Cost Constraints Research Articles

A machine learning-enabled prediction of damage properties for fiber-reinforced polymer composites under out-of-plane loading

For understanding the damage morphology in fiber-reinforced plastics under out-of-plane loading, engineers/ and designers mostly rely on experimental investigations and numerical models, which could be costly in terms of resources and computational requirements. Thus, pragmatic predictive approaches that can offer ready-to-use tools for material optimization have gained traction in recent years. This paper proposes a machine learning model, developed with the use of previous experiments, to predict the damage behaviour of E-glass fiber-reinforced plastics in sandwich structures and laminates. The focus is on quasi-static indentation with different indenter geometries, using acoustic signals for damage monitoring. A total of 10 optimal-performing regression algorithms were employed to develop the prediction models. The developed models based on total energy gave acceptable results for all specimens (R-squared value: 0.9932 – 0.9999), while prediction models using force produced less accurate results (R-squared value: 0.8693) for a sandwich structure subjected to a conical indenter. Results showed that energy absorption of composites provided the most reliable data for the development of predictive models. The load profiles could also be used when the contact area is limited (e.g., for hemispherical and conical indenters). The best-performing model for higher surface areas of the indenter increased was k-NN, thanks to its ability to capture complex and nonlinear relationships between the input features and the target variable. Overall, the developed model could provide more autonomy and control for the material optimisation of composite structures for industrial applications with reduced time and cost constraints.

Qualitative assessment of knowledge, attitude and practice of oncologists about precision medicine in cancer patients- study from Lahore, Pakistan.

Precision medicine (PM) is in great progressive stages in the West and allows healthcare practitioners (HCPs) to give treatment according to the patient's genetic findings, physiological and environmental characteristics. PM is a relatively new treatment approach in Pakistan Therefore, it is important to investigate the level of awareness, attitude, and challenges faced by oncology physicians while practicing PM for various therapies, especially cancer treatment. The present study aims to explore the level of awareness, attitude, and practice of PM in Pakistan along with the challenges faced by the oncologists for the treatment of cancer using the PM approach. Phenomenology-based qualitative approach was used. Face-to-face in-depth interviews were conducted using the purposive sampling approach among oncologists in Lahore, Pakistan. The data were analyzed using thematic content analysis to identify themes and sub-themes. Out of 14 physicians interviewed 11 were aware of PM. They were keen on training to hone their skills and agreed on providing PM. Oncologists believed PM was expensive and given to affluent patients only. Other impeding factors include cost, lack of knowledge, and drug unavailability. Despite basic knowledge and will to practice, resource and cost constraints were marked as significant barriers. Additional training programs and inclusion into the curriculum may help to pave the way to PM implementation in the future. In addition, health authorities and policymakers need to ensure a cheaper PM treatment can be made available for all cancer patients.

Building and sharing medical cohorts for research

Cohort studies are costly and time-consuming. They require not only laboratory equipment and assays but also collaboration from participants and health agencies. Due to cost constraints, they are often confined to a specific population. Nevertheless, they play a crucial role in providing fundamental insights for medical advancements, shedding light on the origins of diseases, and acting their socioeconomic influence in policy making.

Computational Experiments for Complex Social Systems—Part III: The Docking of Domain Models

Powered by advanced information technology, more and more complex systems are exhibiting characteristics of the cyber–physical–social systems (CPSS). In consideration of the cost, legal, and institutional constraints on the study of CPSS in real world, computational experiments have emerged as a new method for quantitative analysis of CPSS. However, with the increase of application scenarios, how to map complex and diverse domain models to artificial society models has become a key challenge to hinder the wide use of computational experiments. In this article, the docking framework between the domain model and the artificial society model was proposed in this article, and the model docking specification is given from three aspects: the agent model, the environmental model, and the rules model. In addition, the effectiveness of the framework was verified by two classic cases: artificial stock market and epidemic prevention and control. The result showed that the proposed model docking framework can provide technical support for the multidisciplinary applications of computational experiments and significantly reduce the difficulty of using the method.

Peningkatan kesadaran kesehatan rongga mulut pada komunitas taman edukasi anak pemulung di Gampong Jawa, Banda Aceh [Increasing awareness of oral health in the scavengers educational park community in Jawa Village, Banda Aceh

Oral health can affect the overall health of the body, yet there are still many people who do not take care of their oral health. Many factors influence this, including lack of knowledge and cost constraints in maintaining oral health. This service aims to increase awareness of the importance of oral health in the Scavenger Children's Educational Park community. The method of the service is through counseling on the importance of maintaining oral health, teaching how to brush teeth, and conducting oral health screenings. From the oral cavity examination, 38 children were examined with the condition of deciduous teeth with cavities, namely 139 teeth. The number of permanent teeth with caries was 74. The caries index of permanent teeth obtained during the oral cavity examination of children in the age of 6-12 years in the educational park community is 1.73, and the caries index of milk teeth is 5.35. In conclusion, this service activity provided preliminary data on the oral health screening of the children of the Scavenger Children's Educational Park Community in Gampong Jawa. The high caries index in these children can be an input for health services, especially in Gampong Jawa, where children in the area deserve special attention.

CBD Oil Concentration and Hemp Flower Drying Preferences of U.S. Consumers

The method used to dry hemp flowers can impact the concentration of cannabidiol (CBD) and other quality characteristics like mold levels in CBD oil. Understanding consumer preferences is vital for hemp producers and processors because there is variation in drying time, energy use, and capital investment between potential drying methods. This research assesses willingness to pay (WTP) for CBD oil at varying CBD content (mg per 1-oz.(30 mL) bottle) and drying methods to better understand consumer preferences and how consumers make tradeoffs between CBD oil attributes. Specifically, the authors elicit preferences toward drying using: i) controlled ambient conditions (air-drying), ii) infrared radiation (IR), and iii) freeze-drying or lyophilization. Few statistically significant WTP differences between CBD oils, when marketed at average attribute levels, were found for higher or lower CBD concentration or drying method. An exception was respondents 55 years of age or older who had a higher WTP for CBD oil with greater CBD content (2,500 mg) and also preferred the freeze dried product over the baseline of a conventionally air-dried product with 1,000 mg of CBD. The lack of distinct preferences among the majority of consumers suggests that producers and processors can choose alternative drying methods based on their cost, time, and energy constraints without risking a WTP penalty. The findings indicate that consumers do not have well-formed preferences for CBD oil as it is a relatively novel product. Yet, the results suggest that consumers may interpret higher prices to reflect higher quality/product safety, thereby offering perceived quality assurance.

RESEARCH ON THE MOTIVATION ISSUES AND COUNTERMEASURES FOR XI'AN LIAN JIA REAL ESTATE SALES STAFF

In recent years, with the increasingly fierce competition in the external market, Xi'an Lian Jia Real Estate Company has also suffered a significant blow, with a notable decline in business performance. To better stimulate the enthusiasm and creativity of employees and promote the healthy and stable development of the enterprise, this paper selects the marketing staff of Xi'an Lian Jia Real Estate Company as a case study for analysis.
 The paper proposes principles and objectives for optimizing the incentive mechanism for sales staff at Xi'an Lian Jia Real Estate Company and designs the following optimization schemes: enhancing employees' sense of honor, broadening the channels for staff promotion, rationalizing organizational performance settings, improving the incentive and remuneration systems, strengthening the reward and punishment mechanism for grassroots employees, adopting flexible welfare systems within the cost constraints, and enhancing the internal motivation of sales staff. To ensure the effective implementation of the sales staff incentive mechanism optimization scheme, this paper suggests the following measures: formulating a scientific execution plan for the scheme; creating a positive atmosphere that genuinely cares for the grassroots employees; and increasing investment in sales personnel by seriously listening to their opinions.

Development pathway and influencing factors of hydrogen energy storage accommodating renewable energy growth

Hydrogen energy is considered an important energy storage mode with medium- and long-term cross-seasonal storage capabilities in scenarios with high penetration of renewable energy (RE). However, there is a lack of research regarding the appropriate scale of hydrogen energy storage (HES) considering different RE power generation scenarios. In response to this gap, the present study aims to establish a power system simulation model to analyze the development pathway of HES while accommodating RE growth with a minimum cost constraint for a power system operation. The impacts of carbon trading prices, advances in HES technology, and RE resource endowment on HES development are also investigated. The findings indicate that the total available solar energy resources in China are 46.94TW, and the total available wind energy resources are 9.52TW. When the share of RE generation reaches 44.33–54.78 % of total power generation, the utilization of HES for balancing power system is advantageous in reducing the overall system costs. Looking ahead to 2060, the year when China is projected to achieve its carbon-neutrality goal, RE is estimated to reach 63.12–91.1 % of total power generation. Consequently, the HES in each province is estimated to contribute to 3.58–18.02 % of total electricity consumption. Provinces with more abundant wind resources relative to solar resources are expected to have a 36.43 % higher demand for HES.

Multi-objective ecological restoration priority in China: Cost-benefit optimization in different ecological performance regimes based on planetary boundaries

In the context of the “United Nations Decade on Ecosystem Restoration”, optimizing spatiotemporal arrangements for ecological restoration is an important approach to enhancing overall socioecological benefits for sustainable development. However, against the background of ecological degradation caused by the human use of most natural resources at levels that have approached or exceeded the safe and sustainable boundaries of ecosystems, it is key to explain how to optimize ecological restoration by classified management and optimal total benefits. In response to these issues, we combined spatial heterogeneity and temporal dynamics at the national scale in China to construct five ecological performance regimes defined by indicators that use planetary boundaries and ecological pressures which served as the basis for prioritizing ecological restoration areas and implementing zoning control. By integrating habitat conservation, biodiversity, water supply, and restoration cost constraints, seven ecological restoration scenarios were simulated to optimize the spatial layout of ecological restoration projects (ERPs). The results indicated that the provinces with unsustainable freshwater use, climate change, and land use accounted for more than 25%, 66.7%, and 25%, respectively, of the total area. Only 30% of the provinces experienced a decrease in environmental pressure. Based on the ecological performance regimes, ERP sites spanning the past 20 years were identified, and more than 50% of the priority areas were clustered in regime areas with increased ecological stress. As the restoration area targets doubled (40%) from the baseline (20%), a multi-objective scenario presents a trade-off between expanded ERPs in areas with highly beneficial effects and minimal restoration costs. In conclusion, a reasonable classification and management regime is the basis for targeted restoration. Coordinating multiple objectives and costs in ecological restoration is the key to maximizing socio-ecological benefits. Our study offered new perspectives on systematic and sustainable planning for ecological restoration.

Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement Learning

A popular framework for enforcing safe actions in Reinforcement Learning (RL) is Constrained RL, where trajectory based constraints on expected cost (or other cost measures) are employed to enforce safety and more importantly these constraints are enforced while maximizing expected reward. Most recent approaches for solving Constrained RL convert the trajectory based cost constraint into a surrogate problem that can be solved using minor modifications to RL methods. A key drawback with such approaches is an over or underestimation of the cost constraint at each state. Therefore, we provide an approach that does not modify the trajectory based cost constraint and instead imitates "good" trajectories and avoids "bad" trajectories generated from incrementally improving policies. We employ an oracle that utilizes a reward threshold (which is varied with learning) and the overall cost constraint to label trajectories as "good" or "bad". A key advantage of our approach is that we are able to work from any starting policy or set of trajectories and improve on it. In an exhaustive set of experiments, we demonstrate that our approach is able to outperform top benchmark approaches for solving Constrained RL problems, with respect to expected cost, CVaR cost, or even unknown cost constraints.

Reward Penalties on Augmented States for Solving Richly Constrained RL Effectively

Constrained Reinforcement Learning employs trajectory-based cost constraints (such as expected cost, Value at Risk, or Conditional VaR cost) to compute safe policies. The challenge lies in handling these constraints effectively while optimizing expected reward. Existing methods convert such trajectory-based constraints into local cost constraints, but they rely on cost estimates, leading to either aggressive or conservative solutions with regards to cost. We propose an unconstrained formulation that employs reward penalties over states augmented with costs to compute safe policies. Unlike standard primal-dual methods, our approach penalizes only infeasible trajectories through state augmentation. This ensures that increasing the penalty parameter always guarantees a feasible policy, a feature lacking in primal-dual methods. Our approach exhibits strong empirical performance and theoretical properties, offering a fresh paradigm for solving complex Constrained RL problems, including rich constraints like expected cost, Value at Risk, and Conditional Value at Risk. Our experimental results demonstrate superior performance compared to leading approaches across various constraint types on multiple benchmark problems.

Handling Long and Richly Constrained Tasks through Constrained Hierarchical Reinforcement Learning

Safety in goal directed Reinforcement Learning (RL) settings has typically been handled through constraints over trajectories and have demonstrated good performance in primarily short horizon tasks. In this paper, we are specifically interested in the problem of solving temporally extended decision making problems such as robots cleaning different areas in a house while avoiding slippery and unsafe areas (e.g., stairs) and retaining enough charge to move to a charging dock; in the presence of complex safety constraints. Our key contribution is a (safety) Constrained Search with Hierarchical Reinforcement Learning (CoSHRL) mechanism that combines an upper level constrained search agent (which computes a reward maximizing policy from a given start to a far away goal state while satisfying cost constraints) with a low-level goal conditioned RL agent (which estimates cost and reward values to move between nearby states). A major advantage of CoSHRL is that it can handle constraints on the cost value distribution (e.g., on Conditional Value at Risk, CVaR) and can adjust to flexible constraint thresholds without retraining. We perform extensive experiments with different types of safety constraints to demonstrate the utility of our approach over leading approaches in constrained and hierarchical RL.

Web Integrated Continuous Positive Airway Pressure System

Keywords: Continuous Positive Airway Pressure(CPAP) 1, Sleep Apnea 2, Neonatal 3, Respiratory Distress Syndrome (RDS) 4, Premature baby 5, Transpiration medical device 6, Internet of Things 7 CPAP, a non-invasive ventilation method crucial in neonatal care, has significant implications for the survival of very low birth weight preterm infants. However, its widespread adoption is hindered by cost constraints and the need for additional features like Apnea Hypopnea Index (AHI) monitoring. This study seeks to overcome these challenges by developing an affordable CPAP device for Neonatal Intensive Care Units (NICUs). By incorporating sensors for data collection and a microcontroller-based control system, this device aims to optimize CPAP function while minimizing energy consumption. Furthermore, the collected data will be securely stored on a cloud platform, potentially benefiting both neonatal care and sleep apnea management. Recognizing the vital role of sleep in human health, this innovation underscores the importance of restorative rest for physical and mental well-being. Highlights: · Web-integrated CPAP devices often come with remote monitoring capabilities. This allows healthcare providers to remotely monitor patients' CPAP usage data, including compliance and efficacy metrics, without requiring in-person visits. This feature facilitates better patient management and allows for timely intervention if issues arise. · These devices typically track and record various parameters such as usage hours, mask leaks, apnea-hypopnea index (AHI), and pressure settings. The data can be accessed by both patients and healthcare providers, providing insights into treatment progress and effectiveness over time. · Many web-integrated CPAP devices offer cloud connectivity, allowing users to sync their data to secure online platforms. This feature enables seamless access to CPAP usage data from multiple devices, such as smartphones, tablets, or computers, enhancing convenience and flexibility for users. · Some modern CPAP machines offer wireless connectivity options, such as Bluetooth or Wi-Fi, enabling users to connect their devices to compatible accessories or smartphone apps. This connectivity allows for features like remote control of device settings, automatic software updates, and integration with health tracking apps for a more comprehensive health management experience. · The integration of web-based CPAP platforms with telemedicine services allows for virtual consultations between patients and healthcare providers. This feature enhances access to care, particularly for patients in remote or underserved areas, and enables real-time adjustments to CPAP settings based on patient feedback and data analysis.

Simulation-Based Learning and Argumentation to Promote Informed Design Decision-Making Processes within a First-Year Engineering Technology Course

Analyzing the integration of scientific knowledge to guide decision-making processes supporting design challenges in engineering education is critical. However, effectively engaging in informed design decision-making processes is challenging, particularly in the context of online education. Simulation-based learning can bring authentic design practices to online education, but effective guidance and scaffolding must be provided to learners. Therefore, this research investigates the implications of integrating simulation-based learning with an argumentation framework to guide students in incorporating scientific knowledge into their design decisions, particularly in designing energy-efficient housing. This study took place during online learning due to the COVID-19 pandemic and was implemented within a first-year engineering technology undergraduate course. It aimed to analyze students’ decision-making processes when designing a zero-energy home for a Midwestern city using Aladdin, an integrated CAD/CAE platform that can be used to design a structure and simulate its function within a single system. This study investigates how students informed their decision-making processes in design for energy-efficient homes and the recurring trends in students’ designs related to economic decision making and energy science. The overall results show how cost constraints significantly influenced students’ observation and argumentation processes during their design challenge, highlighting the pivotal role of economic considerations in shaping their decision making. Moreover, the findings underscore the importance of holistic approaches in providing insights into teaching strategies for online learning, particularly in navigating the intersection of scientific and economic factors in design challenges.

Study on the shielding performance of bismuth oxide as a spent fuel dry storage container based on Monte Carlo simulation

For traditional spent fuel shielding materials, due to physical and chemical defects and cost constraints, they have been unable to meet the needs. Therefore, this paper carries out the first discussion on the application and performance of bismuth in neutron shielding by establishing Monte Carlo simulation on the neutron flux model of shielded spent fuel. Firstly, functional fillers such as bismuth oxide, lead oxide, boron oxide, gadolinium oxide and tungsten oxide are added to the matrices to compare the shielding rates of aluminum alloy matrix and silicone rubber matrix. The shielding rate of silicone rubber mixture is higher than aluminum alloy mixture, reaching more than 56%. The optimal addition proportion of bismuth oxide and lead oxide is 30%, and the neutron radiation protection efficiency reaches 60%. Then, the mass attenuation coefficients of bismuth oxide, lead oxide, boron oxide, gadolinium oxide and tungsten oxide in silicone rubber matrix are simulated with the change of functional fillers proportion and neutron energy. This simulation result shows that the mixture with functional fillers has good shielding performance for low energy neutrons, but poor shielding effect for high energy neutrons. Finally, in order to further evaluate the possibility of replacing lead oxide with bismuth oxide as shielding material, the half-value layers and various properties of bismuth oxide and lead oxide are compared. The results show that the shielding properties of bismuth oxide and lead oxide are basically the same, and the mechanical properties, heat resistance, radiation resistance and environmental protection of bismuth oxide are better than that of lead oxide. Therefore, in the case of neutron source strengths in the range of 0.01–6 MeV and secondary gamma rays produced below 2.5 MeV, bismuth can replace lead in neutron shielding applications.

An Unstructured Mesh Generation Tool for Efficient High‐Resolution Representation of Spatial Heterogeneity in Land Surface Models

AbstractThis study develops an unstructured mesh generation tool for land surface modeling using a multi‐scale hexagon discrete mesh. The tool can automatically determine the required mesh resolution for different regions based on multi‐objective criteria such as elevation, slope, land cover, and land use. The refined unstructured meshes demonstrate significant enhancement in the representation of spatial heterogeneity. The tool is coupled with the Common Land Model (CoLM) to enable land surface simulations using unstructured meshes. Evaluations focused on runoff, river discharge, and inundation indicate improved model performance compared to traditional structured mesh‐based CoLM simulations under the same computational cost constraints. This tool provides new capabilities for more efficiently capturing localized land surface processes and extreme events.

Monitoring gamma type-I censored data using an exponentially weighted moving average control chart based on deep learning networks

In recent years, deep learning methods have been widely used in combination with control charts to improve the monitoring efficiency of complete data. However, due to time and cost constraints, data obtained from reliability life tests are often type-I right censored. Traditional control charts become inefficient for monitoring this type of data. Thus, researchers have proposed various control charts with conditional expected values (CEV) or conditional median (CM) to improve efficiency for right-censored data under normal and non-normal conditions. This study combines the exponentially weighted moving average (EWMA) CEV and CM chart with deep learning methods to increase efficiency for gamma type-I right-censored data. A statistical simulation and a real-world case are presented to assess the proposed method, which outperforms the traditional EWMA charts with CEV and CM in various skewness coefficient values and censoring rates for gamma type-I right-censored data.

Application of Clinical Blood Metabogram to Type 2 Diabetes Mellitus.

The clinical blood metabogram (CBM) was developed to match a tailored analysis of the blood metabolome to the time, cost, and reproducibility constraints of clinical laboratory testing. By analyzing the main blood metabolite groups, CBM offers clinically relevant information about the intake of low-molecular substances into the organism, humoral regulation, liver function, amino acid level, and the lipid and carbohydrate metabolism. The purpose of this work was to investigate the relevance of using the CBM in patients with diabetes mellitus. For this, a CBM was obtained for 18 healthy individuals, 12 individuals with prediabetes, and 64 individuals with type 2 diabetes mellitus, separated into groups according to fasting blood glucose and oral glucose tolerance tests. The results showed that the CBM reveals diabetes-associated metabolic alterations in the blood, including changes in the levels of carbohydrates, ketone bodies, eicosanoids, phospholipids, and amino acids, which are consistent with the scientific data available to date. The CBM enabled the separation of diabetic patients according to their metabolic metabotypes, providing both a general overview of their metabolic alterations and detailing their individual metabolic characteristics. It was concluded that the CBM is a precise and clinically applicable test for assessing an individual's metabolic status in diabetes mellitus for diagnostic and treatment purposes.

An Investigation of Representative Customer Load Collectives in the Development of Electric Vehicle Drivetrain Durability

To ensure the precise dimensioning and effective testing of drivetrain components, it is crucial to have a thorough understanding of customer requirements, with a particular emphasis on customer stress on these components. An accurate interpretation of customer data is essential for determining representative customer requirements, such as load collectives. The automobile industry has faced challenges in analyzing large amounts of customer driving data to obtain representative load collectives as target values in durability design. However, due to technical limitations and cost constraints, collecting data from a large sample size is not feasible. The ongoing digitalization of the automotive industry, driven by an increasing number of connected vehicles, enhances data-based and customer-oriented development. This paper investigates representative customer load collectives using cloud data from over 40,000 customer vehicles to lay the groundwork for realizing robust requirement engineering. A systematic method for analyzing big data on the cloud was introduced. The derived component-specific damage distribution from these collectives adopts a unique approach, utilizing the 1% vehicle term instead of the common 1% customer term to represent typical customer stress. This study shows that the driven mileage and the number of vehicles are crucial factors in 1% vehicle analysis. An analysis of the characteristics of the 1% vehicle is conducted, followed by an exploration to determine the required vehicle quantity for obtaining stable results. The shape parameter of the damage distribution determines the necessary number of vehicles for a reliable conclusion. Additionally, a comparative analysis of market-specific customer requirements between the US and Europe is presented, and real usage differences in customer operations are explained using an operating point frequency heatmap. The information presented in this paper provides valuable input for optimizing durability design and conducting efficient, customer-oriented tests, resulting in significant reductions in development time and costs.

On-the-fly Raman microscopy guaranteeing the accuracy of discrimination

Accelerating the measurement for discrimination of samples, such as classification of cell phenotype, is crucial when faced with significant time and cost constraints. Spontaneous Raman microscopy offers label-free, rich chemical information but suffers from long acquisition time due to extremely small scattering cross-sections. One possible approach to accelerate the measurement is by measuring necessary parts with a suitable number of illumination points. However, how to design these points during measurement remains a challenge. To address this, we developed an imaging technique based on a reinforcement learning in machine learning (ML). This ML approach adaptively feeds back "optimal" illumination pattern during the measurement to detect the existence of specific characteristics of interest, allowing faster measurements while guaranteeing discrimination accuracy. Using a set of Raman images of human follicular thyroid and follicular thyroid carcinoma cells, we showed that our technique requires 3,333 to 31,683 times smaller number of illuminations for discriminating the phenotypes than raster scanning. To quantitatively evaluate the number of illuminations depending on the requisite discrimination accuracy, we prepared a set of polymer bead mixture samples to model anomalous and normal tissues. We then applied a home-built programmable-illumination microscope equipped with our algorithm, and confirmed that the system can discriminate the sample conditions with 104 to 4,350 times smaller number of illuminations compared to standard point illumination Raman microscopy. The proposed algorithm can be applied to other types of microscopy that can control measurement condition on the fly, offering an approach for the acceleration of accurate measurements in various applications including medical diagnosis.