Utility Maximization Research Articles

Housing Developers’ Heterogeneous Decision-Making under Negative Shock after the High-Growth Era: Evidence from the Chinese Real Estate Economy

This research uses difference-in-difference (DID) and other empirical methods to analyze firm-level real estate data to discover how heterogeneous firm characteristics affect managers’ decision-making about development expansion when a firm faces a temporary negative sales shock in the Chinese housing market. The manager’s decision is a utility maximization problem under uncertainty, determined by their risk aversion levels, which managers choose to optimize by considering other factors of interest, including career risk and personal wealth. Also, the advance payment rule encourages real estate developers to maintain high turnover, since new projects allow developers to collect cash first. The results show that state-owned enterprises (SOEs) are much more conservative than other types of developers. SOEs tend to focus on current developing projects. Firms with more concentrated management pursue expansion and seek to use new project sales to compensate for their slower growth. Larger developers with headquarters in large cities tend to slow their development speed when they observe negative signals, as they can quickly engage in new projects given these firms’ easy access to financial resources such as bank loans. This study makes a novel contribution to the literature since previous research has tended to focus on the macro market level rather than the firm level. The findings also have strong policy and regulation value. The results indicate that higher cashflow monitoring needs, especially to monitor family-owned developers, to prevent misuse and excessive project expansion.

An efficient multi-timescale regulation strategy for distribution networks based on active and passive resources combined

Introducing solar and wind power into distribution networks (DNs) is an important initiative to promote the cleanliness and low-carbonization operation of DNs. However, the source and load outputs are uncertain in DNs, which will lead to problems of inaccurate regulation and insufficient renewable energy utilization. Therefore, this study provides an efficient multi-timescale regulation strategy for DNs based on active and passive resources combined. First, the day-ahead regulation model is constructed to minimize the total operating cost in a day for DNs. It aims to determine the on-load tap changer (OLTC) and capacitor bank (CB) switching schemes and consider them as the constraints for intraday regulation by power flow unification. Then, an intraday source and load uncertainty model is considered, which aims to cope with the problems of inaccuracies in the day-ahead source and load prediction. Based on this, the objective function of the intraday regulation model is set. It aims to minimize the deviation from the total day-ahead cost and then ensure the optimal system economy and determine the intraday new energy and energy storage system (ESS) short-time action scheme. Finally, the example results show that the proposed scheme can achieve the maximum utilization of renewable energy and improve the low-carbon economic operation of the whole system.

Optimal sizing of PV–Wind generators with a smart EV charging framework to build grid friendly Net Zero Energy Campus

Electric Vehicles (EVs) are creating a significant challenge on the electric grid as they demand huge amounts of power for their charging. On-site power generation through renewable energy sources (RES) is one of the viable approaches to meet the demands of EVs and other loads of a campus. Hence, an optimal sizing of a hybrid PV–Wind system is considered in this work to build a Net Zero Energy Campus (NZEC). Generally, EVs are scheduled after obtaining the size of RES through a sizing procedure. As a unique approach of this work, a smart EV charging framework is developed to model the profile of EV charging demand and this modeled load profile of EV is considered along with the local load profile of the campus for optimal sizing procedure. The size of PV–Wind sources are optimized by minimizing three objective functions such as the annual operating cost of the system (AOC), loss of power supply probability (LPSP) and total energy transfer (TET). The optimization is carried out by Non-Dominated Sorting Genetic Algorithm II (NSGA II). The maximum utilization of RES and the reduced grid congestion attained through the proposed sizing approach showcase the grid friendliness of the designed NZEC.

Enhancing the sodium-ion storage performance of two-dimensional layered Ti3CN with a molecular riveting strategy

Adjustable 2D layered MXenes hold considerable promising as anode materials for sodium-ion batteries. However, the serious self-stacking and volume variation during sodium-ions insertion/extraction processes inevitably result in inferior rate capability and terrible cycle stability. Herein, we report a novel and effective “molecular riveting” strategy to simultaneously adjust the interlayer spacing and stabilize layered structure of Ti3CN (TCN), which is realized by riveting L-Aspartic acid/L-Glutamic acid/L-2-Aminoadipic acid (LAA/LGA/L2AA, abbreviated as LXA) molecules into interlayers of TCN (named as LAA-TCN/LGA-TCN/L2AA-TCN and abbreviated as LXA-TCN) to form strong amido (HN-C = O) bonds. The riveted LXA molecules between the interlayer of TCN contribute double effects of pillar and strain, achieving the maximum utilization of the 2D layered TCN. As consequences, the interlayer spacing of TCN can be broadened from 1.04 nm to 1.31 nm via altering the carbon chain length of LXA molecules, and the Na+ migration barrier can be reduced from 0.71 eV to 0.08 eV. Meanwhile, the L2AA-TCN with maximum interlayer spacing exhibits a significantly improved reversible capacity of 121.7 mAh/g (45.0 mAh/g for TCN) at 1.0 A/g. The constructed L2AA-TCN//NVP sodium-ion battery (SIB) displays excellent cycle stability with the capacity-retention of 96.6 % after 200 cycles at 10 C. This work opens a new way to broaden interlayer spacing and hinder enormous volume variation of 2D layered MXenes.

Optimization of handoff for joint uplink base station association and power control with max‐min fairness in NOMA small‐cell networks

SummaryThe handoff rate and load balancing are two important issues that have a great impact on the spectrum and energy efficiency in the small‐cell networks (SCN). This paper investigates the handoff optimization in SCN with power‐domain non‐orthogonal multiple access (NOMA) that uses successive interference cancelation (SIC), considering the fairness among base stations (BSs). We study the joint base station association and power control problem by considering the motion of mobile users (MUs) and load balancing in the SCNs. Under the maximum allowable transmit power and minimum average‐rate constraints, two optimization problems are formulated using the number of associated MUs, the number of handoffs, and the transmit power of all MUs. The total power consumption minimization and the system‐wide and handoff utility maximization problems are combined into a single‐stage optimization problem through the weighted‐sum method. We solve the formulated problem using a game theory‐based algorithm and primal decomposition theory. The simulation results show that our proposed algorithm can significantly reduce the frequent handoffs and bring a fair power‐controlled BS association in SCN.

Advances on Single‐Atom‐Based Dual‐Site Photocatalysts: Fundamentals, Mechanisms, and Applications

Single‐atom catalysts (SACs) have emerged as leading‐edge research in the field of photocatalysis due to outstanding photocatalytic performance, maximum atomic utilization efficiency, and well‐defined catalytic active sites. However, the singular functionality of active sites in SACs restricts their applications in complex redox reactions involving multiple intermediates and reaction pathways. To circumvent this limitation, a second site comprising single atoms (SA), alloys, clusters, or nanoparticles is proposed to establish SA‐based dual‐site photocatalysts (SA DSPs). While maintaining 100% atomic utilization, the second site and site–site collaboration endow SA DSPs with superior photocatalytic activities to surpass those of SACs. In this review, the latest investigations on SA DSPs featuring diverse compositions, electronic and geometric configurations, site–site collaboration, and metal–support interactions are summarized. Thereafter, the mechanisms underlying enhanced photocatalytic activities are elucidated regarding the roles of the dual sites in charge‐carrier dynamics and surface reactivity. Furthermore, the state‐of‐the‐art applications in photocatalytic hydrogen evolution, CO2 reduction, and methane oxidation, etc., over SA DSPs are discussed. It is anticipated that this review will offer insights into precise control and design of dual sites in photocatalysts, thereby advancing photocatalysis toward commercial viability.

Breast cancer pre-diagnosis based on incomplete picture fuzzy multi-granularity three-way decisions

PurposeThree-way decision (3WD) and probabilistic rough sets (PRSs) are theoretical tools capable of simulating humans' multi-level and multi-perspective thinking modes in the field of decision-making. They are proposed to assist decision-makers in better managing incomplete or imprecise information under conditions of uncertainty or fuzziness. However, it is easy to cause decision losses and the personal thresholds of decision-makers cannot be taken into account. To solve this problem, this paper combines picture fuzzy (PF) multi-granularity (MG) with 3WD and establishes the notion of PF MG 3WD.Design/methodology/approachAn effective incomplete model based on PF MG 3WD is designed in this paper. First, the form of PF MG incomplete information systems (IISs) is established to reasonably record the uncertain information. On this basis, the PF conditional probability is established by using PF similarity relations, and the concept of adjustable PF MG PRSs is proposed by using the PF conditional probability to fuse data. Then, a comprehensive PF multi-attribute group decision-making (MAGDM) scheme is formed by the adjustable PF MG PRSs and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. Finally, an actual breast cancer data set is used to reveal the validity of the constructed method.FindingsThe experimental results confirm the effectiveness of PF MG 3WD in predicting breast cancer. Compared with existing models, PF MG 3WD has better robustness and generalization performance. This is mainly due to the incomplete PF MG 3WD proposed in this paper, which effectively reduces the influence of unreasonable outliers and threshold settings.Originality/valueThe model employs the VIKOR method for optimal granularity selections, which takes into account both group utility maximization and individual regret minimization, while incorporating decision-makers' subjective preferences as well. This ensures that the experiment maintains higher exclusion stability and reliability, enhancing the robustness of the decision results.

Atomically Dispersed Metal Catalysts for the Conversion of CO2 into High-Value C2+ Chemicals.

The conversion of carbon dioxide (CO2) into value-added chemicals with two or more carbons (C2+) is a promising strategy that cannot only mitigate anthropogenic CO2 emissions but also reduce the excessive dependence on fossil feedstocks. In recent years, atomically dispersed metal catalysts (ADCs), including single-atom catalysts (SACs), dual-atom catalysts (DACs), and single-cluster catalysts (SCCs), emerged as attractive candidates for CO2 fixation reactions due to their unique properties, such as the maximum utilization of active sites, tunable electronic structure, the efficient elucidation of catalytic mechanism, etc. This review provides an overview of significant progress in the synthesis and characterization of ADCs utilized in photocatalytic, electrocatalytic, and thermocatalytic conversion of CO2 toward high-value C2+ compounds. To provide insights for designing efficient ADCs toward the C2+ chemical synthesis originating from CO2, the key factors that influence the catalytic activity and selectivity are highlighted. Finally, the relevant challenges and opportunities are discussed to inspire new ideas for the generation of CO2-based C2+ products over ADCs.

Making Solar PV Street Light – A New Convention

Today’s society is completely based on fossil fuels directly or indirectly for every aspect of its existence. The transformation from fossil fuels to green energy is essential for coming decade to mitigate sudden climate change and most importantly to ensure long term sustainability. Though this shifting works well in theory, but the reality is that shifting from fossil fuels is not so easy as it is considered to be most available convenient source of energy. Sustainable solution with modern creative ideas embedded with traditional practices, can reduce the dependency on fossil fuels on everything from energy to infrastructure. Today Light emitting diode (LED) lamps have replaced High intensity discharge (HID) lamps that were used at urban street light. Solar Street lights work on the principle of photovoltaic cell or solar cell. This paper presents an overview of the design and implementation of the solar PV street lights as a new convention in outdoor lighting technology. Solar PV Street lights eliminate the need of grid electricity, making it cost effective and environment friendly in urban and rural areas. The system’s components include solar panel, batteries, light sources, charge controllers and a pole structure. In short, this paper is based on the idea of maintaining the maximum utilization and minimum loss of available energy and also making an approach to increase the longevity of the system’s components with some common techniques and make a shift from conventional to non-conventional source of energy and thus a new convention is proposed.

Analysis of Unmanned Aerial Vehicle-Assisted Cellular Vehicle-to-Everything Communication Using Markovian Game in a Federated Learning Environment

The paper studies a game theory model to ensure fairness and improve the communication efficiency in an unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (C-V2X) communication network using Markovian game theory in a federated learning (FL) environment. The UAV and each vehicle in a cluster utilized a strategy-based mechanism to maximize their model completion and transmission probability. We modeled a two-stage zero sum Markovian game with incomplete information to jointly study the utility maximization of the participating vehicles and the UAV in the FL environment. We modeled the aggregating process at the UAV as a mixed strategy game between the UAV and each vehicle. By employing Nash equilibrium, the UAV determined the probability of sufficient updates received from each vehicle. We analyzed and proposed decision-making strategies for several representative interactions involving gross data offloading and federated learning. When multiple vehicles enter a parameter transmission conflict, various strategy combinations are evaluated to decide which vehicles transmit their data to the UAV. The optimal payoff in a transmission window is derived using the Karush–Khun–Tucker (KKT) optimality conditions. We also studied the variation in optimal model parameter transmission probability, average packet delay, UAV transmit power, and the UAV–Vehicle optimal communication probabilities under different conditions.

Penalized Schemes for Hamilton-Jacobi-Bellman Quasi-Variational Inequalities Arising in Regime Switching Utility Maximization with Optimal Stopping

Penalized Schemes for Hamilton-Jacobi-Bellman Quasi-Variational Inequalities Arising in Regime Switching Utility Maximization with Optimal Stopping

Effective multi-attribute group decision-making approach to study astronomy in the probabilistic linguistic q-rung orthopair fuzzy VIKOR framework

This study employs a novel fuzzy logic-based framework to address multi-attribute group decision-making problems commonly encountered in modern astronomy. Our approach utilizes the probabilistic linguistic q-rung orthopair fuzzy set (PLq-ROFS) to handle the inherent uncertainties associated with astronomical data. The PLq-ROFS offers significant advantages over existing fuzzy sets like probabilistic hesitant, linguistic intuitionistic, and linguistic Pythagorean fuzzy sets, which comprise both stochastic and non-stochastic uncertainties simultaneously. To aggregate the probabilistic linguistic decision information effectively, we propose two novel operators: the PLq-ROF weighted power average (PLq-ROFWPA) and the PLq-ROF weighted power geometric (PLq-ROFWPG). These operators form the foundation of a novel method within the PLq-ROF environment. Furthermore, this study integrates the PLq-ROF framework with the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) model, a widely used decision-making (DM) tool known for its ability to balance group utility maximization with individual regret minimization. This integration leads to the PLq-ROF-VIKOR model, a novel approach for ranking alternative solutions based on the subjective preferences of decision-makers. The effectiveness of the proposed method is demonstrated through a real-world case study in astronomy, accompanied by both parameter and comparative analyses. These analyses highlight the efficiency and accuracy of the PLq-ROF-VIKOR model, ultimately leading to the conclusion that cosmology is the most optimal key finding in this case study.

Does economic education spoil students’ morality? Economists and the Trolley problem

Economic education is frequently blamed for negatively impacting students’ morality, leading to the so-called indoctrination hypothesis. This view is supported by abundant empirical research. Nevertheless, certain studies do not confirm and even gainsay the existence of such a destructive relationship. This article aims to contribute to the discussion by analysing ethical decisions made by the less and more advanced students of Economics when confronted with moral dilemmas based on the Trolley Problem. Additionally, to address the self-selection hypothesis, we compare the choices made by the first-year students of Economics with those of their Sociology counterparts. Assuming that economics teaching affects students’ moral choices and considering the fundamental role of utility maximization in both orthodox economics and standard economic education, one might expect a proportion of “utilitarian” ethical judgments to increase with the advancement of economic studies. Surprisingly, our research does not confirm such an association. Working with a sample of Polish undergraduate students of Economics (N=408) and Sociology (N=123) during the initial three weeks of the academic years 2020/2021 and 2022/2023, we observed that the choices of more advanced economists-to-be are more “deontological” (grounded in norms) than “utilitarian” (grounded in benefits). Therefore, we argue that economic education does not have the power to shake students' ethical choices considerably and transform them into unscrupulous calculating machines. However, certain support was found for the self-selection hypothesis, as the first-year students enrolled in the Economics programme were slightly more prone to “utilitarian” choices than the first-year sociologists.

Inferring to cooperate: Evolutionary games with Bayesian inferential strategies

Strategies for sustaining cooperation and preventing exploitation by selfish agents in repeated games have mostly been restricted to Markovian strategies where the response of an agent depends on the actions in the previous round. Such strategies are characterized by lack of learning. However, learning from accumulated evidence over time and using the evidence to dynamically update our response is a key feature of living organisms. Bayesian inference provides a framework for such evidence-based learning mechanisms. It is therefore imperative to understand how strategies based on Bayesian learning fare in repeated games with Markovian strategies. Here, we consider a scenario where the Bayesian player uses the accumulated evidence of the opponent’s actions over several rounds to continuously update her belief about the reactive opponent’s strategy. The Bayesian player can then act on her inferred belief in different ways. By studying repeated Prisoner’s dilemma games with such Bayesian inferential strategies, both in infinite and finite populations, we identify the conditions under which such strategies can be evolutionarily stable. We find that a Bayesian strategy that is less altruistic than the inferred belief about the opponent’s strategy can outperform a larger set of reactive strategies, whereas one that is more generous than the inferred belief is more successful when the benefit-to-cost ratio of mutual cooperation is high. Our analysis reveals how learning the opponent’s strategy through Bayesian inference, as opposed to utility maximization, can be beneficial in the long run, in preventing exploitation and eventual invasion by reactive strategies.

Cu-based active sites supported on nitrogen-rich polymer derived porous carbon fibers as an oxygen reduction electrocatalyst for zinc-air batteries

The rational design of electrocatalysts with highly exposed active sites and maximized active sites utilization is challenging and urgent problem. Herein, a facile strategy is employed to fabricate Cu-based active sites anchored in nitrogen doped porous carbon nanofiber as an effective and enduring oxygen reduction reaction (ORR) electrocatalyst, which is achieved by the coordination-pyrolysis way of the pre-designed covalent organic polymer (COP) precursors with unique molecular structure. Meanwhile, COP-derived N-doped carbon nanofiber could provide porous structure and facilitate the rapid mass/electron transport, which can insure the strong fastness and high reaction activity of Cu-based active sites. The as-obtained catalyst (Cu0·4/NPC) displays excellent activity (E1/2 = 0.845 V) and stability for ORR performance. Remarkably, the Cu0·4/NPC as cathode catalyst assembled Zn-air battery achieves the peak power density of 142 mA cm−2 and a large specific capacity of 829 mA h g−1 at large current. Additionally, the Cu0·4/NPC catalyst assembled in the solid Zn-air battery achieves the peak power density of 157 mA cm−2. This work offers great opportunity to design advanced ORR catalysts with high density and maximum utilization of active sites.

Bridging the Gulf Between Development Theory and Innovation Theory: The Imperative of Relational Justice

Phenomena of global development and technological innovation have been theorised and debated from an evolutionary common ground of social, economic, and technical change. However, apart from this common ground, there is a gulf that needs to be bridged. In several respects, development theory is preoccupied with social values such as inclusion and equality whereas innovation theory is preoccupied with economic values of economic growth and maximisation of aggregate utility. This gulf of competing values underpins public policies which are unable to deal with modern day crises such as poverty, inequality, environmental degradation, and unsustainable development. I argue that the gulf between development and innovation theories can be critically re-thought and permanently bridged using a relational notion of social justice that can allow inclusion/equality to be affirmed without disincentivising growth/utility. Such a notion of social justice should be only concerned with equalising resources in as long as this mitigates or eliminates relational inequalities such as hierarchies, oppression, and domination in globalised societies.

Cryptocurrency returns and consumption-based asset pricing

ABSTRACT This paper examines whether the cross-section of cryptocurrency returns is captured by risk factors based on consumption-based asset pricing. It is an imperative task for financial economists to find the fundamental risk behind characteristic-based cryptocurrency factors in order to economically understand cryptocurrency. To address the data availability issue in the analysis of cryptocurrency, we employ mixed data sampling (MIDAS) regression to check the relation between the principal component analysis (PCA) factors in cryptocurrency returns and the factors in the consumption capital asset pricing models (CCAPMs) and intertemporal capital asset pricing models (ICAPMs). We establish significant links between them, which in turn implies that cryptocurrency returns are investors’ compensation for bearing consumption risk, conditional consumption risk, and intertemporal consumption risk. This finding underscores that cryptocurrency returns are the manifestation of macroeconomic equilibrium derived from investors’ utility maximization.

Research on the Improvement of Cable Ampacity in Dense Cable Trench

Due to the influence of many factors, distribution cables are often densely placed at the bottom of the cable trench. As a result, it is easy for distribution cables to become the thermal bottleneck of the whole transmission line. To address this dilemma, this paper establishes a finite element simulation model of a cable trench to analyze the hot spots of cables with different arrangements in the cable trench. Then, the model’s accuracy is verified based on real temperature rise experiments. For an arrangement with overheating risk, the ampacity improvement method of filling the cable trench with high-thermal-conductivity material was proposed, and the ampacity improvement effect under different filling ratios was assessed. Finally, combined with the analysis of economic benefit and cost, the method of determining the optimal filling ratio was used, and the impact resistance of the cables under the impact of new energy load was analyzed. The results indicate that, for the case of the optimal filling ratio, the cables in the dense cable trench showed superior impact resistance. The investigations in this paper make significant contributions to the promotion of the maximum utilization of cables.

Clinical Turing tests with user certainty analysis to create and validate synthetic electrocardiogram images for artificial intelligence-enhanced algorithm development

Abstract Background Artificial intelligence-enhanced electrocardiogram (AI-ECG) algorithms have primarily been created using digitised signal data, owing to a relative absence of publicly available image-based datasets. ECGs are often scanned or photographed into electronic health records. For maximum clinical utility, AI-ECG algorithms should be applicable to these data. Synthetic data could expedite the creation of extensive, fully anonymised image-based ECG datasets to permit training image-based AI algorithms, but it is essential that such datasets contain the artefacts encountered in clinical practice. We investigated whether iterative clinical Turing tests with user certainty analysis could be used to develop and validate synthetic ECG data. Purpose To create synthetic ECG images containing the artefacts typically encountered in clinical practice, and to validate the images through iterative Turing testing and user certainty analysis. Methods Synthetic ECG images containing artefacts were created using the PTB-XL dataset (a publicly available signal-based dataset comprising 21799 ECGs) as source data. Iterative clinical Turing tests were conducted where healthcare professionals completed an online survey comprising 60 real and synthetic ECGs. Participants were asked to select whether they thought ECGs were real or synthetic. For user certainty analysis, participants were asked to rate their confidence in their answers using a five-point Likert scale (Figure 1). Likert scale responses were converted into a signed ordinal scale representing user certainty in the identification of real or synthetic data. This scale was used to perform Receiver Operating Characteristic (ROC) analysis. Following quantitative survey completion, qualitative feedback was sought and used to iteratively improve the realism of the synthetic images. Results A total of 26 healthcare professionals completed the clinical Turing tests over three rounds. Qualitative feedback was used to improve the fidelity of the synthetic ECG images between rounds (Table 1). During iterative testing, the proportion of synthetic ECGs correctly identified fell from 61.5% to 53.7%, and the proportion of real-world ECGs correctly identified fell from 66.3% to 53.0% (Figure 1). Following the final Turing test, ROC analysis revealed no discriminative ability for identifying synthetic data (C-statistic 0.480, 95% confidence interval 0.432-0.529). Conclusion Iterative Turing testing with user certainty analysis and qualitative user feedback may be used to create synthetic ECG images containing the artefacts typically encountered in clinical practice. Iterative Turing testing improved the images’ realism confirming their potential to augment image-based AI algorithm development. The presented methodology establishes a framework to develop high fidelity, synthetic patient datasets presenting a significant opportunity to enhance the uptake of AI within electrophysiology, cardiology, and medicine.

Design and implementation of student management system of integrated programmable device programming system

With the continuous growth and development of society, the reform of higher education is gradually taking shape, and colleges and universities are taking more and more responsibilities in promoting education. The main element of university management is the student management system, which is very important to the development of the university. Under the objective environment of colleges and universities seeking to expand the scale of running schools and rapid economic and social development, the current student management system has been unable to meet the various needs of contemporary students. The integration of programmable device programming systems offers a student management system distinct advantages in terms of reliability, flexibility, and user-friendly operation. This study focuses on developing an effective and affordable student management system by incorporating a programmable device programming system. To evaluate the system's performance, this paper suggests the utilization of a BP neural network, renowned for its high nonlinear approximation capabilities and effectiveness in handling complex nonlinear functional relationships. The experimental findings highlight a significant contribution, demonstrating that the system achieved a throughput of 180 times per second, with a maximum CPU utilization of 99%. Notably, the system's stability exceeded 95%, contrasting sharply with the traditional student management system's stability at a mere 65%. These results underscore the substantial contribution of the proposed system, showcasing its enhanced stability compared to conventional student management systems.