3]oxazines and 3a-R-2,3,3a-trihydro-5H-benzo[d]pyrrolo[2,1-b][1,3]oxazin- 1-ones, respectively. Quantum-chemical calculations of the Fukui reactivity indices and local hardness have substantiated the mechanisms of the reactions carried out. According to the calculated values of the Fukui indices, in the (2-aminophenyl)methanol molecule, compared to the hydroxyl, the amino group has a higher nucleophilicity, which suggests that its initial nucleophilic attack on the electrophilic centers of the substrates is more probable. According to the calculations, the carbonyl carbon atoms in the molecules of 3H-furan-2-ones have much more pronounced local hardness compared to both carbonyl carbon atoms of 4-oxo acids, which suggests an initial attack by a harder nucleophilic center, the hydroxyl group of 2-(aminophenyl)methanol. Thus, it has been shown that the structure of the formed benzopyrroloxazine(one)s depends on the studied substrate.