Collection System Research Articles

Burden of unintentional drowning in Indonesia: insights from the Global Burden of Disease Study 2019

IntroductionA high burden of unintentional fatal drowning has been reported in low- and middle-income countries. However, little is known about unintentional drowning in Indonesia.MethodsThis population-based retrospective cohort study analysed unintentional...

The future of UN peace operations: pragmatism, pluralism or statism?

Abstract Although United Nations peace missions have played an important conflict management role for decades, a backlash against the post-Cold War model of ‘liberal peacebuilding’ and other recent developments have called their future into doubt. Some observers contend that a ‘pragmatic turn’ is now underway—a shift away from ambitious plans to transform war-torn societies into liberal democracies and towards more modest and realistic goals. Whether ‘pragmatism’ offers a viable alternative framework for peacebuilding, however, is less clear. This article challenges key assumptions of the pragmatic approach to peacebuilding, including the notion that UN missions can be ‘agnostic’ about the governance arrangements of societies that host these operations, as some proponents of pragmatism recommend. Historical case-studies show that such assumptions are integral features of collective conflict management systems, including UN peace missions. Moreover, a close examination of the pragmatist approach reveals that it, too, is based on such assumptions—namely, that building peace requires the promotion of pluralist societies and states. Pluralist peacebuilding, the article concludes, could offer a compelling foundation for future UN missions in an era of rising statist–authoritarianism, but developing this approach will first require discarding pragmatism's pretence of ideological agnosticism.

Evaluating the efficacy of biogeochemical cover system in mitigating landfill gas emissions: A large-scale laboratory simulation.

Municipal solid waste (MSW) landfills are a significant source of methane (CH4) emissions in the United States, contributing to global warming. Current landfill gas (LFG) management methods, like the landfill cover system and LFG collection system, do not entirely prevent LFG release. Biocovers have the potential to reduce CH4 emissions through microbial oxidation. However, LFG also contains carbon dioxide (CO2) and trace hydrogen sulfide (H2S) depending on waste composition, temperature, moisture content, and age of waste. An innovative biogeochemical cover (BGCC) was developed to tackle these concerns. This cover comprises a biochar-based biocover layer overlaid with a basic oxygen furnace (BOF) steel slag layer. The biochar-based biocover layer oxidizes CH4 emissions, while the BOF slag layer reduces CO2 and H2S through carbonation and sulfidation reaction mechanisms. The BGCC system's field performance remains unexamined. Therefore, a large-scale tank setup simulating near-field conditions was developed to evaluate the BGCC system's ability to mitigate CH4, CO2, and H2S from LFG simultaneously. Synthetic LFG was passed through the BGCC in five distinct phases, each designed to simulate the varying gas compositions and flux rates typical of MSW landfill. Gas profiles along the depth were monitored during each phase, and gas removal efficiency was measured. After testing, biocover and BOF slag samples were extracted to analyze physico-chemical properties. Batch tests were also conducted on samples extracted from the biocover and BOF slag layers to determine potential CH4 oxidation rates and residual CO2 sequestration capacity. The results showed that the BGCC system's CH4 removal efficiency decreased with higher CH4 flux rates, achieving its highest removal (74.7-79.7%) at moderate influx rates (23.9-25.5g CH4/m2-day) and reducing to its lowest removal (27.4%) at the highest influx rate (57.5g CH4/m2-day). Complete H2S removal occurred during Phase 3 in the biocover layer of BGCC system. CH4 oxidation rates were highest near the upper (277.9µg CH4/g-day) and lowest in the deeper region of the biocover layer. In the tank experiment, CO2 breakthrough occurred after 156days due to drying of the BOF slag layer, with an average residual carbonation capacity of 46 gCO2/kg slag after moisture adjustment. Overall, the BGCC system effectively mitigated LFG emissions, including CH4, CO2, and H2S, at moderate flux rates, showing promise as a comprehensive solution for LFG management.

Tracing the odor source of crumb rubber modified asphalt emissions and evaluating the deodorization effect of attapulgite

Crumb rubber modified asphalt (CRMA) is widely used in road construction due to its excellent performance. However, it generates significant odorous gases during paving. To analyze the odor contribution, both untreated crumb rubber and desulfurized rubber were involved in this study to prepare CRMA and desulfurized rubber modified asphalt (DRMA). Attapulgite was selected as an emission reduction agent to reduce the odorous emissions released from CRMA and DRMA. An indoor fume collection system and gas chromatography-mass spectrometry (GC-MS) analysis were used to trace the odor source and quantify the odorous emissions. A comprehensive olfactory thresholds database for CRMA emissions was further established to evaluate the odor level of various rubberized asphalt binders by using three-point comparative odor bag method, along with known olfactory thresholds of certain compounds reported in the literature. Dynamic shear rheometer (DSR), multiple stress creep recovery (MSCR), bending beam rheometer (BBR) and Brookfield viscosity tests were used to analyze the effect of attapulgite on rheological properties of CRMA and DRMA. Results indicated that odor emission rate of CRMA containing 1 %, 3 %, and 5 % attapulgite was decreased by 42.62 %, 63.29 %, and 71.06 %, respectively. Benzothiazole, derived from tire rubber vulcanization accelerator, can be reduced by 60.25 % with 1 % attapulgite, demonstrating excellent performance in terms of reducing target odor emissions. Although DRMA has a lower emission rate than that of conventional CRMA, the addition of 1 % attapulgite can further reduce the odor emission rate of DRMA by 37.92 %. The addition of attapulgite has less impact on the overall performance of CRMA and DRMA. Besides, it was found that attapulgite can reduce the viscosity of rubberized asphalt binders, which is beneficial for rubberized asphalt mixture paving and compaction. Considering odor reduction, performance, and economic factors, the optimal attapulgite content was determined to be 5 % for CRMA and 1 % for DRMA. This approach successfully reduces odorous emissions while maintaining the desirable properties of CRMA, making it a viable solution for environmentally friendly road construction.

Measurement of the distal bile duct density on computed tomography can differentiate choledocholithiasis from a control population.

Hounsfield unit density of biliary fluid on CT may be a useful clinical marker that has not been described in the literature. This method has been used to differentiate pyonephrosis from hydronephrosis in obstructed collecting systems of the kidney. We aimed to create a user-friendly technique to measure the density of the distal bile duct using CT. The bile duct density of cases with proven choledocholithiasis at ERCP were compared with those of a control group (no biliary pathology). A total of 106 patients with proven choledocholithiasis at ERCP and 50 control patients were analysed. The distal bile duct density was calculated using the 4-point and max ellipse methods. Two blinded, independent investigators calculated the bile duct density. The HU is significantly higher in the presence of choledocholithiasis (P < 0.0001). Using the Youden index a cut-off value of 28.6 HU for the 4-point technique is useful to predict the presence of choledocholithiasis (Sensitivity 58%, Specificity 86%). Calculation of the distal bile duct density can differentiate choledocholithiasis from a control population. It may be useful alone or as a component of a scoring system to select patients more effectively for intervention. The improved use of CT may also decrease use of MRCP and reduce time to ERCP, which have potential cost benefits.

Simulation analysis of overvoltage caused by grounded short circuit in the collection system of far-offshore wind farms

Abstract As the distance from the shore of offshore wind farms increases, MMC-HVDC technology is gradually gaining attention in its grid connection schemes. A broadband electromagnetic transient model of offshore wind farms with a total capacity of 800 MW connected to the grid via a flexible direct current transmission system is developed. After three types of grounded short circuit faults occur at different fault positions within the power collection system, the characteristics of overvoltage are obtained and they are analyzed on the basis of considering the low voltage ride-through capability of the direct-drive permanent magnet wind power generator. Combining the two factors of range and amplitude, the results show that the overvoltage phenomenon is the most serious when a two-phase grounded short circuit occurs at the converging busbar, which can be the focus of the study when setting up overvoltage protection.

CMK: Enhancing Resource Usage Monitoring across Diverse Bioinformatics Workflow Management Systems

The increasing use of multiple Workflow Management Systems (WMS) employing various workflow languages and shared workflow repositories enhances the open-source bioinformatics ecosystem. Efficient resource utilization in these systems is crucial for keeping costs low and improving processing times, especially for large-scale bioinformatics workflows running in cloud environments. Recognizing this, our study introduces a novel reference architecture, Cloud Monitoring Kit (CMK), for a multi-platform monitoring system. Our solution is designed to generate uniform, aggregated metrics from containerized workflow tasks scheduled by different WMS. Central to the proposed solution is the use of task labeling methods, which enable convenient grouping and aggregating of metrics independent of the WMS employed. This approach builds upon existing technology, providing additional benefits of modularity and capacity to seamlessly integrate with other data processing or collection systems. We have developed and released an open-source implementation of our system, which we evaluated on Amazon Web Services (AWS) using a transcriptomics data analysis workflow executed on two scientific WMS. The findings of this study indicate that CMK provides valuable insights into resource utilization. In doing so, it paves the way for more efficient management of resources in containerized scientific workflows running in public cloud environments, and it provides a foundation for optimizing task configurations, reducing costs, and enhancing scheduling decisions. Overall, our solution addresses the immediate needs of bioinformatics workflows and offers a scalable and adaptable framework for future advancements in cloud-based scientific computing.

Indo-Pakistan rivalry and integrated ring balancing: prospects and challenges to regional stability in South Asia

ABSTRACT Pakistan's security landscape faces unprecedented challenges due to India's burgeoning power and its strategic alignment with the United States. This study proposes an innovative ‘Integrated Ring Balancing’ framework to navigate this complex geopolitical environment. The framework operates across three tiers: Tier 1: Direct engagement with immediate neighbors – India, Iran, China, and Afghanistan. Tier 2: Building robust linkages with the neighbors of Pakistan's Tier 1 partners, encompassing South Asia, Central Asia, Southeast Asia, and the Middle East. Tier 3: Leveraging major powers – China, Russia, and the United States – through Tier 1 and 2 partnerships. This multi-layered approach aims to achieve two key objectives: (1) Neutralize India's US-backed advantage: Engaging China and Russia can influence Tier 2 states, particularly in Central Asia, to rebalance their regional positioning towards Pakistan. (2) Develop a collective defense system: Converging geo-economic interests via initiatives like the China–Pakistan Economic Corridor (CPEC) and the Belt and Road Initiative (BRI) can foster a regional security ring around Pakistan. By strategically engaging across these tiers, Pakistan can navigate the complex power dynamics in South Asia, bolster its security, and ultimately safeguard its national interests in the face of a strengthened India.

Implementation Silent Reading for English Subject at Islamic Education Department at STIT Aqidah Usymuni Sumenep

Silent reading refers to the act of reading without vocalizing the words. With this in mind, the researcher aimed to explore students' silent reading comprehension. This study adopted a qualitative research approach. Qualitative research, involves the systematic collection of data to uncover underlying patterns or meanings using scientific methods, typically pursued by researchers interested in understanding the nature of a phenomenon. This research used students of the Islamic Education Department at STIT Aqidah Usymuni Sumenep as objects. The conclusion showed that silent reading can also inspire students to share diverse ideas while reading silently. Students can acquire more information when they read in a quiet environment. When the conditions allow students to read comfortably, their concentration improves, and they can easily grasp the key points of the English text.

Analysis of the resonant converter for multi-terminal collection of DC offshore wind farms

Abstract With the rapid development of power electronics technology, the offshore wind power system using DC collection and DC transmission is the best solution to increasing offshore distance. Resonant converters achieving soft switching have become a common choice in the DC/DC power conversion system. This paper proposed a design scheme for the multi-terminal collection of DC offshore wind farms based on the resonant converter. The operating principles are analyzed, and variable-frequency control of the converter is utilized to control the output voltage. Simulation verifies the effectiveness of the multi-terminal DC collection system and its control strategy.

EXPRESS: Standoff Identification of Plastic Waste Using a Low-Cost Compact Laser-Induced Breakdown Spectroscopy LIBS Detection System.

We report the standoff/remote identification of post-consumer plastic waste by utilizing a low-cost and compact standoff laser-induced breakdown spectroscopy (ST-LIBS) detection system. A single plano-convex lens is used for collecting the optical emissions from the plasma at a standoff distance of 6.5 m. A compact non-gated Czerny-Turner charge-coupled device (CCD) spectrometer (CT-CCD) is utilized to analyze the optical response. The single lens and CT-CCD combination not only reduces the cost of the detection system by ten folds, but also decreases the collection system size, weight compared to heavy telescopic-based intensified CCD (ICCD) systems. All the samples investigated in this study were collected from a local recycling plant. All the measurements were performed with only a single laser shot which enables rapid identification while probing a large number of samples in real time. Furthermore, principal component analysis has shown excellent separation among the samples and an artificial neural network analysis has revealed that plastic waste can be identified within ∼10 ms only (testing time) with accuracies up to ∼99%. Finally, these results have the potential to build a compact and low-cost ST-LIBS detection system for the rapid identification of plastic waste for real-time waste management applications.

Polyethylene terephthalate nanoparticles induce oxidative damage in Chlorella vulgaris

Polyethylene Terephthalate (PET) is a type of plastic largely used for packing food and beverages. Unfortunately, it includes a major portion of the plastic distributed through aquatic systems wherever systematic collection and recycling are lacking. Although PET is known to be non-toxic, it is not obvious whether the nanoparticles (NPs) formed due to their degradation have any direct/indirect effect on aquatic organisms. In order to study the effects on aquatic environment, fresh water algae Chlorella vulgaris was subjected to incremental concentrations of the NPs. We observed a concentration and duration of exposure dependent decrease in algal growth rate along with reduced total chlorophyll content. Scanning electron microscopy revealed deformities in cell shape and the uptake of Propidium Iodide suggested membrane damage in response to NP exposure. Intracellular Reactive Oxygen Species level was also found significantly higher, evidenced by Dichlorodihydrofluorescein diacetate staining. Activity of antioxidant enzymes Superoxide dismutase (SOD), Peroxidase (POD) and Catalase (CAT) were significantly higher in the NP exposed groups suggesting the cellular response to regain homeostasis. Further, expression levels of the genes psaB, psbC, and rbcL associated with photosynthesis increased above two fold with respect to the control inferring the possibility of damage to photosynthesis and the initial molecular responses to circumvent the situation. In short, our studies provide evidence for oxidative stress mediated cellular damages in Chlorella vulgaris exposed to NPs of PET.

Learning Health System to rapidly improve the implementation of a school physical activity policy

BackgroundLearning Health Systems (LHS) – characterised by cycles of evidence generation and application – are increasingly recognised for their potential to improve public health interventions and optimise health impacts; however there is little evidence of their application in the context of public health practice. Here, we describe how an Australian public health unit applied a LHS approach to successfully improve a model of support for implementation of a school-based physical activity policy.MethodsThis body of work was undertaken in the context of a strong research-practice partnership. Core LHS capabilities included: i) partnerships and stakeholder engagement; ii) workforce development and learning health communities; iii) multi-disciplinary scientific expertise; iv) practice data collection and management system; v) evidence surveillance and synthesis; and vi) governance and organisational processes of decision making. Three cycles of data generation and application were used. Within each cycle, randomised controlled trials conducted in NSW primary schools were used to generate data on the support model’s effectiveness for improving schools’ implementation of a government physical activity policy, its delivery costs, and process measures such as adoption and acceptability. Each type of data were analysed independently, synthesised, and then presented to a multi-disciplinary team of researchers and practitioners, in consult with stakeholders, leading to collaborative decisions for incremental improvements to the support model.ResultsCycle 1 tested the first version of the support model (composed of five implementation strategies targeting identified barriers of policy implementation) and showed the model’s feasibility and efficacy for improving schools’ policy implementation. Data-informed changes were made to enhance impact, including the addition of three implementation strategies to address outstanding barriers. Cycle 2 (now, testing a package of eight implementation strategies) established the model’s effectiveness and cost-effectiveness for improving school's policy implementation. Data-informed changes were made to reduce delivery costs, specifically adapting the costliest strategies to reduce in-person contact from external support personnel. Cycle 3 showed that the adaptations minimised the relative cost of delivery without adversely impacting on the effect.ConclusionsThrough this process, we identified an effective, cost-effective, acceptable and scalable policy implementation support model for service delivery. This provides important information to inform or support LHS approaches for other agencies seeking to optimise the health impact of evidence-based interventions.

Machine learning based feedforward voltage control for buck converters in envelope tracking applications

Tracking the voltage envelope of 5G base station power supplies poses a significant long-term challenge. To achieve fast and accurate envelope tracking, an efficient and high-frequency power electronics circuit and an excellent voltage controller are essential. This paper presents a novel machine learning (ML) based feedforward control method for precise voltage reference tracking. The method is exemplified using a standard buck converter in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM). A feedforward neural network (FNN) is trained to capture non-ideal and non-linear effects in real circuits. An automated data collection system, developed with MATLAB and Simulink, facilitates data acquisition and model training process. The trained FNN predicts the duty cycle for the buck converter to generate the targeted output voltage and output power. A simulation model is built to validate the effectiveness of the FNN controller in envelope tracking applications.

Re-exploration following caesarean birth: a prospective national case-control study using the United Kingdom Obstetric Surveillance System (UKOSS) data collection system.

Re-exploration following caesarean birth and the associated maternal morbidity has not been investigated in the UK. Our aims were to determine the national incidence and identify the associated risk factors. We conducted a prospective observational case-control study across 194 UK consultant-led maternity units in women whose caesarean birth was complicated by a re-exploration. Independent factors for re-exploration were analysed using multivariable multi-level mixed effects logistic regression. Over the study period (1 June 2021 and 31 May 2022) 238,423 caesarean births were recorded across the UK of which 187 women underwent re-exploration, giving an incidence of one re-exploration per 1282 caesarean births (95%CI 1:1099-1:1471). Haemorrhage (124/187, 66.3%) and sepsis (31/187, 16.6%) were the most common findings at re-exploration. Median (IQR [range]) time interval to re-exploration following the caesarean birth was 1 (0-4 [0-28]) day. Mechanical ventilation was required in 34 (18.6%) women, cardiac arrest was reported in 5 (2.7%) and 3 (1.6%) women died. Independent preceding factors associated with a re-exploration included: receipt of blood transfusion (adjusted OR (95%CI) 8.25 (2.66-25.61)); use of a general anaesthetic (adjusted OR (95%CI) 3.33 (1.61-6.88)); pre-eclampsia (adjusted OR (95%CI) 3.27 (1.55-6.91)); black ethnicity (adjusted OR (95%CI) 3.14 (1.39-7.11)); postpartum haemorrhage (adjusted OR (95%CI) 2.82 (1.81-4.37)); use of anticoagulants or antiplatelet drugs pre-caesarean birth (adjusted OR (95%CI) 2.26 (1.35-3.81)); and emergency caesarean birth (adjusted OR (95%CI) 1.89 (1.01-3.57)). Re-exploration following caesarean birth in the UK is uncommon but is associated with significant maternal morbidity and mortality. These study findings will help guide informed consent and encourage appropriate surveillance of high-risk women postpartum.

In-Silico Enactment of Musa paradisiaca (L.) Exudate Against the Urotlithiatic Disease by Docking Study

Objectives: Urolithiasis is a condition that occurs when the stones exit the renal pelvis and move onto the remainder of the urinary collecting system. Musa paradisiaca (L.) is a plant with high nutritive value and has been used for the treatment of various diseases including urolithiasis. The objective of the present study was designed to detect the receptor-ligand binding energy and interaction through a molecular docking from the bio-active compounds in an exudates of M. paradisiaca pseudo stem on urolithiatic causative protein named as glycolate oxidase receptor (PDB ID: 2RDU). Methods: In silico study especially molecular docking was performed using AutodockVina and the best confirmation between ligand and protein were selected using Lamarkaina Genetic Algorithm (LGA) and ligand protein interaction was visualized using PyMol viewer. Novelty: Totally three various bio-active compounds are elucidated named as Olean-12-ene-3 beta, 28-diol, Tricyclo[8.4.1.1(3,8)] hexadeca- 3,5,7,10,12,14-hexaene- 2,9-dione, anti and 2H-Pyran,2-(7-heptadecynyloxy)tetrahydro- from the exudate of experimental sample. Three phyto active compounds or secondary metabolites were used for the present prediction by docking. The present result of molecular docking clearly revealed that olean 12 -ene-3 beta, 28 diol is the best biologically effective ligand observed through a highest docking score (-7.2 k cal/mol) on glycolate oxidase receptor than other two ligand compounds. In conclusion the compound olean 12 -ene-3 beta, 28 diol could be act as a potential lead molecule for urolithiasis. Keywords: Musa paradisiaca, Exudate, GCMS, Urolithiasis, AutoDock, PyMol Viewer

Automated debt recovery systems: Harnessing AI for enhanced performance

Amidst an upsurge in the quantity of delinquent loans, the financial industry is experiencing a fundamental transformation in the approaches utilised for debt recovery. The debt collection process is presently undergoing automation and improvement through the utilisation of Artificial Intelligence (AI), an emergent technology that holds the potential to revolutionise this sector. By leveraging machine learning, natural language processing, and predictive analytics, automated debt recovery systems analyse vast quantities of data, generate forecasts regarding the likelihood of recovery, and streamline operational processes. Debt collection systems powered by AI are anticipated to be compliant, precise, and effective. On the other hand, conventional approaches are linked to increasing expenditures and inefficiencies in operations. These solutions facilitate efficient resource allocation, customised communication, and rapid data analysis, all while minimising the need for human intervention. Significant progress has been made in data analytics, predictive modelling, and decision-making through the application of artificial intelligence (AI) in debt recovery; this has the potential to revolutionize the financial sector’s approach to debt management. The findings of the research underscore the criticality of artificial intelligence (AI) in attaining efficacy and precision, in addition to the imperative of a data-centric framework to fundamentally reshape approaches to debt collection. In conclusion, artificial intelligence possesses the capacity to profoundly transform the existing approaches utilized in debt management, thereby guaranteeing financial institutions’ sustained profitability and efficacy. The application of machine learning methodologies, including predictive modelling and logistic regression, signifies the potential of the system.

Harnessing marketing analytics for enhanced decision-making and performance in SMEs

In the contemporary business landscape, small and medium-sized enterprises (SMEs) are increasingly leveraging marketing analytics to enhance decision-making and improve performance. This paper explores the transformative impact of marketing analytics on SMEs, emphasizing its role in driving data-driven decisions that optimize marketing strategies, enhance customer engagement, and boost overall business performance. Through the systematic collection and analysis of data, SMEs can gain deeper insights into customer behaviors, preferences, and trends, enabling more targeted and effective marketing campaigns. The implementation of marketing analytics facilitates the measurement of key performance indicators (KPIs) such as click-through rates, conversion rates, and return on investment (ROI), providing a robust framework for evaluating the effectiveness of marketing initiatives. By utilizing various types of analytics, including descriptive, predictive, and prescriptive analytics, SMEs can make informed decisions that not only respond to current market conditions but also anticipate future trends and opportunities. Moreover, the adoption of marketing analytics supports the personalization of customer experiences, allowing SMEs to deliver tailored content and offers that resonate with specific audience segments. This level of personalization fosters deeper customer relationships and increases the likelihood of conversions and customer loyalty. The continuous monitoring and analysis of marketing data enable SMEs to remain agile and responsive to changing market dynamics, ensuring that their marketing strategies remain relevant and effective. By embracing a data-driven approach, SMEs can minimize the risk of ineffective marketing efforts and allocate resources more efficiently, ultimately driving sustainable growth and competitive advantage. In conclusion, harnessing marketing analytics is crucial for SMEs aiming to enhance decision-making and performance. The ability to derive actionable insights from data empowers SMEs to optimize their marketing strategies, improve customer engagement, and achieve better business outcomes. As SMEs continue to navigate an increasingly data-driven world, the strategic use of marketing analytics will be pivotal in securing long-term success and growth.

The impact of suicide registers and other monitoring systems on suicide prevention: A scoping review.

Decreasing suicide mortality has become an overarching goal for societies worldwide. Suicide registers and other monitoring systems are a valuable source of information that can be used for addressing the suicide phenomenon and evaluating preventative interventions. This scoping review provides an overview of literature published in the last decade that has focussed on the operations (functioning) and characteristics of suicide registers and other suicide monitoring systems. Four electronic databases were searched in 2020 for identifying published material from January 2010 to October 2020. The searches were updated in October 2023 to include material from 2020 to date. Grey literature through Google searches and mental health commissions websites and the reference lists of selected documents were also searched. Twenty-five articles were included in this review. Nearly half the articles were from the United States, followed by Australia. Nine countries were identified as having used suicide registers or suicide-specific monitoring systems to inform suicide prevention. Monitoring mechanisms varied across the countries examined. No article provided evidence that definitively linked suicide registers or other monitoring systems for suicide with the prevention of suicide or reduction in suicide rates. However, a variety of benefits of suicide monitoring for preventative and public health interventions were identified. The number of nations with surveillance systems for suicide prevention is low. Further, there is a lack of consistency in the systematic collection, analysis and interpretation of suicide-related information across the countries examined. Efforts to establish high-quality suicide surveillance systems that can be accessed in a timely and easy manner are needed to inform tailored strategies for suicide prevention.

An optimized framework for implementation of smart waste collection and management system in smart cities using IoT based deep learning approach

An optimized framework for implementation of smart waste collection and management system in smart cities using IoT based deep learning approach