Large Cost Research Articles

Acquired angle error correction based on variation of an angle detection signal intensity in rotary encoders

Abstract Rotary encoders are used in various machines to measure the angular position of a rotating axis. Therefore, rotary encoders are required not only to be highly accurate, but also usable in various environments. When using a rotary encoder on a machine, angle errors occur due to eccentricity between the encoder and the machine, the deformation of the scale, and the shaft runout of a rotation axis. Various methods have been adopted to reduce such angle errors. However, these methods have problems such as the large size and high cost of the rotary encoder, the necessity of additional devices for measuring, and increasing installation labor, so the environment in which they can be implemented is limited. In this study, we have proposed a method to correct the angle errors based on variation of an angle detection signal intensity in the sensor head. This method can correct the errors on machine without using multiple sensor heads or separate devices. The angle error estimated by this method is compared with that measured by the external equipment of a reference encoder. While the maximum angle error was 28.36” without correction, the residual error with correction can be reduced to 1.75” or less. From this result, the proposed method can improve the measurement accuracy of the rotary encoder already installed on the machine. Therefore, this method is an effective means for achieving both high rotational accuracy on machines and ease of correcting an error in rotary encoders to the machines.

Water and Waste: A History Reluctant Policymaking in U.S. Cities

U.S. cities experienced a sanitary revolution during the nineteenth and early twentieth centuries. The abandonment of cesspools, privy vaults for waste containment and cisterns, pumps, and shallow wells for water delivery brought about a large increase in living standards. While the gains are impressive, investments were often made reluctantly. Scientific advances clarifying the origins of waterborne illness improved, and filtration plants and universal access to water and safe sewerage offered a solution. Yet few cities made those investments. Only with chlorination, a low-cost purification solution, did rapid adoption occur. These delays came at a large human cost.

The role of legal system in SME bank credit in ASEAN

This study aims to investigate the role of legal system in the access to bank credit of small and medium enterprises (SMEs) and explores the measures to improve legal system among the Association of South-East Asian Nations (ASEAN). By applying the ordinary least squares (OLS) method, the study examines the bank credit accessibility of over 12,000 firms across eight ASEAN countries, using the data collected from the World Bank’s Business Environment and Enterprise Performance Survey (BEEPS), in relation to each country's contract enforcement indicators. The estimated results reveal a significant positive relationship between contract enforcement and SMEs’ access to bank credit. Furthermore, the results show that long time consumption and large costs for contract enforcement reduces financing opportunities of SMEs. Additionally, the study finds that a country’s legal tradition does not impact the effectiveness of its contract enforcement. Implementing electronic filing systems and limiting times and costs for legal processes can enhance contract enforcement. Consequently, strong contract enforcement appears to encourage banks to increase SME lending, leading to higher SME bank credit in countries with efficient contract enforcement mechanisms. Enhancing contract enforcement can increase SME financing and consequently, will improve a country’s financial intermediation. These findings underscore the importance of contract enforcement including its duration and cost, in the context of SME financing and suggest ways to improve a country’s legal system to foster SME access to bank credit.

MXene-based semi-circle with a thin wire-shaped resonator wideband polarization-insensitive solar absorber

Fossil fuels’ supply peaks, decreases, and shortages are determined by their proven reserves, research, and consumption rates. With a large upfront cost, renewable and alternative energy sources are essential to solving the twin issues of energy and climate change. Solar absorbers are an excellent way to use renewable energy from the environment. This paper suggested an MXene-based semi-circle with a thin wire-shaped resonator (MSCWTWSR) solar absorber where the resonator layer consists of MXene material and Fe is used as substrate layer and the resonator has semi-circle and thin wire geometry which effectively absorbs the sun radiation with wideband. This proposed MSCWTWSR solar absorber works at 200–3000 (nm) wavelength and has more than 93% average absorption. The first band bandwidth of this MSCWTWSR solar absorber is 400 (nm), the second band is 530 (nm), and the third band is 470 (nm). This structure got more than 93% absorption in the AM 1.5 solar irradiation configuration. The structure gives in the Transverse electric (TE) field and Transverse magnetic (TM) field and the structure has polarization for insensitive. Furthermore, there is also investigated different incidence angles. A suggested article includes sections on testing for electric and magnetic intensities with a comparison table. The suggested solar absorber is employed in a distinct thermal heating application since MXene has a low thermal resistance and good thermal stability.

Sea–Land Segmentation of Remote-Sensing Images with Prompt Mask-Attention

Remote-sensing technology has gradually become one of the most important ways to extract sea–land boundaries due to its large scale, high efficiency, and low cost. However, sea–land segmentation (SLS) is still a challenging problem because of data diversity and inconsistency, “different objects with the same spectrum” or “the same object with different spectra”, and noise and interference problems, etc. In this paper, a new sea–land segmentation method (PMFormer) for remote-sensing images is proposed. The contributions are mainly two points. First, based on Mask2Former architecture, we introduce the prompt mask by normalized difference water index (NDWI) of the target image and prompt encoder architecture. The prompt mask provides more reasonable constraints for attention so that the segmentation errors are alleviated in small region boundaries and small branches, which are caused by insufficiency of prior information by large data diversity or inconsistency. Second, for the large intra-class difference problem in the foreground–background segmentation in sea–land scenes, we use deep clustering to simplify the query vectors and make them more suitable for binary segmentation. Then, traditional NDWI and eight other deep-learning methods are thoroughly compared with the proposed PMFormer on three open sea–land datasets. The efficiency of the proposed method is confirmed, after the quantitative analysis, qualitative analysis, time consumption, error distribution, etc. are presented by detailed contrast experiments.

Interval riccati equation-based and non-probabilistic dynamic reliability-constrained multi-objective optimal vibration control with multi-source uncertainties

Structural vibration control is used to ensure the safety of structures in service and has become an active research area in the past few years. In this study, uncertainties in structural dynamics are considered to propose an interval linear quadratic regulator (LQR) method based on an interval Riccati equation. Multi-objective optimization with non-probabilistic dynamic reliability as a constraint is performed to design an optimal uncertain vibration control system. To alleviate the difficulty of quantifying the probabilistic uncertainty and accurately characterize uncertainty information for small samples, multi-source uncertainties are regarded as interval parameters. The bounds on these uncertainties are determined, and the deterministic and uncertain parts of an interval state-space equation for vibration control are then formulated using an expanded-order matrix and the interval analysis method. Next, the interval perturbation method with a matrix series is used to develop a novel solution method for the linear inhomogeneous time-invariant equation in the interval state-space equation for the vibration control system and is used to accurately predict the interval bounds of the state responses. The conventional LQR method from modern control theory is used to propose a novel optimal control method based on a novel interval Riccati equation using the Lyapunov equation and interval uncertainty propagation. The proposed interval control method can be used to estimate the uncertain bounds of the controlled gain and cost function. A non-probabilistic dynamic reliability model is established to evaluate the reliability index of the system, which can overcome the limitations of the large computational cost of determining the probabilistic reliability. The deterministic controlled cost and uncertain states are considered as two optimization objectives with the proposed reliability constraint. The resulting constrained multi-objective optimal vibration control problem is designed and solved using c-DPEA. A flowchart is presented as a clear overview of the uncertain vibration control method, and the effectiveness of the proposed method is validated using two numerical examples. Different optimal control designs are obtained using different reliability constraints for quantitatively analyzing the safety of the control system.

Thermoelectric generators versus photovoltaic solar panels: Power and cost analysis

In the current study, the concept of building a power plant using thermoelectric generator (TEG) modules is investigated, both technically and economically. The hypothesized thermoelectric generation power plant is a modular system, consisting of a large array of electrically connected thermoelectric generator units for generating clean electricity with-out greenhouse gas (GHG) emissions, noise, or hazardous solid wastes. The envisioned thermoelectric generation power plant (TEGPP) considered here is assumed to utilize solar radiation as a heat source, and water as a heat sink. The viability of such a concept is examined in the current study based on available specifications of a high-output thermo-electric generator module released in the market (TEG1-24111-6.0). Benchmarking is car-ried out considering a high-efficiency photovoltaic (PV) panel in the market (SunPower SPR-MAX3-400), assuming that it operates under standard solar radiation of 1,000 W, and with a standard panel temperature of 25 C, causing it to give an output electric power of 400 W (DC or direct current). It is found that in order to have an electric power of a thermoelectric generator unit similar to that of a photovoltaic panel of equal surface area, the temperature at the hot side of the thermoelectric generator unit should be about 70 C if the cold-side temperature is 30 C. However; under this output power equiva-lence, the price of the thermoelectric generator unit is about 90 times that of a photovol-taic panel of equal size (based on prices of October 2023). At an elevated hot-side tem-perature of 300 C for the thermoelectric generator unit (with the cold-side temperature being still 30 C), the thermoelectric generator unit can generate electric power that is about 25 times the power generated by a photovoltaic panel of an equal geometric area. This big boost in the output power still does not counteract the large cost difference be-tween the thermoelectric generator technology and the photovoltaic technology, where the per-watt(electric) cost in the case of thermoelectric generators is 3.5 times its value in the case of photovoltaic panels. Thus, the TEG technology in its intensified generation mode is still relatively more expensive compared to the PV technology. The practical im-plications of the current study are excluding the thermoelectric generation (based on the Seebeck effect) concept from large-scale commercial power plants, and viewing thermoe-lectric generators as sources of small electric power for waste heat management or con-venient power sources for small mobile devices.

Compact convolutional transformers- generative adversarial network for compound fault diagnosis of industrial robot

The safe operation of Industrial robots is a major concern in intelligent manufacturing. Accurate compound fault diagnosis is essential to the safe operation of industrial robots, while it is challenging to achieve since the compound fault samples are hard to be collected. Generative adversarial network (GAN) is a useful tool for addressing the data imbalance issue. However, the computation efficiency of GAN in addressing the data imbalance issue has not been investigated. Hence, this study proposes a lightweight GAN named compact convolutional Transformers-GAN (CCT-GAN) to alleviate the data imbalance issue in compound fault diagnosis modelling. Firstly, the feedback current signals collected from the industrial robot are transformed into time-frequency images via continuous wavelet transformation (CWT). Secondly, CCT-GAN is designed to achieve high-quality fake data generation and compound fault diagnosis modelling without large computational costs. Thirdly, the relation between a single fault and the compound fault is considered in the compound fault diagnosis modelling via multi-hot representation to alleviate the data imbalance issue. An experimental study based on the real-world compound fault dataset of industrial robots reveals that the proposed CCT-GAN shows merits in compound fault diagnosis modelling in comparison with the prevailing algorithms. The results indicate that CCT-GAN can performance of compound fault diagnosis when only 100 data samples from each compound fault category are available.

Cooperative effect of hybrid polyethylene-basalt fibers on crack width control and mechanical properties in ECC

Engineered cementitious composite (ECC) reinforced with polyethylene (PE) fiber has large crack width and high production cost, and these two shortcomings affect its application in practical projects. In this study, basalt fiber (BF) was hybrid with PE fiber to achieve cooperative effect, developing a PE/BF-hybrid fiber ECC (PE/BF-HyECC) with tighter crack width and more economic advantages. The impacts of different fiber content on the pore structure, mechanical, and cost performance of PE/BF-HyECC were investigated. The PE/BF-HyECC with positive cooperative effect of 1.5 vol.% PE and 1.0 vol.% BF had higher strength and tighter crack width compared to PE-ECC with 2.0 vol.% PE. Its cost performance ratio was 1.19 to 1.37 times that of PE-ECC with 2.0 vol.% PE. The better performance and higher cost performance of PE/BF-HyECC is expected to expand its practical application.

Hierarchically structured hollow PVDF nanofibers for flexible piezoelectric sensor

The emergence of flexible sensors addresses the limitations of traditional detection equipment, such as large size, high cost, and inconvenient portability. These sensors have significantly advanced the development of wearable electronic devices for applications in electronic skin, energy harvesting, and energy storage. To enhance the sensitivity of flexible sensors, we developed a hierarchical polyvinylidene fluoride (PVDF) nanofiber-based piezoelectric sensor. By utilizing coaxial electrospinning, we fabricated nanofibers with hollow interiors and porous outer walls. Additionally, microstructures were constructed on the fiber membrane surface by modifying the collector’s shape. The resulting microstructured hollow PVDF nanofiber membrane exhibited high β-phase content (91.31 %), excellent flexibility (maximum strain 56.9 %), and high porosity (88.94 %). The sensor demonstrated a sensitivity of 2.7 V/N (1.08 V/kPa) and a maximum output voltage of 10.1 V, approximately three times higher than that of solid PVDF nanofibers without microstructures. Even after 14,400 cycles of impact, the sensor maintained stable output. The sensor was successfully applied to human activity monitoring and gesture recognition, showcasing its ability to monitor various human activities, respond rapidly (60.4 ms), and synchronously control a mechanical palm to perform different gestures. This work highlights the potential of hierarchically structured hollow PVDF nanofibers in advancing the performance and application scope of flexible piezoelectric sensors.

A multi-dimensional anti-counterfeiting nanocomposite based on fluorescent CDs and Eu-MOFs with dual function for continuous detection of Cu2+ and Bacillus anthracis

Traditional luminescent materials for anti-counterfeiting usually adopt encryption of low-level systems with limited security, which seriously hinders their application in preventing counterfeiting and information leakage. Therefore, it is urgent to develop materials for higher-level anti-counterfeiting. In this work, a stimulus-responsive intelligent luminescent material (AC@CDs-Eu-MOFs) was prepared by co-loading green-fluorescence CDs and red-fluorescence Eu-MOFs on Amino clay (AC). 5D security barcodes, which were easy to observe while difficult to clone, were ulteriorly designed based on the nanocomposite owing to the tunable fluorescence by optical stimulation and chemical stimulus. Owing to the large capacity, low cost, and easy authentication, the 5D security barcodes possess enormous potential in optical data storage and multi-dimensional information encryption. In addition, the chemical stimulus-response enables the nanocomposite achievable in detection of Cu2+ and Bacillus anthracis. Particularly, quantitative determination of copper in environmental water samples could be realized with a detection limit as low as 10.67 nM. Therefore, the material shows potential for detection of environmental pollutants besides advanced anti-counterfeiting.

Metasurface Enabled Multi‐Target and Multi‐Wavelength Diffraction Neural Networks

AbstractBenefiting from low power consumption and high processing speed, there is a growing interest in diffraction neural networks (DNNs), which are typically showcased with 3D printing devices, leading to large volumes, high costs, and low levels of integration. Metasurfaces can desirably manipulate wavefronts of electromagnetic waves, providing a compact platform for mimicking DNNs with novel functions. Although multi‐wavelength and multi‐target recognition provides a richer and more detailed understanding of complex environments, existing architectures are primarily trained to classify a single target at a specific wavelength. A metasurface approach is proposed to design multiplexed DNNs that can classify multiple targets and spatial sequences across various wavelengths in multiple channels. To realize multi‐task processing, the dielectric metasurface is designed based on phase and wavelength multiplexing, which can integrate multi‐target DNNs with different tasks such as operating at distinct wavelengths and classifying diverse targets. The efficacy of this method is exemplified through the numerical simulation and experimental demonstration of recognizing a single target with two wavelengths, two targets at a single wavelength, and two targets at dual wavelengths. This compact metasurface approach enables the design of multi‐target and multi‐wavelength DNNs, opening a new window to develop massively parallel processing and versatile artificial intelligence systems.

The Role of Digital Health in Managing Chronic Pain

Chronic pain, which affects almost a billion people worldwide, is a major health concern that places a large cost strain on healthcare systems. Traditional pain management strategies frequently fall short, demanding novel alternatives. Telemedicine, mobile health applications, and remote monitoring tools are all examples of digital health technologies that show promise for chronic pain management. This study examines the present issues in chronic pain treatment, the importance of digital health treatments, and the efficacy of evidence-based methods including cognitive behavioural therapy (CBT) and machine learning for personalised care. Patients suffering from chronic pain now have new hope thanks to digital health solutions that allow for patient self-management, real-time monitoring, and enhanced access to care. Through evaluating clinical trials and research findings, this review sheds light on the future potential and opportunities for incorporating digital health technologies into chronic pain care. Keywords: Chronic Pain, Digital Health, Telemedicine, Cognitive Behavioral Therapy (CBT), Remote Monitoring.

Dynamic group fuzzy extractor

The group fuzzy extractor allows group users to extract and reproduce group cryptographic keys from their individual non-uniform random sources. It can be easily used in group-oriented cryptographic applications. However, current group fuzzy extractors are not dynamic, i.e. they spend a large cost when dealing with user revocation. In this work, we propose the formal definition and construction of dynamic group fuzzy extractor (DGFE) to address this issue. For the revocation, DGFE allows unrevoked group users to reproduce updated group keys from the existing group help data. Meanwhile, it prevents any revoked group user from generating new group keys using the previously authorized individual help data. We propose a DGFE construction based on the revocable group signature. Furthermore, we give formal proofs of reusability, anonymity and traceability of our construction.

P046 FIRST-LINE TREATMENT OF HYPERTENSION WITH SINGLE PILL COMBINATIONS: A SIMPLE STRATEGY TO SAVE COSTS FOR THE PATIENT AND GOVERNMENT

Background: Single pill combinations (SPCs) of blood pressure (BP) lowering medicines contribute to improved BP control by reducing treatment inertia and improving medication adherence. But high out-of-pocket payments reduce adherence. We compared the costs for SPCs and free-drug combinations for the patient and government in Australia. Methods: We reviewed the “Pharmaceutical Benefits Scheme item drug map” to identify all BP lowering SPCs, their free-drug combinations, and their costs in March 2024. We reviewed costs before and after reaching the “Safety Net” – a threshold where people access medicines at lower rates, differing for general patients and Concession Card holders (typically pensioners or low paid workers). We also calculated the total cost per mmHg systolic BP (SBP) reduction based on a mean reduction of 9, 16 and 23 mmHg for treatment with one, two or three drugs at standard dose. The main outcome measures were the total costs for patients and the government in Australian Dollars. Results: For patients, SPCs always cost less than free drugs with the greatest savings for general patients before reaching the safety net, averaging 30%. For the government SPCs cost less than free drugs for Concession Card holders both before (11%) and after reaching the safety net (26%) and in general patients after the safety net (11%). There was a slight increase in costs (16%) for the government for patients before reaching the safety net. The mean cost per mmHg SBP reduction was 27% lower in SPCs compared to free drugs. Dual SPCs cost little more than monotherapy but provide a much lower cost per mmHg SBP reduction. Conclusion: In Australia, SPCs, instead of free-drug combinations, result in cost savings, for both patient and government. These are the direct costs, and do not factor in the expected benefits from improved adherence and BP control. The large cost savings of SPCs should be considered when prescribing multiple separate pills or starting treatment.

The role of supplementation in traumatic brain injury among athletes and non-athletes population

Introduction: Traumatic brain injury (TBI) is one of the most common causes of death and long-term disability. The consequences following TBI can be persistent and cause life-long implications for survivors. Treatment of TBI may be complex and involve large monetary costs. The frequent injury mechanisms are traffic-related, falls, violence or sport-related accidents. Higher risk of brain injury and its complications indicate that people affected by brain trauma may benefit from the prophylactic and post-traumatic use of certain supplements both in the context of physical performance and in the event of potential injury. Aim of the Study: This narrative review examines the current literature of the role of supplementation before and after TBI and its effects on the patient's outcome. Materials and Methods: A comprehensive search was performed using PubMed and Google Scholar focusing on the literature published up to April 2024. The review included randomized controlled trials, meta-analyses, systematic reviews that focused on the use of creatine monohydrate, omega-3 fatty acids, magnesium and zinc in TBI and among the general population. Data of pathophysiology and epidemiology of TBI and safety profile of supplements were involved. Results: The evidence indicates that the role of supplementation with creatine, omega-3 PUFAs and zinc is important in mitigating secondary damage after TBI, as well as in protecting against damage if administered before injury. Conclusion: Because of the complex and multifactorial nature of the TBI more studies are required to constitute an effective nutrition-based strategy for reducing the cognitive and behavioral deficits associated with this injury. The evidence supporting multiple dietary components to improve a patient's outcome seems strong and the cost of its implementation is low. A group of particular interest that may benefit additionally are athletes who use dietary supplementation in their daily routine.

GAP: A group-based automatic pruning algorithm via convolution kernel fusion

In recent years, the deployment and operation of convolution neural networks on edge devices with limited computing capabilities have become increasingly challenging due to the large network structure and computational cost. Currently, the mainstream structured pruning algorithms mainly compress the network at the filter or layer level. However, these methods introduce too much human intervention with large granularities, which may lead to unpredictable performance after compression. In this paper, we propose a group-based automatic pruning algorithm(GAP) via kernel fusion to automatically search for the optimal pruning structure in a more fine-grained manner. Specifically, we first adopt a novel nonlinear dimensionality reduction clustering algorithm to divide the filters of each convolution layer into groups of equal size. Afterwards, we encode the mutual distribution similarity of the kernels within each group, and its KL divergence is employed as an importance indicator to determine the retained kernel groups through weighted fusion. Subsequently, we introduce an intelligent searching module that automatically explore and optimize the pruned structure of each layer. Finally, the pruned filters are permutated to form a dense group convolution and fine-tuned. Sufficient experiments show that, on two image classification datasets, for five advanced CNN models, our GAP algorithm outperforms most extant SOTA schemes, reduces artificial intervention, and enables efficient end-to-end training of compact models.

Experimental Study of the Effect of Roller Shape Variations on the CVT System using Petrol-Pyrolytic Fuels toward the Power Characteristics of Automatic Motorcycles

There are two types of two-wheeled vehicles when viewed from the transmission system: manual and automatic. The automatic transmission circuit on the automatic motor, the roller (ballast), is on the primary pulley. In the market, various rollers (weights) are sold, starting from those that are standard and different from the standard with lighter or heavier weight variations. However, there are also aftermarket rollers produced in several automotive industries, one of which is UNIONMATERIAL. The difference lies in the shape and materials used. This study aims to analyze the effect of variations in the shape of the roller on the characteristics of power transmission on automatic motorcycles. The type of vehicle used is a motorcycle produced in 2009 using a dyno test test with two test methods. The first test is from low engine speed ± 1400-1600 rpm to 9000 rpm, while the second test is from medium engine speed ± 4000 rpm to 9000 rpm. In addition, the fuel consumption test was also carried out. The variation used is a roller shape consisting of a round roller, a sliding roller, and a flying roller weighing 12 grams. The highest results obtained at a power of 0.7 HP occurred on the flying roller with 7.1 HP/ 3916 rpm, while the torque was 5.19 N.m on the flying roller (23.96 N·m/1250 rpm). There is an effect on fuel consumption, but not too significant. The biggest difference occurred in the engine parameter RPM 5000 of 0.1 between the flying roller (0.9 L/h) and sliding roller (0.9 L/h) with the round roller (1 L/h) - this happens because the shape of the flying roller is not cylindrical and more dynamic, which is adapted to the movable drive face. There is an additional wing on the roller weight. The data from the results of this study have implications for increasing vehicle performance without changing engine capacity, which requires large costs.

Observing the uncertainty of the Amlodipine-Candesartan Incremental Cost-Effectiveness ratio using Deterministic Sensitivity Analysis (DSA)

Background: Hypertension treatment is carried out for a long time and can cause quite large costs. This study delves into the economic evaluation of Amlodipine-Candesartan combination therapy, a prominent treatment for hypertension. While the clinical benefits of this regimen are well-documented, its cost-effectiveness remains a critical consideration for healthcare systems. Employing Deterministic Sensitivity Analysis (DSA). This study aimed to discern the uncertainty surrounding the Incremental Cost-Effectiveness Ratio (ICER) associated with Amlodipine-Candesartan through comprehensive examination, next to refine economic assessments and offer insights for healthcare policymakers and practitioners. Method: This retrospective descriptive study at Andalas University Hospital in 2021 analyzed hypertension outpatient data. Of 284 patients, 73 were included. Among them, 28.77% received Amlodipine monotherapy, while 71.23% received Amlodipine-Candesartan combination therapy. Results: The ICER for systolic blood pressure was Rp. 74,738.10 per mmHg decrease, and for diastolic blood pressure, it was Rp. 205,918.24 per mmHg decrease. Both of them are in the northeast quadrant. Next, DSA will be carried out by creating a tornado diagram by providing a change range of 20% (min-max) for the input variables in ICER. Conclusion: The use of amlodipine-candesartan requires a higher cost, but the resulting effect is also better. Our findings contribute to the broader discourse on combination therapies and inform resource allocation strategies for hypertension management. This research attempts to enhance the precision of economic evaluations, ultimately facilitating more informed and efficient healthcare decision-making processes. Keywords: Amlodipine, Candesartan, Cost-effectiveness, ICER, DSA.

Rental Housing Supply and Build-to-Rent Conundrum in Australia

Traditionally, rental housing has been supplied by a large pool of individual investors who own a few units and invest their savings, with some leverage, to take advantage of the tax regime in Australia. The last five years have seen the emergence of build-to-rent (BTR) units, which are supplied by investors who own a large number of units. The state of Victoria in Australia has the largest share of these projects. In the current market and regulatory environment, the financial viability of BTR projects is low for investors and hinges on the ability of units to be leased at higher than market rents. This paper examines four groups of levers, including those already being pursued by the building industry, that can be used to improve the financial viability of BTRs. These include: (i) revenue maximization, (ii) cost reduction (iii) fiscal and (iv) planning incentives. An archetypical BTR project which mimics current practice is considered, assumed to be in Docklands, Victoria, where several BTR projects are planned. For the robustness check, a feasibility analysis is conducted for a site in North Melbourne, a neighbourhood in Victoria with several BTR projects. The results indicate that for revenue maximization, the mix of unit types in a BTR project should be location-specific, as market preferences (and the characteristics of renters) determine the rent for different types of units that can be achieved. In a conventional BTR project development, where land is bought upfront and the project is developed, the mixed-use BTR (residential in combination with commercial) does not provide significant financial benefits though including small retail (3–4% of the net lettable area) may provide complementary benefits. Incurring large capital costs upfront and having the revenue stream spread over long periods reduces financial viability. While construction costs are more difficult to reduce, ways to reduce land costs could be through zoning land for BTR use, through mechanisms such as joint ventures with landowners, and land leasing. Exemptions on income, land tax, and rates (like CHPs) can result in a higher return for investors. A full GST refund, an incentive that industry is lobbying for, results in a similar IRR as an exemption on income, land tax and rates would offer. These results will assist in determining priorities for policies that are aimed at BTR.