Cost-effective Management Research Articles

A cost utility analysis for the management of acute exacerbations of chronic rhinosinusitis.

The management of acute exacerbations of chronic rhinosinusitis (AECRS) is understudied and the most cost-effective management of AECRS has not been previously investigated. The aim of this study is to determine the most cost-effective strategy for the initial management of AECRS. The study design consisted of a decision-tree economic model comparing three different initial strategies for managing a patient perceived AECRS: observation, upfront rescue medications, or clinic visit with diagnostic nasal endoscopy (DNE). The primary study outcome was the disease burden of a single AECRS, which was determined by the health utility value and the duration of symptoms. Strategies with an incremental cost-effectiveness ratio<$50,000/quality-adjusted life year (QALY) or equivalently<$137/quality-adjusted life day (QALD) were considered cost-effective. Observation was the most cost-effective strategy at a willingness to pay of $137 per QALD. One-way sensitivity analysis demonstrated that observation was more effective than upfront rescue medications when the probability of bacterial infection as the cause of AECRS was<24.0%. Upfront rescue medications wer more cost effective than observation when the probability of bacterial infection exceeded 49.0%. Clinic visit with DNE was the most effective strategy to manage an AECRS, but it was not considered cost-effective. Observation is the most cost-effective strategy for the initial management of AECRS when there is a low likelihood of bacterial infection. When the probability of bacterial etiology of AECRS exceeds 49.0%, upfront rescue medications proved to be the most cost-effective strategy.

Accounting for Predator-Prey Fisheries in the Cost-Effective Management of Eutrophicated Coastal Waters

Accounting for Predator-Prey Fisheries in the Cost-Effective Management of Eutrophicated Coastal Waters

Prioritizing conservation of threatened raptor species using synergistic distribution modeling of high potential areas in foothills of Eastern Himalayas, India

Species Distribution Models are widely used to predict single and multiple species distribution based on different environmental parameters concerned. Such a framework often ignores distribution overlapping of associated species of the same taxon or genus which may lead to high-cost conservation for the concerned authority. Cost effective conservation management can be practiced by prioritizing areas of high suitability, shared by one or more related species. Here, we proposed the concept of the high probable distribution area shared by at least two species i.e. Synergistic potential distribution area in Assam. Analysis of the synergistic distribution map showed that the average combined high potential area of all the species is 3140 km2, and the average synergistic distribution area shared by at least two species accounts for 1367.63 km2 ( Falco severus + Gyps tenuirostris = 1446.19 km2, Falco severus + Haliaeetus leucoryphys = 1404.75 km2, Gyps tenuirostris + Haliaeetus leucoryphus = 1251.95 km2). We found that the highly suitable areas of raptors in Assam were mostly concentrated in the Manas Tiger Reserve landscape, Kaziranga-Karbi Anglong landscape and Upper Brahmaputra valley. This concept would definitely help to identify areas shared by raptors and prioritize cost effective conservation efforts.

Advanced microgrid optimization using price-elastic demand response and greedy rat swarm optimization for economic and environmental efficiency

In this paper, a comprehensive energy management framework for microgrids that incorporates price-based demand response programs (DRPs) and leverages an advanced optimization method—Greedy Rat Swarm Optimizer (GRSO) is proposed. The primary objective is to minimize the generation cost and environmental impact of microgrid systems by effectively scheduling distributed energy resources (DERs), including renewable energy sources (RES) such as solar and wind, alongside fossil-fuel-based generators. Four distinct demand response models—exponential, hyperbolic, logarithmic, and critical peak pricing (CPP)—are developed, each reflecting a different price elasticity of demand. These models are integrated with a flexible elasticity matrix to assess the dynamic consumer response to fluctuating electricity prices. The study evaluates four operational scenarios, focusing on grid participation, DER utilization, and the impact of real-time pricing (RTP), time of use (TOU), and critical peak pricing strategies. Quantitative results demonstrate the significant cost-saving potential of integrating DRPs with microgrid operations. In the optimal scenario, the GRSO achieved a minimum generation cost of 882¥ for the base load profile. Further, when critical peak pricing (CPP) was applied, the generation cost was reduced to 746¥, representing a 15.4% reduction. For a scenario where the grid’s participation was limited, the logarithmic-based demand response model decreased the generation cost to 817¥, while full grid interaction led to higher cost reductions. Additionally, our results show a significant reduction in peak load, with load factor improvements of up to 87.7% across the studied demand profiles. Furthermore, limiting the grid’s upstream power capacity to 30 kW resulted in a 7% increase in generation cost across all cases, confirming the importance of grid participation in reducing operational costs. The GRSO algorithm outperformed traditional metaheuristics in terms of both execution time and convergence, making it a viable solution for real-time microgrid optimization. In conclusion, the proposed GRSO-based framework provides an efficient approach for microgrid cost minimization, achieving up to a 15.4% reduction in operational costs and notable environmental benefits by reducing emissions. This study highlights the importance of dynamic demand response strategies and grid participation for sustainable and cost-effective microgrid management.

Developing a cost-effective and efficient safety case management solution for data coordinating center projects.

A centralized safety function can support multiple clinical trials and provide efficient, standardized processes for the management of serious adverse events. From 2017 to 2022, the centralized safety desk used pharmacovigilance software compliant with FDA regulations, including 21 CFR Part 11. This software assisted with processing safety cases for regulated clinical trials, including allowing capture of event data, and provided process flow management, documentation storage, and transmission of safety reports to FDA. During the software's tenure, we experienced incompatibilities with other clinical research software resulting in unanticipated inefficiencies. Furthermore, the small number of periodic and suspected unexpected serious adverse reaction (SUSAR) cases rendered the annual license fee not cost-effective. An internal analysis of our workflow, systems, and processes revealed that developing a cost-effective and efficient customized solution for our existing data coordinating center (DCC) projects was feasible. The customized solution mimics the key functionality and workflow of the prior software. It utilizes existing electronic data capture systems and document management solutions, ensuring 21 CFR Part 11 compliance without incurring additional expenses. The proposed process received internal approval and launched in December 2022. The process outlined in this paper is particularly advantageous for DCCs or academic clinical research sites that must meet regulatory reporting requirements without depending on specialized pharmacovigilance software. Additionally, it is a cost-effective option for organizations with a low volume of SUSAR events. In this paper, we present the process of developing and implementing a customized safety case management solution and the lessons learned pre and post implementation.

Advancements in Minimally Invasive Techniques in Pediatric Dentistry: A Review.

Minimally invasive dentistry (MID) has revolutionized pediatric dental care by emphasizing the preservation of healthy tooth structures, reducing treatment-related trauma, and improving patient compliance. This narrative review explores advancements in MID techniques, including silver diamine fluoride (SDF), resin infiltration, atraumatic restorative treatment (ART), bioactive materials, laser-assisted therapies, and three-dimensional (3D) printing technologies. These approaches prioritize early diagnosis, prevention, and conservative management, aligning with patient-centered and sustainable practices. SDF demonstrates high efficacy in arresting caries progression but presents esthetic challenges due to discoloration. Resin infiltration provides esthetic and noninvasive treatment for white spot lesions, while ART offers cost-effective and child-friendly caries management in resource-limited settings. Bioactive materials support tissue regeneration, and laser technologies enable precise and painless procedures, although their adoption is limited by high costs and training requirements. Emerging tools, such as artificial intelligence and 3D printing, enhance diagnostic accuracy and treatment precision. Despite challenges related to cost, operator training, and infrastructure, MID techniques continue to evolve, offering promising solutions for pediatric dental care. Future research should focus on optimizing materials, improving accessibility, and integrating digital technologies to broaden the impact of minimally invasive approaches. This review highlights MID's transformative role in improving oral health outcomes and ensuring sustainable, patient-focused care for children.

AI-Enhanced Traffic Prediction and Congestion Control: A Framework for CNF and VNF Networks

This article comprehensively analyzes artificial intelligence-driven approaches to traffic prediction and congestion control in Cloud-Native Functions (CNFs) and Virtual Network Functions (VNFs) based networks. This article examines recent advancements in predictive analytics and dynamic resource allocation, focusing on implementing deep learning frameworks such as Long Short-Term Memory (LSTM) networks for traffic pattern forecasting. This article demonstrates how continuous learning models and real-time telemetry data integration enable adaptive network responses to fluctuating traffic conditions. This article indicates that AI-enhanced load balancing and traffic shaping techniques significantly improve network performance, achieving a more efficient distribution of resources across network nodes while maintaining consistent Quality of Service (QoS). This article highlights the transformative potential of these innovations in meeting the demanding requirements of 5G networks and beyond, offering insights into cost-effective resource management strategies and scalable network solutions. Experimental results show substantial improvements in latency reduction and resource utilization efficiency, presenting a promising direction for next-generation telecom service optimization.

Grey Forecasting for technoeconomic sustainability of fiberglass composite waste recycling in developing countries.

The demand for engineered composites particularly fiberglass reinforced polymers (FRP) is gaining momentum. The manufacturing of virgin input-resins for these composites involves the use of certain materials which poses serious environmental implication. This study has exclusively applied and investigated the Grey Forecasting model for management of FRP waste in developing countries to minimize the virgin inputs and likely environmental impacts. Various recyclate-based solutions are developed, experimented and analyzed. Technoeconomic analytics projected via Grey Forecasting depicted that indigenously developed solutions of reuse and recycling of FRP waste driven via mechanical recycling are sustainable. As such a trial project in Pakistan has initiated a viable benefit-cost ratio (BCR) of 1.27 and a payback period of 26 months. Experiments using FRP recyclate-based inputs were successful in improving the filler, value, and strength of the product. As a result, consumer prices were reduced by 35% and 21% respectively. The recyclate material saved 15% of the manufacturing cost as compared to the product manufactured by 100% virgin inputs. Mechanical recycling proved as a safe and viable method that reduced the hazardous inputs by 25-28% during manufacturing and minimizing the burden on landfills. Likewise, the mixing step focuses on eliminating the use of virgin materials and potentially harmful processes as this is where thermoset resins and glass-fibers are blended together to create the composite material. This comprehensive analysis provides a holistic perspective on policy interventions to achieve cost-effective recyclate management.

Duplex Ultrasonography for Screening and Monitoring of Carotid Artery Stenosis for Risk Stratification of Ischemic Stroke

Aim: To evaluate the efficiency of Duplex Ultrasonography (DUS) for monitoring and screening of Carotid Artery Stenosis (CAS).Methods: PubMed, Web of Science, Cochrane, and SCOPUS were searched for relevant articles. The quality assessment of the included studies was conducted according to the Cochrane Risk of Bias Tool.Results: Our meta-analysis included six articles. Carotid DUS could detect mild CAS in 25%, moderate CAS in 13%, severe CAS in 14.5% of the examined vessels, detection of carotid artery plaques in 36.5% of the examined vessels, and abnormal intima thickness in 21.5% of the examined vessels.Conclusion: DUS is an important exam in the identification of patients with symptomatic CAS. It can improve clinical practice and provide cost-effective management for CAS. Carotid DUS showed high specificity and sensitivity in the diagnosis of CAS, especially a severe degree of stenosis.

Sustainable strategies for cost-effective temporary infrastructure management: insights from fairs in West Midnapore District, India

ABSTRACT Efficient management of temporary infrastructure is pivotal for the success and sustainability of periodic fairs and festivals. Coordination among stakeholders involved in procurement, supply, and timely delivery is essential. The involvement of multiple stakeholders results in varying cost engagements at different levels. This study proposes a centralised operational framework for infrastructure distribution, potentially reducing overall costs. Focusing on West Midnapore district, India, the research aims to introduce a sustainable and cost-effective strategy for temporary infrastructure management. Recognising the significance of transportation in managing temporary infrastructures, the study emphasises reducing travel distances and minimising site occupancy time to enhance cost efficiency. Using a linear regression model, the required infrastructural goods for fairs in the district are quantified. A delivery schedule is devised, employing vehicle route planning techniques to determine reduced truck travel distances. Comparing different distribution frameworks, the study finds that a centralised approach utilising three trucks is the most sustainable and cost-effective solution. Moreover, it examines and quantifies the social cost incurred when infrastructure items exceed the allowed fair duration in the current system. Implementing this proposed approach enables stakeholders to balance sustainability cost-effectiveness and build an efficient infrastructure supply chain management.

CLINICAL EFFICACY OF ERAND SNEHA IN MANAGEMENT OF ACUTE NE-CROTISING PANCREATITIS: A CASE REPORT

Acute pancreatitis with local and systemic complications should be managed through a multi- disciplinary approach. Delays in assessment or mismanagement may lead to serious consequences. The existing treatment of this disease is mollifying and expensive. Cost-effective management of acute necrotizing pancreatitis with Ayurveda medicines within time constraintsand without any complications is encouraging. CASE PRESENTATION: In this case study, a 45-year-old male patient’s diagnosis of pancreatitis was established on the basis of the revised Atlanta classification. As per morphological classification, the patient was of necrotizingpancreatitis and severity grade was moderate as there were local complications. MANAGEMENT AND OUTCOME: The patient was hospitalized and prescribed Murchit Erand Sneha 100ml at 10 pm daily. The result was assessed by applying a numerical pain assessment scale to subjective and objective findings. On the 25th day patient was found free from signs and symptoms. DISCUSSION: In pancreatitis, Srotorodha is an important phenomenon, and Erand Sneha causes Sampraptivighatana by its Srotoshodhana effect. CONCLUSION: Management of acute necrotizing pancreatitis by using Erand Sneha within time constraints and without any complications is encouraging that needs to be further evaluated on a large number of subjects to bring some concrete conclusions and better treatment modality.

Unraveling the Traffic Congestion Due to Bottleneck: A Review

In the metropolitan cities across the world, traffic congestion is a major problem due to growing urbanization, vehicle growth and infrastructure constrains. Bottleneck congestion is a major problem faced in traffic management in many urban centers as it affects travel time, emissions, and fuel consumption. It accounts for nearly 40% of congestion. This extensive literature review aims to discuss the main reasons behind traffic congestion, classify their patterns, and discuss how it can be detected and prevented. This paper focuses on the economic and social consequences of traffic bottlenecks by considering several traffic congestion research. The methods namely; Manual counting, social surveys, CCTV footage analysis, Navigation, and simulation software namely VISSIM are discussed in this paper. It also offers traffic control methods and other difficulties and contemporary control measures for congestion. In addition, this review emphasizes on the need of cost effective traffic congestion management, particularly in underdeveloped countries like Nepal, where video graphic survey is recommended as the most feasible approach. The purpose is to deliver pertinent information to transportation planners and engineers to address issues concerning traffic congestion in big cities and offer ways of improving the efficiency of transport systems in urban cities.

Ultrawide Bandgap Diamond/ε-Ga2O3 Heterojunction pn Diodes with Breakdown Voltages over 3 kV.

Robust bipolar devices based on exclusively ultrawide bandgap (UWBG) semiconductors are highly desired for advanced power electronics. The heterojunction strategy has been a prevailing method for fabricating a bipolar device due to the lack of effective bipolar doping in the same UWBG material. Here, we demonstrate a unique heterojunction design integrating the p-type diamond and n-type ε-Ga2O3 that achieves remarkable breakdown voltages surpassing 3000 V. Despite the lattice mismatch, the heteroepitaxial ε-Ga2O3 film is established on the diamond substrate, forming an atomically sharp interface with C-O-Ga bonding and enabling the O-terminated diamond surface for constructing an effective rectifying heterojunction. The ultra-high-quality interface, together with the lightly doped diamond as the drift layer, largely weakens the commonly met electric field crowding effect in power diodes and provides a cost-effective thermal management route. This study provides an efficient heterojunction design to realize the potential of UWBG semiconductors for ultra-high-power applications.

Application of Dual-Stage Attention Temporal Convolutional Networks in Gas Well Production Prediction

Effective production prediction is vital for optimizing energy resource management, designing efficient extraction strategies, minimizing operational risks, and informing strategic investment decisions within the energy sector. This paper introduces a Dual-Stage Attention Temporal Convolutional Network (DA-TCN) model to enhance the accuracy and efficiency of gas production forecasting, particularly for wells in tight sandstone reservoirs. The DA-TCN architecture integrates feature and temporal attention mechanisms within the Temporal Convolutional Network (TCN) framework, improving the model’s ability to capture complex temporal dependencies and emphasize significant features, resulting in robust forecasting performance across multiple time horizons. Application of the DA-TCN model to gas production data from two wells in Block T of the Sulige gas field in China demonstrated a 19% improvement in RMSE and a 21% improvement in MAPE compared to traditional TCN methods for long-term forecasts. These findings confirm that dual-stage attention not only increases predictive accuracy but also enhances forecast stability over short-, medium-, and long-term horizons. By enabling more reliable production forecasting, the DA-TCN model reduces operational uncertainties, optimizes resource allocation, and supports cost-effective management of unconventional gas resources. Leveraging existing knowledge, this scalable and data-efficient approach represents a significant advancement in gas production forecasting, delivering tangible economic benefits for the energy industry.

Factors related to cost-effective use of palliative care services in Türkiye

Introduction: The aim of this study was to determine the components affecting the cost in palliative care (PC) and to determine how much they affect this cost separately and to evaluate the relationship between PC components and PC cost in order to determine the cost table of both the patient and the hospital in a more planned way and for a more cost-effective management. Methods: This was a retrospective cohort study. Data on patients, demographic characteristics, chronic disease profiles, duration of antibiotic use, antibiotic costs, length of stay, presence and number of hospital infections, feeding methods, opioid use, mortality rate, presence of decubitus ulcer, central venous catheter and tracheostomy were evaluated to determine their effects on cost. Results: No significant relationship was found between cost of palliative care and mortality, age, gender, opioid use and chronic diseases. The cost of palliative care for the cancer group was statistically lower, with an average daily cost of $177. Length of stay, decubitus ulcers, duration of antibiotic use, number of nosocomial infections, presence of central venous catheter and tracheostomy, and nutritional status showed a positive correlation with cost of palliative care. Conclusions: It was observed that some components of PC were associated with high costs, but mortality was independent of costs. While calculating the cost-effectiveness in PC, mortality should also be taken into consideration along with other components, and PC efficiency should not be ignored while targeting cost-effectiveness.

Economic Burden of Endometriosis: A Systematic Review and Meta-Analysis

Introduction: Endometriosis, marked by uterine-like tissue growth outside the uterus, causes severe pelvic pain and infertility. Delayed diagnosis contributes to its significant global economic burden, especially in Europe and the USA. Despite treatment advances, managing endometriosis remains challenging. Comprehensive strategies are urgently needed to reduce its economic impact and improve patient outcomes. Material and Methods: We searched for English-language full-text literature from PubMed, Cochrane, Wiley Library, and ScienceDirect databases from January 2013 to January 2024. Total of 951 studies were identified, and 14 studies were included, providing comprehensive data on the economic burden of endometriosis, including direct healthcare costs and productivity losses. Results: The economic burden of endometriosis, based on 14 studies, is substantial, reflecting both direct healthcare costs and productivity losses. Average annual costs per woman ranged from EUR 8,768 in Sweden to Int $16,970-$20,898 in Australia. Non-surgical treatments in the USA were more cost-effective than surgery, costing $100,000 per QALY. In the UK, the LNG-IUS was the most expensive treatment at £650.94. Costs in China for various therapies ranged up to $10,728 with differing ICERs. Conclusion: These findings underscore the significant economic impact and the need for cost-effective management strategies: costs and productivity losses. Effective management strategies are essential to mitigate these costs.

Optimizing Conservation Planning: An Integrated Approach to Cost-Effective Biodiversity and Carbon Sequestration Management in Yunnan Province, China

This study introduces an innovative, integrated approach to optimizing conservation planning in Yunnan Province, China. By maximizing the synergistic benefits of threatened species protection and carbon sequestration while minimizing economic costs, the research identifies regions with the highest conservation cost-effectiveness. This is achieved by combining species distribution models (SDMs) for threatened species, carbon sequestration capacity estimates, and local economic data through an input-output (I-O) model at 1 kilometer resolution. The study highlights key regions within Yunnan—particularly in the northwestern areas of Shangri-la and Nujiang prefectures—as offering significant ecological benefits at relatively low opportunity costs. These findings provide a scalable framework for policymakers to prioritize regions for protected area (PA) expansion, thus enhancing conservation outcomes while minimizing financial expenditures. The proposed methodology improves conservation investments' cost-effectiveness and supports broader conservation and sustainable land management goals. This research underscores the necessity of integrating multiple ecological and economic factors to address the urgent challenges of biodiversity loss.

Aqua-stream: an IoT based smart water management system for sustainable living

&lt;p class="03IJISAEAbstract"&gt;Aqua-stream, an innovative internet of things (IoT) enabled water management system, utilizes the power of long short-term memory (LSTM) networks, a sophisticated time-series forecasting machine learning technique with Kafka. Aqua-stream seamlessly integrates LSTM within the Kafka streaming architecture for efficient real-time data processing, ensuring quick responses to emerging water management needs. LSTM is employed for real-time anomaly detection, dynamically analyzing streaming data to prevent leaks through automated shut-off valves. The system’s comprehensive dashboard utilizes LSTM insights for live water quality analysis; adaptive scheduling based on individual preferences and personalized recommendations, enhancing cost-effective water management. This streamlined approach extends to the smart gardening system, where LSTM guides automation for optimal plant care incorporating sensors to monitor soil moisture, temperature, and sunlight levels. This system automatically adjusts watering and lighting to ensure optimal conditions for plant growth. Users can control and monitor their garden remotely via a smartphone, facilitating plant care while saving water and energy. Aqua-stream redefines home water management, offering a holistic solution that combines intelligent water conservation with smart gardening for a sustainable and connected living experience. Aqua-stream represents a seamless integration of LSTM-based machine learning and IoT technologies, offering an intelligent, yet simplified, solution for sustainable and connected living.&lt;/p&gt;

Evaluation of dyspepsia symptom severity in pediatric patients: association with endoscopic and pathological findings

Aims: There is diagnostic difficulty in distinguishing between organic and functional causes of dyspepsia, which is characterized by a variety of gastrointestinal symptoms. This study aimed to evaluate the correlation between dyspeptic complaints, clinical symptoms, endoscopic findings, and pathology results in pediatric patients. Methods: A total of 159 children aged 8-17 years with dyspeptic complaints underwent upper gastrointestinal endoscopy (UGE). The Dyspepsia Symptom Severity Index (DSSI), comprising three parts (dysmotility-like, reflux-like, and ulcer-like symptoms) and 19 questions, was used to assess symptom severity. Endoscopic procedures were performed under general anesthesia, and biopsy samples were obtained. Results: The study included 159 patients with a median age of 15 years (ranging from 8 to 17 years). Among them, 29.1% were male (38) and 76.9% were female (121). DSSI scores were calculated for each subgroup and the total score. When assessing the relationship between chronic diseases and DSSI scores, no significant association was found (p=0.48). However, smoking showed a significant correlation with DSSI scores (p=0.01 for dysmotility-like, p=0.02 for reflux-like, and p&lt;0.01 for ulcer-like symptoms). Gender differences were observed in DSSI scores, with girls exhibiting significantly higher median scores for dysmotility-like findings compared to boys (p=0.02). Endoscopy findings also correlated with DSSI scores, with significantly higher scores in patients with ulcers (p&lt;0.01). Pathological findings such as esophagitis were found to have significant associations with reflux-like symptoms (p=0.04), and crypt hyperplasia was associated with ulcer-like symptoms (p=0.04) in patients with DSSI subgroup scores. However, no significant differences were found between pathology findings and dyspepsia severity, significant associations were observed between UGE findings (such as ulcers, pangastritis, erosional areas, and fragility) and ulcer-like symptoms. Conclusion: The study found significant links between dyspepsia severity and endoscopic results, stressing the need for reliable symptom scales for pediatric patients. It also highlights smoking as a factor in dyspepsia severity in children. Further research is needed to evaluate endoscopy's effectiveness in distinguishing between organic and functional dyspepsia. The study points out the limited understanding and consensus on diagnosing and managing dyspepsia in children, calling for the development of validated symptom scales and further research into cost-effective management strategies.

A comprehensive review of energy management in microgrids utilizing energy storage systems

Microgrids (MGs) are essential in advancing energy systems towards a low-carbon future, owing to their highly efficient network architecture that facilitates the flexible integration of various DC/AC loads, distributed renewable energy sources, and energy storage systems. They also offer enhanced resilience and cost-effective control, operation, and energy management for both on-grid and off-grid scenarios. Nevertheless, as new entrants to the utility grid, MGs encounter challenges stemming from the economic deregulation of energy systems, the restructuring of generation, and market-driven operations. This paper provides a thorough overview of existing research on MGs .It categorizes MGs and energy storage systems into several branches and typical combinations, which serve as the foundation for MG energy management.