The Reporting of SDGs by Quality, Environmental, and Occupational Health and Safety-Certified Organizations

Organizations can play a significant role in the advancement of Sustainable Development, and companies with Quality, Environmental, and Occupational Health and Safety (QEOHS)-certified management systems address the three Sustainability Dimensions (economic, environmental, and social). This research aims to map the present level of engagement of those companies in contributing and reporting to the 17 Sustainable Development Goals (SDG) of the United Nations (UN) 2030 Agenda. By publicly disclosing their sustainability reports on their institutional websites, they can, therefore, support this agenda implementation. The content of the company reports that were available by 31 December 2017 in the institutional websites, from a total of 235 Portuguese organizations with QEOHS-certified management systems was analyzed. The results show a moderate reporting of SDGs by those companies, with the top five being SDG 12— Responsible consumption and production (23.8%); SDG 13—Climate action (22.1%); SDG 09—Industry, innovation, and infrastructure (21.3%); SDG 08—Decent work and economic growth (20.0%); and SDG 17—Partnerships for the goals (19.6%). The results of the statistical tests indicate that the communication of SDGs is more prominent in organizations (QEOHS) with the following characteristics: have a high business volume, are members of the United Nations Global Compact Network Portugal, and disclose their sustainability reports on their website. This study can be useful for both managers and decision makers who aim to support organizations in contributing to the Sustainable Development Goals and achieving a better and sustainable future for all.The author(s) thank Manuel Gilberto Freitas dos Santos and Joaquim José de Almeida Soares Gonçalves (IPCA—Polytechnic Institute of Cavado and Ave), and Paulo Alexandre da Costa Araújo Sampaio and José Pedro Teixeira Domingues (UM - Minho University) for their support in previous research projects that preceded this investigation.info:eu-repo/semantics/publishedVersio

Predicting Risk for Deer-Vehicle Collisions Using a Social Media Based Geographic Information System

As an experiment investigating social media as a data source for making management decisions, photo sharing websites were searched for data on deer sightings. Data about deer density and location are important factors in decisions related to herd management and transportation safety, but such data are often limited or not available. Results indicate that when combined with simple rules, data from photo sharing websites reliably predicted the location of road segments with high risk for deer-vehicle collisions as reported by volunteers to an internet site tracking roadkill. Use of Google Maps as the GIS platform was helpful in plotting and sharing data, measuring road segments and other distances, and overlaying geographical data. The ability to view satellite images and panoramic street views proved to be a particularly useful. As a general conclusion, the two independently collected sets of data from social media provided consistent information, suggesting investigative value to this data source. Overlaying two independently collected data sets can be a useful step in evaluating or mitigating reporting bias and human error in data taken from social media

On fuzzy reasoning schemes

In this work we provide a short survey of the most frequently used fuzzy reasoning schemes. The paper is organized as follows: in the first section we introduce the basic notations and definitions needed for fuzzy inference systems; in the second section we explain how the GMP works under Mamdani, Larsen and G¨odel implications, furthermore we discuss the properties of compositional rule of inference with several fuzzy implications; and in the third section we describe Tsukamoto’s, Sugeno’s and the simplified fuzzy inference mechanisms in multi-input-single-output fuzzy systems

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.Comment: 11 PAGE

A comparison of reimbursement recommendations by European HTA agencies : Is there opportunity for further alignment?

Introduction: In Europe and beyond, the rising costs of healthcare and limited healthcare resources have resulted in the implementation of health technology assessment (HTA) to inform health policy and reimbursement decision-making. European legislation has provided a harmonized route for the regulatory process with the European Medicines Agency, but reimbursement decision-making still remains the responsibility of each country. There is a recognized need to move toward a more objective and collaborative reimbursement environment for new medicines in Europe. Therefore, the aim of this study was to objectively assess and compare the national reimbursement recommendations of 9 European jurisdictions following European Medicines Agency (EMA) recommendation for centralized marketing authorization. Methods: Using publicly available data and newly developed classification tools, this study appraised 9 European reimbursement systems by assessing HTA processes and the relationship between the regulatory, HTA and decision-making organizations. Each national HTA agency was classified according to two novel taxonomies. The System taxonomy, focuses on the position of the HTA agency within the national reimbursement system according to the relationship between the regulator, the HTA-performing agency, and the reimbursement decision-making coverage body. The HTA Process taxonomy distinguishes between the individual HTA agency's approach to economic and therapeutic evaluation and the inclusion of an independent appraisal step. The taxonomic groups were subsequently compared with national HTA recommendations. Results: This study identified European national reimbursement recommendations for 102 new active substances (NASs) approved by the EMA from 2008 to 2012. These reimbursement recommendations were compared using a novel classification tool and identified alignment between the organizational structure of reimbursement systems (System taxonomy) and HTA recommendations. However, there was less alignment between the HTA processes and recommendations. Conclusions: In order to move forward to a more harmonized HTA environment within Europe, it is first necessary to understand the variation in HTA practices within Europe. This study has identified alignment between HTA recommendations and the System taxonomy and one of the major implications of this study is that such alignment could support a more collaborative HTA environment in Europe.Peer reviewedFinal Published versio