Trade-off Issue Research Articles

Cost-Effective Active Semi-Supervised Learning on Multivariate Time Series Data With Crowds

Traditional active learning algorithms have several limitations: 1) they cannot obtain satisfactory results on high dimensional datasets, especially for multivariate time series (MTS) data; 2) traditional crowd-based labeling approaches do not consider the swarm intelligence of crowds, which cannot guarantee the confidence of labeling results; and 3) up to now, few works have addressed the issue of tradeoff between the labeling accuracy and the labeling cost with crowdsourcing labelers. There is also a lack of research on crowd-based active learning on MTS data. To efficiently address the above issues, we propose a new framework of active semi-supervised learning. First, two criteria are advanced to measure the importance of an unlabeled sample from different perspectives. A dynamic time wrapping (DTW)-based similarity matrix is used to represent the MTS data. Next, to confidently label the most valuable samples with minimum cost, we advance a cost-effective crowd selection model and an adaptive labeler selection approach (ALS) to select the most suitable labelers, which could minimize the total cost of labeling and achieve better classification performance. Experiments on ten datasets show the effectiveness of our proposed methods.

Do people really support conservation? Evidence from China and the United States

Survey studies have consistently shown strong public support for biodiversity conservation, even though conservation regulations often face strong local resistance when they are implemented. This seemingly contradictory phenomenon is due to the fact that previous polls have sought opinion only in the abstract, yet many conservation measures are implemented at the expense of local economic development, and policymakers often face a ‘Conservation-Development Tradeoff’-a policy dilemma that remains understudied by social scientists. To fill this gap, this study conducts one of the first comparative surveys on public support for biodiversity conservation in China and the United States. I measure public support for conservation using two separate question versions online. The ‘regular version’ asks directly about individual support for biodiversity conservation, and the “tradeoff version” asks the same question but in the context of the policy tradeoff. The results show that both American and Chinese citizens are highly supportive of conservation in the abstract. But after they learn about the tradeoff effect, the aggregate support falls sharply, and the Chinese citizens show significantly less support compared to their American counterparts. The survey findings expanded on existing academic research on public attitudes toward conservation policies and can encourage future conservation policymakers to understand the significance of the tradeoff issue, as conventional wisdom may have overestimated public support for biodiversity conservation. Moreover, as China has become a crucial player in global conservation governance, understanding the Chinese public sentiment on the real-world policy dilemma can have important implications for conservation policymaking in the world.

Decoupling the trade-off between thermoelectric and mechanical performances for polymer composites via interfacial regulation

Despite the explosive achievements in thermoelectric (TE) composites, their mechanical (ME) performance has received little attention relative to the significant enhancement of the TE performances. Furthermore, there always exists a trade-off between TE and ME performances, i.e., both are difficult to be improved simultaneously. Herein, we realize the decoupling of the trade-off between TE and ME performances for flexible polycarbonate (PC)/single-walled carbon nanotube (SWCNT) composites via interfacial adjustment. The barrel-shaped droplet-like PC granules are observed on the surfaces of the acid-treated SWCNTs (a-SWCNTs), indicating high wettability and interfacial affinity. The obtained PC/a-SWCNT composite displays a maximum power factor (PF) value of 6.2 μW m−1 K−2, which distinctly overmatches the PC/pristine SWCNT (p-SWCNT) with a PF value of 4.8 μW m−1 K−2. In addition, the composites reveal the increments of 22% and 16% in the fracture strength and tensile modulus over the PC/p-SWCNT composite, respectively. This decoupling of the trade-off is ascribed to the high electrical conductivity of a-SWCNTs and the strong interfacial interaction between PC and a-SWCNTs. The present work provides an easy and promising methodology to decouple the intractable trade-off issue of TE and ME performances and benefits their practical TE applications.

User-friendly fault detection method for building chilled water flowmeters: Field data validation

In building HVAC systems, a chilled water flowmeter is an important sensor. Its reading can be used to measure the real-time cooling load, which is a critical variable for the automated control of building HVAC systems, especially central chiller plants. To maintain accurate reading data, fault detection and diagnosis (FDD) of flowmeters are necessary. The available FD/FDD methods for chilled water flowmeters have several common disadvantages: (1) high requirements on sensor integrity: multiple sensors are usually involved in the construction of energy balance models; (2) complex methodology: the fault of any monitored sensor could trigger a detection hit, and a diagnosis procedure must be used to isolate the faulty sensor; and (3) the more sensors that are involved, the more difficult it is to collect fault-free historical data for the establishment of a fault-free benchmark. To address these problems, a user-friendly fault detection (FD) method for building chilled water flowmeters is proposed in this study. The proposed method requires three types of variables to function: the pump frequency, the pump power, and the measured chilled water flow rate on the header pipe. The real-time pump frequency and electrical power data that are collected by VFDs/power meters are used to estimate the true value of the chilled water flow rate with random forest regressor. An auxiliary alarm rule is proposed to tackle the tradeoff issue between detector sensitivity and false alarm rate. Field data of a real HVAC system are used in a case study to evaluate the performance of the proposed method in comparison with three existing FD methods. The results of the validation case study suggest that compared to existing FD methods, which are based on the physical pump model, ensemble learning and clustering techniques, the proposed method could realize higher hit rates and higher alarm rates when confronting different faults (bias, noise and drift) at various levels. Besides, due to the proposed alarm rule, false alarms caused by occasional data errors are completely evaded. Sensitivity analysis to the proposed method indicates that the random forest with default hyperparameters is good enough for the FD task. In brief, the good features of the proposed method (i.e., the simple workflow, low sensor requirements, accessible robust input variable data, handy estimation model, auxiliary alarm rule with tight detection threshold) render the proposed method feasible and user-friendly for engineering practice.

Environment and Energy? The Impact of Environmental Management Systems on Energy Efficiency

Problem definition: Despite the widespread adoption of environmental management systems (EMS), their relationship with energy efficiency remains unclear. This study investigates whether and how EMS adoption lowers energy efficiency and, if so, how to mitigate the impact. Academic/practical relevance: Understanding the relationship between EMS and energy efficiency is important because any decrease in energy efficiency may eventually lead to undesired environmental outputs (e.g., CO2), potentially contributing to climate change. This study sheds light on energy as a necessary input required for environmental management, thereby elucidating the issue of environmental trade-offs in sustainable operations. Methodology: Using novel panel data on energy efficiency and EMS standards obtained from 2,690 South Korean plants operating between 2001 and 2014, this study empirically investigates the impact of EMS on energy efficiency at the plant level. Results: We provide evidence of a trade-off relationship between environmental performance and energy efficiency in relation to EMS adoption. We find that the adoption of ISO 14001, the representative EMS standard, results in approximately 6%–12% lower energy efficiency compared with nonadoption although it effectively reduces air, water, and waste pollution. More importantly, our results show that the trade-off, an unintended consequence of EMS implementation, can be moderated through quality management capability. Managerial implications: We suggest that sustainability managers take balancing actions to reduce trade-offs between environmental performance and energy efficiency. Specifically, when implementing EMS, managers are advised to (1) actively incorporate energy management activities, (2) employ more comprehensive measurements for energy efficiency, and (3) redesign incentive schemes to facilitate improvements in energy efficiency.

Multi-CartoonGAN with Conditional Adaptive Instance-Layer Normalization for Conditional Artistic Face Translation

In CycleGAN, an image-to-image translation architecture was established without the use of paired datasets by employing both adversarial and cycle consistency loss. The success of CycleGAN was followed by numerous studies that proposed new translation models. For example, StarGAN works as a multi-domain translation model based on a single generator–discriminator pair, while U-GAT-IT aims to close the large face-to-anime translation gap by adapting its original normalization to the process. However, constructing robust and conditional translation models requires tradeoffs when the computational costs of training on graphic processing units (GPUs) are considered. This is because, if designers attempt to implement conditional models with complex convolutional neural network (CNN) layers and normalization functions, the GPUs will need to secure large amounts of memory when the model begins training. This study aims to resolve this tradeoff issue via the development of Multi-CartoonGAN, which is an improved CartoonGAN architecture that can output conditional translated images and adapt to large feature gap translations between the source and target domains. To accomplish this, Multi-CartoonGAN reduces the computational cost by using a pretrained VGGNet to calculate the consistency loss instead of reusing the generator. Additionally, we report on the development of the conditional adaptive layer-instance normalization (CAdaLIN) process for use with our model to make it robust to unique feature translations. We performed extensive experiments using Multi-CartoonGAN to translate real-world face images into three different artistic styles: portrait, anime, and caricature. An analysis of the visualized translated images and GPU computation comparison shows that our model is capable of performing translations with unique style features that follow the conditional inputs and at a reduced GPU computational cost during training.

Integrating ecosystem markets to co-ordinate landscape-scale public benefits from nature

Ecosystem markets are proliferating around the world in response to increasing demand for climate change mitigation and provision of other public goods. However, this may lead to perverse outcomes, for example where public funding crowds out private investment or different schemes create trade-offs between the ecosystem services they each target. The integration of ecosystem markets could address some of these issues but to date there have been few attempts to do this, and there is limited understanding of either the opportunities or barriers to such integration. This paper reports on a comparative analysis of eleven ecosystem markets in operation or close to market in Europe, based on qualitative analysis of 25 interviews, scheme documentation and two focus groups. Our results indicate three distinct types of markets operating from the regional to national scale, with different modes of operation, funding and outcomes: regional ecosystem markets, national carbon markets and green finance. The typology provides new insights into the operation of ecosystem markets in practice, which may challenge traditionally held notions of Payment for Ecosystem Services. Regional ecosystem markets, in particular, represent a departure from traditional models, by using a risk-based funding model and aggregating both supply and demand to overcome issues of free-riding, ecosystem service trade-offs and land manager engagement. Central to all types of market were trusted intermediaries, brokers and platforms to aggregate supply and demand, build trust and lower transaction costs. The paper outlines six options for blending public and private funding for the provision of ecosystem services and proposes a framework for integrating national carbon markets and green finance with regional ecosystem markets. Such integration may significantly increase funding for regenerative agriculture and conservation across multiple habitats and services, whilst addressing issues of additionality and ecosystem service trade-offs between multiple schemes.

A Novel Channel Model and Optimal Power Control Schemes for Mobile mmWave Two-Tier Networks

We present a unified system model and framework for the analytical performance study of two heterogeneous and physically-distinct, but coexisting, networks that work harmoniously at the same time, space, and frequency domains. The two-tier network model considered in this paper is an overlaying of femtocells on a macrocell. Overlaying femtocells improves the performance by offloading traffic from macrocells and providing spatial diversity. The mmWave channel model employed considers the number of clusters and rays within each cluster to vary due to the end-user mobility. This is a new and different model compared to the widely used channel models for mmWave two-tier networks. Optimal power control is formulated as a sum-rate maximization problem for downlink and uplink transmissions at two-tier networks and a power allocation scheme is proposed by following Shannon-Hartley theorem. A comprehensive and interesting performance investigation is provided, where it is shown that the upper bound on the number of admitted secondary users has a linear relationship with the outage probability threshold, logarithmic relationship with SINR and exponential relationship with channel gain factors. Simulation results show that the proposed scheme with algorithm 2 is very effective at managing the cross-tier interference and can outperform a competitive scheme from literature that is based on cognitive radio technology. The computational complexity analysis of proposed algorithms are also given, since the complexity of algorithm 2 can be a performance-complexity trade-off issue for systems with limited computation power and time requirements.

Integration of game theory and response surface method for robust parameter design

Robust parameter design (RPD) is to determine the optimal controllable factors that minimize the variation of quality performance caused by noise factors. The dual response surface approach is one of the most commonly applied approaches in RPD that attempts to simultaneously minimize the process bias (i.e., the deviation of the process mean from the target) as well as process variability (i.e., variance or standard deviation). In order to address this tradeoff issue between the process bias and variability, a number of RPD methods are reported in literature by assigning relative weights or priorities to both the process bias and variability. However, the relative weights or priorities assigned are often subjectively determined by a decision maker (DM) who in some situations may not have enough prior knowledge to determine the relative importance of both the process bias and variability. In order to address this problem, this paper proposes an alternative approach by integrating the bargaining game theory into an RPD model to determine the optimal factor settings. Both the process bias and variability are considered as two rational players that negotiate how the input variable values should be assigned. Then Nash bargaining game solution technique is applied to determine the optimal, fair, and unique solutions (i.e., a balanced agreement point) for this game. This technique may provide a valuable recommendation for the DM to consider before making the final decision. This proposed method may not require any preference information from the DM by considering the interaction between the process bias and variability. To verify the efficiency of the obtained solutions, a lexicographic weighted Tchebycheff method which is often used in bi-objective optimization problems is utilized. Finally, in two numerical examples, the proposed method provides non-dominated tradeoff solutions for particular convex Pareto frontier cases. Furthermore, sensitivity analyses are also conducted for verification purposes associated with the disagreement and agreement points.

Toward Simultaneous Achievement of Outstanding Durability and Photoelectrochemical Reaction in Cu2O Photocathodes via Electrochemically Designed Resistive Switching (Adv. Energy Mater. 39/2021)

Resistive Switching In article number 2101905, Hyung Koun Cho and co-workers solve the trade-off issue between photo-charge transport and stability originating from the use of insulating protection layers. The electrochemical forming process suggested for the TiO2 protection layer on photocathodes draws inspiration from typical resistive switching behaviors in resistive-type memory with metal/insulator/metal (MIM) structures. From the electrochemical filament forming process and selective Pt-photoelectrodeposition on filaments, the Cu2O/AZO/TiO2 photocathodes exhibit an unprecedented photocurrent density and open-circuit potential and produce vigorous hydrogen and oxygen evolution.

Factors Shaping the Recycling Systems for Plastic Packaging Waste—A Comparison between Austria, Germany and The Netherlands

Setting up strategies for a sound management of plastic packaging waste (PPW) is becoming increasingly crucial at many levels of the value chain in Europe. After the very first implementation of an extended producer responsibility scheme in Germany in 1991, many EU Countries followed. This resulted in a complex network of schemes that differ from one member state to another. This paper brings together the three latest studies describing the current flows of PPW across the waste value chain from Austria (reference year 2013), Germany and the Netherlands (reference year 2017). With this aim, the models of the three single studies have been adapted to fit into a common model, allowing to perform a comparative analysis. Although with a relatively comparable product market, the three countries have different management systems (e.g., separate collection systems, target sorting products and treatment of residual waste), reflecting different national strategies to achieve the circular economy targets. Recycling rates (in terms of washed milled goods at the output of the recycling process) for the three countries resulted in 23%, 43% and 30% of the total mass of PPW generated in, respectively, Austria, Germany and the Netherlands. The fraction of mixed recycled plastics, relevant for Germany and the Netherlands only, was determined to be one of the major determinants of the differences in recycling rates. Furthermore, the discussion revolves around new political targets that have the potential to contribute to addressing the issue of tradeoff between quantity and quality of recycled plastics placed on the market, with measures such as design-for-recycling and eco-modulation of EPR fees playing a critical role, while also pointing out the aspects that inevitably hinder closed-loop recycling.

A Multi-Threshold Ant Colony System-based Sanitization Model in Shared Medical Environments

During the past several years, revealing some useful knowledge or protecting individual’s private information in an identifiable health dataset (i.e., within an Electronic Health Record) has become a tradeoff issue. Especially in this era of a global pandemic, security and privacy are often overlooked in lieu of usability. Privacy preserving data mining (PPDM) is definitely going to be have an important role to resolve this problem. Nevertheless, the scenario of mining information in an identifiable health dataset holds high complexity compared to traditional PPDM problems. Leaking individual private information in an identifiable health dataset has becomes a serious legal issue. In this article, the proposed Ant Colony System to Data Mining algorithm takes the multi-threshold constraint to secure and sanitize patents’ records in different lengths, which is applicable in a real medical situation. The experimental results show the proposed algorithm not only has the ability to hide all sensitive information but also to keep useful knowledge for mining usage in the sanitized database.

Valuing Forest Ecosystem Services. Why Is an Integrative Approach Needed?

Among the many types of terrestrial ecosystems, forests have some of the highest levels of biodiversity; they also have many interdependent economic, ecological and social functions and provide ecosystem services. They supply a range of tangible, marketable goods, as well as a variety of nonmarketable and intangible services derived from various forest functions. These translate into social, cultural, health and scientific benefits for people’s quality of life. However, because they cannot be traded on a market, nonmarketable and intangible services are often perceived as free, inexhaustible and, as a result, underestimated. The human–nature interaction has affected both nature (via resource consumption) and society (via development of human welfare and well-being). Decision-makers, both public and private, often manage natural capital for multiple aims. In recent years it has been found that the single, individual approach estimating the value for these goods and services is not able to provide information that generates and supports decisions and policies in complex areas of current relevance such as the constant loss of biodiversity, climate change and global warming in close connection with the need for social development and ensuring an acceptable level of well-being for the greatest part of humanity. An integrated assessment with advanced techniques and methods using a pluralist framework of a heterogeneous set of values is considered a better approach to the valuation of such complex nature of the ecosystem goods and services. This assessment should take into account both costs and benefits trade-off issues among the multiple uses of ecosystem goods and/or services, especially the relationships between them and how they influence or determine the economic, social and cultural development of society. It should also consider the estimation of the complex inverse effect, from society to nature, whose goods and services can be diminished to exhaustion by the extensive and intensive anthropization of natural ecosystems with major impact on the number and quality of goods and services provided by ecosystems. Research has shown that applying an integrative assessment approach that utilizes tools developed by sustainability sciences could be an important component of future environmental policy making.

Energy Efficiency In Wireless Sensor Network Using Dynamic Duty Cycle Based Sensor Mac

Wireless Sensor Network is created by a huge number of sensor nodes normally powered by batteries and may not easily recharged. To extend the lifetime of the sensor node is a vital issue while designing a protocol in WSN. However, lowering the energy consumption may result in higher latency. To address on such trade-off issues, this paper proposes Sensor MAC (SMAC) dynamic duty cycle to improve SMAC fixed duty-cycle. S-MAC adjusts the duty-cycle based on dynamic utilization and average sleep delay. The proposed topology which is distributive in nature, controls the technique to schedule the nodes to wakeup time slots, and design a SMAC protocol to get benefitted from this topology control for improving the energy-efficiency, delay, and efficiently handling the spatially-correlated contention. Compared to Fixed SMAC the Dynamic SMAC lowers the Energy consumption in Fixed SMAC and performs very good Energy saving in circular, grid and random topologies.

Analysis of Conditions for Reliable Predictions by Moodle Machine Learning Models

In this paper the issue of bias-variance trade-off in building and operating Moodle Machine Learning (ML) models are discussed to avoid traps of get-ting unreliable predictions. Moodle is one of the world’s most popular open source Learning Management System (LMS) with millions of users. Although since Moodle 3.4 release it is possible to create ML models within the LMS system very few studies have been published so far about the conditions of its proper application. Using these models as black boxes hold serious risks to get unreliable predictions and false alarms. From a comprehensive study of differently built machine learning models elaborated at the University of Dunaújváros in Hungary, one specific issue is addressed here, namely the in-fluence of the size and the row-column ratio of the predictor matrix on the goodness of the predictions. In the so-called Time Splitting Method in Moo-dle Learning Analytics the effect of varying numbers of time splits and of predictors has also been studied to see their influence on the bias and the variance of the models. An Applied Statistics course is used to demonstrate the consequences of the different model set up.

An M/PH/1 queue with workload-dependent processing speed and vacations.

Motivated by the trade-off issue between delay performance and energy consumption in modern computer and communication systems, we consider a single-server queue with phase-type service requirements and with the following two special features: Firstly, the service speed is a piecewise constant function of the workload. Secondly, the server switches off when the system becomes empty, only to be activated again when the workload reaches a certain threshold. For this system, we obtain the steady-state workload distribution and its moments of any order. We use this result to choose the activation threshold such that a certain cost function, involving processing costs, activation costs and mean workload, is minimized.

Variants of bat algorithm for solving partitional clustering problems

Clustering is an exploratory data analysis technique that organize the data objects into clusters with optimal distance efficacy. In this work, a bat algorithm is considered to obtain optimal set of clusters. The bat algorithm is based on the echolocation feature of micro bats. Moreover, some improvements are proposed to overcome the shortcoming associated with bat algorithm like local optima, slow convergence, initial seed points and trade-off between local and global search mechanisms etc. An enhanced cooperative co-evolution method is proposed for addressing the initial seed points selection issue. The local optima issue is handled through neighbourhood search-based mechanism. The trade-off issue among local and global searches of bat algorithm is addressed through a modified elitist strategy. On the basis of aforementioned improvements, three variants (BA-C, BA-CN and BA-CNE) of bat algorithm is developed and efficacy of these variants is tested over twelve benchmark clustering datasets suing intra-cluster distance, accuracy and rand index parameters. Simulation results showed that BA-CNE variant achieves more effective clustering results as compared to BA-C, BA-CN and BA. The simulation results of BA-CNE are also compared with several existing clustering algorithms and two statistical tests are also applied to investigate the statistical difference among BA-CNE and other clustering algorithms. The simulation and statistical results confirmed that BA-CNE is an effective and robust algorithm for handling partitional clustering problems.

Simultaneous Transport Promotion and Recombination Suppression in Perovskite Solar Cells by Defect Passivation with Li-Doped Graphitic Carbon Nitride

Among diverse strategies of improving the power conversion efficiency of perovskite solar cells (PSCs), one of the most effective pathways is to simultaneously promote charge transport and suppress charge recombination. Graphite carbon nitride (CN) is a promising additive for blocking the charge recombination in PSCs by passivating the trap states, which, however, usually leads to terrible charge transport performance as a side effect. Herein, we propose that this trade-off issue can be smoothly solved by simply replacing the intrinsic CN with its Li-doped counterpart (CN-Li). Specifically, introducing the CN-Li into perovskite films results in larger grain size with greater crystallinity, which further benefits the reduction of intrinsic defects and the elongation of the fluorescence lifetime. At the device level, the CN-Li modification not only dramatically passivates the trap states and suppresses the charge recombination but also decreases the series resistance and hence boosts the charge transport. Consequently, the CN-Li-modified PSC reaches a champion power conversion efficiency of 18.86% with a prominent short-circuit current density of 24.16 mA cm–2. This work provides new insights into the efficiency optimization strategy of PSCs by simultaneously promoting charge transport and suppressing charge recombination.

Precipitation in nanostructured alloys: A brief review

Precipitates have been utilized to strengthen various alloys since early last century. Nanostructured (NS) or ultrafine grain (UFG) structured alloys were extensively developed in recent decades, facing a common challenge of limited ductility. While introducing precipitates emerged as an effective strategy to solve the strength-ductility tradeoff issue, the precipitation behavior in NS or UFG alloys differs from that in conventional coarse-grained counterparts. This article provides a review on the scientific discovery of precipitation in NS or UFG alloys in the past 20 years, including the formation mechanism and growth of precipitates, the interplay between precipitates and solid solutes, dislocations, or grain boundaries, as well as the consequent mechanical behavior. Specifically, this article covers Al alloys, Mg alloys, Ti alloys, and high-entropy alloys. Modern research trends in this field are highlighted.

Real-time foreground object segmentation networks using long and short skip connections

Foreground object segmentation is an important task with various applications in outdoor surveillance and navigation. Most existing methods focus on accuracy and therefore, are computationally expensive and low in speed, making them difficult to use in actual applications. In this study, we aim to address the issue of accuracy and efficiency trade-off. In particular, in contrast with existing methods that use fine-tuning routine on heavyweight pretrained models and/or optimization techniques to enhance results, we propose a lightweight end-to-end network that can be trained from scratch effectively and efficiently. First, long and short skip connections are used among convolutional blocks and within the bottleneck block. By doing so, information flow within the networks is enhanced during the training stage, and thus, the use rate of parameters in the model is increased, allowing a more compact and efficient network design. Second, we use feature fusions based on element-wise summing before each up-sampling layer to reduce the size of the decoder, accelerate the up-sampling process, and stabilize training convergence. Our proposed method is tested rigorously. In particular, we achieved 1000 times higher speed compared with state-of-the-art methods on CD2014 and SBI2015 datasets with comparable accuracy.