Cost-benefit Research Articles

Critical infrastructure resilience: a guide for building indicator systems based on a multi-criteria framework with a focus on implementable actions

Abstract. Criteria and indicators are frequently used for assessing the resilience of critical infrastructure (CI). Moreover, to generate precise information on conditions, the assessment designed for CI resilience could rely on indicator systems. However, few practical tools exist for guiding CI managers to build specific indicator systems for considering real cases. Therefore, the main objective of this study is to develop a step-by-step guide that contains guidance on operational steps and required resources for criteria and indicator setting, reference definition, and data collection. This guide enables CI managers to build systems of indicators tailored to different real cases. This guide could assist CI managers in their decision-making process, as it is structured based on a multi-criteria framework that takes into account the cost–benefit analysis and side effects of implementable actions. This guide could furthermore advance the application of indicator-based CI resilience assessment in practical management. In addition, this study provides an example to demonstrate how to use this guide. This example is based on specific circumstances for the Nantes Ring Road (NRR) network: when the ring road is flooded and closed, the road network manager suggests alternative roads to the public. An indicator system consisting of 4 criteria, 7 sub-criteria, and 11 indicators was built for these circumstances using the guide developed in this paper. This example relates to criteria and indicators in the technical, social, and environmental dimensions and involves 62 676 data points.

Evaluation of Phyto-extracts for the Management of Alternaria Leaf Blight of Cluster Bean (Cyamopsidis tetragonoloba L.) Caused by Alternaria cucumerina

The present study was carried out to evaluate the effect of selected phyto-extracts for the management of alternaria leaf blight of cluster bean (Cyamopsidis tetragonoloba L.) caused by Alternaria cucumerina under field conditions. Three replications of cluster bean were planted in a randomized block design at the research plot of the Central Research Field, Department of Plant Pathology, SHUATS, Prayagraj during Zaid season of 2023. Ginger rhizome extract @ 10%, onion bulb extract @ 10%, eucalyptus leaf extract @ 10%, neem leaf extract @ 10%, garlic clove extract @ 10% and mancozeb 0.2% were applied as foliar spray. All the treatments were found significantly reduced the disease severity and increased the yield. Among all the treatments garlic clove extract @ 10% was recorded minimum disease intensity (29.63) and maximum yield (2.50 t/ha). Neem leaf extract @ 10% was recorded maximum cost benefit ratio (1: 1.80), as compared to treated mancozeb @ 0.2%(treated) and T0 control.

Assessing the Origin and Mapping the Extension of Salinity Around Shrimp Culture Ponds in Rio Grande Do Norte (Brazil)

The increasing installation of shrimp farms in vulnerable coastal areas around the world generates an environmental impact and makes it urgent to develop methodologies and studies for assessing and scaling the potential risks and sustainability of these activities. One of the main hazards of these activities is that the prolonged inundation of excavated ponds for shrimp farming allows the percolation of saltwater in the surroundings, resulting in increasing groundwater salinity. Saltwater intrusion in coastal aquifers, accompanied by salinization of soils, causes a decrease in available freshwater resources, a decline in crop productivity and the deterioration of the natural ecosystem. The coastal aquifer of Rio Grande do Norte State (Brazil) where, for years, several shrimp farm factories have been operating, reported some issues related to aquifer and soil salinization. The present study aims to assess the origin of and delineate groundwater salinization in a sector of this coastal aquifer using a low-budget procedure. The integration of hydrogeological and hydrogeochemical characterization by drilling shallow piezometers, measuring the hydrostatic level and analyzing the major ion concentrations of the groundwater has made it possible to establish that the origin of groundwater pollution in the studied area is caused by saltwater percolation from shrimp farms. The joint use of both characterization techniques has been shown to have an efficient cost–benefit ratio and less-intrusive methodology, which can be applied in other areas with similar environmental concerns.

Rethinking the Roles of the Social Determinants of Health in Bystander Intervention for Partner Violence Among College Students

Willingness to intervene as an effective bystander is critical in preventing partner violence and fostering safety among college students. Informed by the Social Determinants of Health framework, this conceptual paper provides an overview of the background and historical evolution of bystander intervention and invites a rethinking of how various factors impact bystander intervention for partner violence among college students by highlighting the roles of (1) economic factors and education, (2) sexual beliefs, myths, and media, (3) gender values and norms, (4) environmental factors, (5) college students at risk, (6) social support network and access to services, (7) acculturation and familiarity with the U.S. culture, and (8) cost-benefit analyses. We also discuss potential practice, research, and policy implications. Understanding the Social Determinants of Health is instrumental in addressing the root cause of intersecting health disparities, which is vital in preventing violence, encouraging bystander intervention, and creating a safer community.

Immunity priming and biostimulation by airborne nonanal increase yield of field-grown common bean plants

IntroductionStress-induced volatile organic compounds (VOCs) that induce plant immunity bear potential for biocontrol. Here, we explore the potential of nonanal to enhance the seed yield of common bean (Phaseolus vulgaris) under open field conditions that are realistic for smallholder farmers.Methods and resultsUsing plastic cups with a nonanal-containing lanolin paste as low-cost dispensers, we observed that exposure of Flor de Junio Marcela (FJM) plants over 48h to airborne nonanal was followed by a 3-fold higher expression of pathogenesis-related (PR) genes PR1 and PR4. Both genes further increased their expression in response to subsequent challenge with the fungal pathogen Colletotrichum lindemuthianum. Therefore, we conclude that nonanal causes resistance gene priming. This effect was associated with ca. 2.5-fold lower infection rates and a 2-fold higher seed yield. Offspring of nonanal-exposed FJM plants exhibited a 10% higher emergence rate and a priming of PR1- and PR4-expression, which was associated with decreased infection by C. lindemuthianum and, ultimately, a ca. 3-fold increase in seed yield by anthracnose-infected offspring of nonanal-exposed plants. Seeds of nonanal-exposed and of challenged plants contained significantly more phenolic compounds (increase by ca 40%) and increased antioxidant and radical scavenging activity. Comparative studies including five widely used bean cultivars revealed 2-fold to 3-fold higher seed yield for nonanal-exposed plants. Finally, a cost-benefit analysis indicated a potential economic net profit of nonanal exposure for some, but not all cultivars.OutlookWe consider nonanal as a promising candidate for an affordable tool that allows low-income smallholder farmers to increase the yield of an important staple-crop without using pesticides

Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion

The growth of urbanization, population, and economic activity has led to a substantial increase in freight transportation demand, exceeding the capacity of existing infrastructure and creating new challenges across various regions. This has resulted in significant traffic congestion, increased travel times, and higher operational costs for commercial vehicle fleets. Leveraging topological data, such as road networks and traffic patterns, can enable more efficient routing strategies to navigate around congested areas. This study presents a comprehensive approach to truck rerouting strategy by integrating spatial analysis, truck characteristics, traffic conditions, road geometry, and cost–benefit analysis to select alternative routes suitable for commercial vehicle fleets. Incorporating real-time traffic information and predictive analytics, commercial vehicle operators can optimize their routes, reduce fuel consumption, and improve overall delivery efficiency. Three case studies were presented to demonstrate the proposed diversion decision framework. Two scenarios were designed for each case study: a base scenario with no diversion and an optimized scenario with a diversion strategy. The travel times, fuel consumption, and economic impacts between the two scenarios were compared and quantified as a total annual saving of USD 52 million. This approach goes beyond selecting alternative routes and provides decision makers with measurable benefits that justify diversion strategies.

The Role of Technology in Modern Education: A Comparative Analysis of Benefits and Challenges

The Role of Technology in Modern Education: A Comparative Analysis of Benefits and Challenges

Performance of Molasses Waste as a Cement Replacement to Study Concrete Compressive Strength

To reduce the negative environmental impact of cement production and preserve natural resources, an experimental investigation was conducted to study the performance of concrete specimens at different curing ages and to determine the compressive strength of these specimens by replacing cement with molasses. Experimentation was carried out on the concrete specimens at a temperature range of 25 °C to 30 °C; six specimens were cast for each replacement ratio, except for 0.75% wt. of cement (86.55 g) and 1% wt. of cement (113.6 g), where five samples were considered for each ratio. The average 28-day compressive strength of the conventional concrete specimens came out to be 29 MPa, but increased to 40 MPa with the addition of 0.25% wt. of cement molasses (28.85 g). It was observed that as the percentage of molasses waste in the concrete mix was further increased by replacing the cement, the compressive strength of the concrete specimens increased gradually and then significantly decreased. The findings shed light on the prospect of using molasses waste instead of cement in the concrete mix. Also, it is worth mentioning that about 30% of the cost–benefit was obtained with reference to that of conventional admixtures available in the market for the production of concrete. However, it is notable that a long-term durability study needs to be conducted before making it viable. This work not only addresses a sustainable and innovative method of waste management (SDG12), but also contributes to low carbon emissions (SDG13). The novelty of this work lies in the fact that no such kind of study has been conducted in Pakistan so far, in addition to the very limited international literature available, and, in particular, no evidence on the compressive strength results at higher molasses dosages, i.e., 1% wt. of cement (113.6 g) and 2% wt. of cement (230.8 g).

Assessing the Zinc, Boron and Rhizobium Inoculation on Yield and Economics of Kharif Mungbean (Vigna radiata L.)

The present experiment was carried out during kharif season of 2021 and 2022 at Instructional farm, Kamla Nehru Institute of Physical and Social Sciences Sultanpur, U.P. India. Four levels of zinc (0, 2.5, 5.0 and 7.5 Kg Zn ha-1), three levels of boron (0, 0.5 and 1.0 Kg B ha-1) and two levels of rhizobium i.e. Uninoculated (Control) and inoculated. A split-split plot design with three replications was used. Zinc levels were allocated to the main plots, boron levels in the sub-plot, while Rhizobium inoculation in the sub-sub plot. Results revealed that dry weight and seed and stover yield was observed that Rhizobium inoculation with application of zinc 7.5 kg ha-1 and Application of boron 1.0 kg ha-1. Obtaining seed yield from Rhizobium inoculation (8.53 and 8.71 q/ha-1) with application of zinc (8.53 and 8.79 q/ha-1) and Application of boron (8.45 and 8.70 q/ha-1). The highest net return (Rs. 58211.0 and 65488.50 ha-1) and benefit cost ratio (1.75 and 1.97) were obtained with 7.5kg Zn ha-1 + 1 kg B ha-1 + seed inoculation with rhizobium. It could be recommended for cultivation of mungbean.

Cost and Return Analysis of Tomato Cultivation in Three Different Districts of Bangladesh

This paper attempts to analyze the costs, return and production problems of tomato in three districts namely Gazipur, Rangpur and Cumilla in Bangladesh. It is necessary to enunciate that through purposive sampling technique, the data were collected from 225 tomato cultivators of Cumilla and Rangpur districts. Human labour, fertiliser, bamboo sticks, thread, seeds and seedlings, ploughing, irrigation, insecticides and pesticides, hormone, etc. are all necessary for the production of tomatoes. Transport expenses must be taken into account as well. Together, the aforementioned elements were taken into account to estimate the cost of tomato production. Every data set was subjected to statistical and economic analysis, with validated outcomes via a series of tables. Numerous issues affect farmers, and this study sheds insight on this situation. The findings showed that the total cost was highest in Gazipur (Tk.333,800/ha) than that of Rangpur ( Tk. 304,600/ha) and Cumilla ( Tk.282,670/ha). Gross returns from tomato in Cumilla and Rangpur were Tk. 234,942/acre and Tk. 212,213/acre respectively. The net returns were found higher in Gazipur (Tk. 206,200/ha) than that of Rangpur (Tk. 135,400/ha) and Cumilla (Tk. 97,330/ha). Based on overall costs, the Gazipur, Rangpur and Cumilla districts' benefit cost ratios for tomato production per hectare were determined to be 1.60,1.40 and 1.30, respectively. The most notable limitations in were the high cost of inputs, the absence of storage facilities, price fluctuations, and damage from insects and diseases.

Edge Computing for AI-Based Brain MRI Applications: A Critical Evaluation of Real-Time Classification and Segmentation.

Medical imaging plays a pivotal role in diagnostic medicine with technologies like Magnetic Resonance Imagining (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and ultrasound scans being widely used to assist radiologists and medical experts in reaching concrete diagnosis. Given the recent massive uplift in the storage and processing capabilities of computers, and the publicly available big data, Artificial Intelligence (AI) has also started contributing to improving diagnostic radiology. Edge computing devices and handheld gadgets can serve as useful tools to process medical data in remote areas with limited network and computational resources. In this research, the capabilities of multiple platforms are evaluated for the real-time deployment of diagnostic tools. MRI classification and segmentation applications developed in previous studies are used for testing the performance using different hardware and software configurations. Cost-benefit analysis is carried out using a workstation with a NVIDIA Graphics Processing Unit (GPU), Jetson Xavier NX, Raspberry Pi 4B, and Android phone, using MATLAB, Python, and Android Studio. The mean computational times for the classification app on the PC, Jetson Xavier NX, and Raspberry Pi are 1.2074, 3.7627, and 3.4747 s, respectively. On the low-cost Android phone, this time is observed to be 0.1068 s using the Dynamic Range Quantized TFLite version of the baseline model, with slight degradation in accuracy. For the segmentation app, the times are 1.8241, 5.2641, 6.2162, and 3.2023 s, respectively, when using JPEG inputs. The Jetson Xavier NX and Android phone stand out as the best platforms due to their compact size, fast inference times, and affordability.

Improved machine learning models with a similarity-based approach for remaining useful life prediction

Abstract Cost efficiency is a critical factor in the competitive aviation sector. These efficiency factors force airline operators to develop new approaches in their organizations. Predictive maintenance helps to build scheduling maintenance programs for airline operators or MROs. Scheduled maintenance programs benefit cost efficiency in the aviation sector. Predictive maintenance methods predict the failure time of any equipment. Predictions can be made by analyzing the sensor values from equipment. In this paper, we predicted the remaining useful life (RUL) of turbofan engines using machine learning models and a similarity-based approach. Sensor datasets from the Prognostics Data Repository of NASA, called CMAPPS, were utilized. Using the FD0002 sub-dataset, a health index (HI) was created, and models were trained. Once the models were trained, train and test HIs were estimated. The predicted test HI was matched with the predicted train HI based on a similarity-based approach, and then a RUL prediction was made. The results obtained were compared with the actual results to calculate the accuracy, and the algorithm that resulted in the maximum accuracy was identified. We selected six machine learning algorithms and also created an ensemble model by averaging the predictions of six machine learning algorithms for comparing prediction accuracy. The different algorithms were compared to obtain the prediction model with the closest prediction of remaining useful lifecycle in terms of the number of life cycles. This experiment showed us the effect of the similarity-based approach on the basic version of machine learning models for RUL prediction.

Healthcare Value Assessment: A Critical Review of Economic Outcome Metrics and Future Directions

This paper provides a critical review of economic outcome metrics used in healthcare value assessment, emphasizing the evolving landscape of resource allocation, patient-centered approaches, and standardization efforts. With healthcare costs rising globally, the efficient allocation of limited resources is essential. Metrics like Quality-Adjusted Life Years (QALYs), Disability-Adjusted Life Years (DALYs), Incremental Cost-Effectiveness Ratios (ICERs), and Cost-Benefit Analysis (CBA) are central to guiding funding decisions, influencing insurance coverage, and shaping treatment prioritization. Emerging trends, such as the integration of artificial intelligence (AI) and big data, are enhancing the precision of these assessments, while patient-centered metrics underscore the importance of patient satisfaction and quality of life. Additionally, there is a growing push for the standardization of these metrics to create consistent frameworks across diverse health systems. This paper explores case studies, practical applications, and future directions to provide insights into how healthcare systems can adopt more effective, globally aligned economic assessments.

Effective weed control through post-emergence herbicides to enhance blackgram (Vigna mungo L.) productivity in South India

Pulses are essential components of vegetarian diet and play a pivotal role in addressing malnutrition by providing a vital source of dietary protein. However, weed competition remains as a significant obstacle to blackgram (Vigna mungo L) production, resulting in 25 to 35% yield losses. A field experiment was carried out at the National Pulses Research Centre, Tamil Nadu Agricultural University, Vamban, Pudukkottai, India during Kharif seasons of 2020 and 2021, to assess the suitable post-emergence herbicides for appropriate weed control in blackgram grown under irrigated conditions. The experimental field was observed to contain notable grass weed flora, specifically Dactyloctenium aegyptium and Chloris barbata, as well as broadleaved weeds, such as Flaveria australica, Cleome gynandra, Eclipta alba, Convolvulus arvensis, Digera arvensis, Vicia spp., and Celosia argentea. The results demonstrated that among the chemical weed management methods, spraying of Fomesafen @ 220 g + Fluzifop p-butyl @ 220 g ha−1 at 20 days after sowing (DAS) as a post-emergence herbicide treatment exhibited superior weed control efficiency, recording 66.80% and 68.53% at 30 and 45 DAS, respectively and recorded higher seed yield of 1088 kg ha−1. Additionally, this method generated 8.1% higher net income and 12.5% more benefit–cost ratio than hand weeding, making it an economically profitable strategy for maximizing blackgram yield and effective labour management.

The role of economic evaluation in modelling public health and social measures for pandemic policy: a systematic review

BackgroundDynamic transmission models are often used to provide epidemiological guidance for pandemic policy decisions. However, how economic evaluation is typically incorporated into this technique to generate cost-effectiveness estimates of pandemic policy responses has not previously been reviewed.MethodsWe systematically searched the Embase, PubMed and Scopus databases for dynamic epidemiological modelling studies that incorporated economic evaluation of public health and social measures (PHSMs), with no date restrictions, on 7 July 2024.ResultsOf the 2,719 screened studies, 51 met the inclusion criteria. Most studies (n = 42, 82%) modelled SARS-CoV-2. A range of PHSMs were examined, including school closures, testing/screening, social distancing and mask use. Half of the studies utilised an extension of a Susceptible-Exposed-Infectious-Recovered (SEIR) compartmental model. The most common type of economic evaluation was cost-effectiveness analysis (n = 24, 47%), followed by cost-utility analysis (n = 17, 33%) and cost–benefit analysis (n = 17, 33%).ConclusionsEconomic evaluation is infrequently incorporated into dynamic epidemiological modelling studies of PHSMs. The scope of this research should be expanded, given the substantial cost implications of pandemic PHSM policy responses.

Evaluating the social return on investment of dance movement for wellbeing in the prevention of burnout among healthcare staff: a study protocol.

Improvement in the National Health Service (NHS) employees' mental well-being and staff retention are current key issues. Evidence indicates that dance movement therapy (DMT) has been effective in improving mental health and well-being; however, a social return on investment (SROI) evaluation on DMT interventions aimed at NHS staff has not been performed. This protocol for an SROI study will explore the social value generated from DMT, specifically 'The Body Moving Self-Compassion' programme, as measured by the increase in personal well-being and resilience experienced by participants. The SROI evaluation aims to measure the monetary and social value generated through DMT by placing a monetary value which is essential for sound policy in accessing investment while contributing evidence of dance/movement's impact on health. SROI is a pragmatic form of social cost-benefit analysis which uses quantitative and qualitative methods to value relevant costs, outcomes and associated impact. A mixed-methods approach design (focus group, online questionnaire and semistructured interviews) will be employed in this SROI study. SROI takes a societal perspective and considers relevant and significant outcomes for participants. Monetary values which often do not have a market price are then assigned to these outcomes. Contingent valuation is integrated into this study to estimate individuals' choices, preferences and values associated with DMT. The social value generated by the identified outcomes will then be estimated in a similar way to cost-benefit analysis, and the ratio of social value generated per £1 invested is then calculated. Ethical approval for this study has been granted by the Faculty of Life Sciences and Education Ethics Subgroup at the University of South Wales, with Reference No. 230 236LR. The findings from this SROI study will result in a report and academic publications.

Cost–benefit analysis of mammography screening: the perspective of Nigerian healthcare providers

IntroductionThere is no national breast cancer screening implemented in Nigeria. The National Cancer Control Plan (NCCP) has a goal of making screening services and early detection of cancer available for...

E-commerce and air pollution: Evidence from China

E-commerce serves as an important link between the digital economy and the real economy, and contributes positively to income growth. In the face of resource constraints and serious environmental pollution, it is equally relevant to examine the environmental effects of e-commerce. Based on county panel data for China from 2011 to 2018, this study uses a difference in differences (DID) model to investigate the impact of Rural E-Commerce Demonstration County (REDC) policy on air pollution. This study reveals that the demonstration policy exacerbates air pollution in counties and the pollution effect is regionally heterogeneous. Additionally, the increase in temperature and precipitation weakens the pollution effects of the demonstration policy. Mechanism analysis indicates that the demonstration policy can promote the development of the secondary industry and digital financial inclusion at the county level, which subsequently leads to the deterioration of local air quality. Further analysis of costs and benefits reveals that the significant positive economic effects resulting from the demonstration policy far outweigh the indirect economic losses caused by air pollution. Therefore, when local governments actively promote the development of e-commerce in rural areas, they should also attach great importance to its potential negative environmental externalities.

Enhancing Customer Experience with Generative AI in Financial Services Contact Centers

This article explores the transformative impact of Generative Artificial Intelligence (Gen AI) on customer service operations in financial services contact centers. Through a comprehensive case study approach, we examine the implementation of a Gen AI system designed to handle complex financial queries, provide personalized guidance, and ensure regulatory compliance. The article investigates the system's architecture, key features, and integration with existing infrastructure, while analyzing its performance across critical metrics such as First-Call Resolution rates, call handling times, customer satisfaction scores, and operational costs. Our article reveals significant improvements in these areas, with FCR rates increasing by 50%, call handling times decreasing by 40%, and customer satisfaction scores improving by 45%. The article also addresses the challenges and ethical considerations associated with AI-driven customer service in the financial sector, including data privacy, algorithmic bias, and the need for transparency. By providing a detailed analysis of both quantitative improvements and qualitative benefits, this article offers valuable insights for financial institutions considering the adoption of Gen AI technologies to enhance their customer service capabilities and maintain competitiveness in an increasingly digital financial landscape.

Cost-benefit and equity analysis of Nature-based Solutions in Haiti, India, Indonesia and Uganda

Cost-benefit and equity analysis of Nature-based Solutions in Haiti, India, Indonesia and Uganda