Quality Trade-off Research Articles

Influence of microstructure and surface topography on material removal by the Hirtisation® process

ABSTRACT The Hirtisation® process is an electro-chemical process, wherein the electrolyte can easily access both external and internal surfaces. The process shows promising results in both support structure removal and reduction of surface roughness, and has the potential to solve the productivity and quality trade-off in powder bed fusion – laser beam (PBF-LB) processing. In this study, the role of the microstructure and surface topography on the capability of the Hirtisation® process to lower the PBF-LB produced surface roughness, has been investigated. A detailed microstructure analysis by SEM was used to determine the effect of the Hirtisation® process on removal of sintered powder, the effects on melt pool boundaries and grain boundaries, and thus the final surface quality. The Hirtisation® process significantly reduced surface roughness thanks to the complete removal of sintering powder from the as-built surface. Additionally, preferential material removal was detected along melt pool boundaries, leading to creation of notches of up to 10 µm in depth.

Advancing Persistent Character Generation: Comparative Analysis of Fine-Tuning Techniques for Diffusion Models

In the evolving field of artificial intelligence, fine-tuning diffusion models is crucial for generating contextually coherent digital characters across various media. This paper examines four advanced fine-tuning techniques: Low-Rank Adaptation (LoRA), DreamBooth, Hypernetworks, and Textual Inversion. Each technique enhances the specificity and consistency of character generation, expanding the applications of diffusion models in digital content creation. LoRA efficiently adapts models to new tasks with minimal adjustments, making it ideal for environments with limited computational resources. It excels in low VRAM contexts due to its targeted fine-tuning of low-rank matrices within cross-attention layers, enabling faster training and efficient parameter tweaking. DreamBooth generates highly detailed, subject-specific images but is computationally intensive and suited for robust hardware environments. Hypernetworks introduce auxiliary networks that dynamically adjust the model’s behavior, allowing for flexibility during inference and on-the-fly model switching. This adaptability, however, can result in slightly lower image quality. Textual Inversion embeds new concepts directly into the model’s embedding space, allowing for rapid adaptation to novel styles or concepts, but is less effective for precise character generation. This analysis shows that LoRA is the most efficient for producing high-quality outputs with minimal computational overhead. In contrast, DreamBooth excels in high-fidelity images at the cost of longer training. Hypernetworks provide adaptability with some tradeoffs in quality, while Textual Inversion serves as a lightweight option for style integration. These techniques collectively enhance the creative capabilities of diffusion models, delivering high-quality, contextually relevant outputs.

Improving Lameness Detection in Cows: A Machine Learning Algorithm Application

The deployment of diverse data-generating technologies in livestock farming holds the promise of early disease detection and improved animal well-being. In this paper, we combine routinely collected dairy farm and herd data with weather and high frequency sensor data from 6 farms to predict new lameness events in various future periods, spanning from the following day to 3 weeks. A Random Forest classifier, using input features selected by the Boruta Algorithm, was used for the prediction task; effects of individual features were further assessed using partial dependence plots. We achieve precision scores of up to 93% when predicting lameness for the next 3 weeks and when using information from the last 3 weeks, combined with a balanced accuracy of 79%. Removing sensor data results have tendency to reduce the precision for predictions, especially when using information from the last one,2 or 3 weeks. Moving to a larger data set (without sensor data) of 44 farms keeps the similar balanced accuracy but reduces precision by more than 30%, revealing a substantial a trade-off in model quality between false positives (false lameness alerts) and false negatives (missed lameness events). Sensor data holds promise to further improve the precision of these models, but can be partially compensated by high resolution data from other systems, such as automated milking systems.

Breaking the data barrier: a review of deep learning techniques for democratizing AI with small datasets

Justifiably, while big data is the primary interest of research and public discourse, it is essential to acknowledge that small data remains prevalent. The same technological and societal forces that generate big datasets also produce a more significant number of small datasets. Contrary to the notion that more data is inherently superior, real-world constraints such as budget limitations and increased analytical complexity present critical challenges. Quality versus quantity trade-offs necessitate strategic decision-making, where small data often leads to quicker, more accurate, and cost-effective insights. Concentrating AI research, particularly in deep learning (DL), on big datasets exacerbates AI inequality, as tech giants such as Meta, Amazon, Apple, Netflix and Google (MAANG) can easily lead AI research due to their access to vast datasets, creating a barrier for small and mid-sized enterprises that lack similar access. This article addresses this imbalance by exploring DL techniques optimized for small datasets, offering a comprehensive review of historic and state-of-the-art DL models developed specifically for small datasets. This study aims to highlight the feasibility and benefits of these approaches, promoting a more inclusive and equitable AI landscape. Through a PRISMA-based literature search, 175+ relevant articles are identified and subsequently analysed based on various attributes, such as publisher, country, utilization of small dataset technique, dataset size, and performance. This article also delves into current DL models and highlights open research problems, offering recommendations for future investigations. Additionally, the article highlights the importance of developing DL models that effectively utilize small datasets, particularly in domains where data acquisition is difficult and expensive.

Evolution: A quantity–quality trade-off constrains the evolution of immortality in bacterial endospores

Bacterial endospores are extremely resilient cells, capable of withstanding the most dramatic environmental challenges. New work identifies a trade-off between resistance to UV radiation and germination efficiency, a trade-off mediated by an unexpected sporulation 'contingency locus'.

Rapid evolutionary tuning of endospore quantity versus quality trade-off via a phase-variable contingency locus

The UV resistance of bacterial endospores is an important quality supporting their survival in inhospitable environments and therefore constitutes an essential driver of the ecological success of spore-forming bacteria. Nevertheless, the variability and evolvability of this trait are poorly understood. In this study, directed evolution and genetics approaches revealed that the Bacillus cereus pdaA gene (encoding the endospore-specific peptidoglycan-N-acetylmuramic acid deacetylase) serves as a contingency locus in which the expansion and contraction of short tandem repeats can readily compromise (PdaAOFF) or restore (PdaAON) the pdaA open reading frame. Compared with B.cereus populations in the PdaAON state, populations in the PdaAOFF state produced a lower yield of viable endospores but endowed them with vastly increased UV resistance. Moreover, selection pressures based on either quantity (i.e., yield of viable endospores) or quality (i.e., UV resistance of viable endospores) aspects could readily shift populations between PdaAON and PdaAOFF states, respectively. Bioinformatic analysis also revealed that pdaA homologs within the Bacillus and Clostridium genera are often equipped with several short tandem repeat regions, suggesting a wider implementation of the pdaA-mediated phase variability in other sporeformers as well. These results for the first time reveal (1) pdaA as a phase-variable contingency locus in the adaptive evolution of endospore properties and (2) bet-hedging between what appears to be a quantity versus quality trade-off in endospore crops.

Dual-mode optical projection mapping system: integration of laser speckle contrast and subcutaneous vein imaging

Dual-mode optical imaging can simultaneously provide morphological and functional information. Furthermore, it can be integrated with projection mapping method to directly observe the images in the region of interest. This study was aimed to develop a dual-mode optical projection mapping system (DOPMS) that obtains laser speckle contrast image (LSCI) and subcutaneous vein image (SVI) and projects onto the region of interest, minimizing the spatial misalignment between the regions captured by the camera and projected by a projector. In in vitro and in vivo studies, LSCI and SVI were obtained and projected under single-mode illumination, where either the laser or light-emitting diode (LED) was activated, and under dual-mode illumination, where the laser and LED were activated simultaneously. In addition, fusion image (FI) of LSCI and SVI was implemented to selectively observe blood perfusion in the vein. DOPMS successfully obtained LSCI, SVI, and FI and projected them onto the identical region of interest, minimizing spatial misalignment. Single-mode illumination resulted in relatively clearer and noise-free images. Dual-mode illumination introduced speckle noise to SVI and FI but enabled real-time imaging by simultaneously employing LSCI, SVI, and FI. FI may be more effective for quasi-static evaluations before and after treatment under single-mode illumination and for real-time evaluation during treatment under dual-mode illumination owing to its faster image processing, albeit with a potential tradeoff in image quality.

Large Language Models and the Wisdom of Small Crowds.

Recent advances in Large Language Models (LLMs) have raised the question of replacing human subjects with LLM-generated data. While some believe that LLMs capture the "wisdom of the crowd"-due to their vast training data-empirical evidence for this hypothesis remains scarce. We present a novel methodological framework to test this: the "number needed to beat" (NNB), which measures how many humans are needed for a sample's quality to rival the quality achieved by GPT-4, a state-of-the-art LLM. In a series of pre-registered experiments, we collect novel human data and demonstrate the utility of this method for four psycholinguistic datasets for English. We find that NNB > 1 for each dataset, but also that NNB varies across tasks (and in some cases is quite small, e.g., 2). We also introduce two "centaur" methods for combining LLM and human data, which outperform both stand-alone LLMs and human samples. Finally, we analyze the trade-offs in data cost and quality for each approach. While clear limitations remain, we suggest that this framework could guide decision-making about whether and how to integrate LLM-generated data into the research pipeline.

Assessing the tradeoffs in emissions, air quality and health benefits from excess power generation due to climate-related policies for the transportation sector

As the transportation sector continues to decarbonize through electrification, there is growing interest in quantifying potential tradeoffs in air pollution and health impacts due to potential excess emissions from the power sector. This study investigates air pollution and health impacts of policy-driven changes in the transportation sector and the associated power generation demand in the Northeast and Mid-Atlantic United States. Five illustrative scenarios were designed to capture the effects of different policies under the first mandatory market-based program to reduce greenhouse gases in the US power sector (Regional Greenhouse Gas Initiative—RGGI) and the Transportation and Climate Initiative (TCI). Considering future power generation with new renewable energy investments to meet demands from decarbonized transportation, the scenarios were framed using: 1. 2030 reference cases for both sectors and a hybrid TCI portfolio, 2. Departure from the reference cases defined by Pennsylvania included or not in RGGI, and 3. Power grid emissions estimated under clean energy standard (CES) policy and hybrid TCI portfolio. While the cross-sectoral policy effect on domain-wide concentrations is modest (max ΔPM2.5 ∼ 0.06 μg m3, ΔNO2 ∼ 0.3 ppbv, ΔO3 ∼ 0.15 ppbv), substantial increases in Ohio and West Virginia were attributed to Pennsylvania joining RGGI. With CES enacted and Pennsylvania in RGGI, significant reductions are seen in average concentrations (max ΔPM2.5 ∼ 1.2 μg m3, ΔNO2 ∼ 1.1 ppbv, ΔO3 ∼ 1.7 ppbv) except for Louisiana and Mississippi with corresponding disbenefits. When focusing exclusively on emissions reductions from transportation, the hybrid TCI portfolio had health benefits of 530 avoided adult deaths, and 46 000 avoided asthma exacerbations. With a ‘business as usual’ power grid, these benefits remain comparable and are mainly driven by NO2, followed by PM2.5 and O3. However, if Pennsylvania joins RGGI, total health benefits and spatial distribution change substantially, with a large portion of adverse health impacts moving from TCI states to Ohio and West Virginia. The overall monetized impact of a CES scenario can substantially exceed the estimated average range of 66–69 Billion US$, depending on the interaction with transportation decarbonization strategies and other drivers of exposure.

Managing and aggregating group evidence under quality and quantity trade-offs

Trade-offs between quality and quantity arise in an abundance of contexts concerning group decision making. With the starting point being that group members provide more accurate evidence when they are involved with fewer tasks, team managers often encounter the following dilemma: Should they assign their group members with many tasks (attempting to gather more evidence with lower quality), or with fewer tasks (aiming at receiving less, but more high-quality evidence)? Secondly, what is the optimal way to aggregate the collected evidence from a group, which may be contrasting and varying in accuracy? Should more weight be given to the more accurate group members, or to the larger number of those who provide the same answer? This topic is already studied within the mathematical framework of Terzopoulou and Endriss (2019). In this paper we complement it experimentally, by investigating to what extent people's decision-making patterns are in accordance with the optimal ones proposed by the normative model. Our findings suggest that people understand the task at hand and generally opt for optimal choices, especially in conflict-free cases. Still, a tendency towards overvaluing the importance of additional evidence, despite their accuracy, is observed; this translates into choosing options that align with the majority rule in aggregation problems.

Optimization of nutrient management improves productivity, quality and sustainability of albino tea cultivar Baiye-1.

Proper nutrient management is crucially important to the sustainable development of tea production. Compared to normal green-leaf cultivars, albino tea cultivars produce green tea of superior quality characterized by high contents of amino acids as a result of the hydrolysis of chloroplast proteins at albinism. However, the advantage of albino tea cultivars was offset by inferior growth and yield performance because of low contents of chlorophylls and limited photosynthesis capacity. Our understanding about the nutrition characteristics of albino tea cultivars was very limited. A four-year field experiment was conducted to develop proper nutrient management for Baiye-1 to overcome its weakness of low productivity without a tradeoff in tea quality and environmental risks. The nutrient management schemes were formulated by optimizing the rate and ratio of nitrogen (N), phosphorus, potassium and magnesium together with substitution of chemical fertilizers with organic manures. The total amounts of nutrients in the optimized schemes were reduced by 25% compared to the local farmers' practice (FP). Results showed that optimized rates and ratio of nutrients together with partial substitution of chemical fertilizers with rapeseed cake manure more considerably improved albino tea yield, the contents of free amino acids, total polyphenol and catechins relative to FP. Partial substitution of chemical fertilizers with commercial livestock manure decreased tea quality, which was likely caused by a dilution effect of increasing tea yield and decreasing N status of tea plants. Full organic substitution of chemical fertilizers by rapeseed cake manure improved tea yield and quality but had relatively low agronomic efficiency and profit. The effect of optimized nutrient management schemes was associated with the improvement of nutritional status in tea plants. The present work demonstrated that the optimization of nutrient management considerably improved albino tea yield, quality and profit while decreased the application rate of fertilizers and the intensity of greenhouse gas emissions.

Being an only child and children’s prosocial behaviors: evidence from rural China and the role of parenting styles

Are only children little emperors and more selfish than children with siblings? Drawing on data from the National Children’s Study of China, this research investigates the causal relationship between being an only child and the performance of prosocial behaviors. Results indicate that only children tend to show more prosocial tendencies than their counterparts with siblings, particularly with respect to trait-related, relational, and altruistic prosocial behaviors. In addition, the results are robust having accounted for potential biases due to endogeneity, sex selection preferences, and educational choices. Smaller children are also more sensitive to the status of being an only child. The investigation into why only children might exhibit more prosocial behavior suggests that only children perceive more positive parenting styles compared to their peers with siblings. The findings are consistent with the quantity–quality trade-off theory and present evidence about the formation of prosocial behaviors before the age of 15, challenging the stereotype that only children are more selfish than those with siblings.

Characterizing BVOC emissions of common plant species in northern China using real world measurements: Towards strategic species selection to minimize ozone forming potential of urban greening

Biogenic volatile organic compounds (BVOCs) emitted by plants play a crucial role in ozone (O3) formation. Avoiding urban air quality trade-offs of urban greening involves selecting low-emission and O3 forming potential (OFP) plants. Using a dynamic enclosure system and the TD-GC-MS technique, we measured BVOC emissions from 47 common urban greening plant species in northern China. The potential to form O3 was estimated based on the emission quantification of 63 compounds and their maximum incremental reactivity (MIR) values. The results indicated that isoprene and most of monoterpene dominated BVOC emissions. Most of the broad-leaved trees were isoprene emitter, whereas needle-leaved and some broad-leaved trees emitted monoterpene and sesquiterpene. Alkene emissions, such as 1-hexene, were relatively low, while Phoebe zhennan exhibited higher aromatics such as m-ethyltoluene. Broad-leaved trees had a higher potential for O3 generation compared to needle-leaved trees. BVOC emission rates and OFP varied significantly among plant species, ranging from 0.08 to 112.23 μg/(g h) and 0.33–1130.75 μg/(g h), respectively. To minimize BVOC emissions and O3 pollution, we determined a list of preferred urban greening plants using hierarchical cluster analysis combined BVOC emission rates and OFPs. Based on our findings, we recommend incorporating plants with low BVOC emissions and OFPs, such as Eucommia ulmoides, Firmiana platanifolia, and Cercis chinensis, in future urban greening plans to prevent strengthening O3 pollution in northern China.

Cropland soil nitrogen oxide emissions vary with dairy manure incorporation methods

AbstractSoils contribute 15%–75% of total atmospheric nitrogen oxide (NOx) emissions in agricultural regions during the growing season. However, the impacts of cropland fertilizer management on spatially heterogeneous, temporally episodic NOx emission patterns are highly uncertain. We examine the effects of liquid slurry dairy manure application practices on soil NOx emissions in rainfed, corn‐soybean rotations during spring 2016 and 2017. Daily soil NOx emissions and weekly soil inorganic N measurements were performed in a randomized split–split plot design for 1–4 weeks following manure applications. NOx emissions and soil N with shallow‐disk injection and chisel‐disk manure incorporation methods were compared with unincorporated broadcast practices. Injected manure and chisel‐disk incorporation exhibited two–four times larger mean NOx emissions than those with unincorporated broadcast manure. Larger soil NOx emissions with manure incorporation practices were driven by the predominance of nitrification in these treatments with evidence of soil nitrate production. Soil NOx emission differences between treatments were detectable across order of magnitude changes in daily NOx emissions during two growing seasons. Larger soil NOx emissions associated with manure incorporation practices compared with unincorporated broadcast practices occur alongside larger N2O and smaller NH3 emissions, highlighting important air quality and climate impact tradeoffs for cropland manure fertilizer management choices.

Framework for optimizing AR waveguide in-coupler architectures.

Waveguide displays have been shown to exhibit multiple interactions of light at the in-coupler diffractive surface, leading to light loss. Any losses at the in-coupler set a fundamental upper limit on the full-system efficiency. Furthermore, these losses vary spatially across the beam for each field, significantly decreasing the displayed image quality. We present a framework for alleviating the losses based on irradiance, efficiency, and MTF maps. We then derive and quantify the innate tradeoff between the in-coupling efficiency and the achievable modulation transfer function (MTF) characterizing image quality. Applying the framework, we show a new in-coupler architecture that mitigates the efficiency vs image quality tradeoff. In the example architecture, we demonstrate a computation speed that is 2,000 times faster than that of a commercial non-sequential ray tracer, enabling faster optimization and more thorough exploration of the parameter space. Results show that with this architecture, the in-coupling efficiency still meets the fundamental limit, while the MTF achieves the diffraction limit up to and including 30 cycles/deg, equivalent to 20/20 vision.

Stochastic modeling of integrated order fulfillment processes with delivery time promise: Order picking, batching, and last-mile delivery

To guarantee high customer service and short and accurate lead times, many e-commerce retailers have started to home deliver their customer orders within a few hours or even minutes, also known as quick-commerce order fulfillment. Quick-commerce order fulfillment consists of three main processes: order picking in the warehouse, order batching for delivery, and last-mile delivery. The ultimate delivery performance depends on managing all three processes, which are highly stochastic, and interdependent. We capture this stochasticity and interdependency in an integrated analytical framework and derive approximate analytical expressions for the mean and variance of the total order fulfillment time. We validate the analytical expressions with both in-house detailed process simulations and external-party output measures. We then analyze the delivery cost-service quality trade-offs using an optimization model that minimizes the expected order fulfillment cost with a delivery probability (DP) constraint, focusing on meeting delivery time deadlines. The optimization model determines the number of pickers, the optimal delivery batch size, and the number of vehicles required to deliver the customer orders. Achieving a high delivery reliability comes at a cost. In comparison to the model with DP constraints, we observe that the expected order fulfillment cost averaged over all data parameter settings obtained from the model without DP constraints is 8.9% lower; however, the mean and standard deviation of order fulfillment time increase by 44.1% and 18.6%, respectively, which results in low delivery reliability. We further demonstrate that an integrated analysis of the order fulfillment process is essential to set reliable fulfillment due times.

Quality and quantity trade-offs in clear ice making

This paper is aimed at clear ice making. To this end, a purpose-built apparatus that controls the solidification rate was specially designed and constructed to assess the trade-offs between ice clearness (quality) and production rate (quantity). An image-based ice clearness evaluation technique was devised and adopted to compute a figure-of-merit for the ice quality. 30 ml ice cube samples were produced in a closed ice tray for either prescribed heat flux (650, 1300 and 1700 W m−2) or surface temperature (−4, −8 and −12 °C) at the bottom wall, while a 25 mm EPS insulation was adopted for top and lateral walls. The experiments were carried out using both tap and boiled water, whose initial amount of dissolved gas was monitored. The results pointed out the heat transfer and initial air concentration conditions that rule the quality and quantity trade-offs in clear ice making.

Preference Sensitive Care and Shared Decision-Making in Lumbar Spinal Stenosis: A Scoping Review.

Scoping review. The objective of this study was to conduct a scoping review exploring the extent to which preference sensitivity has been studied in treatment decisions for lumbar spinal stenosis (LSS), utilizing shared decision-making (SDM) as a proxy. Preference-sensitive care involves situations where multiple treatment options exist with significant tradeoffs in cost, outcome, recovery time, and quality of life. LSS has gained research focus as a preference-sensitive care scenario. A scoping review protocol in accordance with "Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews" regulations was registered with the Open Science Framework (ID: 9ewup) and conducted across multiple databases from January 2000 to October 2022. Study selection and characterization were performed by 3 independent reviewers and an unbiased moderator. The search resulted in the inclusion of 16 studies varying in design and sample size, with most published between 2016 and 2021. The studies examined variables related to SDM, patient preferences, surgeon preferences, and decision aids (DAs). The outcomes assessed included treatment choice, patient satisfaction, and patient understanding. Several studies reported that SDM influenced treatment choice and patient satisfaction, while the impact on patient understanding was less clear. DAs were used in some studies to facilitate SDM. The scoping review identified a gap in comprehensive studies analyzing the preference sensitivity of treatment for LSS and the role of DAs. Further research is needed to better understand the impact of patient preferences on treatment decisions and the effectiveness of DAs in LSS care. This review provides a foundation for future research in preference-sensitive care and SDM in the context of lumbar stenosis treatment.

A cost-effective image-based system for 3D geomorphic monitoring: An application to rockfalls

Change-detection monitoring plays a crucial role in geoscience, facilitating the examination of earth surface processes and the mitigation of potential risks due to natural hazards. A significant aspect of this monitoring involves the use of images, enabling 2D to 4D monitoring approaches. Our objective is to bridge the knowledge gap in developing very low-cost camera units by providing insights into specific products, assembly processes, and utilized codes. The presented approach involves prioritizing cost reduction albeit a trade-off in system quality. The results obtained in the study area of Puigcercós cliff in Spain demonstrates the system's efficacy in detecting rockfalls and pre-failure deformation with a notable level of detection of only 8 cm in the change detection analysis. Additionally, two system versions are presented; one emphasizing real-time image transmission, while the other provides a simpler, energy-efficient approach conducive to long-term data capture using a single battery. Both solutions showcase the potential of leveraging very low-cost technology in geohazard monitoring.

A Multi-Objective Mathematical Programming Model for Project-Scheduling Optimization Considering Customer Satisfaction in Construction Projects

The aim of this study was to develop a multi-objective mathematical programming model for the trade-off of time, cost, and quality in the project-scheduling problem (PSP) by taking priorities and resource constraints as well as activity preemption into account. First, a small-sized problem instance that was a sub-project of an oil and gas construction project was used for te validation of the proposed model and algorithm. Subsequently, considering the sensitivity, complexity, and importance of oil and gas projects, the proposed model was implemented in a large-sized oil and gas construction project. Considering the NP-hardness of this problem, the NSGA-II metaheuristic algorithm was used to deal with the time, cost, and quality trade-off problem. Finally, a sensitivity analysis was implemented on the three main parameters of time, cost, and quality to investigate the effects of changes on the results. The findings show that the proposed model is more sensitive to cost changes, so an increase in project costs leads to a drastic change in the values of other objective functions.