Galway-Mayo Institute of Technology

Abstract The mining industry is an important sector that contributes to economic growth and employment creation in Mongolia. Water access, water quality, and community engagement are the major challenges the Mongolian mining industry faces. Integrated Water Resource Management (IWRM) is a holistic water management approach that applies principles of economic efficiency, social equity, and environmental sustainability to ensure water sustainability. A research study was carried out to understand stakeholders’ views and perspectives on IWRM and to identify water use practices, challenges, and barriers in the Gobi Desert mining region. The aim was to identify processes that help to improve access to water in the Gobi Desert region. This research applied a qualitative approach and employed three data collection methods: (1) semi-structured interviews; (2) field observations and (3) documents and academic articles reviews. Research participants were representatives from mining companies, local communities, government, and river basin administrations. In the Gobi Desert region, processes contributing to improving water management are: (1) participatory water monitoring, (2) coal processing plant educational visits, (3) local stakeholders council’s meetings, (4) herder’s well improvement projects, (5) independent water auditing, and (6) water advocacy events. These practices, aligned with the core principles of IWRM provide practical solutions for sustainable water management in mining regions, with the potential for global adaptation.

The proposed study introduces a robust computational framework meant towards leveraging behavioral analysis of permission usage integrated with sophisticated Machine Learning (ML) techniques. The novelty associated with the model is the generation of a synthetic permission access dataset and facilitating scalable simulation of scenarios of diverse attacks in the Internet of Things (IoT). The study model has been implemented considering 4 use-cases of potential adversaries, viz., spyware, data exfiltration, root exploits, and location tracking. The model leverages a Gradient Boosting Classifier for differentiating regular threats from malicious ones based on the activities of users on IoT devices. A risk profiling system has been integrated towards critical permission, where various forms of supervised and unsupervised learning models have also been tested. The simulated study outcome shows the proposed scheme to accomplish 20

Typically, in Ireland, male/female marine fish gonads are discarded at sea or processed onshore for low-value fishmeal. This study aimed at a) developing two new ready-to-eat fish spread products from underutilized herring milt with either cod roe or plaice roe and b) testing the products acceptance with consumers. Chi-square tests on contingency tables were used to analyze open-ended questions relating to consumer likes and dislikes of the products. The main characteristics showed sensory drivers for liking was flavor, color, and texture with similar characteristics also used for disliking. Colorimetry and texture analysis showed significant differences (P < .05) between the products which were also detected by panelists during sensory evaluation (color, texture, appearance, P < .05). Penalty analysis of JAR showed overall liking was most impacted by level of fishiness. Positive acceptance of products suggests that underutilized fish by-products could be used successfully in Irish ready-to-eat foods thereby adding value while reducing waste.

A new paradigm has been adopted in agricultural techniques, tools, and technology. To guarantee the implementation of site-specific crop management, which includes soil nutrient treatments according to crop requirements, precision agriculture is crucial. Soil nutrients are a major component in determining the growth of precision agriculture, which has gained global attention. One of the main challenges is more effective nutrient content detection as it guides well-planned nutrient-level-boosting routines. Many approaches, such using research labs and additional mobile labs, haven't shown to be very useful in helping farmers control their soil fertility. Taking advantage of the breakthroughs is severely hampered by factors like lack of knowledge and distance from research centers. In the work proposed, LSTM based RNN is employed to predict the Ph and nutrient values of the soil measured through microcontroller using sensor from the agricultural field. At the same time, RELU-CNN approach is applied to the soil images for measuring the same values. The values obtained from both the approaches are compared against each other so that the method may be made directly available to the farmers for evaluating the nutrient level of the soil and take necessary action. The approaches are measured in terms of quality parameters Recall, F1-Score, Accuracy, Precision and RMS value.

Objective Determine the effectiveness of interventions in changing concussion knowledge and attitudes in athletes, coaches, parents. Design Systematic literature review. Setting Amateur and communiity sports. Participants Human populations ranging from child to adult involved in sport in any capacity. Interventions (or Assessment of Risk Factors) Influence of enhancing concussion knowledge and attitudes in athletes, coaches, parents. Outcome Measures Changes in concussion knowledge and athletes. Main Results Search strategy identified 7534 articles. Following the three stage study screening process, 25 were eligible for inclusion in this review. Sixteen studies examined the effects of an educational intervention on both attitudes and knowledge, five studies examined the effects of an educational intervention on knowledge only and four interventions examined the effects on attitudes only. Conclusions Knowledge transfer frameworks have been shown to be effective for both concussion knowledge and concussion attitudes This abstract has been published in full manuscript format and has the following citation: BMJ Citation https://doi.org/10.1080/02640414.2020.1835223