Cost Metrics Research Articles

Software Development Effort Estimation Using Relational Database and Optimized Learning Mechanism

Accurately estimating the cost of software development is crucial for effective project planning and resource allocation. However, traditional cost estimation methods rely heavily on expert judgment and historical data, which can be time consuming and prone to errors. This study suggests a learning-based cost estimation model that leverages relational databases to improve accuracy. The proposed approach estimates project cost based on the effort required to complete software development, which is a key driver of the project cost. The proposed model is designed to address the challenges posed by the variability in open-source development, including variable team sizes, working hours, and expertise. The study collects and pre-processes data from open-source platforms and selects cost drivers and metrics based on logical rules and SQL queries. Moreover, we propose an optimized Artificial Neural Network (ANN) with augmented topology to automate the selection of neuron units, layers, and adjustment of learnable parameters according to the input variables. The proposed model is evaluated on a 100 open-source software repositories dataset and demonstrates its effectiveness in accurately estimating development cost. The system is implemented using Python and evaluated using performance parameters such as MSE, RMSE, MAE, and MMRE. Results indicate that our proposed model offers a more accurate and efficient approach to software cost estimation, especially for freelancers and outsourcing firms. The proposed model has the potential to save time and resources and improve the reliability and accuracy of software cost estimation.

Robust Cost Volume Generation Method for Dense Stereo Matching in Endoscopic Scenarios.

Stereo matching in binocular endoscopic scenarios is difficult due to the radiometric distortion caused by restricted light conditions. Traditional matching algorithms suffer from poor performance in challenging areas, while deep learning ones are limited by their generalizability and complexity. We introduce a non-deep learning cost volume generation method whose performance is close to a deep learning algorithm, but with far less computation. To deal with the radiometric distortion problem, the initial cost volume is constructed using two radiometric invariant cost metrics, the histogram of gradient angle and amplitude descriptors. Then we propose a new cross-scale propagation framework to improve the matching reliability in small homogenous regions without increasing the running time. The experimental results on the Middlebury Version 3 Benchmark show that the performance of the combination of our method and Local-Expansion, an optimization algorithm, ranks top among non-deep learning algorithms. Other quantitative experimental results on a surgical endoscopic dataset and our binocular endoscope show that the accuracy of the proposed algorithm is at the millimeter level which is comparable to the accuracy of deep learning algorithms. In addition, our method is 65 times faster than its deep learning counterpart in terms of cost volume generation.

The quest for a better solvent for the direct hydration of cyclohexene: From molecular screening to process design

Cylcohexanol is an essential bulk chemical that can be produced via cyclohexene hydration, a liquid-liquid two-phase reaction that is limited by the low reaction rate and the equilibrium conversion. Adding an appropriate solvent is the most promising method to break through these limitations. However, in previous works the solvent was almost blindly selected without a global consideration. In this work, a rational multiscale method is proposed for the effective selection of an economical and sustainable solvent for the direct hydration of cyclohexene. At the molecular scale, liquid-liquid phase equilibrium was estimated using group contribution methods to rapidly screen the potential solvent candidates from a range of organics, based on the partition coefficient. At the reactor scale, the candidates were experimentally investigated to pick out the solvents that could significantly improve the conversion, without introducing side reactions or deactivating the catalyst. At the process scale, the total annual cost (TAC), CO2 emission, and other metrics were calculated to evaluate the eco-efficiency of all solvents. Using this multi-scale method, acetophenone was selected as an eco-efficient solvent from over 100 organics, resulting in the reduction of TAC by 8 % and CO2 emission by 17 % in the production process. Using acetophenone also led to the increase of cyclohexanol yield from 12.3 % to 27.6 % without the occurrence of side reactions and catalyst deactivation.

Quantum rebound attacks on reduced‐round ARIA‐based hash functions

AbstractARIA is a block cipher proposed by Kwon et al. at ICISC 2003 that is widely used as the national standard block cipher in the Republic of Korea. Herein, we identify some flaws in the quantum rebound attack on seven‐round ARIA‐DM proposed by Dou et al. and reveal that the limit of this attack is up to five rounds. Our revised attack applies to not only ARIA‐DM but also ARIA‐MMO and ARIA‐MP among the PGV models, and it is valid for all ARIA key lengths. Furthermore, we present dedicated quantum rebound attacks on seven‐round ARIA‐Hirose and ARIA‐MJH for the first time. These attacks are only valid for the 256‐bit key length of ARIA because they are constructed using the degrees of freedom in the key schedule. All our attacks are faster than the generic quantum attack in the cost metric of the time–space tradeoff.

IMF 2 O 2 : A Fully Connected Sensor Deployment Algorithm for Underwater Sensor Networks

To address the problems of node deployment schemes in existing underwater sensor networks that lack consideration of network connectivity and high deployment costs, this article constructs an optimization model that maximizes network coverage and minimizes deployment costs while ensuring full connectivity. For the NP-hard property of this optimization model, an improved moth flame optimization node deployment algorithm based on fuzzy operators (IMF 2 O 2 ) is proposed. First, comprehensively considering the two performance metrics of network coverage and network connectivity, a multi-objective selection mechanism based on fuzzy operators is proposed to improve network coverage while ensuring full connectivity. Second, a fixed number of nodes are used to monitor the target event points, transforming the node deployment of sensors into an optimal problem and proposing an improved moth flame optimization algorithm to solve this problem. Finally, the two metrics of coverage and deployment cost are measured and the fuzzy operator is used to select the optimal number of nodes to be deployed. Numerical results showed that the proposed algorithm improved network coverage rate by 10%, 22%, and 25%, and improved network connectivity rate by 12%, 20%, and 8% as compared to PSSD, RAWS, and VODA, respectively, while ensuring full connectivity.

A Nowcasting Central Controller with Continuous RTP for Residential Device Scheduling using Swap-Based BFPSO.

Energy management plays an important role in the residential sector allowing consumers to take control over their energy consumption w.r.t. the market fluctuations. For a long time, forecasting model-based scheduling was thought as a way to mitigate the expected versus reality electricity pricing gap. However, it does not always present a working model owing to uncertainties involved around it. This paper presents a scheduling model having a Nowcasting Central Controller. This model is designed for residential devices using continuous RTP and targets on optimizing the device schedule in the current time slot as well as the subsequent time slots. It is dependent on the current input data and less on the past dataset, making it implemen at any situation. To solve the optimization problem, four variants of PSO in conjunction with swapping operation are implemented on the proposed model by considering a normalized objective function made up of two cost metrics. The results demonstrate a quickness and reduction in costs by BFPSO at each time slot. A comparison is carried out among different pricing schemes that clearly establish the effectiveness of CRTP over DAP and TOD. With CRTP performing the best of the lot, the NCC model is found to be highly adaptable and robust to sudden changes in pricing schemes.

Eliminating Food Insecurity in the USA: a Target Trial Emulation Using Observational Data to Estimate Effects on Health-Related Quality of Life.

Food insecurity is associated with many aspects of poor health. However, trials of food insecurity interventions typically focus on outcomes of interest to funders, such as healthcare use, cost, or clinical performance metrics, rather than quality of life outcomes that may be prioritized by individuals who experience food insecurity. To emulate a trial of a food insecurity elimination intervention, and quantify its estimated effects on health utility, health-related quality of life, and mental health. Target trial emulation using longitudinal, nationally representative data, from the USA, 2016-2017. A total of 2013 adults in the Medical Expenditure Panel Survey screened positive for food insecurity, representing 32 million individuals. Food insecurity was assessed usingthe Adult Food Security Survey Module. The primary outcome was the SF-6D (Short-Form Six Dimension) measure of health utility. Secondary outcomes were mental component score (MCS) and physical component score (PCS) of the Veterans RAND 12-Item Health Survey (a measure of health-related quality of life), Kessler 6 (K6) psychological distress, and Patient Health Questionnaire 2-item (PHQ2) depressive symptoms. We estimated that food insecurity elimination would improve health utility by 80 QALYs per 100,000 person-years, or 0.008 QALYs per person per year (95% CI 0.002 to 0.014, p = 0.005), relative to the status quo. We also estimated that food insecurity elimination would improve mental health (difference in MCS [95% CI]: 0.55 [0.14 to 0.96]), physical health (difference in PCS: 0.44 [0.06 to 0.82]), psychological distress (difference in K6: -0.30 [-0.51 to -0.09]), and depressive symptoms (difference in PHQ-2: -0.13 [-0.20 to -0.07]). Food insecurity elimination may improve important, but understudied, aspects of health. Evaluations of food insecurity interventions should holistically investigate their potential to improve many different aspects of health.

Dietary Costs among Midwestern Adult Food-Pantry Users by Food-Security Status.

Evidence of the relationship between dietary cost, diet quality, and socio-economic status is mixed. No studies have directly evaluated food-security status and dietary cost. This study investigated whether food-pantry clients with low and very low food-security status had less expensive daily diets than food-secure clients by comparing total cost, cost per gram, and cost per calorie of total daily dietary intake both per person and by individual food item, followed by evaluations of each food group. Mixed-model regression and Tukey-Kramer comparisons were used to compare food-security groups. There was no clear association between food-security status and cost of daily diet. Analyzed per person, total price and price per gram showed significant differences between low food-secure and food-secure groups. When analyzing individual food items, prices per calorie were significantly different between food-secure and very low food-secure groups. The directionality of the relationships by food-security status was inconsistent. Per person, those with lower food security had lower mean prices, and for individual foods this association was reversed. Therefore, the metric of food cost and the unit of analysis are critical to determining the relationship between food-security status and dietary cost.

Optimal Control of Dynamical Systems using Calculus of Variations

This paper explores the application of calculus of variations techniques for optimizing the control of complex dynamical systems. Determining control strategies that minimize cost metrics and satisfy constraints is critical across engineering disciplines like robotics, aerospace, and process operations. Classical optimal control methods, such as Pontryagin's Maximum Principle, transform an optimal control problem into a calculus of variations framework amenable to analytical and numerical optimization. We present a unified framework for applying these techniques, enabling dynamical systems defined by ordinary and partial differential equations to be optimized by conversion into a nonlinear programming form. Analytical approaches provide theoretical guarantees on control performance while numerical methods, like direct collocation and transcription, enable large-scale optimal control problems to be efficiently solved. The connections between dynamical systems, calculus of variations, and modern numerical optimization methods establish a holistic methodology for control engineers and applied mathematicians to design optimal controllers for physical systems. Case studies on real-time trajectory optimization, adaptive path planning, and dynamic process operations demonstrate the efficacy of the proposed optimal control framework across robotics, aerospace, power systems, and other applications.

Optimal Control of Dynamical Systems using Calculus of Variations

This paper explores the application of calculus of variations techniques for optimizing the control of complex dynamical systems. Determining control strategies that minimize cost metrics and satisfy constraints is critical across engineering disciplines like robotics, aerospace, and process operations. Classical optimal control methods, such as Pontryagin's Maximum Principle, transform an optimal control problem into a calculus of variations framework amenable to analytical and numerical optimization. We present a unified framework for applying these techniques, enabling dynamical systems defined by ordinary and partial differential equations to be optimized by conversion into a nonlinear programming form. Analytical approaches provide theoretical guarantees on control performance while numerical methods, like direct collocation and transcription, enable large-scale optimal control problems to be efficiently solved. The connections between dynamical systems, calculus of variations, and modern numerical optimization methods establish a holistic methodology for control engineers and applied mathematicians to design optimal controllers for physical systems. Case studies on real-time trajectory optimization, adaptive path planning, and dynamic process operations demonstrate the efficacy of the proposed optimal control framework across robotics, aerospace, power systems, and other applications.

Probabilistic Resource-Aware Session Types

Session types guarantee that message-passing processes adhere to predefined communication protocols. Prior work on session types has focused on deterministic languages but many message-passing systems, such as Markov chains and randomized distributed algorithms, are probabilistic. To implement and analyze such systems, this article develops the meta theory of probabilistic session types with an application focus on automatic expected resource analysis. Probabilistic session types describe probability distributions over messages and are a conservative extension of intuitionistic (binary) session types. To send on a probabilistic channel, processes have to utilize internal randomness from a probabilistic branching or external randomness from receiving on a probabilistic channel. The analysis for expected resource bounds is smoothly integrated with the type system and is a variant of automatic amortized resource analysis. Type inference relies on linear constraint solving to automatically derive symbolic bounds for various cost metrics. The technical contributions include the meta theory that is based on a novel nested multiverse semantics and a type-reconstruction algorithm that allows flexible mixing of different sources of randomness without burdening the programmer with complex type annotations. The type system has been implemented in the language NomosPro with linear-time type checking. Experiments demonstrate that NomosPro is applicable in different domains such as cost analysis of randomized distributed algorithms, analysis of Markov chains, probabilistic analysis of amortized data structures and digital contracts. NomosPro is also shown to be scalable by (i) implementing two broadcast and a bounded retransmission protocol where messages are dropped with a fixed probability, and (ii) verifying the limiting distribution of a Markov chain with 64 states and 420 transitions.

Facilitating viral vector movement enhances heterologous protein production in an established plant system.

Molecular farming technology using transiently transformed Nicotiana plants offers an economical approach to the pharmaceutical industry to produce an array of protein targets including vaccine antigens and therapeutics. It can serve as a desirable alternative approach for those proteins that are challenging or too costly to produce in large quantities using other heterologous protein expression systems. However, since cost metrics are such a critical factor in selecting a production host, any system-wide modifications that can increase recombinant protein yields are key to further improving the platform and making it applicable for a wider range of target molecules. Here, we report on the development of a new approach to improve target accumulation in an established plant-based expression system that utilizes viral-based vectors to mediate transient expression in Nicotiana benthamiana. We show that by engineering the host plant to support viral vectors to spread more effectively between host cells through plasmodesmata, protein target accumulation can be increased by up to approximately 60%.

Superactive: a priority, latency, and SLA-aware resource management scheme for software defined space-air-ground integrated networks

The software-defined space-air-ground integrated network (SAGIN) is regarded as future-generation networking solution due to its wide-area coverage and seamless communication support for ground networks and resource-intensive application support for space-air networks. The SDN-based literary works are restricted only to resource management for space-air or ground networks, not both. The resource scheduling for multi-users caching, computing and data-transfer task accomplishment over software-defined SAGIN were out of their investigations by taking proactive local SDN, heterogenous users and applications, SLA requirements, priority, latency, and users' budget into account. To outperform the problems, this paper advocates a proactive SDN-based resource management scheme considering SLA requirements, latency, priority, and users' budget for multi-user task completion over software-defined SAGIN. This paper contributes an analytical model that covers task completion time, energy expenditure, financial cost, and SLA fulfilment metrics. The performance results illustrate that proposed scheme produces 72% time and 69% financial gain over the compared scheme.

Use of Customer Satisfaction to Measure Performance of Systems and Contractors: Post-Occupancy Evaluation (POE) Method

A roofing manufacturer wants to differentiate themselves from other roofing manufacturers based on performance information. However, construction industry has revealed poor performance documentation in the last couple of decades. With no current developed performance measurement model in the industry, two roofing manufacturers approached the research group to implement a warranty program that measures the performance information of their systems and applicators. Moreover, the success of any project in the construction industry heavily relies upon the capability of the contractor(s) executing the project. Low-performing contractors are correlated with increased cost and delayed schedules, resulting in end-user dissatisfaction with the final product. Hence, the identification and differentiation of the high performing contractors from their competitors is also crucial. The purpose of this study is to identify and describe a new model for measuring manufacturer performance and differentiating contractor performance and capability for two roofing manufacturers (Manufacturer 1 and Manufacturer 2) in the roofing industry. The research uses multiple years of project data and customer satisfaction data collected for two roofing manufacturers for over 1,000 roofing contractors. The performance and end-user satisfaction were obtained for over 7,000 manufacturers' projects and each contractor associated with that project for cost, schedule, and quality metrics. The measurement process was successfully able to provide a performance measurement for the manufacturer based on the customer satisfaction and able to identify low performing contractors. This study presents the research method, the developed measurement model, and proposes a performance measurement process that entities in the construction industry can use to measure performance.

Availability, affordability and access to essential medications for asthma and chronic obstructive pulmonary disease in three low- and middle-income country settings.

Despite the rising burden of chronic respiratory disease globally, and although many respiratory medications are included in the World Health Organization Essential Medications List (WHO-EML), there is limited information concerning the availability and affordability of treatment drugs for respiratory conditions in low- and middle-income countries (LMICs). All public and private pharmacies in catchment areas of the Global Excellence in COPD outcomes (GECo) study sites in Bhaktapur, Nepal, Lima, Peru, and Nakaseke, Uganda, were approached in 2017-2019 to assess pricing and availability of medications for the management of asthma and COPD. We surveyed all 63 pharmacies in respective study areas in Nepal (95.2% private), 104 pharmacies in Peru (94.2% private) and 53 pharmacies in Uganda (98.1% private). The availability of any medication for respiratory disease was higher in private (93.3%) compared to public (73.3%) pharmacies. Salbutamol (WHO-EML) monotherapy in any formulation was the most commonly available respiratory medication among the three sites (93.7% Nepal, 86.5% Peru and 79.2% Uganda) while beclomethasone (WHO-EML) was only available in Peru (33.7%) and Nepal (22%). LABA-LAMA combination therapy was only available in Nepal (14.3% of pharmacies surveyed). The monthly treatment cost of respiratory medications was lowest in Nepal according to several cost metrics: the overall monthly cost, the median price ratio comparing medication costs to international reference prices at time of survey in dollars, and in terms of days' wages of the lowest-paid government worker. For the treatment of intermittent asthma, defined as 100 mcg Salbutamol/Albuterol inhaler, days' wages ranged from 0.47 days in Nepal and Peru to 3.33 days in Uganda. The availability and pricing of respiratory medications varied across LMIC settings, with medications for acute care of respiratory diseases being more widely available than those for long-term management.

Markov Renewal Prediction and Radial Kronecker Neural Network Based Handover for Seamless Mobility

Prevailing personal mobile network architectures make use of streamlined mobility control system, where the complete understanding is concentrated on single-end that results in scarce of dynamic mobility support when data volume is found to be large. The present-day networks necessitate seamless connections regardless of node position and connectivity that has to be accomplished between personal are network (PAN). In this work, a novel method called, Markov Renewal Prediction and Radial Kronecker Neural Network (MRP-RKNN) based optimized handover for seamless mobility in PAN is proposed. By employing a Markov Renewal Prediction model for Seamless Mobility along with the two-hop network architecture, in this paper, we propose a transition probabilities (TP) function to mitigate the persistent handover issue in conventional wireless communication systems. The proposed Markov Renewal Prediction model for Seamless Mobility significantly reduces handover execution time and seamless mobility handover accuracy with efficient transition probabilities. In PANs, the unavoidable deployment of low power sink nodes permits the mobile nodes with many issues in terms of Quality of Service (QoS) due to complication of recurrent handovers due to high mobility. Addressing this issue of handover optimization in the deployment of PAN, this work proposes a model called to optimize the handovers in a cost-efficient manner. In this work, Radial Kronecker Delta Neural Network is utilized for handling frequent handovers based on received signal strength and cost metrics. Here, the resultant desired output is obtained using the Radial Kronecker function being a function of two variables with which optimized handover is performed. Simulation results presented in the study exhibits the performance and prediction rate of the proposed method in terms of handover execution time, seamless mobility prediction accuracy, mobility handover cost and packet loss rate.

Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance

Finding the best configuration of algorithms’ hyperparameters for a given optimization problem is an important task in evolutionary computation. We compare in this work the results of four different hyperparameter optimization (HPO) approaches for a family of genetic algorithms (GAs) on 25 diverse pseudo-Boolean optimization (PBO) problems. More precisely, we compare previously obtained results from a grid search with those obtained from three automated configuration techniques: 1) iterated racing; 2) mixed-integer parallel-efficient global optimization (MIP-EGO); and 3) mixed-integer evolutionary strategies. Using two different cost metrics: 1) expected running time (ERT) and 2) the area under the empirical cumulative distribution function (ECDF) curve, we find that in several cases the best configurations with respect to ERT are obtained when using the area under the ECDF curve as the cost metric during the configuration process. Our results suggest that even when interested in ERT performance, it might be preferable to use anytime performance measures for the configuration task. We also observe that tuning for ERT is much more sensitive with respect to the budget that is allocated to the target algorithms.

Black coating of quartz sand towards low-cost solar-absorbing and thermal energy storage material for concentrating solar power

Solid particles based direct solar absorbing heat transfer fluid (HTF) and thermal energy storage (TES) material is gaining increasing interests for high-temperature concentrating solar power (CSP). However, there is no particulate material that simultaneously possesses the performance and cost metrics needed to achieve the target levelized cost of electricity of future CSP, including high solar absorptance, high thermal conductivity, good mechanical and thermal stability, and importantly, low cost. In this work, we demonstrate a facile and scalable method to turn low-cost quartz sand into a direct solar-absorbing HTF and TES material by coating a thin silica shell containing black spinel nanoparticles (Cu0.5Cr1.1Mn1.4O4and CuCr2O4). The black coating increases the solar absorptances of the sand from ∼0.43 to ∼0.89. The coated quartz sand also shows excellent stability and nearly unchanged optical properties after 100-hr of isothermal annealing at 800 °C and up to 24-hr ball mixing with Yttria Stabilized Zirconia (YSZ) grinding balls. The measured effective thermal conductivity of the coated quartz sand increases 0.30 W m−1 K−1 to 0.65 W m−1 K−1 from 20 °C to 700 °C and is comparable to that of uncoated sand as well as Carbo ceramic particle beds currently used in CSP plants. The coating process could represent a cost-effective and generic strategy to enhance the solar absorptance of solid particles, and when applied on low-cost sand, could turn them into low-cost and high-performance HTF and TES media for future CSP systems.

Location constrained virtual optical network embedding in space-division multiplexing elastic optical networks

Virtual optical network embedding (VONE) involves two processes, node mapping, and link mapping. Node mapping decides which data center to place the virtual nodes (VNs). Link mapping decides how to route virtual links between placed VNs. In location constrained VONE (LC-VONE), each VN can be mapped only to some substrate nodes. In this paper, we study the problem of LC-VONE in space-division multiplexing elastic optical networks (SDM-EONs). Aiming at load balancing, we formulate the first integer linear programming (ILP) to solve the static LC-VONE problem, where the set of VON requests is given. To achieve scalability, an integrated framework is devised to jointly conduct node and link mappings for each VON request. Three cost metrics are designed for finding the least-cost VON solution. Combining them with the integrated framework, three LC-VONE algorithms are proposed: minimum spectrum consumption (MSC), bottleneck link avoidance (BLA), and lowest traffic load (LTL). By applying them to solve both static and dynamic LC-VONE problems, we found that LTL always outperforms MSC and BLA.

Fixed-Time Coverage Control of Mobile Robot Networks Considering the Time Cost Metric.

In this work, we studied the area coverage control problem (ACCP) based on the time cost metric of a robot network with an input disturbance in a dynamic environment, which was modeled by a time-varying risk density function. A coverage control method based on the time cost metric was proposed. The area coverage task that considers the time cost consists of two phases: the robot network is driven to cover the task area with a time-optimal effect in the first phase; the second phase is when the accident occurs and the robot is driven to the accident site at maximum speed. Considering that there were movable objects in the task area, a time-varying risk density function was used to describe the risk degree at different locations in the task area. In the presence of the input disturbance, a robust controller was designed to drive each robot, with different maximum control input values, to the position that locally minimized the time cost metric function in a fixed time, and the conditions for maximum control input were obtained. Finally, simulation results and comparison result are presented in this paper.