Resource Requirements Research Articles

Factors Influencing School‐Based Mental Health Program Selection: Insights From Educational Stakeholders

ABSTRACTSchool mental health programs are an important component of the national response to the current student mental health crisis. However, evidence‐based programs often face numerous barriers to their selection, scale‐up, and sustainability in real‐world settings. In this study, we interviewed 15 educational stakeholders to examine what influences their selection of school‐based mental health programs. Interviews focused on factors influencing the selection and sustainability of mental health programs in schools. We coded transcribed interview data using a hybrid, iterative process, and generated themes from these codes. We identified five categories of themes related to key decision‐making criteria: program fit with student and school needs, evidence of program effectiveness, stakeholder buy‐in, logistical considerations for staff delivering programs, and cost and resource requirements. Findings have implications for researchers who wish to improve dissemination strategies for school‐based mental health prevention programs and for policymakers who wish to shape funding priorities to support greater diversity in program availability. Findings are also useful to practitioners who may use these insights to reflect on their own program selection practices or may see their own experiences reflected and normalized through the lens of the key themes presented here.

Security Analysis of Classical and Post-Quantum Blockchains

ABSTRACT In recent years, the emergence of quantum computing has created substantial problems to the security of cryptographic methods often utilized in blockchain technology. As quantum computers become more powerful, the possibility of them breaking classical cryptography algorithms such as ECDSA grows, forcing researchers to look for quantum-resistant alternatives. Post-quantum cryptographic algorithms, such as Falcon, have emerged as possible alternatives for long-term blockchain security. This study examines the performance and security of classical (ECDSA) and post-quantum (Falcon) blockchains under a variety of attack scenarios, including DDoS, replay, and Sybil attacks. Our research will evaluate the computational and resource needs of several cryptographic approaches, as well as examine how these infrastructures respond under various attack situations and emphasize the trade-offs between higher security and resource consumption. Our results show that, while post-quantum blockchains are more resistant to quantum computing threats, they need much more CPU and memory than their classical equivalents. This increased resource requirement affects scalability and efficiency, especially in high-load scenarios. Despite their resistance to quantum attacks, post-quantum blockchains are still vulnerable to traditional attack vectors, as demonstrated by the significant computing cost seen throughout our testing. These research results underscore the need of balanced blockchain systems that successfully handle the trade-offs between security and performance.

Intelligibility Sound Therapy Enhances the Ability of Speech-in-Noise Perception and Pre-Perceptual Neurophysiological Response

Aural rehabilitation with hearing aids can decrease the attentional requirements of cognitive resources by amplifying deteriorated-frequency sound in hearing loss patients and improving auditory discrimination ability like speech-in-noise perception. As aural rehabilitation with an intelligible-hearing sound also can be hopeful, the aim of this study was to evaluate the effectiveness of aural rehabilitation with intelligible-hearing sound for hearing loss patients. Adult native Japanese speakers (17 males and 23 females, 68.43 ± 9.23 years) with hearing thresholds exceeding 30 dB at any of the following frequencies: 125, 250, 500, 1000, 2000, 3000, 4000, 8000, 10,000, and 12,000 Hz in either ear, were recruited. on any side were recruited and underwent the Mini-Mental State Examination Japanese. We conducted a self-evaluation questionnaire for hearing problems of voice, a gap detection test, a fast speech test, a speech-in-noise test, a pure tone audiogram, and a speech perception test using a Japanese 67-S, cortical auditory-evoked fields, and magnetic mismatch negativity before and after the non-intelligible-hearing (N = 20) and intelligible-hearing (N = 20) sound therapy, which involved listening to music for one hour a day for 35 days. The better hearing ear was defined using a four-frequency pure-tone average at the thresholds of 500, 1000, 2000, and 4000 Hz. After the sound therapy, the speech-in-noise test with a signal-to-noise ratio +10 in the better hearing ear showed significant improvement (p < 0.05), and N1m-P2m amplitudes showed a significant increase in the Lt superior temporal gyrus in response to the stimulus from the better hearing ear (p < 0.05). A significant enhancement of the magnetic mismatch negativity amplitude at the Lt superior temporal gyrus was exhibited after the sound therapy (p < 0.01). Intelligible-hearing sound therapy can improve the ability of speech-in-noise perception in the better hearing ear and enhancement of central cortex response, which reflects the ability of working memory, was proved by cortical auditory-evoked fields and magnetic mismatch negativity. Intelligible-hearing sound therapy can be a valuable aural rehabilitation method for sensory neural hearing loss, the same as hearing aids.

Animal-assisted intervention services across UK intensive care units: A national service evaluation.

Animal-assisted interventions (AAI) can provide psychological support to critical care patients during their intensive care unit (ICU) admission. However, there are currently no data on AAI services across UK ICUs. The current study therefore aims to (i) determine how many ICUs in the UK offer services, (ii) characterise available services and (iii) explore and review local documentation for service oversight. A service evaluation comprising two parts; a national survey of UK ICU's, analysed using descriptive statistics, and review of local service oversight documents, analysed using a framework approach. Responses from 74 sites (/242, 30.6%) were included in survey analysis. AAI services were present at 32 sites (/74, 43.2%), of which 30 offered animal-assisted activity services alone and 2 offered both animal-assisted activity and animal-assisted therapy services. Animal-assisted activity services were typically delivered on a weekly basis, lasting 30-60 min and with dogs the sole animal employed. Concern over infection prevention and control was the most common barrier to service provision, as well as a lack of supporting evidence. Sixteen sites provided 27 oversight documents for analysis, that highlighted unique and shared responsibilities between critical care staff and animal therapy handlers, including aspects of administration, welfare and infection control. From a small sample, AAI services were available in less than half of ICUs. Empirical value of interventions is countered by current lack of definitive evidence of effectiveness, which should be addressed before wider implementation of AAI services and the associated resource requirements, is undertaken.

Are Consumers Ready to Consider Insect-Based Foods as a Sustainable Food Choice? An Application of the Extended Protection Motivation Theory to Italian Consumers

Adopting sustainable food choices is crucial to mitigating the environmental impacts of food production. Insect-based foods offer a promising alternative with low resource requirements and reduced greenhouse gas emissions. This study examines the psychological factors influencing consumers’ intentions to purchase insect-based foods using an extended protection motivation theory (PMT) model that includes disgust as a critical factor. Data from 233 Italian participants were analyzed through partial least squares structural equation modeling (PLS-SEM). Results indicate that perceived response efficacy—the belief in the environmental benefits of insect consumption—and self-efficacy—confidence in one’s ability to incorporate insect-based foods—positively influence purchase intentions. However, strong aversions rooted in disgust and low intrinsic motivation present major barriers, highlighting cultural resistance to entomophagy in Western contexts. Extrinsic motivators such as social recognition, perceived costs, and perceived severity or vulnerability to environmental issues had no significant effect. The findings suggest that effective strategies should focus on reducing disgust and strengthening consumer confidence, emphasizing the environmental benefits to shift attitudes toward sustainable dietary choices.

Machine learning application to disaster damage repair cost modelling of residential buildings

Restoring residential buildings following earthquake damage requires a significant level of resources. Being able to predict these resource requirements in advance and accurately improves the effectiveness of disaster preparedness and subsequent recovery activities. This research explored how the latest ML algorithms could be used for antecedent earthquake loss modelling. A cost database for repairing residential buildings damaged by the Emilia Romagna earthquake in Italy was analysed using six state-of-the-art ML models to explore their ability to predict repair cost rates(cost per floor area) for a domestic building damaged by earthquakes. A Gradient Boost Regression model outperformed five other models in predicting earthquake damage repair cost rate. The performance of this model was significantly accurate and covers about 76% of the cases. A further SHAP analysis revealed that operational level, damage level and non-housing area of the buildings as top 3 important features when predicting the resultant damage repair cost rate. Overall this research advanced antecedent earthquake loss modelling approaches to increase the accuracy of estimates by incorporating more variables than the widely used damage level based simple methodology.

Pediatric Triage Accuracy in Pediatric and General Emergency Departments.

Accurate triage at the time of emergency department (ED) presentation is critical for timely acuity assessment and anticipating resource requirements. Commonly, triage is conducted using the Emergency Severity Index (ESI); however, the accuracy of this approach for children in general EDs is uncertain. The purpose of this study was to quantify pediatric triage accuracy in a national sample of ED visits and evaluate whether presentation to a pediatric vs general ED is associated with mistriage. This was a cross-sectional study of the 2017-2021 National Hospital Ambulatory Medical Care Survey of pediatric (aged <18years) ED visits with an ESI score from 3 to 5. The outcome was mistriage (resource utilization discordant with ESI prediction). Standardized ESI definitions were applied to count resources. We used multivariable logistic regression to evaluate whether presentation to a pediatric or general ED was associated with triage accuracy. Of 149 million visits, mistriage occurred in 53.7% of ESI 3, 57.7% of ESI 4, and 22.9% of ESI 5 visits. Children in general EDs were more likely to be mistriaged than children in pediatric EDs (adjusted odds ratio [OR], 1.29; 95% CI, 1.11-1.50). Young age was associated with mistriage (aged <1year vs aged 13-17: adjusted OR [95% CI], 2.42 [2.00-2.94], 1-5years: 1.79 [1.53-2.10], 6-12years: 1.38 [1.16-1.64]). Mistriage was common among children with an initial ESI of 3 to 5 and more common among children visiting general EDs. Our findings highlight the need for improved resource prediction at the time of triage.

Optimizing Scheduled Virtual Machine Requests Placement in Cloud Environments: A Tabu Search Approach

This paper introduces a novel model for virtual machine (VM) requests with predefined start and end times, referred to as scheduled virtual machine demands (SVMs). In cloud computing environments, SVMs represent anticipated resource requirements derived from historical data, usage trends, and predictive analytics, allowing cloud providers to optimize resource allocation for maximum efficiency. Unlike traditional VMs, SVMs are not active concurrently. This allows providers to reuse physical resources such as CPU, RAM, and storage for time-disjoint requests, opening new avenues for optimizing resource distribution in data centers. To leverage this opportunity, we propose an advanced VM placement algorithm designed to maximize the number of hosted SVMs in cloud data centers. We formulate the SVM placement problem (SVMPP) as a combinatorial optimization challenge and introduce a tailored Tabu Search (TS) meta-heuristic to provide an effective solution. Our algorithm demonstrates significant improvements over existing placement methods, achieving up to a 15% increase in resource efficiency compared to baseline approaches. This advancement highlights the TS algorithm’s potential to deliver substantial scalability and optimization benefits, particularly for high-demand scenarios, albeit with a necessary consideration for computational cost.

Pixel Distillation: Cost-Flexible Distillation Across Image Sizes and Heterogeneous Networks.

Previous knowledge distillation (KD) methods mostly focus on compressing network architectures, which is not thorough enough in deployment as some costs like transmission bandwidth and imaging equipment are related to the image size. Therefore, we propose Pixel Distillation that extends knowledge distillation into the input level while simultaneously breaking architecture constraints. Such a scheme can achieve flexible cost control for deployment, as it allows the system to adjust both network architecture and image quality according to the overall requirement of resources. Specifically, we first propose an input spatial representation distillation (ISRD) mechanism to transfer spatial knowledge from large images to student's input module, which can facilitate stable knowledge transfer between CNN and ViT. Then, a Teacher-Assistant-Student (TAS) framework is further established to disentangle pixel distillation into the model compression stage and input compression stage, which significantly reduces the overall complexity of pixel distillation and the difficulty of distilling intermediate knowledge. Finally, we adapt pixel distillation to object detection via an aligned feature for preservation (AFP) strategy for TAS, which aligns output dimensions of detectors at each stage by manipulating features and anchors of the assistant. Comprehensive experiments on image classification and object detection demonstrate the effectiveness of our method.

An X-ray image-based Pruned Dense Convolution Neural Network for Tuberculosis Detection

According to the Ministry of Health in Kenya, tuberculosis (TB) is the fifth greatest cause of death and the main infectious disease killer in Kenya and across the world. In Kenya and throughout Africa, TB continues to wreak havoc on many vulnerable populations, homes, and communities despite being preventable and treatable. Common TB diagnostics, like blood and skin tests, frequently fail to identify the precise kind of TB. As a result, the World Health Organization (WHO) advises expanding the use of X-rays, for screening. In TB-prevalent regions of Kenya, a shortage of radiologists hampers effective screening and diagnosis, highlighting the need for scalable solutions for accurate X-ray analysis.Recent advancements in deep learning techniques have shown promise in the healthcare sector, particularly in radiology. However, many deep convolutional neural network (CNN) architectures are computationally intensive due to their size and resource requirements. This study designed and developed a Pruned CNN to address this issue by applying pruning techniques to baseline architectures. This approach significantly reduced model sizes while maintaining accuracy levels. Specifically, the pruned version of the DenseNet model achieved an impressive 99% accuracy with a reduction rate of 65.8%. These results highlight the potential of this pruned CNN as an effective and efficient tool for TB detection, particularly in resource-constrained environments. This study addresses the shortage of radiological expertise in many regions by providing a tool that can assist in the interpretation of X-ray images. This capability can help healthcare providers deliver timely and accurate diagnoses, thereby improving patient care.

A novel recyclable surface ion-imprinted graphene aerogels for high selective adsorption of lithium in salt lake brine

To mitigate the growing pressure of lithium resource supply and requirement, the exploration of abundant lithium from salt lake brine has emerged as a novel avenue for lithium resource acquisition. Surface ion imprinting technology with specific recognition could achieve selective lithium adsorption. However, existing Li+-imprinted adsorption materials exhibit poor adsorption performance and difficult recovery. To address these issues, the novel recyclable Li+-imprinted graphene aerogels were synthesized by using graphene oxide as the skeleton material, and thiourea dioxide as the reducing agent, assisted by DMF and NH3·H2O. The imprinted aerogels demonstrate excellent structural stability (bearing 10000 times of its weight), superior adsorption capacity (31.66 mg g−1), high selectivity and good regeneration performance (91.0% after six cycles). The selection factors for Li+ over Na+, K+ and Mg2+ reach 10.00, 5.71 and 4.00, respectively. This is attributed to the strong metal coordination interreaction between 2-Hydroxymethyl-12-crown-4 and Li+, thereby specifically recognizing and adsorbing Li+. This research offers a fresh material for the enrichment and recovery of lithium resources in salt lake brine.

Immersive virtual reality aggression and violence management education for nursing students: A pre-test, post-test, follow-up evaluation

BackgroundNursing students attending clinical placement encounter similar exposure to aggression and violence as qualified clinicians. Universities encounter barriers in aggression and violence management education delivery due to resourcing and time requirements. Immersive Virtual Reality (IVR) education may reduce barriers while maintaining comparable learning outcomes to face-to-face simulation-based education. MethodNursing students (n = 72), recruited from one public university in Perth, Western Australia, completed a 15-20 minute IVR education program. Pre-, post-, and 4-month post-intervention questionnaires investigated self-reported confidence. Cross-sectional data were collected for system usability and motion sickness. ResultsSignificant differences in self-reported confidence were observed (p < .001, partial η2 = 0.53), with large improvements from pre- to post-intervention (p < .001, d = 1.21) and follow-up (p < .001, d = 1.25). Cross-sectional data indicated the program is easy to use and incurs negligible motion sickness. ConclusionIVR can deliver effective aggression and violence education for nursing students. The short duration, high usability, and persistence of confidence improvements over time indicate potential for IVR to support current nursing education practices.

The International Society of Nephrology collaborative quality framework to support safe and effective dialysis provision in resource-challenged settings.

Global under-provision of affordable dialysis results in inequitable access to safe and effective treatment. Identified as a key priority for the International Society of Nephrology (ISN) Kidney Failure Strategy, the need was identified for a dialysis quality framework that would set minimum standards for safe and effective treatment. In addition, this framework, which was developed through collaboration and iterative review to ensure external validity, links these standards to resource requirements and suggested reporting tools. It includes elements that are designed to minimise catastrophic healthcare expenditure by mandating transparent individualised affordable dialysis planning and encourages continuous quality improvement by including a tiered approach to standards. Although a set of minimum mandatory standards is included, with a strong focus on patient safety, this is not intended as a goal for all, but to support the incremental development of dialysis in the most challenging resource settings. Providers and funders are expected to engage with the framework at a level commensurate with their resources, clearly identifying where lack of resource is leading to sub-optimal quality. In this way the framework empowers policy makers, healthcare commissioners and patient groups to work with providers to ensure quality and meet demand.

Potential of Bintangur (Calophyllum spp.) Diversity in West Kalimantan as a Biology Learning Resource in Senior High School

Indonesia is one of the countries with a very high level of biodiversity. West Kalimantan is one of the provinces with the highest level of biodiversity in Indonesia. One form of biodiversity found in West Kalimantan's forests is bintangur (Calophyllum spp.). The very high diversity of bintangur species is fascinating to be utilized as a source of learning biology, especially in the material of biodiversity and their interactions and roles. This study aimed to analyze the potential of bintangur diversity in West Kalimantan as a biology learning resource on the material of biodiversity and their interactions and roles in senior high school. This research uses the qualitative method. This research consists of two stages. The first stage is a literature review to find information related to bintangur species in West Kalimantan. In contrast, the second stage is an analysis of learning resource requirements based on the results of the literature review that has been carried out. The results of the literature review stage show that there are 27 species of bintangur in West Kalimantan, as well as their classification, main characteristics, habitats, and values to the environment and other organisms. The literature review stage results are then continued to the learning resources analysis. Six requirements must be met for learning resources: clarity of the potential availability of objects and problems raised, suitability for learning objectives, clarity of targets and their designation, clarity of information disclosed, clarity of exploration guidelines, and clarity of the results to be achieved. The analysis of learning resource requirements that have been carried out shows that the diversity of bintangur species in West Kalimantan meets the requirements of learning resources so that it can be utilized as a biology learning resource on the material of biodiversity and their interactions and roles in senior high school.

AI-driven anomaly detection in cloud computing environments

The rapid adoption of cloud computing has changed the way businesses manage and store data, but it has also introduced new security challenges. One of the most pressing concerns in cloud environments is the detection of anomalies, which can signal potential security breaches, system failures, or performance issues. Traditional anomaly detection methods often fall short due to the complexity, scalability, and dynamic nature of cloud infrastructures. In recent years, Artificial Intelligence (AI)-driven anomaly detection techniques, particularly those leveraging machine learning and deep learning, have shown promise in overcoming these limitations. This paper reviews AI-driven approaches to anomaly detection in cloud computing environments, exploring their applications in enhancing cloud security, optimizing performance, and ensuring efficient resource management. The paper examines the strengths of various AI techniques, including supervised and unsupervised learning, deep learning, and hybrid models, highlighting their capacity to detect complex, previously unknown anomalies. Despite their advantages, implementing AI-based systems in cloud environments presents challenges, including data quality issues, scalability concerns, and computational resource requirements. Solutions such as federated learning and model optimization techniques are explored as methods to address these challenges. Furthermore, the paper discusses future research directions, including the integration of AI-driven anomaly detection with emerging technologies like blockchain and IoT, and the potential for advancements in self-supervised learning and explainable AI (XAI). This review concludes by emphasizing the critical role of AI in securing cloud infrastructures and the promising future of anomaly detection in the cloud computing landscape.

Should We Switch to Tenecteplase for all Ischaemic Strokes? Evidence and Logistics.

Recent clinical trials provide robust evidence of non-inferiority of tenecteplase 0.25mg/kg over alteplase 0.9mg/kg in acute ischaemic stroke treated within 4.5h of time last known well. Aggregate data meta-analysis suggests likely superiority of tenecteplase with respect to excellent (modified Rankin Scale 0 or 1) outcomes at 90 days. Less complex single intravenous bolus administration of tenecteplase brings significant logistical benefits compared to alteplase. Real-world implementation data demonstrate reduced door-to-needle and door-to-puncture times, and potentially improved clinical outcomes. Avoiding the need for infusion pumps and monitoring reduces resource requirements and facilitates inter-hospital transfer. Guidelines favour tenecteplase over alteplase due to its logistical advantages. Transitioning services to tenecteplase requires consideration of education and training for all relevant staff (medical, nursing, pharmacy) and should address physician concerns. Use of stroke-specific tenecteplase 25mg dose vials is strongly preferable to minimise the chance of dosing errors that might arise from use of cardiac-dose tenecteplase. Some off-label uses of alteplase are supported by positive randomised controlled trial data (wake-up and unknown onset stroke, and imaging-supported late window use 4.5-9h after onset) while equivalent data for tenecteplase are less conclusive. Trial data comparing tenecteplase to control give relevant safety data for both wake-up / unknown onset stroke and for late time windows, and some efficacy data favour tenecteplase in a late time window. Given the weight of evidence for biologically similar efficacy and safety of tenecteplase 0.25mg/kg, and potential for dosing errors, retention of alteplase for off-label indications should not be recommended. Some off-label uses of alteplase are supported by positive randomised controlled trial data (wake-up and unknown onset stroke, and imaging-supported late window use 4.5-9h after onset) while equivalent data for tenecteplase are less conclusive. Trial data comparing tenecteplase to control give relevant safety data for both wake-up / unknown onset stroke and for late time windows, and some efficacy data favour tenecteplase in a late time window. Given the weight of evidence for biologically similar efficacy and safety of tenecteplase 0.25mg/kg, and potential for dosing errors, retention of alteplase for off-label indications should not be recommended.

Automated MRI quantification of pediatric abdominal adipose tissue using convolutional neural networks and novel total intensity maps

ObjectiveChildhood obesity is a significant global health concern. Visceral adipose tissue (VAT) is more closely linked to the development of metabolic and cardiovascular diseases than abdominal subcutaneous adipose tissue (ASAT). This study aimed to develop a straightforward and automated method to quantify both ASAT and VAT in children from MRI. MethodsDixon-based 3 T-MRI scans were conducted on Mexican boys aged 7–9 years. Novel Total Intensity Maps were generated from these scans for input into simple 2D Convolutional Neural Networks (CNNs) for automatic prediction/quantification of ASAT and VAT. The results were compared with the commercial semi-automated quantification and validated method provided by AMRA® Researcher which is the gold standard. ResultsThe CNN-based method produced ASAT and VAT quantifications comparable to those obtained by AMRA® Researcher (ASAT, p = 0.1765; VAT, p = 0.7757), with a high correlation between the two methods for ASAT (R2 = 0.98, p < 10−14) and VAT (R2 = 0.95, p < 10−11). The reproducibility coefficients (RPC) were 0.32 L for ASAT and 0.17 L for VAT with our novel methodology. ConclusionThis study presents a simple automated method for quantifying ASAT and VAT in children from MRI data using 2D-CNN analysis of novel inputs, Total Intensity Maps. The new method produced measurements comparable to those obtained by AMRA® Researcher on the same dataset. SignificanceThe proposed automatic and straightforward quantification method, with minimal computational resource requirements, may serve as an accessible tool for researchers and clinicians in the diagnosis, follow-up, and prevention of childhood obesity.

Optimizing same-day delivery with vehicles and drones: A hierarchical deep reinforcement learning approach

The advent of same-day delivery services has achieved immense popularity, driven by escalating customer expectations on fast shipping and the need for market competitiveness. To optimize such services, the use of heterogeneous fleets with vehicles and drones has proven effective in reducing the resource requirements needed for delivery. This paper focuses on investigating the same-day delivery dispatching and routing problem with a fleet of multiple vehicles and drones. In this problem, stochastic and dynamic requests, coupled with their stringent time constraints, require dispatchers to make real-time decisions about optimally assigning vehicles and drones, ensuring both efficiency and effectiveness in delivery operations while taking into account the routing. To tackle this complex problem, we model it with a route-based Markov decision process and develop a novel hierarchical decision approach based on deep reinforcement learning (HDDRL). The first level of the hierarchy is tasked with determining the departure times of vehicles, balancing the trade-offs between the delivery frequency and efficiency. The second level of the hierarchy is dedicated to determining the most suitable delivery mode for each request, whether by vehicles or drones. The third level is responsible for planning routes for vehicles and drones, thereby enhancing route efficiency. These three levels in the hierarchical framework collaborate to solve the problem in a synchronized manner, with the objective of maximizing the service requests within a day. Empirical results from computational experiments highlight the superiority of the HDDRL over benchmark methods, demonstrating not only its enhanced efficacy but also its robust generalization across diverse data distributions and fleet sizes. This underscores the HDDRL’s potential as a powerful tool for enhancing operational efficiency in same-day delivery services.

A Case Study on the Application of Evolutionary Algorithms for Solving Non-linear Programming Problems in Water Resource Management

Evolutionary algorithms have been proposed and used in this case study for the solution to non-linear programming problems related to water resources. Heuristic, a type of optimization algorithm characterized by population-based search as well as using the genetic algorithms, are used to model and determine the optimal distribution and utilization of aquatic resources based on environmental and demand factors. These materials include examples based on water distribution networks, managing a reservoir, and forecasting demands of customers, demonstrating the effectiveness of the aforementioned algorithms in solving non-linear constraint problems and in dealing with multiple objectives. The comparative analysis is based on a number of simulation experiments to examine the efficiency of such microevolutionary algorithms as Genetic Algorithm, Particle Swarm Optimization, and Differential Evolution in contrast to the traditional optimization algorithms. These results show that there is significant value in using evolutionary algorithms as a basis for managing and modelling water resources as it offers a pragmatic approach and has the potential to meet the demands of the water industry in terms of sustainability, cost and resource requirements. There seems to be considerable scope for extension of these algorithms to a wide range of natural resources management and administration.

Wolbachia incompatible insect technique program optimization over large spatial scales using a process-based model of mosquito metapopulation dynamics

BackgroundWolbachia incompatible insect technique (IIT) programs have been shown in field trials to be highly effective in suppressing populations of mosquitoes that carry diseases such as dengue, chikungunya, and Zika. However, the frequent and repeated release of Wolbachia-infected male mosquitoes makes such programs resource-intensive. While the need for optimization is recognized, potential strategies to optimize releases and reduce resource utilization have not been fully explored.ResultsWe developed a process-based model to study the spatio-temporal metapopulation dynamics of mosquitoes in a Wolbachia IIT program, which explicitly incorporates climatic influence in mosquito life-history traits. We then used the model to simulate various scale-down and redistribution strategies to optimize the existing program in Singapore. Specifically, the model was used to study the trade-offs between the intervention efficacy outcomes and resource requirements of various release program strategies, such as the total number of release events and the number of mosquitoes released. We found that scaling down releases in existing sites from twice a week to only once a week yielded small changes in suppression efficacy (from 87 to 80%), while requiring 44% fewer mosquitoes and release events. Additionally, redistributing mosquitoes from already suppressed areas and releasing them in new areas once a week led to a greater total suppressive efficacy (83% compared to 61%) while also yielding a 16% and 14% reduction in the number of mosquitoes and release events required, respectively.ConclusionsBoth scale-down and redistribution strategies can be implemented to significantly reduce program resource requirements without compromising the suppressive efficacy of IIT. These findings will inform planners on ways to optimize existing and future IIT programs, potentially allowing for the wider adoption of this method for mosquito-borne disease control.