Realistic Estimates Research Articles

Research on financial risk early warning system model based on second-order blockchain differential equation

A financial risk early warning system (FREWS) is a disclosure and tracking mechanism that provides advance notice of potential issues, hazards, and potentials that might affect the business’s finances. Some elderly individuals living alone may experience financial difficulties, which may hinder their ability to pay for appropriate medical care, property maintenance, and other essential expenses. Financial difficulties can add tension and diminish their quality of life. Financial results, investment risk, and possible insolvencies may all be detected by implementing early warning systems. Management might use the window of opportunity provided by early warning systems to avert or lessen the impact of possible issues. Almost all FREWS rely on some financial statement analysis. Financial measures are combined with the EWS, accounting information, to determine the firm’s success in its field. Organizational success depends on effective financial oversight, which is at the heart of each business. Studying the enhancement of early warning capacities is relevant because there are no adequate risk evaluation methods to generate realistic estimates. To minimize the FREWS, this research provides a systemic model based on a second-order block chain differential equation (SBDE). China’s systemic financial liabilities have also been quantified using the expected investment returns of 64 selected financial enterprises in China between February 2006 and September 2020 as the datasets. The financial risk warning approach is compared and analyzed primarily using analytical and comparative techniques. The suggested method is 96% accurate in experiments. Consequently, the proposed algorithm compares favorably to others regarding both computing efficacy and precision and has strong predictability.

The impact of sampling scale: A comparison of methods for estimating external contaminant exposure in free-ranging wildlife

The impacts of contaminants on wildlife are dose dependent, and thus being able to track or predict exposure following contamination events is important for monitoring ecosystem health. However, the ability to track exposure in free-ranging wildlife is often severely limited. Consequently, researchers have predominantly relied on simple methods for estimating contaminant exposures in wildlife with little regard for spatial contaminant heterogeneity or an animal's use of diverse habitats. We evaluated the influence sampling scale (i.e., how finely contaminant distribution and organism's spatial use of the landscape is mapped) has on (1) realism and (2) conservativeness of exposure estimates. To do this, we monitored the actual exposure of wild boar (Sus scrofa) in Fukushima, Japan to radioactive contamination using GPS-coupled contaminant monitors placed on individual animals. We compared empirical exposures to estimates generated by combining varying amounts of information about an individual boar's location and/or movement, with the distribution of contamination on the landscape. We found that the most realistic exposure estimates were produced when finer-scale contaminant distribution surveys (e.g., airborne surveys) were combined with more accurate estimates of an individual's space use (e.g., home ranges or core areas). Importantly, estimates of exposure based on single point surveys at a trap site (a simple method commonly used in the literature), did not correlate with actual exposure rates, suggesting dose-effects studies using this method may result in spurious conclusions. These results suggest that researchers seeking realistic estimates of exposure, such as in dose-effect studies, should ensure they have adequately accounted for fine-scale contaminant distribution patterns and areas of higher use by study organisms. However, conservative estimates of exposure (i.e., intentionally over-predicting exposure as is done in initial tiers of ecological risk analyses) were not as scale sensitive and could be achieved with a single known location and coarse contaminant distribution maps.

Electro-Nuclear Dynamics of Single and Double Ionization of H2 in Ultrafast Intense Laser Pulses.

We present an efficient method for modeling the single and double ionization dynamics of the H2 molecule in ultrashort, intense laser fields. This method is based on a semianalytical approach to calculate the time-dependent single and double molecular ionization rates and on a numerical approach to describe the vibrational motion that takes place in the intermediate molecular ion H2+. This model allows for the prediction of the single and double ionization probabilities of the H2 molecule to be made over a wide range of frequencies and laser intensities with limited computational time while providing a realistic estimate of the energy of the products of the dissociative ionization and of the Coulomb explosion of the H2 molecule. The effect of vibrational dynamics on ionization yields and proton kinetic energy release spectra is demonstrated and, in the case of the latter, is discussed in terms of basic strong-field molecular fragmentation mechanisms.

Prescriptions of psychotropic and somatic medications among patients with severe mental disorders and healthy controls in a naturalistic study

Purpose Psychotropic and somatic medications are both used in treating severe mental disorders (SMDs). Realistic estimates of the prevalence of use across medication categories are needed. We obtained this in a clinical cohort of patients with SMD and healthy controls (HCs). Materials and methods Prescriptions filled at Norwegian pharmacies the year before and after admittance to the Thematically Organized Psychosis (TOP) study were examined in 1406 patients with SMD (mean age 32.5 years, 48.2% women) and 920 HC (34.1 years, 46.2% women). Using data from the Norwegian Prescription Database (NorPD), the number of users in different anatomical therapeutic chemical (ATC) categories was compared using logistic regression. Population estimates were used as reference data. Results Use of antipsychotics (N05A), antiepileptics (N03A), antidepressants (N06A), anxiolytics (N05B), hypnotics and sedatives (N05C), anticholinergics (N04A), psychostimulants, attention deficit hyperactivity disorder and nootropic agents (N06B) and drugs for addiction disorders (N07B) was significantly more prevalent in patients with SMD than HC. Use of diabetes treatment (A10), antithrombotic drugs (B01), beta blockers (C07), lipid modifiers (C10), and thyroid and endocrine therapeutics (H03) was also more prevalent in patients with SMD, but with two exceptions somatic medication use was comparable to the general population. Among HC, there was low prevalence of use for most medication categories. Conclusion Patients were using psychiatric medications, but also several types of somatic medications, more often than HC. Still, somatic medication use was mostly not higher than in the general population. The results indicate that HC had low use of most medication types.

Large sampling errors when using the Unmatched Count Technique to estimate prevalence: A simulation study

The Unmatched Count Technique (UCT) is a method for ensuring respondent anonymity and thereby providing an unbiased estimate of the prevalence of a characteristic in a population. I illustrate that under realistic conditions UCT estimates can have ten times more sampling error than estimates derived from direct questions, and that UCT estimates can take nonsensical negative values. Therefore, the UCT should be used with caution.

Validating a UK Geomagnetically Induced Current Model Using Differential Magnetometer Measurements

AbstractExtreme space weather can damage ground‐based infrastructure such as power lines, railways and gas pipelines through geomagnetically induced currents (GICs). Modeling GICs requires knowledge about the source magnetic field and the electrical conductivity structure of the Earth to calculate ground electric fields during enhanced geomagnetic activity. The electric field, in combination with detailed information about the power grid topology, enable the modeling of GICs in high‐voltage (HV) power lines. Directly monitoring GICs in substations is possible with a Hall probe, but scarcely realized in the UK. Therefore we deployed the differential magnetometer method (DMM) to measure GICs at 12 sites in the UK power grid. The DMM includes the installation of two fluxgate magnetometers, one directly under a power line affected by GICs, and one as a remote site. The difference in recordings of the magnetic field at each instrument yields an estimate of the GICs in the respective power line segment via the Biot‐Savart law. We collected data across the UK in 2018–2022, monitoring HV line segments where previous research indicated high GIC risk. We recorded magnetometer data during several smaller storms that allow detailed analysis of our GIC model. For the ground electric field computations we used recent magnetotelluric (MT) measurements recorded close to the DMM sites. Our results show that there is strong agreement in both amplitude and signal shape between measured and modeled line and substation GICs when using our HV model and the realistic electric field estimates derived from MT data.

A modified stochastic finite-fault method for estimating strong ground motion: Validation and application

We developed a modified stochastic finite-fault method for estimating strong ground motions. An adjustment to the dynamic corner frequency was introduced, which accounted for the effect of the location of the subfault relative to the hypocenter and rupture propagation direction, to account for the influence of the rupture propagation direction on the subfault dynamic corner frequency. By comparing the peak ground acceleration (PGA), pseudo-absolute response spectra acceleration (PSA, damping ratio of 5%), and duration, the results of the modified and existing methods were compared, demonstrating that our proposed adjustment to the dynamic corner frequency can accurately reflect the rupture directivity effect. We applied our modified method to simulate near-field strong motions within 150 km of the 2008 MW7.9 Wenchuan earthquake rupture. Our modified method performed well over a broad period range, particularly at 0.04–4 s. The total deviations of the stochastic finite-fault method (EXSIM) and the modified EXSIM were 0.1676 and 0.1494, respectively. The modified method can effectively account for the influence of the rupture propagation direction and provide more realistic ground motion estimations for earthquake disaster mitigation.

Aggravation of global maize yield loss risk under various hot and dry scenarios using multiple types of prediction approaches

AbstractHigh temperature and drought are widely known to cause a reduction of crop yield, but the simultaneously occurring risks in major producing countries and the associated uncertainty across various climate change scenarios remain unclear at the global scale. Here, we evaluate global maize yield loss risk (i.e., the probability of yield reduction by over 10% relative to historical trend yield during 1981–2010) across 30 hot and dry scenarios using regression, machine learning and process‐based models. Besides examining yield loss risk in a single country, we predict the potential risks simultaneously occurring in the top two and top ten producing countries. The three approaches agree on the aggravation of yield loss risk under dry and hot scenarios, but show large discrepancy in the magnitude and sensitivities. Specifically, 2°C warming alone could lead to a global yield loss risk of 73%, 100% and 62% based on regression, long‐short term memory (LSTM) and process‐based models, respectively, and warming‐induced risks can be further aggravated by droughts especially in process models. Global yield loss by over 10% would even become the new norm (i.e., yield loss probability is 100%) when temperature increases by over 2°C in some models. Importantly, the probabilities of yield loss simultaneously occurring in the top two countries (i.e., United States and China) and top ten countries are unexpectedly high and could even become 100% under extreme hot and dry scenarios. Our results highlight the large risks that future climate change may bring to multiple exporting and importing countries simultaneously, thus threating global food market and security. We also emphasize the important value of using different types of prediction approaches for yield projection under hot and dry scenarios, which enables more realistic estimation of uncertainty range than a single type of model.

Availability and mobilization of forest resources in Sweden

The available amount of wood supply is essential for national strategic planning and evaluation of forestry in Sweden. Since Sweden holds a large part of the forests in the European Union and plays a significant role in the global trade of wood-based products, a precise estimate of the potential of the Swedish forest resource is also important in regional and global outlook studies. In this study, we analyse factors influencing the availability and mobilization of wood supply. By comparing data from the Swedish National Forest Inventory with the stand registers of the five largest forest owners in Sweden, we estimate the productive forest area not included in the forest owners' stand databases. Our results show that 0.4 million hectares, or 5% of these large-scale forest owners productive forest area, is outside their stand registers and therefore neither included in their long-term harvesting plans nor in their nature conservation plans. For small-scale forest owners, we analyse the final felling rate during 2004–2020 using satellite imagery to estimate the proportion of properties that abstain from final fellings and thereby could affect the potential mobilization of wood supply. During this period, 32% of the forest properties owned by small-scale forest owners have not done any final felling. These forest estates hold in total 1.1 million hectares of productive forest land or 9% of the area owned by small-scale forest owners. This implies a gap between the potential and realistic estimates for Forest Available for Wood Supply.

Realistic long-term stress levels in a deep segmented tunnel lining, from hereditary mechanics-informed evaluation of strain measurements

Realistic estimation of stresses in segmented tunnel linings is a challenge tackled herein by means of a hybrid, i.e. computational-experimental, approach. Thermistor-equipped vibrating wire strain gauges were installed (i) into concrete samples undergoing one year-long uniaxial creep tests under the environmental conditions of the tunnel site, and (ii) into the tubbings making up the tunnel lining of the Koralm tunnel. The data obtained from the creep tests allow for calibration and validation of an integro-differential thermo-viscoelastic model. The creep function combines a power-law for short-term creep with a logarithmic law for long-term creep. The corresponding relaxation function is determined by means of Laplace-Carson transformation, inversion, and back-transformation. This is the basis for translating the circumferential strain histories measured in the tubbings of Ring 2013 in Koralm tunnel KAT3 into circumferential and longitudinal stress evolutions. They are mainly due to mechanical ground-shell interactions. The corresponding degree of utilization increases during the first four months after ring installation, and remains virtually constant thereafter. Stress fluctuations due to seasonal temperature variations play only a minor role. With regards to a long-term prognosis, it is very interesting to note that the strain measurements recorded in the tubbings, when plotted as function of the logarithm of time, follow bi-linear trends. These trends can be extrapolated to 150 years, the targeted service life of newly built tunnels in Austria. Throughout this period, the viscoelasticity-based estimates of the stresses in the vicinity of the strain sensors stay temporally constant, at some 40% of the strength of concrete.

Meaning and prediction of 'excess mortality': a comparison of Covid-19 and pre-Covid-19 mortality data in 31 Eurostat countries from 1965 to 2021.

Determining 'excess mortality' makes it possible to compare the burden of disasters between countries and over time, and thus also to evaluate the success of mitigation measures. However, the debate on coronavirus disease 2019 (Covid-19) has exposed that calculations of excess mortalities vary considerably depending on the method and its specification. Moreover, it is often unclear what exactly is meant by 'excess mortality'. We define excess mortality as the excess over the number of deaths that would have been expected counter-factually, that is without the catastrophic event in question. Based on this definition, we use a very parsimonious calculation method, namely the linear extrapolation of death figures from previous years to determine the excess mortality during the Covid-19 pandemic. But unlike most other literature on this topic, we first evaluated and optimized the specification of our method using a larger historical data set in order to identify and minimize estimation errors and biases. The result shows that excess mortality rates in the literature are often inflated. Moreover, they would have exhibited considerable excess mortalities in the period before Covid-19, if this value had already been of public interest at that time. Three conclusions can be drawn from this study and its findings: (i) All calculation methods for current figures should first be evaluated against past figures. (ii) To avoid alarm fatigue, thresholds should be introduced which would differentiate between 'usual fluctuations' and 'remarkable excess'. (iii) Statistical offices could provide more realistic estimates.

Sediment–vegetation interactions determine the fate of fluvial sediment in the upper reaches of a large estuary

AbstractFeedbacks between sediment and plants in the submersed aquatic vegetation (SAV) beds of the Susquehanna Flats help modulate sediment delivery into the upper Chesapeake Bay from its major tributary (Susquehanna River). Recent modeling work has shown that SAV can steer the river plume, directly controlling sediment transport and fate. However, transport mechanisms likely differ between flood and non‐flood conditions and depend on event timing. An opportunity to evaluate these insights in the field occurred with several flood events in summer 2018. Sediment and biomass samples were collected after the flood in the large, continuous main SAV bed of the Flats and in smaller patches to the west of the main bed to evaluate the effect of SAV bed size (width). Sediment characteristics (grain size, organic content) and deposition rates were compared to previous observations during non‐flood conditions in the main bed in 2014–2015 and in the smaller patches in 2019. Overall, sediment deposition rates could be predicted by an empirical model including sediment load, vegetation biomass, and SAV bed width, reflecting the role of vegetation in steering the Susquehanna River plume and modulating sediment input to the upper Chesapeake Bay. Understanding spatiotemporal patterns of sedimentation is essential for realistic estimates of sediment and nutrient (especially nitrogen and carbon) storage in freshwater SAV systems, which have not received as much attention as their saltwater counterparts.

Quasi-2D thermal network based heat soakage model for gas turbine transient performance modification

To overcome the limitations of traditional heat soakage analysis, a novel quasi-2D thermal network based heat soakage model is established with the consideration of component internal thermal resistance, combustor temperature profile, cooling technologies and thermal barrier coating. The method has been integrated into Turbomatch, a gas turbine performance software developed by Cranfield University, to offer a more realistic estimation of the heat soakage effect during the gas turbine transient manoeuvre. The accuracy of the heat soakage model has been validated against results obtained from GasTurb. Furthermore, a parametric analysis has been conducted to evaluate the effects of combustor temperature profile, cooling technologies, and thermal barrier coating. It is found that the combustor temperature profile will cause a significant increase in heat flow rate, which is approximately four times the conventional lumped parameter method based model. The implementation of cooling technologies and thermal barrier coating will reduce the heat flow rate by 48.97%. More importantly, the change in heat flow rate causes a further delay in engine transient response. Corrected rotational speed was decreased by 0.89%, and thrust was reduced by 2.24%. Moreover, a delay of 8s (233% increase) in the transient acceleration time was found when comparing the quasi-2D thermal network based heat soakage model with the traditional lumped parameter method based model. These highlight the significance of accurately evaluating the heat soakage effects during gas turbine transient simulation.

A refined ICD-10 diagnoses-based approach for retrospective analysis of potential palliative care need and coverage in claims data of deceased.

ICD-10-based approaches often provide the basis for retrospective estimation of potential palliative care need. Applying the ICD-10-based Murtagh et al. classification from 2014 (Murtagh classification), developed using mortality data, to administrative claims data leads to inconsistencies in estimating palliative care need. The aim of the study was to refine the classification for palliative care need estimation in deceased individuals with cancer and non-cancer diagnosis. A retrospective population-based study comparing Murtagh classification to a new ICD-10-based classification (revised by expert opinion) was conducted using outpatient and inpatient claims data, including billing codes for palliative care. Palliative care need was estimated for diagnoses groups and was contrasted with palliative care utilization rates in the last year of life. Our dataset included records of 417,405 individuals who deceased in 2016-2019. Out of individuals deceased in 2019 (n = 117,436), 81.4% had at least one diagnosis from the new classification, while 97.0% had at least one diagnosis from the Murtagh classification. Classification revision thus identified fewer individuals as potentially in need of palliative care. Among individuals with cancer, 70.7% (vs. 55.7% via Murtagh classification) received palliative care. In non-cancer subgroups, the utilization rate was considerably lower, with a maximum of 36.7% (vs. 33.7% via Murtagh classification) in 2019. Similar results were observed for the other years. Compared to the ICD10-based Murtagh classification, the revised ICD-10-based classification enables more realistic estimations if the cause of death is unavailable and reveals higher rates of palliative care coverage and differences especially in cancer versus non-cancer diseases. German Clinical Trials Register (DRKS00024133).

A pragmatic approach for the fatigue life estimation of hybrid joints

The need for more environmentally sustainable ways of transportation and for a reduction in emissions and fuel consumption make lightweight structures essential. Together with the use of lightweight materials and design optimisation, the use of hybrid joints represents one way to reduce the weight of components and it is becoming increasingly popular in the transportation industry. The name ‘hybrid joint’ refers to a connection where adhesive bonding is used in conjunction with traditional joining techniques, such as spot welds and rivets with the aim of combining and exploiting the advantages of the individual joining techniques. To optimise the design of hybrid joints and minimise the risk of in-service fatigue failures, the transportation industry needs efficient, robust, and easy-to-use approaches for the modelling and fatigue life estimation of hybrid joints.This work presents two practical methodologies for estimating the fatigue life of hybrid joints that can be easily adopted by companies in the transportation industry. The first methodology neglects the life given by the mechanical joints after failure of the adhesive (the joint is considered failed when the adhesive fails), while the second methodology considers the life of both the adhesive and the mechanical joints. In the first methodology, just one configuration would need to be analysed (‘hybrid’ joint or ‘purely bonded’ joint, if this simplification is considered reasonable). In the second methodology, instead, the analysis of two configurations would be required (the previous configuration followed by a configuration where only the mechanical fasteners are considered). The second methodology would produce more realistic fatigue life estimations compared to the first methodology, but it would be more onerous both in terms of modelling and computationally. For both methodologies, FE modelling guidelines to recover the required stresses are suggested. These guidelines require limited changes to the typical FE modelling strategies currently used, especially in the automotive industry. Furthermore, the proposed modelling guidelines provide FE models that are not computationally too onerous, reasonably mesh insensitive and that do not require congruent meshes. The relevant stresses recovered from the FE model are then used as an input into nCode DesignLife to estimate the fatigue life of the hybrid joints in the analysed structure. The fatigue life estimation is carried out using standard Stress-Life (SN) based nCode DesignLife analysis engines and bespoke SN curves obtained through testing of hybrid joint specimens, representative of the joints in the production parts.

Efficient data collection for camera trap‐based density estimation: A preliminary assessment

Abstract Camera traps have great potential for generating wildlife insights by providing high resolution site‐specific data. Methods of data collection and analysis reliant on these tools for population density estimation can be relatively resource intensive, hindering their mainstream adoption. Here, we explore the potential of population density estimates derived from a distance sampling method based on optics theory, which greatly simplifies the process of setting up camera sites and analysing data. Specifically, we (1) tested the method on human subjects in an artificial environment, (2) compared it to another method relying on virtual grids on images using wild populations of black‐backed jackal (Canis mesomelas) and African civet (Civettictis civetta) in South Africa and (3) deployed it to estimate wild boar (Sus scrofa) population densities in Hungary. The initial human trials resulted in an estimate that was extremely close to true population density. When compared to the virtual grid method, results suggest that our distance sampling method can deliver accurate estimates with increased convenience and robustness against disruptions of the camera sites. The wild boar study resulted in a realistic density estimate, which can be used as a baseline when assessing future fluctuations in population density. As this new approach does not have special requirements for setting up camera sites, it is efficient and widely applicable across other density estimation methods requiring an estimate for effective detection distance. Additionally, the method can be applied in the retrospective analysis of existing datasets.

Realistic Estimation of COVID-19 Infection by Seroprevalence Surveillance of SARS-CoV-2 Antibodies: An Experience From Korea Metropolitan Area From January to May 2022.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally, leading to the coronavirus disease 2019 (COVID-19) pandemic. Because a significant proportion of the COVID-19 confirmed cases were concentrated in the capital metropolitan area of South Korea, and a large proportion of the population in the area had been adequately vaccinated against COVID-19, we conducted a seroprevalence surveillance study focusing on the residents of the capital metropolitan area in South Korea. We used a quota-sampling method to obtain blood samples from 1,000 individuals per round, equally stratified across seven age categories and sexes and regions, from five medical institutions located within the capital metropolitan area of South Korea. During five consecutive months (rounds) between January 2022 and May 2022, a total of 5,000 samples were analyzed for anti-spike (S) and anti-nucleocapsid (N) antibodies. High anti-S seropositivity was observed in all age groups, which corresponded to the vaccine coverage during the study period. Both the cumulative incidence based on polymerase chain reaction (PCR) and the estimated seroprevalence based on anti-N seropositivity increased in the fourth and fifth rounds, which corresponded to April 2022 and May 2022. Seroprevalence coincided with the cumulative incidence during the first three rounds, but exceeded from the fourth survey onwards when infection with omicron variants was increased rapidly in Korea. Seroprevalence confirmed the number of infection cases outside of PCR testing-based surveillance. Seroepidemiological surveillance can help us understand vaccine responses and detect hidden infections, thereby providing appropriate public health guidance for achieving population-level immunity.

Domain of Stability Characterization for Power Systems: A Novel Individual Invariance Method

In this paper, we approach the problem of stability in nonlinear systems through a new perspective that views them as a combination of individual artificial systems carefully chosen to simplify the complex structure of nonlinear systems. This is achieved by recasting nonlinear vector fields as an algebraic sum of individual vector fields for which artificial systems with known invariant sets or at least in forms that allow for tractable approximation of their invariant sets. This attempt to restructure nonlinear systems stands out in comparison to other previous attempts like Lure' systems or network based models as a purely mathematical structuring technique that transcends the physical constraints and dependencies within dynamical models and allows the user to creatively construct artificial systems with the sole focus on the overall stability. The theoretical foundation is provided for a theorem about individual invariance to relate the invariant sets of individual artificial systems to the invariant set of their original system in a way that significantly simplifies the task of approximating regions of attraction. Several examples are used to demonstrate this theorem and we also evaluate the use this theorem for the challenging power system stability problems in both AC and DC grids. The proposed method is successfully applied to the IEEE 39- bus New England system, and a DC converter with constant power load giving accurate and realistic estimations of the critical clearing time and stability regions in comparison to state of the art approaches.

Unapređenje procene odnosa troškova i efektivnosti lekova za retke bolesti

Background/Aim. Incremental cost/effectiveness ratio (ICER) of many drugs for rare diseases is often much higher that the accepted cost/effectiveness threshold for reimbursement, primarily due to their extremely high prices, raising the question of their availability. The aim of this article was to review necessary adjustments of methods used for cost/effectiveness analysis of drugs for rare diseases. Methods. This article is a narrative review of methods for adjusting cost/effectiveness analysis of drugs for rare diseases in order to get more realistic estimate of ICER threshold, which is essential information for decision-makers. Results. Inputs in cost/effectiveness analysis of a drug for rare diseases should be adjusted by changing discount rates, estimating utilities in a more precise way, excluding treatment-unrelated costs, calculating local C/E threshold, and most importantly, by negotiating drug price until the C/E threshold is not surpassed. With intensified adjusted cost/effectiveness research within the area, many uncertainties will be ended, and real-life value of many of the drugs for rare diseases will be known, influencing pricing in a sustainable direction. Conclusion. With the adjustments, the true cost/effectiveness of a drug for rare disease will be approached, enabling evidence-based and completely transparent reimbursement decisions.

Exploring the Role of Quantity Surveyors in Mechanical and Electrical Services Cost Estimation – Challenges, and Client Implication

Mechanical and Electrical (M&E) services costs constitute a substantial proportion of the total cost of a building. Realistic cost estimates for this element are a desirable option by industry stakeholders because the total building cost can potentially be reduced if the cost of M&E services can effectively be managed throughout the life of the project. In the building construction industry, quantity surveyors are engaged to prepare cost estimates on all aspects of the building and provide cost advice services at every stage of the building construction projects. However, M&E services are one aspect of a building that quantity surveyors find very difficult to prepare cost estimates for. This study explored the role of quantity surveyors and, the challenges involved in the delivery of M&E services cost estimation.