Cost Efficiency Research Articles

AI Applications in Pediatrics: Past, Present, and Future

In pediatric medicine, Artificial Intelligence (AI) currently remains as a novel area for research and development. Most AI applications created for adults are often complicated to fully transfer over to pediatrics due to several limiting factors. These factors include physiological differences, lack of data, and unknown implications. Nevertheless, applications in adult medicine have influenced extensive reviews for algorithms that show significant potential in pediatrics. As pediatric data gradually increases, several AI algorithms for adults can be fine-tuned to provide similar accuracies and sensitivities for diagnosing specific pediatric diseases. This chapter will discuss the pioneering applications of AI in medicine as well as current and arising applications or subspecialties. Areas of discussion encompass precision medicine, oncology, cardiology, radiology, chatbots, neurology, and ophthalmology. AI algorithms within these domains range from assistance with diagnoses, shorter waiting times, to continuous monitoring. Common benefits exhibited by many applications include increased accuracy, time and cost efficiency, patient compliance, and flexibility. For example, medical wearable technology and chatbots are two AI applications that can provide certain patient care in both outpatient and inpatient settings. These algorithms are convenient to use and offer continuous monitoring when a provider may not be readily available. Furthermore, other topics explored in this chapter relate to common concerns for using AI in pediatric medicine and education for health professionals, parents, and children. Most apprehension pertains to the misuse of data and quality of care. Reviewing these concerns is crucial for AI to exponentially flourish in medicine. Without established comprehension, hesitance and distrust may prevail over the promising potential AI can offer. Education and training can help alleviate these common misunderstandings by explaining how AI can affect both medicine and health care. Moreover, learning environments can allow health professionals, parents, and children to openly communicate their concerns that could guide ethical frameworks for developing AI applications. Analyzing these past, present, future applications, and related concerns can help demonstrate how AI will transform pediatric medicine.

A Comprehensive Review on Niosomes as a Tool for Advanced Drug Delivery.

Over the past few decades, advancements in nanocarrier-based therapeutic delivery have been significant, and niosomes research has recently received much interest. The self-assembled nonionic surfactant vesicles lead to the production of niosomes. The most recent nanocarriers, niosomes, are self-assembled vesicles made of nonionic surfactants with or without the proper quantities of cholesterol or other amphiphilic molecules. Because of their durability, low cost of components, large-scale production, simple maintenance, and high entrapment efficiency, niosomes are being used more frequently. Additionally, they enhance pharmacokinetics, reduce toxicity, enhance the solubility of poorly water-soluble compounds, & increase bioavailability. One of the most crucial features of niosomes is their controlled release and targeted diffusion, which is utilized for treating cancer, infectious diseases, and other problems. In this review article, we have covered all the fundamental information about niosomes, including preparation techniques, niosomes types, factors influencing their formation, niosomes evaluation, applications, and administration routes, along with recent developments.

Enhanced reaction extraction distillation via multi-objective optimization and 4E analysis for sustainable recovery of organic compounds from wastewater

Resource utilization and sustainable treatment of wastewater, which can achieve efficient energy utilization and environmental protection, have received hot social attention. Tetrahydrofuran and methanol are the two most common substances in pharmaceutical wastewater. It is very necessary to explore the wastewater treatment process containing these two substances. In this study, extractive distillation and reactive distillation were proposed to effectively separate the tetrahydrofuran-methanol-water component of pharmaceutical wastewater. To further reduce energy consumption, two strengthening processes, double-column reactive-extractive distillation and reactive-extractive dividing wall column distillation were designed. Taking the minimum operating and equipment cost as the objective function, the processes were optimized by using the multi-objective genetic optimization algorithm to obtain the best process parameters. Four processes were evaluated in terms of total annual cost, energy consumption, gas emissions and thermodynamic efficiency. Compared to the traditional three-column extractive distillation process, the above-mentioned four aspects of the reactive-extractive dividing wall column process was reduced by 40.63%, 43.13%, 43.14% and 17.71%, respectively. The reactive-extractive dividing wall column distillation process is of great significance for the efficient purification of valuable organic matter in wastewater and environmental protection.

An efficient and low cost realization of LoRa based real-time forest protection system

The forest is a natural habitat for a variety of fauna and flora, and helps to maintain the ecosystem equilibrium. However, wildfire incidents and deforestation lead to forest degradation. Moreover, most of the existing methods, to preserve the forest resources, are ineffective due to their large establishment cost, more power consumption, and poor coverage. This paper brings out a sustainable solution by developing a forest protection system (FPS) that uses internet of things (IoT) technology together with long range (LoRa) communication. The work focuses on the development of an IoT framework for the detection of any intrusion into the forest as well as the detection of fire incidents in the vicinity of the equipment. Powering the equipment through solar energy makes the system cost-effective. The system is examined in terms of acquisition of data from sensor nodes pertaining to forest protection, relaying the same to the cloud using LoRa wide area network (LoRaWAN) technology and analyzing using cloud based visualization tools. The developed system has been deployed at Eturnagaram Wildlife Sanctuary, Mulugu district, Telangana, India for validation in the forest environment. The obtained results have shown that the system has an accuracy of 97.14% for intrusion detection and 100% for fire detection.

Planning Innovation for Implementing Modular Prefabricated Construction in Housing Development in Indonesia Using A Riskbased ISO 56002:2019 Approach to Improve Project Performance

The development of housing in Indonesia, growing rapidly due to urbanization and population growth, faces challenges in terms of land, labor, and housing backlog. Despite being innovative, modular prefabrication methods are limited by unattractive designs and high costs, slowing down construction, harming the productivity of construction companies, and hindering the growth of the national construction sector. These challenges are prevalent in various countries and are influenced by low knowledge, conservative mindsets, and inhibiting regulations. In Indonesia, additional issues include a lack of mastery of modular prefabrication methods and awareness of long-term benefits. This research identified innovative risk-based applications of modular prefabrication with an ISO approach to improve project performance. The methodology employed includes a literature review, a questionnaire survey, and phased expert validation. The study found that out of 38 risk factors based on the 4 processes of implementing modular prefabrication methods, the average risk assessment is high. As a result, 5 innovations are proposed as efforts to reduce risk assessments. The research analyzed data from various literature, resulting in low inter-data connectivity. However, the processes and outcomes of this research can be relied upon for further understanding in subsequent research. This study combined proposed new innovations with those from external literature that have the potential to be applied in Indonesia for optimizing the cost efficiency of modular prefabrication methods.

Standardization versus adaptation of marketing mix in international markets: A systematic literature review

This study examined standardization versus adaptation of marketing mix in international markets. It was carried out to explore the scholarly arguments for both standardization versus adaptation of marketing mix in international markets. It also explored the position of extant scholars on the choice between standardization versus adaptation of marketing mix in international markets. The study adopted systematic literature review methodology. The population of the study comprised 4,128 relevant articles obtained from five credible research databases. The article selection procedure was facilitated by a set of inclusion-exclusion criteria and the PRISMA framework. This resulted in the inclusion of 79 relevant articles published between 2017-2023 for the review. The articles were reviewed using in-depth content analysis method. The first finding of the study revealed that the major scholarly arguments for standardization can be grouped into 16 categories, including: economies of scale and cost efficiency; global brand image and recognition; among others. It was also found that the major scholarly arguments for adaptation can be grouped into 13 categories, including: cultural differences; legal and regulatory environment; among others. The last finding is that majority of extant scholars proposed a hybrid approach (glocalization), which combines elements of both standardization and adaptation to create a strategy that is globally consistent yet locally relevant. Therefore, the study advises international marketers to conduct comprehensive market research before adopting a glocalization strategy. This entails understanding local consumer behaviours, preferences, cultural nuances, and economic conditions in order to craft marketing campaigns that align with local culture, values, and traditions of the target market.

B-LPoET: A middleware lightweight Proof-of-Elapsed Time (PoET) for efficient distributed transaction execution and security on Blockchain using multithreading technology

A private permissioned chain is typically considered a fully protected authorized blockchain. The concept of a permissioned chain has become better single-entity control operation over the network and allows entry only to selected nodes, which most probably supports different small-medium enterprises and government officials. It is one of the prominent features of blockchain technology that maintains transaction integrity and security throughout the deliverance. This paper initially highlights the benefits of a permissioned private chain. Present the current issues of the Proof-of-Elapsed Time consensus mechanism for the distributed applicational environment. In order to make blockchain systems more adaptive for private chains, this paper proposes a middleware lightweight consensus based on PoET, named “B-LPoET”. Objecting to being improved as a lightweight PoET from the predefined PoET in terms of verifying transactions and creating more blocks, to decide the mining right, and participants allows to acknowledge their identities before joining. The design of B-LPoET reduces the cost of multi-node efficiency by establishing a lightweight topology for selecting and waiting time of winning node using multithreading, which affects positively by increasing systems scalability. The experimental results of the B-LPoET illustrate that it performs better as compared with the existing PoET and other state-of-the-art consensus.

Enhancing Adhesion and Reducing Ohmic Contact through Nickel–Silicon Alloy Seed Layer in Electroplating Ni/Cu/Ag

Due to the lower cost compared to screen-printed silver contacts, the Ni/Cu/Ag contacts formed by plating have been continuously studied as a potential metallization technology for solar cells. To address the adhesion issue of backside grid lines in electroplated n-Tunnel Oxide Passivating Contacts (n-TOPCon) solar cells and reduce ohmic contact, we propose a novel approach of adding a Ni/Si alloy seed layer between the Ni and Si layers. The metal nickel layer is deposited on the backside of the solar cells using electron beam evaporation, and excess nickel is removed by H2SO4:H2O2 etchant under annealing conditions of 300–425 °C to form a seed layer. The adhesion strength increased by more than 0.5 N mm−1 and the contact resistance dropped by 0.5 mΩ cm2 in comparison to the traditional direct plating Ni/Cu/Ag method. This is because the resulting Ni/Si alloy has outstanding electrical conductivity, and the produced Ni/Si alloy has higher adhesion over direct contact between the nickel–silicon interface, as well as enhanced surface roughness. The results showed that at an annealing temperature of 375 °C, the main compound formed was NiSi, with a contact resistance of 1 mΩ cm−2 and a maximum gate line adhesion of 2.7 N mm−1. This method proposes a new technical solution for cost reduction and efficiency improvement of n-TOPCon solar cells.

Optimization of mixed cargo allocation and stowage with heterogeneous vehicles

In order to solve the problem of low efficiency and high loading costs in business logistics and distribution operations, this paper classifies non-standard cargoes based on their spatial and temporal characteristics. Based on this, a dual-layer planning model for multi-vehicle cargoes loading and transportation is constructed to jointly optimize the loading process. This paper designs a space-inspired heuristic algorithm to solve the model and verifies the feasibility of the model. The results show that compared to a single vehicle type, the joint optimization of multi-vehicle combinations for loading and transportation has stronger adaptability and can better accommodate changes in cargo volume. Compared to the single vehicle type, not only did the loading rate increase by 18%, but also effectively reduced the transportation cost by 3%. Different proportions of spatial and temporal characteristic cargoes have different effects on the loading and transportation. Adjusting the proportion of cargoes that can be placed on both upper and lower layers to 50% can better utilize transportation resources such as vehicles. Increasing the proportion of urgent cargoes can improve the economic efficiency of loading and transportation.

Rapid Sample Pretreatment Facilitating SERS Detection of Trace Weak Organic Acids/Bases in Complex Matrices.

Fast and efficient sample pretreatment is the prerequisite for realizing surface-enhanced Raman spectroscopy (SERS) detection of trace targets in complex matrices, which is still a big issue for the practical application of SERS. Recently, we have proposed a highly performed liquid-liquid extraction (LLE)-back extraction (BE) for weak acids/bases extraction in drinking water and beverage samples. However, the performance efficiency decreased drastically on facing matrices like food and biological blood. Based on the total interaction energies among target, interferent, and extractant molecules, solid-phase extraction (SPE) with a higher selectivity was introduced in advance of LLE-BE, which enabled the sensitive (μg L-1 level) and rapid (within 10 min) SERS detection of both koumine (a weak base) and celastrol (a weak acid) in different food and biological samples. Further, the high SERS sensitivity was determined unmanned by Vis-CAD (a machine learning algorithm), instead of the highly demanded expert recognition. The generality of SPE-LLE-BE for various weak acids/bases (2 < pKa < 12), accompanied by the high efficiency, easy operation, and low cost, offers SERS as a powerful on-site and efficient inspection tool in food safety and forensics.

Optimizing Healthcare Expenditure for Spinal Cord Stimulation in Italy: The Value of Battery Longevity Improvement and a Direct-to-Implant Approach

Background: Spinal cord stimulation (SCS) is a treatment for chronic intractable pain powered by an implantable pulse generator that may be rechargeable or not rechargeable (NR). It is performed in 2 stages (a trialing phase followed by permanent device implantation) and necessitates 2 hospitalizations, which may increase infection risk. Objective: This analysis explores the cost impact of improvements in battery longevity and the adoption of 1-step (direct-to-implant [DTI]) SCS implantation. Methods: Since 2019, 3 leading NR-SCS devices have been launched: Device A (2019), Device B (2020), and Device C (2021). The battery longevity of the newest Device C was estimated at comparable stimulation settings for Devices A and B. A Markov model simulated individual patient pathways across 2 scenarios: Device A vs Device C and Device B vs Device C (both with the DTI approach and 2-step approach). Costs considered were the initial device implantation procedure, device replacements, and serious adverse event (SAE) management. Italian diagnosis-related group (DRG) tariffs were applied for costs, and a 15-year time horizon was used. Results: Over 15 years, using a DTI approach, the undiscounted total costs for Device A vs Device C were €26 860 and €22 633, respectively, and €25 111 and €22 399 for Device B vs Device C, respectively. Compared with Devices A and B, Device C offered savings of €4227 and €2712, respectively; similar savings were predicted with a 2-step implant approach. Discussion: The battery longevity of NR-SCS devices directly impacts long-term costs to a payer. The longer the device lasts, the lower mean total cumulative costs the patient will have, especially with regard to device replacement costs. With novel devices and specific programming settings, the lifetime cost per patient to a payer can be decreased without compromising the patient’s safety and positive clinical outcome. Conclusions: Extended SCS battery longevity can translate into tangible cost savings for payers. The DTI approach for SCS supports National Healthcare System cost efficiencies and offers the additional benefits of optimizing operating room time while having only one recovery period for the patient.

A production and transport scheduling strategy of energy and resources of pelagic clustering islands based on generalized movable energy storage

At present, most pelagic islands rely on the mainland for resource replenishment. And due to the limited island area, it is not sufficient to simultaneously place enough basic load facilities and new energy generation and consumption facilities on a single island. Considering pelagic islands are distributed in groups, vigorously developing renewable energy around islands can be considered, and the islands can be divided into resource islands and load center island. Resource islands can use renewable energy to produce and transport electric power, fresh water and hydrogen which are needed for load center island. The whole islands group forms a cleaner and green integrated energy system. However, this kind of system will face the coexistence challenge of intra-island independent optimization and inter-island collaborative optimization, continuity of energy production and discreteness of transportation, production scheduling and route planning, which is less studied. To ensure reasonable operation of the energy system, according to multi-agent theory, the inter-island energy and resource production and transportation scheduling model based on generalized movable energy storage can be divided into two parts: load center island agent scheduling model and resource islands agent scheduling model. And scheduling objectives and constraints of each agent in two scenarios are determined. The simulation results show that the new model can comprehensively take into account the production efficiency of equipment, production effectiveness and transportation cost and realize the rational production scheduling and transportation of energy and resources under the premise of meeting the demand of load center island.

ENHANCING RECRUITMENT AND SELECTION PROCESSES: INSIGHTS FROM PNB METLIFE’S TALENT ACQUISITION

This paper is on recruitment and selection process at PNB MetLife Insurance Company Limited. The process of identifying, screening, shortlisting, and choosing the best applicants to occupy the necessary vacant jobs is known as recruitment and selection. The primary objectives of this research are to determine how candidates are recruited, on what criteria the selection process is based, and what skill set is necessary for a certain position. The aim of this research is to investigate the PNB MetLife's recruitment and selection procedure and to investigate whether the procedure is simple to comprehend and use different methods that are used by the organisation. The study's research design is descriptive. Primary data have been collected for this project. 50 candidates make up the sample size for the questionnaire method used to acquire the data. The data indicates that employee referrals (72%) are the dominant source for internal recruitment, and employment agencies (42%) lead in external recruitment. High satisfaction (88%) with the resume screening process and clear job descriptions (94%) reflect positively on organizational practices, while a strong awareness (94%) of referral schemes supports internal hiring strategies. Such insights offer valuable guidance for refining recruitment approaches and enhancing employee satisfaction, thereby fortifying organizational success. This project makes an effort to offer suggestions for improving recruitment efficiency and lowering hiring costs. Keywords: Recruitment Process, Selection process, Interview, Job vacancy, Qualification and Skills

The Causal Linkages between Market Power and Cost Efficiency: Testing Quiet Life Hypothesis for the Banking Industry

This study aims to test the quiet life hypothesis considering the impact of market power on cost efficiency in Pakistani banks where managers of the banking industry slacken their efforts to enjoy a ‘quiet life’ due to market power and are not focused on the cost efficiency of their banks. Thus, banks with high market power are relatively less focused on the efficient allocation of resources, resulting in a loss of consumer surplus and efficiency. We take panel data for a sample of 25 commercial banks throughout 2006-2019. Our findings suggest Uni-directional causality running from cost efficiency towards market power in the case of Pakistan. In addition, to control for endogeneity, we adopt a dynamic two-step system GMM approach to model the relationship between market power and cost efficiency. Our findings suggest significantly negative effects of cost efficiency over market power, disproving the presence of quiet life in the case of Pakistan. This study provides substantial theoretical contributions in the form of the Quiet life hypothesis and welfare performance of banks. However, to the best of our knowledge, the causal linkages and the effects of market power on cost efficiency or vice versa are not documented for the banking industry of Pakistan.

Advances in long-read single-cell transcriptomics.

Long-read single-cell transcriptomics (scRNA-Seq) is revolutionizing the way we profile heterogeneity in disease. Traditional short-read scRNA-Seq methods are limited in their ability to provide complete transcript coverage, resolve isoforms, and identify novel transcripts. The scRNA-Seq protocols developed for long-read sequencing platforms overcome these limitations by enabling the characterization of full-length transcripts. Long-read scRNA-Seq techniques initially suffered from comparatively poor accuracy compared to short readscRNA-Seq. However, with improvements in accuracy, accessibility, and cost efficiency, long-reads are gaining popularity in the field of scRNA-Seq. This review details the advances in long-read scRNA-Seq, with an emphasis on library preparation protocols and downstream bioinformatics analysis tools.

Towards robust state estimation for LFP batteries: Model-in-the-loop analysis with hysteresis modelling and perspectives for other chemistries

The accurate estimation of a battery’s state of charge (SOC) is critical in battery management systems for various applications. Lithium Iron Phosphate (LFP) batteries, preferred for their long cycle life, cost efficiency, and enhanced safety, have emerged as favourable choices for stationary storage. Yet, they still face challenges in precise SOC estimation due to the flatness and hysteresis of their open circuit voltage. Addressing this, our study integrates a hysteresis model into a third-order battery model for BMS controlling a stationary storage system in frequency containment reserve (FCR) application. We analysed three advanced SOC estimation techniques — extended Kalman filter (EKF), dual unscented Kalman filter (DUKF), and particle filter (PF) — with the hysteresis model using a model-in-the-loop (MiL) toolchain. Performance testing under a 48-hour FCR load profile showed EKF with a 4% error, DUKF achieving the best result with a 1.1% error, and PF’s performance varying between 2.9% and 4% depending on particle count. Robustness tests against initialization and current sensor errors under an 8hr profile revealed DUKF maintained a 2% error boundary irrespective of the error introduced, highlighting the hysteresis model’s effectiveness. Broadening the scope, the study also explores extending the method to other lithium-ion chemistries.

A Blockchain-Based Electronic Health Record (EHR) System for Edge Computing Enhancing Security and Cost Efficiency

Blockchain technology offers unique features, such as transparency, the immutability of data, and the capacity to establish trust without a central authority. Such characteristics can be leveraged to support the collaboration among several different software systems operating within the healthcare ecosystem, while ensuring data integrity and make electronic health records (EHRs) more easily accessible. To provide a solution based on blockchain technology, this paper has evaluated the main issues that arise when large amounts of data are expected, i.e., mainly cost and performance. A balanced approach that maximizes the benefits and mitigates the constraints of the blockchain has been designed. The proposed decentralized application (dApp) architecture employs a hybrid storage strategy that involves storing medical records locally, on users’ devices, while utilizing blockchain to manage an index of these data. The dApp clients facilitate interactions among participants, leveraging a smart contract to enable patients to set authorization policies, thereby ensuring that only designated healthcare providers and authorized entities have access to specific medical records. The blockchain data-immutability property is used to validate data stored externally. This solution significantly reduces the costs related to the utilization of the blockchain, while retaining its advantages, and improves performance, since the majority of data are available off-chain.

Trifluoromethoxylation of Arynes using 2,4‐Dinitro‐1‐(trifluoromethoxybenzene) as Trifluoromethoxide Anion Source

The trifluoromethoxy moiety, an emerging fluorinated group, shows interesting properties for pharmaceutical and agrochemical applications. However, only few methods have been reported for the direct introduction of this group on molecules with high efficiency, safety and low cost. Indeed, most of direct trifluoromethoxylation reagents are either expensive, hazardous or require preliminary synthesis before their use. Here we describe the use of 2,4‐dinitro‐1‐(trifluoromethoxy)benzene (DNTFB) as an easy‐to‐handle, safe, commercially available and cheap source of trifluoromethoxide anion for the trifluoromethoxylation of arynes.

Optimal scheduling of electric vehicle aggregators for frequency regulation and cost efficiency in renewable-powered grids

The growing integration of intermittent renewable energy sources (RESs), such as wind and solar, into power grids presents significant operational challenges, particularly in maintaining grid stability and managing generation forecasting errors. This study underscores the critical role of Electric Vehicles (EVs) as a flexible load in providing ancillary services, especially for secondary frequency regulation, to address these challenges. Moreover, a novel scheduling model for EV aggregators is introduced that aims to optimize frequency regulation and cost efficiency. This model strategically manages the State of Charge (SOC) to ensure EV owner satisfaction while minimizing operational costs. Employing Mixed-Integer Linear Programming (MILP), solved via CPLEX-GAMS in both deterministic and stochastic scenarios, the proposed approach evaluates various EV integration strategies, demonstrating potential reductions in charging costs and enhancing owner participation by considering parking constraints. This research highlights the importance of innovative grid management solutions in the era of renewable energy proliferation, offering significant insights into cost-effective and reliable energy practices. The results in the deterministic show that by considering the EV parking constraint, the total charging cost will be increased by about 20%. Also, by applying the proposed strategy to the down-regulation value of 18%, the total cost of EV charging is decreased to 11,400 $ which shows about a 5% decrement in comparison with the down-regulation value of 32%.

Multi-objective optimization and 4E (energy, exergy, economy, environmental impact) analysis of a triple cascade refrigeration system

The post-pandemic energy crisis and ever-increasing environmental degradation necessitate researchers to scrutinize refrigeration systems, major contributors to these issues, for minimal environmental impact and maximum performance. Thus, this study aims to comprehensively examine a triple cascade refrigeration system (TCRS) equipped with hydrocarbon refrigerants (1-butene/Heptane/m-Xylene). This system is specifically designed for ultra-low temperature applications, including vaccine storage, quick-freezing, frozen food preservation, cryogenic processes, and gas liquefaction. The investigation integrates conventional thermodynamic analysis with economic and environmental impact assessments, and finally multi-objective optimization (MOO) to ascertain optimal operating conditions for the system. The effect of (1) evaporator temperature, Tevap (2) condenser temperature, Tcond (3) Lower Temperature Circuit (LTC) condenser temperature, TLTC (4) Mid Temperature Circuit (MTC) condenser temperature, TMTC and (5) Cascade Condenser temperature difference, ΔT on three objective functions (COP, exergy efficiency, and overall plant cost) have been investigated employing a parametric analysis. Subsequently, quadratic equations for these objective functions are generated using the Box-Behnken method, and MOO utilizing the Genetic algorithm has been performed to maximize COP and exergy efficiency while minimizing the overall cost rate. The decision-making techniques TOPSIS and LINMAP are used to retrieve a unique solution from the Pareto Front, and the system performance has been assessed at the optimal point. The optimization result demonstrates that for the 10-kW capacity TCRS, COP, exergy efficiency, and total plant cost are 0.71, 0.51, and 38262.05 $/year respectively, at optimum condition (Tevap = −101.023 °C, Tcond = 36.545 °C, TLTC = - 69.047 °C and TMTC = - 34.651 °C). Exergy analysis identifies HTC compressor (19.3 %) and throttle valve (15.5 %) as key contributors to total exergy destruction, while economic analysis underscores capital and maintenance costs (72 %) as the primary contributors to the overall cost, with evaporator (43 %) and condenser (20 %) accounting for 63 % of this cost.