Information Set Research Articles

Does genetic diversity on corporate boards lead to improved environmental performance?

We study the effects of boards’ genetic diversity on corporate environmental performance. Using a multidimensional information set for 3690 US firms during the period from 2005 to 2019, and three different measures of genetic diversity, we find that, pursuant to the diversity theory, which posits that diversity improves the quality of management decisions and business ethics, genetic diversity leads to improved environmental performance. We also find that genetic diversity improves carbon and governance performance, and ESG disclosure. Particularly, a one percentage point increase in boards’ genetic diversity will increase the carbon performance, measured by the inverse of the carbon emissions to total assets ratio, and environmental performance by 3.54% and 5.57%, respectively. Our results remain robust to different model specifications, while also controlling for endogeneity. In terms of policy implications, results suggest that the key to tackling climate challenges is to promote boards’ genetic diversity.

Correction of the Values of the Classification Feature of Objects on the Example of the Diagnosis of Multiple Myeloma

The clinical features of changes in multiple myeloma indicators of different types associated with the gender of patients (objects) are considered. The methods of data mining examine the truth of the statement about the presence of many patients for whom gender is not significant in making a diagnosis. It is proposed to use the preprocessing of heterogeneous data to unify the description of objects in the binary space. The conditions for selecting and removing noise features from the set are determined. In order to reduce the dimensionality of the space, latent features are calculated by groups of binary generalized estimates of objects. A criterion is proposed for dividing patients into the optimal number of groups, taking into account their gender authenticity. From these groups, a new classification of objects is formed, differentiated by gender. The formation process is illustrated through the visualization of object descriptions, recognition accuracy and selection of informative feature sets according to the new classification. The selection procedure is implemented according to the rules of a hierarchical agglomerative algorithm. The property of invariance to the measurement scales of quantitative traits is an important argument for using the obtained results on data samples from the general population.

IPoLNG-An unsupervised model for the integrative analysis of single-cell multiomics data.

Single-cell multiomics technologies, where the transcriptomic and epigenomic profiles are simultaneously measured in the same set of single cells, pose significant challenges for effective integrative analysis. Here, we propose an unsupervised generative model, iPoLNG, for the effective and scalable integration of single-cell multiomics data. iPoLNG reconstructs low-dimensional representations of the cells and features using computationally efficient stochastic variational inference by modelling the discrete counts in single-cell multiomics data with latent factors. The low-dimensional representation of cells enables the identification of distinct cell types, and the feature by factor loading matrices help characterize cell-type specific markers and provide rich biological insights on the functional pathway enrichment analysis. iPoLNG is also able to handle the setting of partial information where certain modality of the cells is missing. Taking advantage of GPU and probabilistic programming, iPoLNG is scalable to large datasets and it takes less than 15min to implement on datasets with 20,000 cells.

Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response

A community integrated energy system (CIES) is an important carrier of the energy internet and smart city in geographical and functional terms. Its emergence provides a new solution to the problems of energy utilization and environmental pollution. To coordinate the integrated demand response and uncertainty of renewable energy generation (RGs), a data-driven two-stage distributionally robust optimization (DRO) model is constructed. A comprehensive norm consisting of the 1-norm and ∞-norm is used as the uncertainty probability distribution information set, thereby avoiding complex probability density information. To address multiple uncertainties of RGs, a generative adversarial network based on the Wasserstein distance with gradient penalty is proposed to generate RG scenarios, which has wide applicability. To further tap the potential of the demand response, we take into account the ambiguity of human thermal comfort and the thermal inertia of buildings. Thus, an integrated demand response mechanism is developed that effectively promotes the consumption of renewable energy. The proposed method is simulated in an actual CIES in North China. In comparison with traditional stochastic programming and robust optimization, it is verified that the proposed DRO model properly balances the relationship between economical operation and robustness while exhibiting stronger adaptability. Furthermore, our approach outperforms other commonly used DRO methods with better operational economy, lower renewable power curtailment rate, and higher computational efficiency.

Inattention and the impact of monetary policy

SummaryWe measure aggregate inattention as the common component in agents' inattentiveness to many economic variables. Applying this measure to the US Survey of Professional Forecasters enables us to establish the following empirical evidence. Professional forecasters update their information sets every 5 months on average but do so more frequently in response to high inflation and unemployment, as well as rising market volatility and policy uncertainty. Monetary policy shocks have larger real effects when the degree of inattention is higher. To explain our empirical findings, we propose a general equilibrium model with state‐dependent information rigidity in both the production and household sectors.

Comparing Outcomes between HEDIS Persistent Asthma Definition and a Non-Restrictive Model

The Healthcare Effectiveness Data and Information Set (HEDIS) uses the asthma medication ratio (AMR) to measure care across health settings for persistent asthma. HEDIS criteria requires a restrictive asthma definition to identify children for analysis. A low AMR (<0.5) has been used to assess asthma control and is associated with poorer control and adverse outcomes. High AMR (=>0.5) conveys better outcomes. In Arkansas, when compared to the nation, children overall have higher asthma burden. This disparity is most apparent in racial and ethnic minority children.

Data governance and the secondary use of data: The board influence

The business analytics and strategic management literatures suggest that organizations should seek to exploit data as a key mechanism for competitive advantage. However, the rules of engagement are evolving, the regulatory landscape is becoming increasingly complex, and examples of poor outcomes are increasingly common. The board – in its role of setting and monitoring risk appetite – needs to be able to govern the risk/reward trade-off of the data asset. Contemporary data governance approaches are inadequate: they are overly rigid and risk oriented, limited in scope to an organization's self-interest rather than considering the broad set of stakeholders, and do not provide a platform for the board to manage this critical risk. This paper uses a unique set of informants – 41 board directors – to demonstrate that differences in board perspectives influence how organizations explore the secondary use of data. Furthermore, this paper identifies a set of relevant individual, organizational and environmental factors and presents empirically based configurations of these factors that lead organizations to consider (or neglect) the secondary use of data as a critical enabler of competitive advantage.

The Use of Biologics in NFL Athletes: An Expert Consensus of NFL Team Physicians.

There is a lack of published information outlining the use of biologics in National Football League (NFL) athletes and limited data to guide biologic treatment strategies. To develop a consensus on the use of biologics among NFL team physicians. Consensus statement. A working group of 6 experts convened a consensus process involving NFL team physicians using validated Delphi methodology. Physicians from 32 NFL teams as well as NFL London were invited to take part. This iterative process was used to define statements on the use of biologics in NFL athletes. A recent scoping review exploring biologics in professional athletes was used to inform the first of 3 rounds of surveys, with statements considered under 7 headings: biologics in general, challenges of treating NFL athletes, terminology/nomenclature, autologous blood products, cell-based therapies, guidance for NFL team physicians, and biologic research in the NFL. In addition to rating agreement, experts were encouraged to propose further items or modifications. Predefined criteria were used to refine item lists after each survey. For a consensus within the final round, defined a priori, items were included in the final information set if a minimum of 75% of respondents agreed and fewer than 10% disagreed. Physicians from 26 NFL teams and NFL London responded to the initial invitation to participate in the Delphi process; 88.9% of participating team physicians completed the round 1 survey, with response rates of 87.5% in round 2 and 95.2% in round 3. After 3 rounds, 47 statements reached a consensus. A consensus was achieved that platelet-rich plasma has a positive impact on patellar tendinopathy and on symptoms in early osteoarthritis but not for other indications. NFL team physicians agreed that while cell therapies have the potential to improve symptoms, the misrepresentation of uncharacterized preparations as "stem cells" has contributed to the widespread use of unproven therapies. This study established an expert consensus on 47 statements relating to the use of biologics in NFL athletes. In addition to providing clinical guidance for the use of biologics in NFL athletes, this study identified key areas for future focus including the development of athlete education materials.

Marketing Library and Information Services in Nigeria

In a rapidly changing world, the perception to adapt to change is inexplicable and a constant as well. There is virtually the possibility of upturning many assumptions such like the issue of marketing library information services (LIS) which many libraries/librarians deemed to be unsellable. The scope of marketing in itself is evolving and portray different connotations; to different personalities. The marketing of LIS is an ongoing project determined to give the customer a place of pride in terms of providing much needed satisfaction. This is because the whole essence of library and information set up is to serve the clientele also referred to as the king, or the master. Most library staff of developing countries have low compliance level to change or innovation especially as it pertains to ICT and use of modern gadgets in performing their duties. All these could be linked to the lack of skills/ knowledge needed to handle the sufisticated facilities. There's no doubt that some of the Librarians are well equipped in terms of ICT compliance level, but there is urgent need to train vast majority of the others who are lagging behind.

Automatic COVID-19 severity assessment from HRV

COVID-19 is known to be a cause of microvascular disease imputable to, for instance, the cytokine storm inflammatory response and the consequent blood coagulation. In this study, we propose a methodological approach for assessing the COVID-19 presence and severity based on Random Forest (RF) and Support Vector Machine (SVM) classifiers. Classifiers were applied to Heart Rate Variability (HRV) parameters extracted from photoplethysmographic (PPG) signals collected from healthy and COVID-19 affected subjects. The supervised classifiers were trained and tested on HRV parameters obtained from the PPG signals in a cohort of 50 healthy subjects and 93 COVID-19 affected subjects, divided into two groups, mild and moderate, based on the support of oxygen therapy and/or ventilation. The most informative feature set for every group’s comparison was determined with the Least Absolute Shrinkage and Selection Operator (LASSO) technique. Both RF and SVM classifiers showed a high accuracy percentage during groups’ comparisons. In particular, the RF classifier reached 94% of accuracy during the comparison between the healthy and minor severity COVID-19 group. Obtained results showed a strong capability of RF and SVM to discriminate between healthy subjects and COVID-19 patients and to differentiate the two different COVID-19 severity. The proposed method might be helpful for detecting, in a low-cost and fast fashion, the presence and severity of COVID-19 disease; moreover, these reasons make this method interesting as a starting point for future studies that aim to investigate its effectiveness as a possible screening method.

Fish demand in the U.S. Great Lakes region in the face of seafood mislabeling

The rapid globalization of seafood trade over the past few decades has triggered heightened vulnerability for fraud within the seafood supply network. Consumer perceptions of these vulnerabilities are not limited to imported seafood products, as spillover effects are likely to influence purchasing behavior for domestically produced seafood as well. Using a between-sample survey experiment with 1,272 respondents in differing informational settings, we investigate whether consumer WTP for local seafood is impacted by information regarding seafood fraud. Consistent with previous studies, our results indicate that consumers broadly derive positive utility from consuming locally sourced relatively to imported seafood. Upon further disaggregation, however, we find that for one consumer segment, which we term the price-sensitive group, localness does not command a significant positive premium. Most importantly, we demonstrate that information regarding international seafood fraud largely did not alter local seafood demand. That said, we find some evidence of a negative spillover effect of the information treatment on US-labeled seafood in one of the consumer subgroups.

The differential pricing of cash flows and total accruals

PurposeRecent studies on the securities market's differential pricing of earnings components indicate that cash flows from operations are valued more highly than extreme total accruals. However, no previous study has examined whether cash flows from operations have a higher valuation than moderate total accruals. Therefore, this study examines the securities market's differential pricing of cash flows from operations and both moderate and extreme total accruals.Design/methodology/approachThe study's sample is divided into two sub-samples: a moderate total accruals sub-sample; and an extreme total accruals sub-sample. To evaluate whether cash flows have a higher valuation when compared to total accruals, for the entire sample and for each of the two sub-samples, the study examines the statistical significance of the difference between slope coefficients of cash flows and total accruals for regression of returns on both unexpected cash flows from operations and unexpected total accruals.FindingsConsistent with prior research, results from the entire sample show a differential higher valuation of cash flows when compared to total accruals. Another finding, consistent with recent studies, is that cash flows from operations have a higher valuation when compared to extreme total accruals. However, there is no higher differential valuation of cash flows over moderate total accruals. These findings support the decomposition of earnings into the components of cash flows from operations and total accruals only when total accruals are extreme (rather than moderate).Practical implicationsA possible explanation for these results is that since accruals predict cash flows, total accruals – when moderate (i.e. not extreme) – are priced similarly to cash flows. These results reveal that when total accruals are moderate, earnings are a better proxy for the underlying cash flows (over the entire future horizon, not just the current period) than is cash flows. However, since total accruals are unlikely to persist in a permanent way over the years, these results indicate that the decomposition of earnings into the components of cash flows from operations and total accruals is consistent with the information set used to value equity securities. Therefore, separate disclosure of cash flows is value relevant. In addition, users of financial statements certainly need the cash flows information as an ex-post validation of the prior earnings.Originality/valueThis study's contribution stems from its determination of the preferred level of disaggregation of earnings components (i.e. operating cash flows and total accruals). This is expected to help investors in their attempt to enhance the outcome of their informed investment and credit decisions.

ABI3BP is a prognosis biomarker related with clinicopathological features and immunity infiltration of lung tumor.

Background: The primary factor of cancer mortality is lung tumor. ABI3BP gene encodes an extracellular matrix bind protein associated to multiplication and derivation. However, the prognosis score of ABI3BP for lung tumor and its relation with immunity cellular infiltration for lung tumor have not been reported. Methods: Public repository systems (Timer, GEPIA, TCGA, HPA) were utilized to explore expression of ABI3BP for lung tumor, and explored the relation of ABI3BP and clinicopathological parameters. TCGA information set was utilized for cox analysis for data with one or more variables of ABI3BP for lung tumor. STRING was utilized to explore ABI3BP regulatory networks. GO/KEGG enrichment analysis as well as enrichment analysis of gene sets were carried out for ABI3BP co-expression via R package. And finally we explored the relation of expression of ABI3BP and lung tumor immunity invasion, exploring the influence of ABI3BP level of expression on immunotreatment and whether immunity invasion would affect the prognosis of patients with lung tumor. Results: ABI3BP is downregulated in LUAD and LUSC, and associated to lung tumor phase and prognosis. Univariate and multivariate cox regression showed that ABI3BP was an independent prognostic factor in patients with lung tumors. The extracellular matrix protein-coding gene and the ABI3BP-related gene were intersected to obtain 10 hub genes. On the basis of GO/KEGG enrichment analysis, hub genes are closely associated to immunity-associated pathways including T cell receptor signaling pathway, immune response-activating cell surface receptor signaling pathway. Finally, the expression of ABI3BP is closely related to immune cell infiltration and immune cell marker set, and the expression of ABI3BP can help predict the therapeutic effect of immune checkpoint inhibitors and improve the prognosis of patients. Conclusion: ABI3BP could be a new target for lung tumor that could be utilized as a diagnostic and therapeutic tool.

Diversity Learning Based on Multi-Latent Space for Medical Image Visual Question Generation.

Auxiliary clinical diagnosis has been researched to solve unevenly and insufficiently distributed clinical resources. However, auxiliary diagnosis is still dominated by human physicians, and how to make intelligent systems more involved in the diagnosis process is gradually becoming a concern. An interactive automated clinical diagnosis with a question-answering system and a question generation system can capture a patient's conditions from multiple perspectives with less physician involvement by asking different questions to drive and guide the diagnosis. This clinical diagnosis process requires diverse information to evaluate a patient from different perspectives to obtain an accurate diagnosis. Recently proposed medical question generation systems have not considered diversity. Thus, we propose a diversity learning-based visual question generation model using a multi-latent space to generate informative question sets from medical images. The proposed method generates various questions by embedding visual and language information in different latent spaces, whose diversity is trained by our newly proposed loss. We have also added control over the categories of generated questions, making the generated questions directional. Furthermore, we use a new metric named similarity to accurately evaluate the proposed model's performance. The experimental results on the Slake and VQA-RAD datasets demonstrate that the proposed method can generate questions with diverse information. Our model works with an answering model for interactive automated clinical diagnosis and generates datasets to replace the process of annotation that incurs huge labor costs.

Random Matrix Transformation and Its Application in Image Hiding

Image coding technology has become an indispensable technology in the field of modern information. With the vigorous development of the big data era, information security has received more attention. Image steganography is an important method of image encoding and hiding, and how to protect information security with this technology is worth studying. Using a basis of mathematical modeling, this paper makes innovations not only in improving the theoretical system of kernel function but also in constructing a random matrix to establish an information-hiding scheme. By using the random matrix as the reference matrix for secret-information steganography, due to the characteristics of the random matrix, the secret information set to be retrieved is very small, reducing the modification range of the steganography image and improving the steganography image quality and efficiency. This scheme can maintain the steganography image quality with a PSNR of 49.95 dB and steganography of 1.5 bits per pixel and can ensure that the steganography efficiency is improved by reducing the steganography set. In order to adapt to different steganography requirements and improve the steganography ability of the steganography schemes, this paper also proposes an adaptive large-capacity information-hiding scheme based on the random matrix. In this scheme, a method of expanding the random matrix is proposed, which can generate a corresponding random matrix according to different steganography capacity requirements to achieve the corresponding secret-information steganography. Two schemes are demonstrated through simulation experiments as well as an analysis of the steganography efficiency, steganography image quality, and steganography capacity and security. The experimental results show that the latter two schemes are better than the first two in terms of steganography capacity and steganography image quality.

The links between COVID-19 vaccine acceptance and non-pharmaceutical interventions

The information set from which individuals make their decision on vaccination includes signals from trusted agents, such as governments, community leaders and the media. By implementing restrictions, or by relaxing them, governments can provide a signal about the underlying risk of the pandemic and indirectly affect vaccination take-up. Rather than focusing on measures specifically designed to increase vaccine acceptance, this paper studies how governments' non-pharmaceutical policy responses to the pandemic can modify the degree of preventive health behavior, including vaccination. To do so, we use repeated waves of a global survey on COVID-19 Beliefs, Behaviors and Norms covering 18 countries from October 2020 to March 2021. Controlling for the usual determinants, we explore how individuals’ willingness to get vaccinated is affected by changes in government restriction measures (as measured by the Oxford Stringency Index). This relationship is mediated by individual characteristics, social norms (social pressure to conform with what most people do), and trust in government institutions. Our results point to a complex picture as the implementation of restrictions is associated with increased acceptance in some contexts and decreased acceptance in others. The stringency of government restrictions has significant positive correlations with vaccine acceptance in contexts of weak social norms of vaccine acceptance and lower trust in government. In countries or communities with tighter social norms and high trust in health authorities, vaccine acceptance is high but less sensitive to changes in policies. These results suggest that the effect of government policy stringency is stronger among individuals who report lower trust and weaker social norms of vaccine acceptance.

Process Technology and Sustainability Assessment of Wastewater Treatment

Removal of heavy metals in wastewater treatment is crucial to protect the environment, wildlife, and human health. Various techniques have been developed focusing on removal of heavy metal ions, pharmaceuticals, and other contaminants from different wastewater sources. The main methods include adsorption, filtration, ion exchange, electrochemical, reverse osmosis, precipitation, flotation/coagulation/flocculation, and photocatalytic-based treatments. This paper comprehensively assesses the sustainability of those common technologies used for wastewater process treatment. The sustainability profile depends mostly on the exact approach followed for each technology, including its energy consumption, type of radiation (where appropriate), auxiliary materials used (e.g., catalysts, adsorbents), and further specific experimental process settings. Thus, while sustainability inevitably provides a multifaceted answer, the review finally aims for sustainability benchmarking of all technologies, by compressing the manifold outcomes toward a compact information set, such as a table and radar plot.

A founding-team model of creating a venture's culture

As many new ventures are started by founding teams, it is these founding teams that likely engage in creating their venture's culture. We draw on theories of cultural dynamics and the literature on team cognitive diversity to investigate the creation of a new venture's culture. Specifically, we theorize how a founding team's cognitive diversity impacts the team's production of cultural information and the transmission of that information throughout the venture. Cognitive diversity directly influences the founding team's production of cultural information by shaping the diversity of the information set and the speed of its production. Moreover, cognitive diversity can give rise to faultlines within the venture, impacting how venture members interpret cultural information. Importantly, our model suggests a complex interplay between the production and interpretation of cultural information. Understanding culture creation in new ventures is important because a new venture's culture shapes its legitimacy and thus its access to stakeholder resources for venture emergence.

A multi-source information fusion model for outlier detection

Multi-source information fusion (MSIF) is a useful strategy for combining complimentary data from numerous information sources to produce an overall precise description, which can help with effective decision-making, prediction, and categorization, etc. In order to find the objects that are different from the expected ones after fusion, i.e., anomalies, or outliers, an MSIF model is put forward for outlier detection. This is a two-stage model that includes fusion of multiple information sources and outlier detection of fused data. The first stage uses information sets to construct uncertainty criteria for information source values and combines multiple information sources into a single information source based on the minimum uncertainty strategy. The second stage uses the Gaussian kernel method for possibility modeling based on the fused data to construct knowledge granules. From the perspective of granular computing, outliers in the fused data can be assigned to each knowledge granule. Then, we can find all outliers just by evaluating these knowledge granules. Inspired by this, the fuzzy knowledge measure (FKM) is proposed to evaluate the knowledge granule. Moreover, several metrics are induced on the basis of FKM to describe outliers in knowledge granules and an FKM-based outlier detection algorithm (FKMOD) is designed. Finally, we conduct the experiments on sixteen open access outlier detection datasets. The experimental results show that the proposed FKMOD method has more accurate detection performance than nine classical methods.

Population genetic analysis of 12 X-STR markers in Slovakia

Background: During the last 20 years, X-chromosomal STR markers have become widely used in forensic genetics and paternity testing. Nevertheless, to exploit their full potential in any given population, a reliable reference dataset needs to be established. Since no relevant studies concerning these markers have been performed on the Slovak population so far, we decided to analyse several commonly used markers in this population. Aim: To create an informative set of Slovak population data concerning X-STR markers. Subjects and methods: We genotyped 378 individuals and analysed 12 loci (DXS10148, DX10135, DXS8378, DXS7132, DXS10079, DXS10074, DXS10103, HPRTB, DXS10101, DXS10146, DXS10134 and DXS742) localised in four distinct linkage groups. Results: Our analysis showed that the most informative marker is DXS10135 (PIC = 0,927) and the most informative linkage group (LG) is LG1 with 149 different haplotypes. This analysis also confirmed linkage disequilibrium for two pairs of markers (DX10101-DX10103 and DX10101-HPRTB) within LG3 in female samples. No statistically significant departure from HWE was observed for any locus. Moreover, the interpopulation comparison of 8 European populations based on haplotype frequencies showed no statistically significant FST values in any LG, except for LG2 in comparison with the German population. Conclusion: We created a haplotype database for forensic analyses and kinship testing in Slovakia, as well as the CE dataset which can be used to further increase the decision power in similar analyses in the future.