Traditional Framework Research Articles

Смерть А. Чехова как финал эпохи: между журналистикой, литературой и философией (на материале публикаций в газете «Новое время»)

The purpose of the article is to analyze publications in “Novoye Vremya” devoted to the death of A. Chekhov. During the viewing of the newspaper for 1904–1905 it was possible to find more than 50 relevant materials of different genres. These are, in particular, notes on the writer's last days; the meeting of the coffin in St. Petersburg at the Nikolaevsky railway station; the arrangement of the grave at the cemetery of the Novodevichy Monastery; memorial meetings and lectures. The most interesting are the obituaries and memoirs of the leading journalists of “Novoye Vremya”. N. Engelhardt, V. Rozanov and V. Burenin, recognizing Chekhov’s outstanding talent, nevertheless assess him as a minor author and refuse to put him on a par with the great novelists — Goncharov, Dostoevsky, Tolstoy. M. Menshikov and A. Suvorin, in turn, talk about the end of the “golden age” of Russian literature. At the origins of this era stood Pushkin, who overcame romanticism and turned to the poetry of reality, and its finale is associated with the death of Chekhov, who always remained within the framework of the realistic tradition, but mastered techniques that were soon adopted by modernists — new romantics. In a sense, Chekhov mirrored Pushkin’s creative path.

A Transdisciplinary Approach and Design Thinking Methodology: For Applications to Complex Problems and Energy Transition

In this paper, we outline a transdisciplinary approach and design thinking methodology (TADTM) to tackle complex problems. Our premise is that these problems need a fundamental understanding of technological solutions and those for human interactions, business operations, financing, socioeconomic governance, legislation, and regulations. They must be approached by different decisionmakers from different disciplines to establish seamless interactions and structured teamwork. In this regard, we emphasize the need for a transdisciplinary framework that accounts for personal preferences based on human behavior as well as the traditional interdisciplinary frameworks. To test and prove our hypothesis, three case studies are discussed. Case Study 1 is based on our studies at a major medical establishment, and Case Study 2 is about the integrated engineering and architecture approach we used at our university campus. Case Study 3 is based on an ongoing project to lead industrial corporations to change their energy policies with practical energy efficiency measures and by adapting renewable/alternative energy adaptations for their operations. Developing creative solutions and strategies to decrease atmospheric greenhouse gas emissions requires such an energy transition framework and should involve every person, company, entity, and all governments. It can only be achieved with efforts on both local and global levels, which needs to convince (a) industries to change their traditional operation modalities, (b) people to alter their consumption behaviors, and (c) governments to change their rules, regulations, and incentives. The complexity and magnitude of this enormous task demand the coordination and collaboration of all stakeholders, as well as the need for technological innovations.

The Impact of Assessment for 21st Century Skills in Higher Education Institutions: A Narrative Literature Review

This review examines the impact of assessing 21st-century skills, such as critical thinking, communication, collaboration, and creativity, in higher education institutions. Through a narrative analysis of recent literature, the study identifies diverse assessment methodologies and their effectiveness in fostering skill development. Findings suggest that while innovative assessment practices can enhance skill acquisition, challenges remain in aligning these practices with traditional educational frameworks. The review highlights the necessity for institutions to adopt a holistic approach that integrates 21st-century skill assessment into curriculum design and pedagogy. It concludes with recommendations for educators to embrace flexible, competency-based assessments and for policymakers to support the development of frameworks that recognize and reward the acquisition of 21st-century skills. This study underscores the importance of evolving assessment practices to prepare students for the demands of the modern workforce, suggesting directions for future research in optimizing assessment strategies for 21st-century skills in higher education.

Deep-feature-based asymmetrical background-aware correlation filter for object tracking

Correlation filter-based video object-tracking algorithms have gained widespread attention due to their efficiency and excellent tracking performance. However, traditional correlation filtering tracking algorithms possess several limitations. (1) They extract image features only by using rule sampling, which ignores the shape information of the target, resulting in insufficient discriminative power of the features. (2) They focus only on the difference between the background and the object, ignoring the effects of more challenging intra-class interference. (3) They do not further evaluate the reliability of the optimal candidate samples, resulting in easy failure in occlusion scenarios. This paper proposes an asymmetric background-aware correlation filter method for object tracking with deep features to solve the above limitations. First, an asymmetrical background-aware sampling method based on the shape information of the object is proposed. This sampling method significantly differs from the symmetrical sampling method in the traditional correlation filter framework. By exploring the shape information of the object, improving the otherness between the background and object samples is easy, thus suppressing the intra-class interferences. Second, deep neural networks are introduced in the correlation filter framework to extract the deep object features and a spatio-temporal regularization factor is adaptively assigned to suppress the boundary effects, intra-class distractors and aberrance between frames. Finally, a multi-modal object pool is constructed to evaluate the optimal candidate sample in each frame. This template pool fully exploits object diversity and solves the tracking drift and failure caused by invalid appearance changes in scenes such as occlusion and vigorous motion scenarios. To validate the effectiveness of the proposed method, it was compared with the state-of-the-art methods on public datasets. The experimental results show that the tracking performance of the proposed method is competitive.

Nonlinear model updating of the rotor-bearing system by multi-harmonic balance method and analytical sensitivity derivation

Responses of the bearing system are usually predicted or tested, to analyze its nonlinear dynamic characteristics. However, responses predicted from the bearing model constructed by Hertz contact theory usually show differences with their tested counterparts, denoting the improper setting of the nonlinear contact parameters. It is compulsory to reduce such differences and obtain a precise bearing model using nonlinear model updating techniques. In this paper, a novel nonlinear model updating framework is proposed following the Multi-Harmonic Balance Method (MHBM) and the analytical sensitivity analysis, for updating of the rotor-bearing system using the nonlinear responses in the frequency domain. With the derivation of bearing response sensitivities to the nonlinear contact parameters, increments of the nonlinear parameters can be ascertained by the sensitivity inverse and updating of the nonlinear bearing model can be implemented in an iterative way, until a precise bearing model with the response differences reduced to minima is obtained. Firstly, a novel MHBM model is derived for the bearing system, which is fully described by matrix operations and used to further deduce the analytical sensitivity formulations of the bearing responses to the design parameters. Afterwards, the bearing responses and sensitivities in the frequency domain are predicted by the continuation strategy. Finally, model updating is conducted for the bearing system using the traditional inverse-sensitivity framework. Two cases are used to demonstrate the feasibility of the method. The first case is to update bearing parameters of contact stiffness, clearance and eccentricity of a 2-DOF bearing system in order to match the predicted bearing responses with the measured ones in strong nonlinearity with the multivalued phenomenon. The next is to update the Hertz contact parameters of the left and right bearings, together with the disc eccentricity, of a Finite Element (FE) rotor-bearing system. These results show the validity and superiority of the proposed method.

Reconceptualizing Tax Compliance Behavior: A Theoretical Matrix Approach

Tax compliance behavior is a multifaceted and extensively explored phenomenon within behavioral economics. However, due to its intricate nature, achieving a full grasp of this topic remains a challenge. This paper is motivated by the lack of coherent structure and lucidity in the existing theoretical frameworks employed to elucidate tax compliance behavior. Firstly, this paper puts forth a novel perspective that seeks to transcend the traditional binary framework of taxpayer behavior, which typically categorizes taxpayers as either compliant or non-compliant. Instead, this paper posits that taxpayer behavior may exist along a spectrum spanning from complete compliance to absolute non-compliance. This conceptual shift aims to provide a more nuanced understanding of taxpayer behavior by acknowledging the potential for varying degrees of compliance. Secondly, to address the existing gaps in the theoretical foundations of factors influencing tax compliance behaviour, the paper introduces a theoretical matrix. This matrix is intended to serve as an organized framework that succinctly encapsulates the prevalent theories underpinning studies concerning the determinants of tax compliance behavior. By methodically categorizing these theories based on ‘types of compliance’ and ‘types of factors’ dimensions, the matrix provides researchers and practitioners with a cohesive overview of the diverse factors influencing taxpayer behavior.

Asynchronous Federated Learning: Methods and applications

Federated Learning (FL) is a distributed alternative to traditional machine learning frameworks that computes a global model on a centralized aggregation server according to the parameters of local models, which can address the privacy leakage problem caused by collecting sensitive data from local devices. However, the classic FL methods with synchronous aggregation strategies, in many cases, shall suffer from limitations in resource utilization due to the need to wait for slower devices (stragglers) to aggregate during each training epoch. In addition, the accuracy of the global model can be affected by the uneven distribution of data among unreliable devices in real-world scenarios. Therefore, many Asynchronous Federated Learning (AFL) methods have been developed on many occasions to improve communication efficiency, model performance, privacy, and security. This article elaborates on the existing research on AFL and its applications in many areas. The paper first introduces the concept and development of FL, and then discusses in detail the related work and main research directions of AFL, including dealing with stragglers, staleness, communication efficiency between devices, and privacy and scalability issues. Then, this paper also explores the application of AFL in different fields, especially in the fields of mobile device edge computing, Internet of Things devices, and medical data analysis. Finally, the article gives some outlook on future research directions and believes that it is necessary to design efficient asynchronous optimization algorithms, reduce communication overhead and computing resource usage, and explore new data privacy protection methods.

Organic Farming in Agricultural Practices: A Review

Creating sufficient food to address the issues of a developing populace has forever been the biggest worry of food strategy creators all over the planet. The review showed that the main benefits of organic farming are ecological insurance and a higher flexibility to natural changes, expanding ranchers' pay and lessening outside input cost, upgrading social limit and expanding work open doors. As well as upgrading food security essentially by expanding the food buying force of nearby individuals. Notwithstanding, the fundamental difficulties of this food creation framework incorporate lower yields in contrast with traditional frameworks, challenges with soil supplement the board, confirmation and market obstructions, and the instructive and research necessities of little holders. The paper infers that despite the fact that OF could introduce a few huge difficulties to limited scope ranchers, it could/ ought to enhance the overall soil health.

Genealogy of Islamic Business Organization: The Institutional Approach Towards Current Islamic Corporate Law

This article examines the social foundation which posed challenges to the adoption of corporate form as a business entity within the framework of Islamic legal tradition. This article employs juridical-normative research analysis with socio-legal approach. This paper concludes: (1) The corporate legal form was not required by the medieval Islamic legal system as well as the case in Western Europe due to several reason such as: an institutional vacuity; withdrawal of community capital resources into waqf institutions; stagnancy in institutional development of business organizations in Islamic law; and inability of the Muslim business elite to consolidate power. (2) In classical fiqh discussions, Islamic business organizations do not have legal personality. The closest approximation to corporate legal entities found in Islam have been bayt al-māl (public treasury), mosque property, and waqf (trusts). Recently, the scholars have approved the corporate form on the basis of fiqh principles of qiyas (analogy) and istihsan, or masaliha mursalah (public interest).

Rethinking the Taxonomic Category “Sect/School” (Zong 宗) in the Construction of Modern Buddhism in China—Focusing on Hešeri Rushan’s Eight Schools and Two Practices (“Ba-Zong-Er-Xing 八宗二行”)

This paper explores the origin and role of the Buddhist taxonomic category “zong 宗” (“sect” or “school”) in the formation of modern Buddhism in China. It does so by examining a highly significant late-Qing Buddhist text titled Ba-zong-er-xing 八宗二行 (Eight Schools and Two Practices), which the author discovered recently in Japan. Authored by the 19th-century Manchu bannerman official Hešeri Rushan 赫舍裏如山, Eight Schools and Two Practices had a direct influence on the prominent Chinese lay Buddhist Yang Wenhui (1837–1911)’s Shi-zong-lue-shuo 十宗略说 (Brief Outline of the Ten Schools) (1913), which subsequently became the most important narrative model, known as the ten-school model, for describing Chinese Buddhist history in modern times. Historians have long recognized that Yang Wenhui’s Brief Outline of the Ten Schools (1913) was influenced by the medieval Japanese text hasshū kōyō 八宗綱要 (Essentials of the Eight Schools) composed by the 13th-century Japanese monk Gyōnen. Identifying, in detail, Hešeri Rushan’s influence on Yang Wenhui sheds light on how a narrative model for Buddhism in its national form grew out of trans-national intellectual sharing and interactions, and how Chinese Buddhism emerged from the interactive and mutually enabling Sino-Japanese discursive field of the 19th century. Gyōnen, Rushan, and Yang Wenhui all used the category zong, referring to both doctrine and school/sect, to organize narratives of Buddhist history. Their uses were, however, different. Gyōnen’s conception of zong (shū in Japanese) was fixed and exclusive, whereas zong for Rushan and Yang meant more of a mobile, nonexclusive identity. Without knowledge of Japanese Buddhism, Rushan made creative use of zong for describing the history and current condition of Chinese Buddhism, thereby superseding the traditional framework of lineage, doctrine, and precept, or zong 宗, jiao 教, lu 律. Rushan’s zong provided the necessary prerequisite knowledge for Yang Wenhui to understand Gyōnen’s theories, which he studied for constructing his own historical narrative and vision for modern Buddhism.

A revisit of the normalized eight-point algorithm and a self-supervised deep solution

The normalized eight-point algorithm has been widely viewed as the cornerstone in two-view geometry computation, where the seminal Hartley’s normalization has greatly improved the performance of the direct linear transformation algorithm. A natural question is, whether there are and how to find other normalization methods that can further improve the performance for each input sample. In this paper, we provide a novel perspective and propose two contributions to this fundamental problem: (1) we revisit the normalized eight-point algorithm and make a theoretical contribution by presenting the existence of different and better normalization algorithms; (2) we introduce a deep convolutional neural network with a self-supervised learning strategy for normalization. Given eight pairs of correspondences, our network directly predicts the normalization matrices, thus learning to normalize each input sample. Our learning-based normalization module can be integrated with both traditional (e.g., RANSAC) and deep learning frameworks (affording good interpretability) with minimal effort. Extensive experiments on both synthetic and real images demonstrate the effectiveness of our proposed approach.

Ahmad Moftizadeh and the Idea of the Islamic Government: A Discursive Tradition Analysis in the Field of Political Islam

This article explores the idea of political Islam as a discursive tradition within the context of Iranian Kurdistan. It challenges the prevailing essentialist and universalist approaches commonly used in the analysis of political Islam, advocating for a more adaptable and comprehensive interpretation. By conceptualizing political Islam as a discursive tradition, this study sheds light on the complex interconnections, configurations, and historical contingencies influencing Islamist discourses and movements. The paper argues that political Islam should be examined in relation to other phenomena and discourses, acknowledging its dynamic nature within specific temporal and spatial contexts. Focusing on an influential yet underexplored Islamist discursive tradition in Iranian Kurdistan during the 1970s and 1980s, the research acknowledges the hybrid nature of Islamist discourses, drawing from diverse traditions to fulfill particular objectives. In particular, it explores the perspectives of Ahmad Moftizadeh, a Sunni Kurdish Islamist and the founder of the Maktab Quran (MQ). This study investigates Moftizadeh’s views on the Islamic government, positioning it within the framework of discursive tradition. It analyzes his core categories and inquiries within Islamist thought, notably emphasizing Moftizadeh’s alignment of the Islamic government with the Islamic tradition of the Shura, highlighting its central role in the Islamist discursive tradition.

Bridging the Gap between Theoretical Learning and Practical Application: A Qualitative Study in the Italian Educational Context

In the contemporary educational landscape, there is a growing recognition of the transformative impact of practical experiences within traditional learning frameworks. This shift reflects a pedagogical evolution that values contextualized learning and the acquisition of practical skills together with theoretical knowledge. In the Italian educational context, School–Work Alternation (SWA) represents a proactive response to the evolving needs of the workforce and the imperative for educational institutions to prepare students for professional life. This study’s objectives include a deep exploration of students’ SWA experience, evaluating its impact on employability perceptions and the sense of agency, examining the influence of Self-Orientation, and contributing insights to the discourse on integrating practical experiences in education. Employing a mixed methodology and a bottom-up approach, 63 high school students of different Italian regions participated in an online in-depth interview and an ad hoc questionnaire designed to measure the experience of SWA in relation to variables of interest, utility, advantage, perception of support, quality of received mentoring, engagement, and satisfaction. The quantitative results indicate that personal choice significantly influences the perceived usefulness of and satisfaction with SWA, with those students guided by curiosity exhibiting higher utility and satisfaction. Qualitative analysis underscores both positive and negative aspects, with respondents viewing SWA as a useful experience bridging work, corporate, and school realms provided that students are key players in the SWA experience’s choice and that the SWA’s partners are motivated to guide them in this practical training. Moreover, results highlight SWA’s relevance in guiding academic and career paths, emphasizing its potential to offer valuable support to students. This study contributes nuanced insights into integrating practical experiences in education, offering recommendations for educators and policymakers to better prepare students for the dynamic demands of the modern job market.

Analysis of Residents’ Livelihoods in Transformed Shantytowns: A Case Study of a Resource-Based City in China

Research on shantytown transformation in the context of building sustainable human settlements has tended to concentrate on macro and objective factors. However, there is still room for expanding research on the livelihoods of residents in transformed poor communities of resource-based cities. This study is based on household survey data after the coal mine shantytown transformation in Datong, China, and uses the entropy method and Logit regression model to analyze the livelihood level and impact of livelihood capital on the choice of livelihood strategies of the residents after shantytown transformation. Based on the development characteristics of the Datong coal mining community, the traditional sustainable livelihood analysis framework is improved, constructing a livelihood capital evaluation system including natural capital, physical capital, financial capital, social capital, human capital and cultural capital. The research indicates that the livelihoods of the residents after shantytown transformation are in a state of low-value aggregation and differentiation, their livelihood strategies are still dependent on coal mining, financial capital and cultural capital have significant positive impacts on the residents’ livelihood strategies of choosing coal mine-related industries, and males and elderly people have a greater likelihood of choosing coal mine-related industries. This study provides micro-level explanations for the livelihood status and livelihood strategy choices of residents after shantytown transformation in a resource-based city and provides policy enlightenment for local governments on how to promote the sustainable development of residents in coal mining communities.

Physics-informed differentiable method for piano modeling

Numerical emulations of the piano have been a subject of study since the early days of sound synthesis. High-accuracy sound synthesis of acoustic instruments employs physical modeling techniques which aim to describe the system’s internal mechanism using mathematical formulations. Such physical approaches are system-specific and present significant challenges for tuning the system’s parameters. In addition, acoustic instruments such as the piano present nonlinear mechanisms that present significant computational challenges for solving associated partial differential equations required to generate synthetic sound. In a nonlinear context, the stability and efficiency of the numerical schemes when performing numerical simulations are not trivial, and models generally adopt simplifying assumptions and linearizations. Artificial neural networks can learn a complex system’s behaviors from data, and their application can be beneficial for modeling acoustic instruments. Artificial neural networks typically offer less flexibility regarding the variation of internal parameters for interactive applications, such as real-time sound synthesis. However, their integration with traditional signal processing frameworks can overcome this limitation. This article presents a method for piano sound synthesis informed by the physics of the instrument, combining deep learning with traditional digital signal processing techniques. The proposed model learns to synthesize the quasi-harmonic content of individual piano notes using physics-based formulas whose parameters are automatically estimated from real audio recordings. The model thus emulates the inharmonicity of the piano and the amplitude envelopes of the partials. It is capable of generalizing with good accuracy across different keys and velocities. Challenges persist in the high-frequency part of the spectrum, where the generation of partials is less accurate, especially at high-velocity values. The architecture of the proposed model permits low-latency implementation and has low computational complexity, paving the way for a novel approach to sound synthesis in interactive digital pianos that emulates specific acoustic instruments.

Comparative analysis of trust taxation: a deep dive into Australian and Canadian regimes

Abstract This article examines trust taxation in Australia and Canada, focusing on the Australian Income Tax Assessment Act 1936's Section 97(1) and the Canadian Income Tax Act’s Section 94.1. It explores the Australian Carter and the Canadian Fundy Settlement cases, highlighting their implications for tax avoidance strategies. The article employs a comparative methodology to provide insights into the policy and legislative nuances shaping trust distribution taxation in these jurisdictions. The goal is to bridge traditional legal frameworks with modern fiscal challenges and inform future reforms in common-law countries.

Are propagandists combatants? Analysing the ethical status of propagandists in warfare

Abstract Belligerents increasingly rely on media manipulation, propaganda, and communication to attain strategic advantages in conflict. Given the civilian propagandists’ clear role in creating tactical or strategic advantages for one side in the conflict, should these propagandists be considered combatants, and can they therefore be legitimately targeted because of their activities? This article overcomes traditional binary frameworks of distinction to argue that propagandists are indirectly participating civilians, i.e. participants who cannot be intentionally or directly killed in conflict. Because of their activities, propagandists, it argues, are liable to less-than-lethal harm, of which it identifies three types (destruction of property, privation of liberty, isolation). The article then proposes a necessary criterion – necessity – to decide if less-than-lethal harm is warranted against propagandists. It then creates four new criteria – denial of agency, falsehood, influence, gratification – to serve as assessment criteria able to decide what degree of harm a propagandist may maximally face. In making this argument, the article contributes to just war literature by proposing a novel way to evaluate the ethical status of a highly diverse, yet undoubtedly influential, category of war participants: those who undertake the ‘media battle’, outside of the military.

Design and Mechanical Properties of Maximum Bulk Modulus Microstructures Based on a Smooth Topology with Grid Point Density

The aim of topology optimisation is to determine the optimal distribution of material phases within the periodic cells of a microstructure. In this paper, the density of grid points under element volume fraction is constructed to replace the finite elements in the traditional SIMP framework, avoiding jagged and blurry boundaries in the computational process due to grid dependence. This is then combined with homogenisation theory, a microstructure topology optimisation algorithm with maximum bulk modulus under prescribed volume constraints is proposed, which can obtain 2D and 3D topologies with smooth boundaries. In addition, a closed form expression for the two-dimensional topological concave edge structure (taking the most typical topology as an example) was derived, and a compression experiment was conducted on the topological microstructure based on 3D metal printing technology. Scanning electron microscopy showed that the powder bonded on the surface of the printed structure was not completely melted and the step effect caused the finite element analysis results to be higher than the experimental results. Overall, the finite element simulation and experimental results of the concave surface structure have good consistency, with high strength and energy absorption effects. Topologies based on grid point density obtain microstructures with smooth boundaries, and the introduction of the Heaviside smoothing function and multiple filtering steps within this algorithm leads to more robust optimisation, facilitating 3D or 4D printing of microstructures that meet specific design requirements and confirming the feasibility of the proposed topology for lightweighting studies.

Multimodal Biomedical Image Segmentation using Multi-Dimensional U-Convolutional Neural Network

Deep learning recently achieved advancement in the segmentation of medical images. In this regard, U-Net is the most predominant deep neural network, and its architecture is the most prevalent in the medical imaging society. Experiments conducted on difficult datasets directed us to the conclusion that the traditional U-Net framework appears to be deficient in certain respects, despite its overall excellence in segmenting multimodal medical images. Therefore, we propose several modifications to the existing cutting-edge U-Net model. The technical approach involves applying a Multi-Dimensional U-Convolutional Neural Network to achieve accurate segmentation of multimodal biomedical images, enhancing precision and comprehensiveness in identifying and analyzing structures across diverse imaging modalities. As a result of the enhancements, we propose a novel framework called Multi-Dimensional U-Convolutional Neural Network (MDU-CNN) as a potential successor to the U-Net framework. On a large set of multimodal medical images, we compared our proposed framework, MDU-CNN, to the classical U-Net. There have been small changes in the case of perfect images, and a huge improvement is obtained in the case of difficult images. We tested our model on five distinct datasets, each of which presented unique challenges, and found that it has obtained a better performance of 1.32%, 5.19%, 4.50%, 10.23% and 0.87%, respectively.

A Population-Based Search Approach to Solve Continuous Distributed Constraint Optimization Problems

Distributed Constraint Optimization Problems (DCOPs) are an efficient framework widely used in multi-agent collaborative modeling. The traditional DCOP framework assumes that variables are discrete and constraint utilities are represented in tabular forms. However, the variables are continuous and constraint utilities are in functional forms in many practical applications. To overcome this limitation, researchers have proposed Continuous DCOPs (C-DCOPs), which can model DCOPs with continuous variables. However, most of the existing C-DCOP algorithms rely on gradient information for optimization, which means that they are unable to solve the situation where the utility function is a non-differentiable function. Although the Particle Swarm-Based C-DCOP (PCD) and Particle Swarm with Local Decision-Based C-DCOP (PCD-LD) algorithms can solve the situation with non-differentiable utility functions, they need to implement Breadth First Search (BFS) pseudo-trees for message passing. Unfortunately, employing the BFS pseudo-tree results in expensive computational overheads and agent privacy leakage, as messages are aggregated to the root node of the BFS pseudo-tree. Therefore, this paper aims to propose a fully distributed C-DCOP algorithm to solve the utility function form problem and avoid the disadvantages caused by the BFS pseudo-tree. Inspired by the population-based algorithms, we propose a fully decentralized local search algorithm, named Population-based Local Search Algorithm (PLSA), for solving C-DCOPs with three-fold advantages: (i) PLSA adopts a heuristic method to guide the local search to achieve a fast search for high-quality solutions; (ii) in contrast to the conventional C-DCOP algorithm, PLSA can solve utility functions of any form; and (iii) compared to PCD and PCD-LD, PLSA avoids complex message passing to achieve efficient computation and agent privacy protection. In addition, we implement an extended version of PLSA, named Population-based Global Search Algorithm (PGSA), and empirically show that our algorithms outperform the state-of-the-art C-DCOP algorithms on three types of benchmark problems.