A systems perspective on national prioritisation of sustainable development goals:Insights from Australia

Achieving the Sustainable Development Goals(SDGs)requires effective national initiatives and resource allo-cation.Yet,the simultaneous attainment of all goals is hindered by constraints such as limited budgets and resources,varied national priorities,and the intricate nature of the goals.As we approach 2030 and beyond,an urgent need for an effective,data-driven prioritisation system exists to optimise what can be accomplished.A considerable knowledge gap persists in identifying the priority areas that demand concentrated attention and how their improvement would propel overall sustainability goals.To bridge this gap,our study presents a priori-tisation approach that identifies significant SDG indicators based on urgency and impact,utilising Benchmarking,Bivariate,and Network analysis.Furthermore,we introduce an innovative Impact Index(IMIN)to assess an indi-cator’s extensive effect on the SDG network.This system carries significant international relevance by establishing a robust framework to identify key,potent,and interconnected indicators.It supports decision-makers worldwide in comprehending their nation’s SDG performance and promotes efficient resource allocation.In the specific con-text of Australia,our analysis spotlights several impactful,yet underperforming SDG indicators.These include the protection of Freshwater,Terrestrial,and Mountain Key Biodiversity Areas(KBAs),the share of renewable energy and energy intensity level of primary energy,targeted research and development,gender equality in national parliaments,and carbon-efficient manufacturing,amongst others.

Anomaly Based Camera Prioritization in Large Scale Surveillance Networks

Digital surveillance systems are ubiquitous and continuously generate massive amounts of data,and manual monitoring is required in order to recognise human activities in public areas.Intelligent surveillance systems that can automatically identify normal and abnormal activities are highly desirable,as these would allow for efficient monitoring by selecting only those camera feeds in which abnormal activities are occurring.This paper proposes an energy-efficient camera prioritisation framework that intelligently adjusts the priority of cameras in a vast surveillance network using feedback from the activity recognition system.The proposed system addresses the limitations of existing manual monitoring surveillance systems using a three-step framework.In the first step,the salient frames are selected from the online video stream using a frame differencing method.A lightweight 3D convolutional neural network(3DCNN)architecture is applied to extract spatio-temporal features from the salient frames in the second step.Finally,the probabilities predicted by the 3DCNN network and the metadata of the cameras are processed using a linear threshold gate sigmoid mechanism to control the priority of the camera.The proposed system performs well compared to state-of-theart violent activity recognition methods in terms of efficient camera prioritisation in large-scale surveillance networks.Comprehensive experiments and an evaluation of activity recognition and camera prioritisation showed that our approach achieved an accuracy of 98%with an F1-score of 0.97 on the Hockey Fight dataset,and an accuracy of 99%with an F1-score of 0.98 on the Violent Crowd dataset.

A SPE-HPLC-MS/MS method for the simultaneous determination of pr ior itised pharmaceuticals and EDCs with high environmental risk potential in freshwater

This work describes the development,optimisation and validation of an analytical method for the rapid determination of 17 priority pharmaceutical compounds and endocrine disrupting chemicals(EDCs).Rather than studying compounds from the same therapeutic class,the analyses aimed to determine target compounds with the highest risk potential(with particular regard to Scotland),providing a tool for further monitoring in different water matrices.Prioritisation was based on a systematic environmental risk assessment approach,using consumption data;wastewater treatment removal efficiency;environmental occurrence;toxicological effects;and pre-existing regulatory indicators.This process highlighted 17 compounds across various therapeutic classes,which were then quantified,at environmentally relevant concentrations,by a single analytical methodology.Analytical determination was achieved using a single-step solid phase extraction(SPE)procedure followed by high-performance liquid chromatography with tandem mass spectrometry(HPLC-MS/MS).The fully optimised method performed well for the majority of target compounds,with recoveries>71%for 15 of 17 analytes.The limits of quantification for most target analytes(14 of 17)ranged from 0.07 ng/L to 1.88 ng/L in river waters.The utility of this method was then demonstrated using real water samples associated with a rural hospital/setting.Eight compounds were targeted and detected,with the highest levels found for the analgesic,paracetamol(at up to 105,910 ng/L in the hospital discharge).This method offers a robust tool to monitor high priority pharmaceutical and EDC levels in various aqueous sample matrices.