A roadmap for triggering the convergence of global ESG disclosure standards: lessons from the IFRS foundation and stakeholder engagement

PurposeMotivated by the growing and urgent demands for a unified set of internationally accepted, and high-quality environmental, social and governance (hereafter ESG) disclosure standards, this exploratory study aims to propose a roadmap for setting out the proper technical groundwork for global ESG disclosure standards. Design/methodology/approachAn exploratory study is conducted to gain initial understanding and insights into establishing a worldwide set of standards for reporting on sustainability, as this topic has not been extensively studied. This study examines the viewpoints of various stakeholders, including sustainability practitioners, academics and organizations focused on ESG issues, to generate knowledge that is more solid than knowledge produced when one group of stakeholders work alone. FindingsThe results revealed that there is an ongoing and incompatible debate regarding several conceptual and practical challenges for setting a unified set of ESG disclosure standards. Practical implicationsThe study results provide multidimensional insights for regulatory parties and standard-setters to develop a high-quality package of global ESG reporting standards. This, in turn, enables different groups of stakeholders to understand the firm's impact on the environment, society and economy. Originality/valueResearch into this timely and relevant global issue is considered an appealing area of study and deserves significant attention. Thereby, working on this topic merits remarkable attention. Furthermore, this exploratory article provides valuable and informative suggestions for creating a unified and high-quality set of internationally accepted sustainability reporting standards.

Comparative Analysis of Swarm Algorithms to Classification of covid19 on X-Rays

The use of Chest radiograph (CXR) images in the examination and monitoring of different lung disorders like infiltration, tuberculosis, pneumonia, atelectasis, and hernia has long been known. The detection of COVID-19 can also be done with CXR images. COVID-19, a virus that results in an infection of the upper respiratory tract and lungs, was initially detected in late 2019 in China's Wuhan province and is considered to majorly damage the airway and, thus, the lungs of people afflicted. From that time, the virus has quickly spread over the world, with the number of mortalities and cases increasing daily. The COVID-19 effects on lung tissue can be monitored via CXR. As a result, This paper provides a comparison regarding k-nearest neighbors (KNN), Support-vector machine (SVM), and Extreme Gradient Boosting (XGboost) classification techniques depending on Harris Hawks optimization algorithm (HHO), Salp swarm optimization algorithm (SSA), Whale optimization algorithm (WOA), and Gray wolf optimizer (GWO) utilized in this domain and utilized for feature selection in the presented work. The dataset used in this analysis consists of 9000 2D X-ray images in Poster anterior chest view, which has been categorized by using valid tests into two categories: 5500 images of Normal lungs and 4044 images of COVID-19 patients. All of the image sizes were set to 200 × 200 pixels. this analysis used several quantitative evaluation metrics like precision, recall, and F1-score. © 2022 IEEE.

Peer shadowing graduate assistantships: an autoethnographic study

The unique nature of the COVID-19 pandemic prevented many typical graduate assistantships from occurring due to school-building closures, virtual classes, and stay-at-home orders. As such, the authors address the increase of ‘shadowing' graduate assistantships at a large land-grant institution. To uphold the governmental stay-at-home regulations per COVID-19, shadowing provided first-year students with exposure and access to more experienced graduate student instructors (‘the shadowed') who taught undergraduate and graduate coursework. In this autoethnographic study, the authors investigate the following questions: (1) In what ways did our experiences align with Mentoring Enactment Theory and Social Exchange Theory ? (2) How might we better align our conception of shadowing to both theories? The article finishes with modifications to the theories in light of the shadowing experiences. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

An Efficient Hybrid Classification Approach for COVID-19 Based on Harris Hawks Optimization and Salp Swarm Optimization

Feature selection can be defined as one of the pre-processing steps that decrease the dimensionality of a dataset by identifying the most significant attributes while also boosting the accuracy of classification. For solving feature selection problems, this study presents a hybrid binary version of the Harris Hawks Optimization algorithm (HHO) and Salp Swarm Optimization (SSA) (HHOSSA) for Covid-19 classification. The proposed (HHOSSA) presents a strategy for improving the basic HHO’s performance using the Salp algorithm’s power to select the best fitness values. The HHOSSA was tested against two well-known optimization algorithms, the Whale Optimization Algorithm (WOA) and the Grey wolf optimizer (GWO), utilizing a total of 800 chest X-ray images. A total of four performance metrics (Accuracy, Recall, Precision, F1) were employed in the studies using three classifiers (Support vector machines (SVMs), k-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost)). The proposed algorithm (HHOSSA) achieved 96% accuracy with the SVM classifier, and 98% accuracy with two classifiers, XGboost and KNN © 2022, International journal of online and biomedical engineering.All Rights Reserved.

The BISTIM study: first RCT comparing dual ovarian stimulation on the same cycle (duostim) vs 2 conventional ovarianstimulations in poor ovarian responders undergoing IVF

Study question: Is the number of cumulated oocytes with dual ovarian stimulation on the same cycle (duostim) higher compared to 2 consecutive antagonist cycles in poor responders? Summary answer: Considering the number of total and mature oocytes collected in poor ovarian responders, there is no benefit of duostim vs two consecutive antagonist cycles. What is known already: Several waves of follicular development exist on the same cycle. Recent studies have shown the ability to obtain oocytes with equivalent quality in the luteal phase, even after a previous ovarian stimulation in the follicular phase. During stimulation, smaller follicles are recruited and sensitized, which may increase the selection of follicles available on the second stimulation. In poor ovarian responders (POR) this potentialization may have a great interest, as 2 stimulations on the same cycle could give a higher number of oocytes compared to two conventional stimulations. However, these preliminary data need to be confirmed with a randomized controlled trial. Study design, size, duration: This is a multicenter, open-labeled randomized control trial (2018, september-2021, march). The primary objective was to demonstrate that two ovarian stimulations within the same cycle (first in the follicular phase, followed by a second in the luteal phase) lead to the retrevial of 1.5 more oocytes than the cumulative number of oocytes from two consecutive conventional stimulation, in POR women. According to this hypothesis, 44 patients were needed in each group. Participants/materials, setting, methods: 88 POR women, defined with Bologna criteria (AFC≤5 and/or AMH≤1.2ng/ml and ≤3 oocytes if previous IVF) were randomized, 44 in duostim group (D) and 44 in conventional group (C). Fertistart Kit®300IU/day with antagonist protocol was used except in luteal phase stimulation of group D. In group D, oocytes were pooled and inseminated after the second retrieval, with freeze all embryos. Fresh transfer was performed in group C. The analysis is presented in intention to treat. Main results and the role of chance: There was no difference between the groups regarding demographics, ovarian reserve markers (AFC, AMH) and stimulation parameters. The mean number of cumulated oocytes retrieved with 2 ovarian stimulation was not statistically different in group D and C, respectively 5.0+/-3.4 and 4.6+/-3.4 (p=0.56). The mean number of cumulated mature oocytes was not statistically different, 3.7+/-3.3 in group D vs 3.1+/-3.0 in group C (p=0.38). The mean number of embryos was significantly lower in the group D, 0.8+/-1.3 vs group C 1.6+/-1.3 (p<0.01). There was no statistical difference of the mean number of oocytes retrieved per cycle in cycle 1 vs cycle 2 in both group D and C. The delay, between the first and the second day 1 of stimulation was statistically different in group D 14.4 days (10-19) vs group C 90.6 (28-232). The ongoing pregnancy rate in group D 17.9% (7/39) was not statistically different with group C 29.3% (12/41), (p=0.23). And the mean time to ongoing pregnancy tends to be longer in group D (144 days) vs group C (115 days) but was not statistically different (p=0.21). Limitations, reasons for caution: The RCT was impacted by Covid pandemia and stop of IVF activities for 10 weeks. Delays were recalculated to exclude this period, however one women in group D cannot have the luteal stimulation. We also faced unexpected good ovarian responses and pregnancies after the first oocyte pick-up in group C. Wider implications of the findings: In routine practice, the benefit of duostim in patients with POR is not confirmed. Firstly, because there is no potentialization on the number of oocyte retrieved in luteal phase after follicular phase stimulation. Secondly, because the freeze all strategy avoids a pregnancy with fresh embryo transfer after the first cycle.

Diphtheria Outbreak During Covid-19 Pandemic in Katsina, North-Western Nigeria: Epidemiological Characteristics and Predictors of Death

Context: The impact of coronavirus disease 2019 (COVID-19) pandemic on vaccine-preventable diseases, including diphtheria, may hamper the previous gains made in the eradication of the disease. Aims: We report the epidemiological profile, clinical features, laboratory findings, and hospitalization outcomes amongst cases of diphtheria managed at Federal Medical Centre, Katsina, Nigeria during the first wave of COVID-19 pandemic. Settings and Design: This was a retrospective review of cases of diphtheria managed between July and December 2020. Methods and Material: We extracted the clinical (socio-demographics, clinical features, and hospitalization outcomes) and laboratory findings (full blood counts, electrolytes, urea and creatinine) from the record of the children. Statistical Analysis Used: Using SPSS, we carried out a descriptive analysis and applied binary logistic regression to determine factors associated with death. Level of statistical significance was set at P < 0.05. Results: A total of 35 cases of diphtheria were admitted and managed from 1 July to 31 December 2020. The mean age of the children was 7.6 +/- 3.1 years. Males were 15 (42.9%). There were 24 deaths (case fatality of 68.6%). Clinical findings were comparable between survivors and non-survivors except the bull neck, which was more common among non-survivors (P = 0.022). The median duration of hospitalization was shorter in those that died (P = 0.001). The age, sex, immunization status, leukocytosis, and biochemical features of renal impairments were not predictive of deaths. Prescence of bull neck was predictive of death (adjusted odds ratio 2.115, 95% CI 1.270, 3.521). Conclusions: The study shows a high number of cases of diphtheria over a short period of six months with high mortality. Amongst the clinical and laboratory variables, only presence of bull neck was predictive of death.

Pulmonary Diseases Decision Support System Using Deep Learning Approach

Pulmonary diseases are common throughout the world, especially in developing countries. These diseases include chronic obstructive pulmonary diseases, pneumonia, asthma, tuberculosis, fibrosis, and recently COVID-19. In general, pulmonary diseases have a similar footprint on chest radiographs which makes them difficult to discriminate even for expert radiologists. In recent years, many image processing techniques and artificial intelligence models have been developed to quickly and accurately diagnose lung diseases. In this paper, the performance of four popular pretrained models (namely VGG16, DenseNet201, DarkNet19, and XceptionNet) in distinguishing between different pulmonary diseases was analyzed. To the best of our knowledge, this is the first published study to ever attempt to distinguish all four cases normal, pneumonia, COVID-19 and lung opacity from ChestX-Ray (CXR) images. All models were trained using Chest-X-Ray (CXR) images, and statistically tested using 5-fold cross validation. Using individual models, XceptionNet outperformed all other models with a 94.775% accuracy and Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC) of 99.84%. On the other hand, DarkNet19 represents a good compromise between accuracy, fast convergence, resource utilization, and near real time detection (0.33 s). Using a collection of models, the 97.79% accuracy achieved by Ensemble Features was the highest among all surveyed methods, but it takes the longest time to predict an image (5.68 s). An efficient effective decision support system can be developed using one of those approaches to assist radiologists in the field make the right assessment in terms of accuracy and prediction time, such a dependable system can be used in rural areas and various healthcare sectors.

The potential of putative zinc-binding motifs of haemagglutinin (HA) protein for categorization and prediction of pathogenicity of H5 subtypes of avian influenza virus.

In the present study, we used the potential of bioinformatics and computational analysis to predict the existence and biological relevance of zinc finger (ZF) motifs in heamagglutinin (HA) protein of Avian Influenza (AI) virus. Sequence data of Avian Influenza (AI) viruses were retrieved from accessible databases (GenBank, GISAID, IRD) and analyzed for the existence, as well as functional prediction of the putative zinc finger or ''zinc-binding'' motif(s) of HA protein. It is hypothesized that the ZF motif(s) in HA of AI virus can be used as a ''novel'' biomarker for categorization of the virus and/or its virulence. As a model for analysis, we used the H5 subtypes of highly pathogenic, non-pathogenic and low pathogenic avian influenza (HPAI, NPAI and LPAI) viruses of H5N1 and H5N2 of avian and human origins. Interestingly, our method of characterization using the zinc-finger agrees with the existing classification in distinguishing between highly pathogenic and non-pathogenic or low pathogenic subtypes. The new method also clearly distinguished between low and non-pathogenic strains of H5N2 and H5N1 which are indistinguishable by the existing method that utilizes the sequence of the polybasic amino acids of the proteolytic cleavage site for pathogenicity. It is hypothesized that zinc through the activities of zinc-binding proteins modulates the virulence property of the viral subtypes. Our observation further revealed that only the HA protein among the eight encoded proteins of influenza viruses contain high numbers of Cys-His residues. It is expected that the information gathered from the analysis of the data will be useful to generate more research hypotheses/designs that will give further insight towards the identification and control of avian influenza virus through the molecular manipulation of zinc finger motifs present in viral HA protein.