Small Cost Research Articles

Spectral-Galerkin methods for the fully nonlinear Monge-Ampère equation

In this paper, we develop two numerical methods, the Legendre-Galerkin method and the generalized Log orthogonal functions Galerkin method for numerically solving the fully nonlinear Monge-Ampère equation. Both methods are constructed based on the vanishing moment approach. To address both solution stability and computational efficiency, we propose a multiple-level framework for resolving discretization schemes. The mathematical justifications of the new approaches and the error estimates for the Legendre-Galerkin method are established. Numerical experiments validate the accuracy of our methods, and a comparative experiment demonstrates the advantage of Log orthogonal functions for problems with corner singularities. The results highlight that our methods have high-order accuracy and small computational cost.

Optimization of Coreless PCB Coils Based on a Modified Taguchi Tuning Method for WPT of Pedelec

The printed circuit board (PCB) winding coil offers advantages such as small size, high precision, high repeatability, and low cost, making it conducive to the miniaturization of electronic equipment and a popular choice in wireless power transmission systems. This paper aims to clarify the correlation between induction parameters and inductive capabilities using the orthogonal array of the modified Taguchi method for Pedelec applications. The conventional Taguchi method typically achieves only local optimization; however, this paper considers practical application conditions and combines experimental data to establish the initial values of the orthogonal array, thereby achieving global optimization. Additionally, the tuning process of the Taguchi method replaces physical experiments with simulations, enhancing optimization speed and reducing hardware implementation costs. The performance index for the proposed modified Taguchi tuning method is selected as a combination of the quality factor (Q) and coupling coefficient (k) to minimize AC resistance and improve system efficiency. To validate the proposed method, the designed coils were implemented and tested in a WPT system based on S–S compensation with a half-bridge topology. The experimental results demonstrate that the optimized PCB coil parameters derived from the proposed tuning method accurately validate the method’s effectiveness and accuracy. From the measured results with the proposed modified tuning method, the system efficiency is increased by 43.87% and the system transmitting power is increased by 28.51%.

Resolving subgrid-scale structures for multiphase flows using a filament moment-of-fluid method

Multiphase flows are present in many industrial and engineering applications as well as in some physical phenomena. Capturing the interface between the phases for complex flows is challenging and requires an accurate method, especially to resolve fine-scale structures. The moment-of-fluid (MOF) method improves drastically the accuracy of interface reconstruction compared to previous geometrical methods. Instead of refining the mesh to capture increased levels of detail, the MOF method, which uses zeroth and first moments as well as a conglomeration algorithm, enables subgrid structures such as filaments to be captured at a small extra cost. Coupled to a finite volume Navier–Stokes solver, the MOF method has been tested on a fixed grid and validated using well-known benchmark problems such as the dam break flows, the Rayleigh–Taylor and Kelvin–Helmholtz instability problems, and a rising bubble. The ability of the novel filament MOF method to capture the filamentary structures that eventually form for the Rayleigh–Taylor instability and rising bubble problems is assessed. Good agreement has been found with other numerical results and experimental measurements available in the literature.

Efficient Approximation Algorithms for Minimum Cost Seed Selection with Probabilistic Coverage Guarantee

Given a social network G , a cost associated with each user, and an influence threshold η, the minimum cost seed selection problem (MCSS) aims to find a set of seeds that minimizes the total cost to reach η users. Existing works are mainly devoted to providing an <u>e</u>xpected <u>c</u>overage <u>g</u>uarantee on reaching η, classified as MCSS-ECG, where their solutions either rely on an impractical influence oracle or cannot attain the expected influence threshold. More importantly, due to the expected coverage guarantee, the actual influence in a campaign may drift from the threshold evidently. Thus, the advertisers would like to request for a probability guarantee of reaching η. This motivates us to further solve the MCSS problem with a <u>p</u>robabilistic <u>c</u>overage <u>g</u>uarantee, termed MCSS-PCG. In this paper, we first propose our algorithm CLEAR to solve MCSS-ECG, which reaches the expected influence threshold without any influence oracle or influence shortfall but a practical approximation ratio. However, the ratio involves an unknown term (i.e., the optimal cost). Thus, we further devise the STAR method to derive a lower bound of the optimal cost and then obtain the first explicit approximation ratio for MCSS-ECG. In MCSS-PCG, it is necessary to estimate the probability that the current seeds reach η, to decide when to stop seed selection. To achieve this, we design a new technique named MRR, which provides efficient probability estimation with a theoretical guarantee. With MRR in hand, we propose our algorithm SCORE for MCSS-PCG, whose performance guarantee is derived by measuring the gap between MCSS-ECG and MCSS-PCG, and applying the theoretical results in MCSS-ECG. Finally, extensive experiments demonstrate that our algorithms achieve up to two orders of magnitude speed-up compared to alternatives while meeting the requirement of MCSS-PCG with the smallest cost.

Economic Assessment of Small-Scale Mountain Dairy Farms by Using Accounting Data: Evidence from an Italian Case Study

Dairy mountain farms are economically disadvantaged due to small farm sizes and high production costs. This situation was exacerbated firstly by the opening of the market linked to the abolition of milk quotas; secondly by the rising prices due to the energy crisis due to covid 19 and the war in Ukraine. However, these farms are important for the preservation of traditional landscapes, the economy of these areas and the offered ecosystem services, especially when they are managed extensively. The objective of this research is to understand if mountain dairy farms are economically sustainable and competitive through the analysis of a case study. The economic analysis takes into account the production cost of milk, profitability, and some economic indicators using accounting data. The results show that the farm profitability is decreasing, and the costs are unsustainable. As a consequence, more efficient policy support is needed to overcome this crisis.

Miniaturized on-chip spectrometer enabled by electrochromic modulation.

Miniaturized on-chip spectrometers with small footprints, lightweight, and low cost are in great demand for portable optical sensing, lab-on-chip systems, and so on. Such miniaturized spectrometers are usually based on engineered spectral response units and then reconstruct unknown spectra with algorithms. However, due to the limited footprints of computational on-chip spectrometers, the recovered spectral resolution is limited by the number of integrated spectral response units/filters. Thus, it is challenging to improve the spectral resolution without increasing the number of used filters. Here we present a computational on-chip spectrometer using electrochromic filter-based computational spectral units that can be electrochemically modulated to increase the efficient sampling number for higher spectral resolution. These filters are directly integrated on top of the photodetector pixels, and the spectral modulation of the filters results from redox reactions during the dual injection of ions and electrons into the electrochromic material. We experimentally demonstrate that the spectral resolution of the proposed spectrometer can be effectively improved as the number of applied voltages increases. The average difference of the peak wavelengths between the reconstructed and the reference spectra decreases from 1.61 nm to 0.29 nm. We also demonstrate the proposed spectrometer can be worked with only four or two filter units, assisted by electrochromic modulation. In addition, we also demonstrate that the electrochromic filter can be easily adapted for hyperspectral imaging, due to its uniform transparency. This strategy suggests a new way to enhance the performance of miniaturized spectrometers with tunable spectral filters for high resolution, low-cost, and portable spectral sensing, and would also inspire the exploration of other stimulus responses such as photochromic and force-chromic, etc, on computational spectrometers.

Signal processing for miniature mass spectrometer based on LSTM-EEMD feature digging

Miniature mass spectrometers exhibit immense application potential in on-site detection due to their small size and low cost. However, their detection accuracy is severely affected by factors such as sample pre-processing and environmental conditions. In this study, we propose a data processing method based on long short-term memory-ensemble empirical mode decomposition (LSTM-EEMD) to improve the quality of on-site detection data from miniature mass spectrometers. The EEMD method can clearly decompose the different physical feature components in the small-scale spectrometer signals, while the LSTM method can adaptively learn the internal feature relationships of the signals. Thus, by combining the two, the parameters for the EEMD signal reconstruction can be optimized in an adaptive manner, obtaining the optimized coefficients. Compared to the previous EEMD feature enhancement approach, the LSTM-EEMD method not only significantly improves the coefficient of determination (R2) and relative standard deviation (RSD) of the data, enhancing the linear range, but also achieves fully adaptive processing throughout the workflow, greatly boosting the efficiency. By leveraging a miniature mass spectrometer, data for N-acetyl-l-aspartic acid (NAA), 2-Hydroxyglutarate (2-HG), and γ-Aminobutyric acid (GABA) in actual blood samples have been obtained. The experimental results demonstrate that the LSTM-EEMD method can markedly enhance the accuracy and usability of the biological sample data in practical testing, providing new perspectives and possibilities for research and applications in the relevant domain.

A cooperative task assignment framework with minimum cooperation cost in crowdsourcing systems

Crowdsourcing provides a new problem-solving paradigm that utilizes the intelligence of crowds to solve computer-hard problems. Task assignment is a foundation problem in crowdsourcing systems and applications. However, existing task assignment approaches often assume that workers operate independently. In reality, worker cooperation is necessary. In this paper, we address the cooperative task assignment (CTA) problem where a worker needs to pay a monetary cost to another worker in exchange for cooperation. Cooperative working also requires one task to be assigned to more than one worker to ensure the reliability of crowdsourcing services. We formalize the CTA problem with the goal of minimizing the total cooperation cost of all workers under the workload limitation of each worker. The challenge is that the individual cooperation cost that a worker pays for a specific task highly depends on the task distribution. This increases the difficulty of obtaining the assignment instance with a small cooperation cost. We prove that the CTA problem is NP-hard. We propose a two-stage cooperative task assignment framework that first assigns each task to one worker and then makes duplicate assignments. We also present solutions to address the dynamic scenarios. Extensive experimental results show that the proposed framework can effectively reduce the cooperation cost.

Efficient general method for numerically modeling laser pulse propagation, overlap, and lifetime effects in amplifiers

An efficient numerical time-dependent general method is developed to address incoherent pulse overlap and lifetime effects in laser amplifiers. The alternating propagation-population laser energetics method (APPLE) has been validated against a semi-discrete coupled rate equation numerical method (SDRE) and analytic formalisms in bounding cases. APPLE is based on decoupled rates applied to a time-dependent framework where both space-time-dependent populations and pulse energetics are consistently updated in each time step. A significant advantage of APPLE lies in its conceptual simplicity, ease of implementation, and relatively small computational cost. SDRE tracks the populations through coupled rates and uses the method of lines to discretize the hyperbolic partial differential transport equations allowing for use of ordinary differential equation solvers. With reasonably sized mesh, we report both energetic and power pulse shape relative differences on the order of one percent between the models over a large range of initial conditions.

Characterization of an Advanced Blast Simulator for Investigation of Large Scale Blast Traumatic Brain Injury Studies.

Blast traumatic brain injury (bTBI) is a prominent military health concern. The pervasiveness and long-term impacts of this injury highlight the need for investigation of the physiological outcomes of bTBI. Preclinical models allow for the evaluation of behavioral and neuropathological sequelae associated with bTBI. Studies have implemented rodent models to investigate bTBI due to the relative small size and low cost; however, a large animal model with similar neuroanatomical structure to humans is essential for clinical translation. Small blast simulators are used to induce bTBI in rodents, but a large animal model demands a larger device. This study describes a large advanced blast simulator (ABS4) that is a gas-detonation-driven system consisting of 5 sections totaling 40ft in length with a cross-section of 4 × 4 ft at the test section. It is highly suitable for large animals and human surrogate investigations. This work characterized the ABS4 in preparation of large-scale bTBI testing. An array of tests were conducted with target overpressures in the test section ranging from 10 to 50 psi, and the pressure-time profiles clearly illustrate the essential characteristics of a free-field blast wave, specifically a sharp peak pressure and a defined negative phase. Multiple blast tests conducted at the same target pressure produced very similar pressure profiles, exhibiting the reproducibility of the ABS4 system. With its extensive range of pressures and substantial size, the ABS4 will permit military-relevant translational blast testing.

Quartz enhanced low sampling trace gas detection

Abstract Dectection of the exhaled gas, which can effectively reflect human health status, analyze and predict diseases has become a research focus. Quartz enhanced photoacoustic spectroscopy (QEPAS) technology has the advantages of small volume, low cost, and high sensitivity, exhibiting great potential in exhaled gas detection. In this work, small volume fractions of CO were detected by QEPAS technology. Optimizing the structure based on finite element method and designing circuit, a detection system with adjustable photoacoustic cell pressure was established. Then, the corresponding relationship between gas volume fraction and response signal was calibrated by the detection of multiple volume fractions of CO. The system has shown a volume fraction detection limit of 531.2×10−9, with a normalized noise equivalent absorption coefficient of 2.86×10−8 cm−1/Hz1/2 and an average detection error of less than 0.8%. The results indicate that our QEPAS system could monitor the exhaled CO trace gas, showing broad application prospects.

Trends and Outcomes in Elective Pediatric Surgery During Weekends

PurposeLimited operating room availability constrains hospital scheduling capacity for elective surgical cases. Leveraging weekends for elective surgical cases could increase operative capacity but must be balanced with practical considerations. Our study aimed to characterize trends and outcomes for elective pediatric surgeries performed during weekends. MethodsThis retrospective cohort study used the Pediatric Health Information System database from 2016 to 2019 to identify surgeries in children <18 years of age from 38 hospitals. Six elective surgeries, commonly performed on the weekend, were selected for analysis. Trends in elective surgeries during weekends (Saturday or Sunday) were evaluated using the Mann-Kendall trend test. Multivariable regression models were used to compare complications and costs between weekend and weekday surgeries. ResultsOf the 233,266 elective surgeries evaluated, 357 (0.15%) were performed during weekend hours. The proportion of surgeries performed on weekends was stable over time (p = 0.65). Following adjustment for clinicodemographic and hospital-level factors, no differences were observed when comparing weekend to weekday surgeries in terms of surgical complications [adjusted Odds Ratio: 1.59; 95% Confidence Interval (CI): 0.65–3.90; p = 0.32] or mortality (n = 1 in cohort). Weekend surgeries were associated a small additional cost compared to weekday surgeries (β-coefficient $312; 95% CI: $152 to $473; p < 0.01). ConclusionElective pediatric surgeries performed during weekends were uncommon, stable in occurrence, and not associated with substantial increases in complications or costs compared to weekday surgeries. Increasing surgical capacity by extending into weekend scheduling merits further assessment of patient and provider satisfaction, unexpected human resource costs, and thoughtful case selection to ensure patient safety. Level of EvidenceIII. Type of StudyRetrospective Cohort Study.

A refined reweighing technique for nondiscriminatory classification.

Discrimination-aware classification methods remedy socioeconomic disparities exacerbated by machine learning systems. In this paper, we propose a novel data pre-processing technique that assigns weights to training instances in order to reduce discrimination without changing any of the inputs or labels. While the existing reweighing approach only looks into sensitive attributes, we refine the weights by utilizing both sensitive and insensitive ones. We formulate our weight assignment as a linear programming problem. The weights can be directly used in any classification model into which they are incorporated. We demonstrate three advantages of our approach on synthetic and benchmark datasets. First, discrimination reduction comes at a small cost in accuracy. Second, our method is more scalable than most other pre-processing methods. Third, the trade-off between fairness and accuracy can be explicitly monitored by model users. Code is available at https://github.com/frnliang/refined_reweighing.

Fuzzy Evaluation Model for Critical Components of Machine Tools

The rapid progression of emerging technologies like the Internet of Things (IoT) and Big Data analytics for manufacturing has driven innovation across various industries worldwide. Production data are utilized to construct a model for quality evaluation and analysis applicable to components processed by machine tools, ensuring process quality for critical components and final product quality for the machine tools. Machine tool parts often encompass several quality characteristics concurrently, categorized into three types: smaller-the-better, larger-the-better, and nominal-the-better. In this paper, an evaluation index for the nominal-the-better quality characteristic was segmented into two single-sided Six Sigma quality indexes. Furthermore, the process quality of the entire component product was assessed by n single-sided Six Sigma quality indexes. According to numerous studies, machine tool manufacturers conventionally base their decisions on small sample sizes (n), considering timeliness and costs. However, this often leads to inconsistent evaluation results due to significant sampling errors. Therefore, this paper established fuzzy testing rules using the confidence intervals of the q single-sided Six Sigma quality indices, serving as the fuzzy quality evaluation model for components of machine tools.

Consulting and advising business startups

Startups are recently established companies, which are mainly based on selling products and/or services through information and communication technologies. They are companies that differ from others in that they have fairly small costs compared to their profits and that also tend to develop significantly. The objective of this investigation was to study the organizations that provide startup consulting and advice to Venezuelan entrepreneurial companies. A qualitative, case study-type investigation was carried out with the support of documentary research, for which 17 experts who work in three organizations that advise startup companies were interviewed. It was determined that the organizations studied converge in that all the experts are specialists in technology, financial, legal aspects, marketing, business models, likewise, the organizations studied specify mission and reasons for undertaking. It was concluded that each institution advises these ventures from its experience and the dynamics of the market that is present, therefore it is recommended to propose lines of action or guidelines, such as financing, technology, marketing, values, among others.

Impact of Social Stories on social and emotional health of autism spectrum primary school children: the ASSSIST2 RCT with economic evaluation.

Differences in the way autistic children experience the world can contribute to anxiety and stress. Carol Gray's Social Stories™ are a highly personalised intervention to support children by providing social information about specific situations in an individual story. This randomised controlled trial aimed to establish whether Social Stories are clinically effective and cost-effective in improving social responsiveness and social and emotional health in children on the autism spectrum in schools. A multisite pragmatic cluster randomised controlled trial comparing Social Stories with care as usual. Eighty-seven schools (clusters) across Yorkshire and the Humber. Two hundred and forty-nine children were randomised via a bespoke system hosted at York Trials Unit (129 Social Stories and 120 care as usual). Recruitment was completed in May 2021. Participants were children aged 4-11 years with a diagnosis of autism, alongside teachers, interventionists and caregivers. Recruitment was via schools, NHS trusts, support groups and local publicity. The intervention included training for educational professionals and caregivers covering psychoeducation and implementation of Social Stories. Stories were written around contextualised goals around the child's need for social information. Interventionists read the Social Story™ with the child at least six times over 4 weeks during school. The primary outcome was the Social Responsiveness Scale-2 completed by teachers at 6 months (the primary end point), which measures social awareness, cognition, communication and behaviour. Data were collected from caregivers and educational professionals at 6 weeks and 6 months through questionnaires. Blinding of participants was not possible. At 6 months, the estimated difference in expected teacher-reported Social Responsiveness Scale-2 T-score (the primary end point) was -1.61 (95% confidence interval -4.18 to 0.96, p = 0.220), slightly favouring the intervention group. The estimated differences for the parent-reported secondary outcomes at 6 months were small and generally favoured the control group except the measure of children's quality-adjusted life-year (+ 0.001, 95% confidence interval -0.032 to 0.035) and parental stress (-1.49, 95% confidence interval -5.43 to 2.46, p = 0.460), which favoured the intervention group. Children in the intervention group met their individual goals more frequently than children who received usual care alone (0.97 confidence interval 0.21 to 1.73, p = 0.012). The intervention is likely to save small costs (-£191 per child, 95% confidence interval -767.7 to 337.7) and maintain a similar quality of life compared to usual care. The probability of Social Stories being a preferred option is 75% if the society is willing to pay £20,000 per quality-adjusted life-year gained. Limitations include considerable disruptions during the coronavirus disease 2019 pandemic. Social Stories are used in schools and represent a low-cost intervention. There is no clinically evident impact on social responsiveness, anxiety and/or depression, parental stress or general health. Benefits were observed for specific behavioural goals as assessed by the teacher, and Social Stories may serve as a useful tool for facilitating dialogue between children and school staff to address specific behavioural challenges. Usage should be at the school's discretion. Given the uncertainty of the results in light of coronavirus disease 2019, further work to establish the impact of Social Stories is merited. This trial is registered as ISRCTN11634810. This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 16/111/91) and is published in full in Health Technology Assessment; Vol. 28, No. 39. See the NIHR Funding and Awards website for further award information.

Analytical Inverse Kinematics for 2-Segment Extensible Continuum Manipulators

Abstract Fast inverse kinematics (IK) algorithm is significant for real-time precise motion control of continuum and soft manipulator. In this paper, we studied an explicit expression of two-segment continuum manipulator based on the constant curvature assumptions and discussed the existence of the IK solutions. This model used pseudo-rigid-body method, unlike 6-axis rigid industrial robot arm, we found that the two-segment extensible continuum manipulators have limited rotation angle around the direction vector of its tip, i.e., this type manipulator has limited dexterity. Then we pre-constrained five degrees of freedom (DOFs) constrained and discussed the definition of the left DOF. The IK solving process is simplified to realize small calculation cost for most applications. This model will provide a robust and real-time way to control the two-segment extensible continuum manipulators.

Is biased mutation sufficient to save runaway sexual selection?

In the 1980s, groundbreaking theoretical studies showed that ornaments displayed during courtship can coevolve with preferences for such ornaments, leading to extreme exaggeration of both traits. Later models cast doubt on such "runaway" sexual selection, showing that even a small cost of preferences can prevent exaggerated ornaments from persisting long-term. It was subsequently shown that if mutations acting on the ornament are biased-tending to produce smaller rather than larger ornaments-then exaggeration can persist even in the presence of preference costs, seemingly vindicating the original models. Here, we unpack an implicit assumption of these "biased mutation" models: Mutations are assumed to lead, on average, to both smaller and less costly ornaments. Biased mutation consequently generates both a fitness cost (due to reduced mating success) and a fitness benefit (due to increased survival). We lift this assumption by separating an individual's investment in an ornament from their efficiency in converting such investment into ornament size. We assume that biased mutation acts only on efficiency but not on investment, and discuss the plausibility of this alternative assumption. Our model predicts that exaggerated ornaments and preferences can persist stably once they arise, but that strong initial preferences are needed to kick-start the runaway process. Consequently, biased mutation alone may not always be sufficient to save runaway sexual selection.

High sensitivity metasurface sensor for estimating the complex permittivity of ionic liquids

In this paper, a sensor based on a metasurface structure for the complex permittivity of ionic liquids is designed. The structure consists of an F4B substrate with a metal resonance pattern, and a polystyrene foam box is used to contain the ionic liquids to achieve non-contact measurements. The simulation results show that without the presence of the ionic liquids, the sensor can achieve a significant reflection spectrum depth of −37 dB at 11.2 GHz, after adding the ionic liquids, the sensor can generate different responses depending on the types of ionic liquids in the frequency range of 8–9.3 GHz. Furthermore, by employing a characteristic matrix model inversion based on the relationships between the complex permittivity, resonance frequency, and quality factor variations, the standard deviations of the real and imaginary parts of the complex permittivity for seven ionic liquids are estimated to be 0.18 and 0.3, respectively. The estimated results show a good agreement with the standard values obtained from probe measurements. The sensor designed in this paper features a small size, simple structure, and low fabrication cost. With a volume of 0.216 milliliters of the sample, it achieves a high sensitivity of 462.87 MHz/ԑ′and frequency detection resolution of 391.57 MHz/ԑ′. It demonstrates excellent discriminatory detection capability for ionic liquids, providing a new direction for the application research of metasurfaces.

Improving the efficiency of combined displacement of ultra-viscous oil from the reservoir by carbon dioxide and coolant

Currently, in the world practice, special attention is paid to the processing of carbon dioxide in the aspect of obtaining it at the field itself, which reduces the cost of the technology of gas capture and injection into the well, because it is important for Russia due to the remoteness of the main part of oil fi elds. Realization of the gas method of oil feedstock extraction increases its efficiency several times taking into account the elimination of the ecological problem of utilization of carbon dioxide formed on a large scale industrially, with the growth of qualitative parameters of extracted commercial oil feedstock, taking into account the relatively small cost of carbon dioxide, the ability to recycle, as well as the use at any stage of fi eld operation. It is obvious that the increase of efficiency of technologies of carbon dioxide influence on the formation mass in complex with a thermal agent for super viscous oil feedstock requires an integrated approach in terms of methodical and empirical studies of regime parameters, applied technological processes and materials. On the basis of systematization of known theoretical data, results of empirical studies and their industrial approbation the authors recommend an algorithm for research of the problem. The article presents empirical substantiation of the ability to increase the effi ciency of exploitation of carbonate deposit of super viscous oil at great depth through the impact of thermal agent and carbon dioxide through the inclusion of the matrix component of the reservoir mass in more mobile development. It is shown that it is possible to incorporate the oil-saturated matrix by means of increasing the mobility of carbon dioxide when the temperature in the reservoir rises, the high degree of solubility of extra-viscous oil there and the additive drop in its dynamic viscosity. The data of empirical studies lead to the conclusion that the level of heating of the deposit and the volume of carbon dioxide supplied serve as optimizable factors, with a rational combination of which can increase the effectiveness of their influence.