Trade-off Research Articles (Page 1)

China's Clean Heating Policy (CHP), aimed at shifting households from coal to electricity for space heating, represents a major residential energy transition initiative. We evaluated its multi-year impacts on outdoor, indoor, and personal exposures to PM2.5 and black carbon (BC) across 50 villages and 1,236 households in rural Beijing. Using a difference-in-differences (DiD) design, we observed a substantial (31 μg/m3) reduction in winter (3 month) indoor PM2.5 (95% CI: -53, -9), but an increase in 24-h indoor BC by 2.6 (0.4, 4.7) μg/m3. CHP-driven reductions in personal exposures were limited, emphasizing the limitations of using single 24-h measurements to estimate "usual" exposure. Outdoor air quality improved in all villages, with no difference between treated versus untreated villages. Exposure-energy trade-off analysis showed that untreated households achieved similar personal PM2.5 reductions at lower cost, with smaller coal use reductions and less electricity expenditures. The CHP significantly reduced seasonal indoor PM2.5, but continued burning of biomass, which was accessible at no cost, limited air quality improvements and may have contributed to the observed increase in 24-h indoor BC. This illustrates how behavioral choices, economic feasibility, and selection of exposure metrics influence the measured impact of household energy transitions.

Purpose This paper aims to analyze the tradeoffs inherent in using text analysis, an increasingly popular methodology for analyzing teams, using McGrath’s (1981) ABC framework as a guide. Design/methodology/approach Noting team research examples, the authors review the ABC framework, which outlines that all research choices have implications for the generalizability over actors (A), precision of measuring behavior (B) and realism of context (C). The authors then use the framework to situate team researchers’ choices around text data collection and text analysis techniques. Findings The paper suggests that there are systematic tradeoffs in how team interaction text is collected and analyzed to investigate team processes. With respect to collection, text data from naturalistic settings offers high contextual realism but lacks the behavioral precision of text obtained in experimental studies. With respect to text analysis techniques, human annotation can capture team behaviors with nuanced contextual details but may lack generalizability. Word count methods offer broad generalizability but sacrifice contextual realism. Meanwhile topic modeling, natural language processing and large language models can uncover team dynamics in large text data sets but often with lower behavioral precision. Originality/value This paper underscores the importance of aligning text analysis methods with research objectives based on the ABC framework. The authors build on the analysis of the team text analysis tradeoff to provide key recommendations for researchers studying teams and team processes.

Background: The choice of mesh in totally extraperitoneal (TEP) inguinal hernia repair remains a subject of debate, particularly regarding the optimal mesh shape. While anatomical and flat meshes are widely used, few studies have rigorously compared the impact of mesh shape while standardizing other surgical factors like fixation. Consequently, there is limited evidence to quantify the clinical and economic value of potential early recovery benefits. We aimed to determine if anatomical mesh shape provides superior early patient-reported outcomes compared to flat mesh when a standardized metallic fixation technique is used in both groups.Methods: This retrospective cohort study included 80 patients undergoing elective TEP inguinal hernia repair. Patients were categorized to receive either a three-dimensional anatomical mesh (n=39) or a standard flat mesh (n=41), with both groups undergoing standardized metallic tack fixation. The primary outcomes were patient-reported outcome measures (PROMs) assessed at one and six months postoperatively. Instruments included the validated Thai version of the Carolinas Comfort Scale (TCCS) for hernia-specific symptoms and the EQ-5D-5L with a Thai value set for health-related quality of life (HRQoL). Results: At the one-month follow-up, the anatomical mesh group demonstrated significantly better outcomes, with lower mean scores across all TCCS domains - Pain (7.77 ± 0.97 vs. 10.14 ± 0.96), Mesh Sensation (7.02 ± 0.92 vs. 9.17 ± 1.14), and Movement (6.95 ± 0.89 vs. 8.91 ± 1.08) - and a higher EQ-5D-5L utility score (0.819 ± 0.019 vs. 0.784 ± 0.016; p < 0.001 for all). By six months, there were no statistically significant differences between the groups in either TCCS domains or EQ-5D-5L scores.Conclusions: When the fixation method is standardized, the anatomical shape of the mesh is associated with a significantly better quality of life and fewer symptoms during the first month of recovery, a benefit that is transient. This study quantifies the value of early postoperative recovery, providing crucial evidence for a trade-off analysis between the higher cost of anatomical mesh and its tangible, short-term clinical benefits. These findings are essential for facilitating informed, value-based decisions in surgical practice.

Does prioritization of COVID vaccine distribution to communities with the highest COVID burden reduce health inequity?

As a common endocrinopathy of reproductive-aged women, polycystic ovary syndrome (PCOS) is characterized by ovarian hyperandrogenism, insulin resistance and preferential abdominal fat accumulation. These characteristics in normal-weight women with PCOS are accompanied by subcutaneous (SC) abdominal adipose stem cells that intrinsically exaggerate lipid accumulation during adipocyte development in vitro in combination with an increased amount of highly-lipolytic visceral fat. PCOS-related adipose characteristics are intimately linked with hyperandrogenism through genetic inheritance and epigenetic events programmed during prenatal and postnatal life. Accordingly, evolutionary theory submits that such events in PCOS may have ancestral origins, providing survival advantages in three contexts: (1) food scarcity with risk of starvation, (2) infectious disease risks, alleviated by visceral and omental fat, and (3) benefits from increased muscularity. But such adaptations also involve costs, given that PCOS-related traits also tend to reduce reproduction, due to oligo-anovulation. This review examines the evolutionary origins of PCOS risk as a syndrome potentiated by environmental mismatches (especially contemporary obesity and low physical activity), combined with adaptive physiological systems governed by trade-offs between survival and reproduction. This hypothesis is supported by a plethora of recent studies on physiological and behavioral differences between subsistence-level and modern westernized populations, and by analyses of survival-reproduction trade-offs in non-human mammals. Studies of PCOS models using prenatally testosterone-treated and naturally-hyperandrogenic animal models provide crucial insights for understanding how today's illnesses likely emerge from ancient developmental-metabolic strategies, and how knowledge about the evolutionary past can help guide current research and the development of more effective therapies.

Prediction-based trade-off analysis of polymer membranes for organic solvent reverse osmosis of hydrocarbons

Environmental and socio-economic Pareto-front trade-off analysis of U.S. PET packaging material in a circular economy

This study proposes a Transformer–NSGA-III multi-objective optimization framework for high-rise residential buildings in Haikou, a coastal city characterized by a hot summer and warm winter climate. The framework addresses four conflicting objectives: Annual Energy Demand (AED), Predicted Percentage of Dissatisfied (PPD), Global Cost (GC), and Life Cycle Carbon (LCC) emissions. A localized database of 11 design variables was constructed by incorporating envelope parameters and climate data from 79 surveyed buildings. A total of 5000 training samples were generated through EnergyPlus simulations, employing jEPlus and Latin Hypercube Sampling (LHS). A Transformer model was employed as a surrogate predictor, leveraging its self-attention mechanism to capture complex, long-range dependencies and achieving superior predictive accuracy (R2 ≥ 0.998, MAPE ≤ 0.26%) over the benchmark CNN and MLP models. The NSGA-III algorithm subsequently conducted a global optimization of the four-objective space, with the Pareto-optimal solution identified using the TOPSIS multi-criteria decision-making method. The optimization resulted in significant reductions of 28.5% in the AED, 24.1% in the PPD, 20.6% in the GC, and 18.0% in the LCC compared to the base case. The synergistic control of the window solar heat gain coefficient and external sunshade length was identified as the central strategy for simultaneously reducing energy consumption, thermal discomfort, cost, and carbon emissions in this hot and humid climate. The TOPSIS-optimal solution (C = 0.647) effectively balanced low energy use, high thermal comfort, low cost, and low carbon emissions. By integrating the Energy Performance of Buildings Directive (EPBD) Global Cost methodology with Life Cycle Carbon accounting, this study provides a robust framework for dynamic economic–environmental trade-off analyses of ultra-low-energy buildings in humid regions. The work advances the synergy between the NSGA-III and Transformer models for high-dimensional building performance optimization.

GenAI has rapidly advanced natural language processing, vision, and multimodal applications and has led to breakthroughs that have never existed before. Nevertheless, such capabilities are mostly driven by large-scale models, which require heavy computational capabilities, consume large amounts of energy, and incur expensive infrastructures to deploy. These needs limit the availability and maintainability of GenAI systems, especially to edge devices and resource-starved settings. This paper examines architectural designs that are lightweight in an effort to optimize efficiency and performance in generative design. The research shows the importance of compact models in preserving competitive performance and reducing knowledge distillation techniques and parameter reduction strategies by a large margin in reducing memory and computational overhead, as well as making the process of compact model generation more manageable and systematic. The analysis goes on to discuss the trade-offs between model size, speed of inference and generative quality, and provides a framework that can be used to assess optimization decisions in the real world. Experimental findings on both image and text generation challenges indicate that lightweight architectures designed with strategic planning can produce the state-of-the-art results with great efficiency advantage, thus eliminating the disparity between research and practice excellence. The results point to the importance of reconsidering the architectural priorities towards the dominance of the raw performance to the priorities of sustainable and inclusive generative intelligence.

Abstract: We present Federated Multi-Omics Transformers (FMOT), a framework for cross-border precision medicine that fuses genomics, transcriptomics, proteomics, and imaging under privacy-preserving federated learning. FMOT introduces a subgroup-fairness head with equalized-odds regularization and auditable training. Synthetic multi-country experiments show monotonic AUROC gains, shrinking EO gaps, and manageable latency across rounds. We provide a mathematical formulation, trade-off analysis, and a governance-ready pipeline. Keywords: Federated learning, multi-omics, transformer, subgroup fairness, equalized odds, cross-border compliance.

Diagnostic Test Accuracy (DTA) analyses are essential in clinical research and medical decision-making. Despite the availability of R packages such as pROC and ROCR, their use often requires programming expertise, limiting accessibility for many clinical researchers. An interactive, code-free method is therefore needed to enhance usability and understanding. We developed DTAShiny, an R Shiny based web application that allows users to upload diagnostic data sets in CSV or Stata formats, perform DTA metric calculations, and generate dynamic visualizations. The application is built using shiny, bs4Dash, pROC, ggplot2, and other packages. It includes heuristic-based automatic detection of reference and test variables and offers real time threshold adjustment via an interactive slider. DTAShiny computes standard sensitivity, specificity, PPV, NPV, AUC and advanced F1 score, balanced accuracy. These DTA metrics are accompanied with approximate confidence intervals. The tool generates ROC and PR curves, distribution plots, and a calibration style plot. Real-time interactivity enables users to observe trade offs as thresholds change. This Zenodo deposit contains the DATAShiny source code, an example anonymised dataset, and documentation to run the app locally.

Since the introduction of the EQ-5D-Y-3L valuation protocol, a considerable number of EQ-5D-Y-3L value sets have been published. This provides an opportunity to explore the differences and similarities between EQ-5D-Y-3L value sets across countries, and their similarity to their EQ-5D-5L counterparts. EQ-5D-Y-3L value set publications for 11 countries identified key methodological, sampling and value set characteristics. Similarity between value sets was assessed using kernel density plots and other key characteristics. Preference patterns between groups of value sets were explored. EQ-5D-Y-3L value set properties were compared with those of EQ-5D-5L value sets from the same country. All EQ-5D-Y-3L valuation studies used the same DCE design. Six studies used expanded health state designs in the composite Time Trade Off. Analytical strategies differed between studies. Values for state 33333 ranged from - 0.691 (Slovenia) to 0.289 (Japan); the number of negative values ranged from 0 to 21%. Pain/discomfort level 3 received the largest weight in all EQ-5D-Y-3L studies, while self-care level 3 received the smallest weight in 8 out of 11 studies. Similarities were found between European value sets, and between Asian value sets. Value sets for Australia and Brazil had similar scale lengths as the Asian value sets, but differed in other ways. Although substantial differences were observed between EQ-5D-Y-3L value sets (e.g. regarding the length of the value scale), striking similarities between them existed (e.g. pain/discomfort consistently received the largest weight). Comparing EQ-5D-Y-3L value sets to EQ-5D-5L values generally suggests less willingness to trade life years for life quality for children.

ABSTRACT It has become commonplace for non‐democratic regimes to integrate information and communication technologies (ICTs) into their governance system. However, how citizens make use of these new digital opportunities in an authoritarian setting remains understudied. Based on an online survey in China in 2021, we find that a surprisingly large share of Chinese citizens engages with the new ICT‐based participatory venues. But ICTs seem to unfold their potential only for certain groups such as tech‐savvy younger citizens with a higher income. Moreover, while citizens seem to be quite aware of the risks of using ICTs, such as data security breaches or digital surveillance, these do not seem to hamper participation. Rather, citizens trade off perceived risks for perceived benefits, such as higher levels of convenience, efficiency, or expected government responsiveness. Our study contributes to the literature on digital governance in authoritarian regimes, and the digital divide and technology acceptance literatures. Moreover, amid the tightening of political control in China, it offers insights into participatory developments under Xi Jinping.

Sexual communication allows individuals to find and choose a mate, but also to avoid hybridization with individuals from different species. Sexual signals can thus play an important role in speciation. However, many sexual signals come with potential trade-offs with other life history traits, which may only be revealed under stressful conditions. To determine if intra-specific variation in sexual signals are affected by limited resource intake during the larval stage, we studied variation in the sex pheromone of the noctuid moth Heliothis subflexa when larvae were reared on limited resources. Females of this species produce acetate esters in their sex pheromone blend that attract males from the same species, while repelling a sympatrically occurring species, Heliothis virescens. As H. subflexa females produce high amounts of acetates when the interfering species is present but low amounts in its absence, we hypothesized that high-acetate sex pheromone signals trade off with female fitness. To identify potential trade-offs between high acetate levels and female fitness, we manipulated the quantity of resources available at the larval stage. We showed that under larval food stress, females with high ratios of acetates in their sex pheromone had longer developmental times and lower fertility compared to females producing less acetates. These results thus support our hypothesis that a balance between costly acetates and the benefit of deterring heterospecific mates may at least partly explain the intra-specific variation in the H. subflexa female sex pheromone blend.

This report evaluates the role of Hybrid Renewable Energy Systems (HRESs) in supporting South Africa’s energy transition amidst persistent power shortages, coal dependency, and growing decarbonisation imperatives. Drawing on national policy frameworks including the Integrated Resource Plan (IRP 2019), the Just Energy Transition (JET) strategy, and Net Zero 2050 targets, this study analyses five major HRES configurations: PV–Battery, PV–Diesel–Battery, PV–Wind–Battery, PV–Hydrogen, and Multi-Source EMS. Through technical modelling, lifecycle cost estimation, and trade-off analysis, the report demonstrates how hybrid systems can decentralise energy supply, improve grid resilience, and align with socio-economic development goals. Geographic application, cost-performance metrics, and policy alignment are assessed to inform region-specific deployment strategies. Despite enabling technologies and proven field performance, the scale-up of HRESs is constrained by financial, regulatory, and institutional barriers. The report concludes with targeted policy recommendations to support inclusive and regionally adaptive HRES investment in South Africa.

Abstract We study college admissions markets where students can attend the same college under different financial terms. The deferred acceptance algorithm identifies a stable allocation where funding is allocated based on merit and the set of meritbased stable allocations is small. When students are heterogeneous in the way they trade off program characteristics and contractual terms, the set of stable allocations is large and different stable allocations differ in the number of assigned students. In Hungary, where such heterogeneity is present, a non-merit-based stable allocation would increase the number of assigned applicants by 1.9% relative to any merit-based stable allocation.

As fully fault-tolerant quantum computers capable of solving useful problems remain a distant goal, we anticipate an era of during which limited error correction is available. We propose a framework for designing early fault-tolerant algorithms by trading between error-correction overhead and residual logical noise, and apply it to quantum phase estimation (QPE). We develop a quantum-Fourier-transform- (QFT) based QPE technique that is robust to global depolarizing noise and outperforms the previous state of the art at low and moderate noise rates. We further introduce the explicitly unbiased maximum-likelihood estimator (EUMLE), a data-processing technique that mitigates errors in QFT-based QPE schemes. EUMLE provides consistent asymptotically normal error-mitigated estimates, addressing the open problem of extending error mitigation beyond expectation-value estimation. Applying this scheme to the ground-state problem of the two-dimensional Hubbard model and various molecular Hamiltonians, we find that we can roughly halve the number of physical qubits with a ∼ 10 times wall-clock time overhead, but that further reduction causes a steep run-time increase. This work provides an end-to-end analysis of early fault-tolerance cost reductions and space-time trade-offs, and identifies which areas can be improved in the future.

Today, as engineering problems become more complex in terms of the effective variables in these problems and the range of their changes and their multidimensionality (in terms of not being able to look at a problem from one dimension and its various dimensions must be considered) And the need to trade off on the goals of these issues, especially issues that are in conflict with each other and the need for simultaneous optimization of these goals, the need to use multi-objective optimization methods has become more apparent. For example, in the design of VLSI circuits that have both power and latency, given that these two parameters have opposite behaviors, that is, by increasing one of them, the other decreases, and we need to optimize both of these parameters together. In such problems, using multi-objective optimization methods, the most optimal state for these two parameters can be obtained simultaneously. Since one-objective optimization methods have shown their ability to deal with various engineering problems, so often with the introduction of any one-objective optimization method after a while, the multi-objective optimization method Based on the same method has been presented by researchers to the scientific community to test and evaluate the ability of optimization based on the same single-objective method in the field of multi-objective optimization. For example, the NSGAII multi-objective optimization algorithm is based on the GA single-objective optimization algorithm, the MOPSO algorithm is based on the PSO, the MOGSA algorithm is based on the GSA algorithm, and the MOIPO algorithm is based on the IPO algorithm. Today, more than 350 one-objective methods have been introduced to the scientific community, which are widely used in various researches and examined and tested, and in terms of quantity and quality, the development of these one-purpose methods is a competition field Researchers have provided, and no necessary effort has been made in multi-objective versions, and as many as the single-objective method has been introduced to the scientific community, the multi-objective method has not been introduced based on those methods. Therefore, in this dissertation, we have tried to multi-objective this method by considering a recently introduced one-objective optimization method called Arithmetic Optimization Algorithm (AOA). Add existing multi-objective methods and enable researchers to use the ability of this single-objective method to deal with multi-objective problems. In this regard, in order to multiply the goals of the arithmetic optimization algorithm, the concept of Pareto optimality has been used to detect non dominated solutions and repository to store these solutions. To measure the performance of the Multi-objective Arithmetic Optimization Algorithm (MOAOA), this algorithm is applied to the famous benchmark functions and to compare the performance of this algorithm with the famous NSGAII and MOPSO algorithms, spacing and generational distance criteria have been used. Based on the results of these experiments, it was found that this algorithm shows acceptable performance compared to popular optimization algorithms.

The reporting of unaccounted intangibles remains a complex and unresolved issue in financial accounting. Recent regulatory developments, including possible revisions to IAS 38 and new sustainability reporting requirements, have renewed interest in how such resources should be measured, reported and disclosed. Through a survey-based analysis this study firstly explores the contingency factors that affect the views of preparers, users, and professionals on the treatment of unaccounted intangibles as well as their costs-benefits considerations. Results show that professional role significantly shapes preferences. From a tradeoffs perspective, preparers generally adopt more conservative positions, consultants and auditors call for reform and users tend to rely on resources other than the financial statements. By examining all key stakeholder groups jointly, the study highlights important considerations for different regimes for accounting on intangibles. These findings offer timely and practical insights for standard setters aiming to improve the relevance of financial reporting.

The integration of Vision-Language Models (VLMs) into autonomous systems is of growing importance for improving Human-Robot Interaction (HRI), enabling robots to operate within complex and unstructured environments and collaborate with non-expert users. For mobile robots to be effectively deployed in dynamic settings such as domestic or industrial areas, the ability to interpret and execute natural language commands is crucial. However, while VLMs offer powerful zero-shot, open-vocabulary recognition capabilities, their high computational cost presents a significant challenge for real-time performance on resource-constrained edge devices. This study provides a systematic analysis of the trade-offs involved in optimizing a real-time robotic perception pipeline on the NVIDIA Jetson AGX Orin 64GB platform. We investigate the relationship between accuracy and latency by evaluating combinations of two open-vocabulary detection models and two prompt-based segmentation models. Each pipeline is optimized using various precision levels (FP32, FP16, and Best) via NVIDIA TensorRT. We present a quantitative comparison of the mean Intersection over Union (mIoU) and latency for each configuration, offering practical insights and benchmarks for researchers and developers deploying these advanced models on embedded systems.