Showing papers on "Listing (finance) published in 2022"

The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms

Abstract: We herein present an overview of the upcoming 5th edition of the World Health Organization Classification of Haematolymphoid Tumours focussing on lymphoid neoplasms. Myeloid and histiocytic neoplasms will be presented in a separate accompanying article. Besides listing the entities of the classification, we highlight and explain changes from the revised 4th edition. These include reorganization of entities by a hierarchical system as is adopted throughout the 5th edition of the WHO classification of tumours of all organ systems, modification of nomenclature for some entities, revision of diagnostic criteria or subtypes, deletion of certain entities, and introduction of new entities, as well as inclusion of tumour-like lesions, mesenchymal lesions specific to lymph node and spleen, and germline predisposition syndromes associated with the lymphoid neoplasms.

Solar cell efficiency tables (Version 60)

Abstract: Consolidated tables showing an extensive listing of the highest independently confirmed efficiencies for solar cells and modules are presented. Guidelines for inclusion of results into these tables are outlined, and new entries since January 2022 are reviewed. An appendix describing temporary electrical contacting of large-area solar cells approaches and terminology is also included.

Solar cell efficiency tables (Version 61)

Abstract: Consolidated tables showing an extensive listing of the highest independently confirmed efficiencies for solar cells and modules are presented. Guidelines for inclusion of results into these tables are outlined, and new entries since July 2022 are reviewed. Graphs showing progress with each cell technology over the 30-year history of the tables are also included plus an updated list of designated test centres.

DrugShot: querying biomedical search terms to retrieve prioritized lists of small molecules

Abstract: PubMed contains millions of abstracts that co-mention terms that describe drugs with other biomedical terms such as genes or diseases. Unique opportunities exist for leveraging these co-mentions by integrating them with other drug-drug similarity resources such as the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 signatures to develop novel hypotheses.DrugShot is a web-based server application and an Appyter that enables users to enter any biomedical search term into a simple input form to receive ranked lists of drugs and other small molecules based on their relevance to the search term. To produce ranked lists of small molecules, DrugShot cross-references returned PubMed identifiers (PMIDs) with DrugRIF or AutoRIF, which are curated resources of drug-PMID associations, to produce an associated small molecule list where each small molecule is ranked according to total co-mentions with the search term from shared PubMed IDs. Additionally, using two types of drug-drug similarity matrices, lists of small molecules are predicted to be associated with the search term. Such predictions are based on literature co-mentions and signature similarity from LINCS L1000 drug-induced gene expression profiles.DrugShot prioritizes drugs and small molecules associated with biomedical search terms. In addition to listing known associations, DrugShot predicts additional drugs and small molecules related to any search term. Hence, DrugShot can be used to prioritize drugs and preclinical compounds for drug repurposing and suggest indications and adverse events for preclinical compounds. DrugShot is freely and openly available at: https://maayanlab.cloud/drugshot and https://appyters.maayanlab.cloud/#/DrugShot .

DrugShot: querying biomedical search terms to retrieve prioritized lists of small molecules

Abstract: PubMed contains millions of abstracts that co-mention terms that describe drugs with other biomedical terms such as genes or diseases. Unique opportunities exist for leveraging these co-mentions by integrating them with other drug-drug similarity resources such as the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 signatures to develop novel hypotheses.DrugShot is a web-based server application and an Appyter that enables users to enter any biomedical search term into a simple input form to receive ranked lists of drugs and other small molecules based on their relevance to the search term. To produce ranked lists of small molecules, DrugShot cross-references returned PubMed identifiers (PMIDs) with DrugRIF or AutoRIF, which are curated resources of drug-PMID associations, to produce an associated small molecule list where each small molecule is ranked according to total co-mentions with the search term from shared PubMed IDs. Additionally, using two types of drug-drug similarity matrices, lists of small molecules are predicted to be associated with the search term. Such predictions are based on literature co-mentions and signature similarity from LINCS L1000 drug-induced gene expression profiles.DrugShot prioritizes drugs and small molecules associated with biomedical search terms. In addition to listing known associations, DrugShot predicts additional drugs and small molecules related to any search term. Hence, DrugShot can be used to prioritize drugs and preclinical compounds for drug repurposing and suggest indications and adverse events for preclinical compounds. DrugShot is freely and openly available at: https://maayanlab.cloud/drugshot and https://appyters.maayanlab.cloud/#/DrugShot .

The Antibody Registry: ten years of registering antibodies

Abstract: Abstract Antibodies are ubiquitous key biological research resources yet are tricky to use as they are prone to performance issues and represent a major source of variability across studies. Understanding what antibody was used in a published study is therefore necessary to repeat and/or interpret a given study. However, antibody reagents are still frequently not cited with sufficient detail to determine which antibody was used in experiments. The Antibody Registry is a public, open database that enables citation of antibodies by providing a persistent record for any antibody-based reagent used in a publication. The registry is the authority for antibody Research Resource Identifiers, or RRIDs, which are requested or required by hundreds of journals seeking to improve the citation of these key resources. The registry is the most comprehensive listing of persistently identified antibody reagents used in the scientific literature. Data contributors span individual authors who use antibodies to antibody companies, which provide their entire catalogs including discontinued items. Unlike many commercial antibody listing sites which tend to remove reagents no longer sold, registry records persist, providing an interface between a fast-moving commercial marketplace and the static scientific literature. The Antibody Registry (RRID:SCR_006397) https://antibodyregistry.org.

Do Not Rug on Me: Leveraging Machine Learning Techniques for Automated Scam Detection

Abstract: Uniswap, as with other DEXs, has gained much attention this year because it is a non-custodial and publicly verifiable exchange that allows users to trade digital assets without trusted third parties. However, its simplicity and lack of regulation also make it easy to execute initial coin offering scams by listing non-valuable tokens. This method of performing scams is known as rug pull, a phenomenon that already exists in traditional finance but has become more relevant in DeFi. Various projects have contributed to detecting rug pulls in EVM compatible chains. However, the first longitudinal and academic step to detecting and characterizing scam tokens on Uniswap was made . The authors collected all the transactions related to the Uniswap V2 exchange and proposed a machine learning algorithm to label tokens as scams. However, the algorithm is only valuable for detecting scams accurately after they have been executed. This paper increases their dataset by 20K tokens and proposes a new methodology to label tokens as scams. After manually analyzing the data, we devised a theoretical classification of different malicious maneuvers in the Uniswap protocol. We propose various machine-learning-based algorithms with new, relevant features related to the token propagation and smart contract heuristics to detect potential rug pulls before they occur. In general, the models proposed achieved similar results. The best model obtained accuracy of 0.9936, recall of 0.9540, and precision of 0.9838 in distinguishing non-malicious tokens from scams prior to the malicious maneuver.

The Shifting Baseline Syndrome and Generational Amnesia in Heritage Studies

Abstract: It is widely understood that the preservation of cultural heritage sites and objects is underpinned by values projected by the public onto essentially inanimate objects, that these values vary in strength, and that they are mutable qualities. Using hindsight, the contemporary values are projected on past creations that persist into the present. If deemed significant, these past creations will be listed on heritage lists and afforded various levels of protection. As time moves on, new places or objects will be deemed significant and added to the lists. Using a case study, this paper examines the concept of shifting baselines and how they impact on the identification and listing/protection of heritage places. It will demonstrate that generational biases play a significant role in the initial listing and exert a lasting legacy through the static nature of heritage listings.

From financial performance to sustainable development: A great evolution and an endless debate

Abstract: The concept of ‘Performance’ is one of the most used words, both in the academic and professional spheres, due to its importance in all fields. In addition to its very high frequency of use, its definition is polysemous. This paper aims to focus on the surrounding of the performance, by listing several definitions and tracing its evolution over time. This paper also proposes the treatment of performance in all its facets, from the financial one to the global and sustainable one, and by highlighting the complementary aspect of the different approaches of treatment of this concept. To do this, we were interested in articles and books referenced in the Scopus, Cairn, Electre and Google Scholar databases, and we selected the scientific production between 1960 and 2020, which deals with either the definition or the link between the concepts ‘Performance’, ‘CSR’, ‘CSP’ and ‘Sustainable Development’, to synthesize them in this article following a chronological and logical order. This article is intended as a synthetic guide for any researcher or professional interested in the concept of performance, since it traces its evolution and its ramifications through the highlighting of the complementarity and the relevant use of this concept.

Corporate governance and financial performance: evidence from listed SMEs in India

Abstract: PurposeCorporate governance across small and medium enterprises (SMEs) is undergoing unremitting changes, primarily due to the listing of SMEs on SME exchanges. The changing aspects of governance may influence the financial performance of SMEs. This paper examines how corporate governance influences the financial performance of listed SMEs in the context of developing economies like India. Ownership concentration (promoters' holding) and information disclosures measure corporate governance in this examination.Design/methodology/approachThe sample for this study includes 88 listed SMEs from the Bombay Stock Exchange (BSE) SME platform in India. The data are collected for the period between 2018 and 2020. The study employs panel data analysis. The fixed effects model, coupled with the computation of cluster robust standard errors, is used to test the relationship between variables.FindingsThe results demonstrate that ownership concentration is not significantly related to financial performance. Further, information disclosures are inversely significant for financial performance. The results show that agency problems and information asymmetry plague the sampled firms. Further, the results of the study are indicative of inefficiencies in the governance structures of SMEs. Thus, it is evident that listed SMEs fail to reap the benefits of corporate governance.Practical implicationsThe study's findings should enlighten SME owners and managers on the benefits of corporate governance for SMEs. This is a pressing need at current times as the listing of SMEs is shifting the landscape of SME governance. Today, all firms, including SMEs, are expected to adopt and maintain near internationally benchmarked corporate governance standards. Secondly, the study's implications on how the ownership and information disclosures can be used to influence the financial outcomes of SMEs will benefit the overall business ecosystem. The policyholders and academics can use this study to boost the regulations and research in line with each other.Originality/valueReforming monitoring mechanisms of firm activities and restructuring disclosure practices are essential for SMEs to produce better financial outcomes. The true benefits of corporate governance cannot be realized without attention to financial performance. The study is relevant to practitioners, lawmakers and academics to advance corporate governance for SMEs.

Listing Maximal k-Plexes in Large Real-World Graphs

Abstract: Listing dense subgraphs in large graphs plays a key task in varieties of network analysis applications like community detection. Clique, as the densest model, has been widely investigated. However, in practice, communities rarely form as cliques for various reasons, e.g., data noise. Therefore, k-plex, – graph with each vertex adjacent to all but at most k vertices, is introduced as a relaxed version of clique. Often, to better simulate cohesive communities, an emphasis is placed on connected k-plexes with small k. In this paper, we continue the research line of listing all maximal k-plexes and maximal k-plexes of prescribed size. Our first contribution is algorithm ListPlex that lists all maximal k-plexes in O*(γD) time for each constant k, where γ is a value related to k but strictly smaller than 2, and D is the degeneracy of the graph that is far less than the vertex number n in real-word graphs. Compared to the trivial bound of 2n, the improvement is significant, and our bound is better than all previously known results. In practice, we further use several techniques to accelerate listing k-plexes of a given size, such as structural-based prune rules, cache-efficient data structures, and parallel techniques. All these together result in a very practical algorithm. Empirical results show that our approach outperforms the state-of-the-art solutions by up to orders of magnitude.

Dynamic pricing in Airbnb: Individual versus professional hosts

Abstract: Dynamic pricing is a strategic revenue management tool used by various businesses to optimize profits, and sharing economy lodging has become an interesting context for its use. However, hosts managing multiple listings and nonprofessional hosts have structural and managerial differences. This paper explores the relevance of the degree of professionalization of Airbnb hosts and its relationships with pricing strategies and listing performance. The longitudinal (2016–2019) study analyzes two icons of Italy, Rome and Milan and includes more than 1.2 million observations. The findings show that both the listing performance and the average intensity of price variability tend to increase with the degree of professionalization (number of listings). At the same time, the impact of price variability on performance remains solid regardless of the number of listings. Thus, the study suggests that time-varying pricing is an important and relatively accessible strategy for both professional and nonprofessional hosts.

Identification of Branched and Linear Forms of PFOA and Potential Precursors: A User-Friendly SMILES Structure-based Approach

Abstract: Perfluorooctanoic acid (PFOA) and related compounds are per- and polyfluorinated alkyl substances (PFASs) of concern from toxicological, environmental, and regulatory perspectives. In 2019, the Conference of the Parties to the Stockholm Convention on Persistent Organic Pollutants listed PFOA, its salts, and PFOA-related compounds in Annex A to the Convention. Additionally, the listing specifically included PFOA branched isomers and compounds containing a perfluoroheptyl (C7F15)C moiety, with some noted exclusions. A draft updated “Indicative List” of 393 PFASs (335 with defined structures), each specified as falling within or outside the listing, was released for comment in 2021. The U.S. Environmental Protection Agency’s CompTox Chemicals Dashboard has published a curated PFAS list containing more than 10,700 structures. Applying the PFOA and related compounds listing definition to screen this list required a structure-based approach capable of discerning salts and branched or linear forms of the (C7F15)C moiety. A PFOA SMILES workflow and associated Excel macro file, developed to address this need, applies a series of text substitution rules to a set of canonicalized SMILES structure representations to convert branched forms of the (C7F15)C moiety to linear forms to aid their detection. The approach correctly classified each Stockholm Convention draft Indicative List structure relative to the PFOA and related compounds definition, and accurately discerned branched and linear forms of the (C7F15)C moiety in over 10,700 PFAS structures with 100% sensitivity (no false negatives) and 99.7% accuracy (35 false positives). Approximately 20% of structures in the large PFAS list fell within the PFOA and related compounds definition, and 10% of those were branched. The present work highlights the need to computationally detect branched forms of PFASs and promotes the use of unambiguous, structure-based definitions, along with tools that are publicly available and easy to use, to support clear communication and regulatory action within the PFAS community.

Stronger 3-SUM Lower Bounds for Approximate Distance Oracles via Additive Combinatorics

Abstract: The “short cycle removal” technique was recently introduced by Abboud, Bringmann, Khoury and Zamir (STOC ’22) to prove fine-grained hardness of approximation. Its main technical result is that listing all triangles in an n1/2-regular graph is n2−o(1)-hard even when the number of short cycles is small; namely, when the number of k-cycles is O(nk/2+γ) for γ<1/2. Its corollaries are based on the 3-SUM conjecture and their strength depends on γ, i.e. on how effectively the short cycles are removed. Abboud et al. achieve γ≥ 1/4 by applying structure versus randomness arguments on graphs. In this paper, we take a step back and apply conceptually similar arguments on the numbers of the 3-SUM problem, from which the hardness of triangle listing is derived. Consequently, we achieve the best possible γ=0 and the following lower bound corollaries under the 3-SUM conjecture: * Approximate distance oracles: The seminal Thorup-Zwick distance oracles achieve stretch 2k± O(1) after preprocessing a graph in O(m n1/k) time. For the same stretch, and assuming the query time is no(1) Abboud et al. proved an Ω(m1+1/12.7552 · k) lower bound on the preprocessing time; we improve it to Ω(m1+1/2k) which is only a factor 2 away from the upper bound. Additionally, we obtain tight bounds for stretch 2+o(1) and 3−є and higher lower bounds for dynamic shortest paths. * Listing 4-cycles: Abboud et al. proved the first super-linear lower bound for listing all 4-cycles in a graph, ruling out (m1.1927+t)1+o(1) time algorithms where t is the number of 4-cycles. We settle the complexity of this basic problem by showing that the O(min(m4/3,n2) +t) upper bound is tight up to no(1) factors. Our results exploit a rich tool set from additive combinatorics, most notably the Balog-Szemerédi-Gowers theorem and Rusza’s covering lemma. A key ingredient that may be of independent interest is a truly subquadratic algorithm for 3-SUM if one of the sets has small doubling.

Quality of voluntary modern slavery disclosures: top Australian listed companies

Abstract: Purpose There is a growing concern over the need for greater transparency of quality information by companies about modern slavery to contribute toward elimination of the practice. Hence, this paper aims to examine factors behind the quality of voluntary modern slavery disclosures and major sources of pressure on Australian company disclosures in a premodern slavery legislated environment. Design/methodology/approach Content analysis and cross- sectional regression modeling are conducted to analyze factors determining the quality of voluntary modern slavery disclosures of the top 100 firms listed on the Australian Stock Exchange and their implications for institutional pressures. Findings Results indicate that size, assurance by Big-4 firms and publication of stand-alone modern slavery statements are significant drivers of disclosure quality in the sample. Profitability, listing status and the degree of internationalization are found to be unrelated to the quality of voluntary modern slavery disclosures. Industry classification is significant but only partly supports the prediction, and further investigation is recommended. Practical implications This paper provides a foundation for regulators and companies toward improving the quality of their modern slavery risk disclosures with a particular focus on prior experience, assurance and size. In practice, contrary to suggestions in the literature, results indicate that monetary penalties are unlikely to be an effective means for improving the quality of modern slavery disclosure. Results of the study provide evidence of poor quality of disclosures and the need for improvement, prior to introduction of modern slavery legislation in Australia in 2018. It also confirms that regulation to improve transparency, through the required publication of a modern slavery statement, is significant but not enough on its own to increase disclosure quality. Originality/value To the best of the authors’ knowledge, this is the first research examining company level factors with an impact on voluntary modern slavery disclosure quality and the links to institutional pressures, prior to the introduction of the Commonwealth Modern Slavery Act 2018.

Intangible cultural heritage listing and tourism growth in China

Abstract: ABSTRACT Using city-level data over the 2000–2018 period, we examine the impact of intangible cultural heritage (ICH) listing on international tourism (i.e. international tourist arrivals and income from international tourism) in China. China uses two parallel ICH classifications: ICH projects and ICH inheritors. Empirical evaluation based on difference-in-difference (DID) methodology shows that both ICH projects and ICH inheritors listing used in China have a positive and statistically significant effect on international tourism. Trend analysis shows that ICH listing contributes to a long-term positive trend in international tourism in China. Finally, based on the positive relationship between ICH listing and the probability of being designated as an excellent tourism city (and its positive effect on international tourism), we argue that city-level cultural brand development is a plausible mechanism behind the positive effect of ICH listing on international tourism in China.

Blockchain for Transformation in Digital Marketing

Abstract: Today, an increasing number of firms are embracing blockchain as part of their efforts to achieve operational efficiency and improve performance, thereby acting as a catalyst to bring about digital transformation. Gartner listed blockchain as the most promising technology in digital marketing in the year 2019. Blockchain is driving digital transformation by forcing organizations to rethink how they operate, in terms of identifying ineffectiveness of traditional approaches to doing business, to address their business needs, promote innovation, and through establishment of standard frameworks. Blockchain shows massive disruption potential in the area of customer relationship management and enhancing consumer experience, besides improving trust, security, and privacy. Therefore, this chapter focuses on providing an enlightenment on how blockchain can specifically address the areas of transformation in digital marketing, prominent frameworks in use, and listing the benefits and challenges of implementing this technology.

Airbnb rental price modeling based on Latent Dirichlet Allocation and MESF-XGBoost composite model

Abstract: Airbnb price modeling is an important decision-making tool that determines the acceptability and profitability of the service. In this study, we demonstrated how proper descriptions of an Airbnb listing and location could influence determining the prices. We assumed the proper description of a listing property positively influences the renter’s decision making; therefore, we applied a Latent Dirichlet Allocation (LDA) based topic model for generating synthetic variables from the textual description of property aiming to improve price prediction accuracy. Additionally, we applied a Moran Eigenvector Spatial Filtering based XGBoost (MESF-XGBoost) model to address the spatial dependence of location data and improve prediction accuracy. Our study at the San Jose County Airbnb dataset found that the number of bedrooms, accommodations, property types, and the total number of reviews positively influence the listing price, whereas the absence of a super host badge and cancellation policy negatively influence the price. The experiment demonstrates that incorporating synthetic variables from both LDA and MESF into the model specification improves the prediction accuracy. The experiment reveals that the XGBoost model with only non-spatial features is not strong enough to address spatial dependence; therefore, it cannot minimize spatial autocorrelation issues.

Airbnb rental price modeling based on Latent Dirichlet Allocation and MESF-XGBoost composite model

Abstract: Airbnb price modeling is an important decision-making tool that determines the acceptability and profitability of the service. In this study, we demonstrated how proper descriptions of an Airbnb listing and location could influence determining the prices. We assumed the proper description of a listing property positively influences the renter’s decision making; therefore, we applied a Latent Dirichlet Allocation (LDA) based topic model for generating synthetic variables from the textual description of property aiming to improve price prediction accuracy. Additionally, we applied a Moran Eigenvector Spatial Filtering based XGBoost (MESF-XGBoost) model to address the spatial dependence of location data and improve prediction accuracy. Our study at the San Jose County Airbnb dataset found that the number of bedrooms, accommodations, property types, and the total number of reviews positively influence the listing price, whereas the absence of a super host badge and cancellation policy negatively influence the price. The experiment demonstrates that incorporating synthetic variables from both LDA and MESF into the model specification improves the prediction accuracy. The experiment reveals that the XGBoost model with only non-spatial features is not strong enough to address spatial dependence; therefore, it cannot minimize spatial autocorrelation issues.