Unified Evaluation Research Articles

Mapping the Drivers of Multisectoral Nutrition Governance and Its Link to Nutrition Outcomes in Kenya: A Qualitative Inquiry.

Malnutrition remains a significant public health issue in Kenya. Multisectoral Nutrition Governance (MNG) is increasingly being acknowledged as a catalyst for enhancing nutrition programming and outcomes. Effective MNG establishes policies, systems, and mechanisms that enable coordinated, adequately funded, and sustainable nutrition actions across sectors; however, its understanding and progress assessment remain inadequate. This study aimed to qualitatively assess the status of MNG and propose strategies to strengthen MNG mechanisms for improved nutrition actions and outcomes in Kenya. We hypothesized that effective performance across the MNG domains is associated with effective multisectoral nutrition actions and improved nutrition outcomes. This study used a qualitative design to assess the MNG status over the past 10 years (2012-2023). Nineteen program managers and officers from government and non-governmental institutions implementing nutrition at the national level were included. Data collection was conducted between January and March 2024 through key informant interviews (KIIs). Thematic analysis, guided by both inductive and deductive coding, was carried out using MAXQDA (Maximizing Qualitative Data Analysis) software. The findings indicate progress in strengthening MNG in the previous decade, though gaps persist. The progress was driven by improved political awareness and commitment, the adoption of nutrition policy and planning frameworks, and improved coordination. Constraints that impede MNG progress include inadequate financing and over-reliance on donor funding, limited translation of commitments to actions, lack of unified monitoring and evaluation (M&E) systems and fragmented policies. Strengthening multisectoral M&E systems that allow timely collection and utilization of data, ensuring sustainable financing for nutrition, enhancing accountability mechanisms and improving coherence across sectors are important for further improvement of MNG.

Fish Tracking, Counting, and Behaviour Analysis in Digital Aquaculture: A Comprehensive Survey

ABSTRACTDigital aquaculture leverages advanced technologies and data‐driven methods, providing substantial benefits over traditional aquaculture practices. This article presents a comprehensive review of three interconnected digital aquaculture tasks, namely, fish tracking, counting, and behaviour analysis, using a novel and unified approach. Unlike previous reviews which focused on single modalities or individual tasks, we analyse vision‐based (i.e., image‐ and video‐based), acoustic‐based, and biosensor‐based methods across all three tasks. We examine their advantages, limitations, and applications, highlighting recent advancements and identifying critical cross‐cutting research gaps. The review also includes emerging ideas such as applying multitask learning and large language models to address various aspects of fish monitoring, an approach not previously explored in aquaculture literature. We identify the major obstacles hindering research progress in this field, including the scarcity of comprehensive fish datasets and the lack of unified evaluation standards. To overcome the current limitations, we explore the potential of using emerging technologies such as multimodal data fusion and deep learning to improve the accuracy, robustness, and efficiency of integrated fish monitoring systems. In addition, we provide a summary of existing datasets available for fish tracking, counting, and behaviour analysis. This holistic perspective offers a roadmap for future research, emphasizing the need for comprehensive datasets and evaluation standards to facilitate meaningful comparisons between technologies and to promote their practical implementations in real‐world settings.

A Comparative Analysis of Advanced Routing and Cluster Head Selection Algorithm Using Lagrange Interpolation

This paper presents a unified performance metric for evaluating the chronological wild geese optimization (CWGO) algorithm in wireless sensor networks (WSNs). The metric combines key performance factors—energy consumption, delay, distance, and trust—into a single measure using Lagrange interpolation, providing a more comprehensive assessment of WSN algorithms. We evaluate CWGO against E-CERP, EECHIGWO, DUCISCA, and DE-SEP across static and dynamic sensor node configurations in various wireless technologies, including LoRa, Wi-Fi, Zigbee, and Bluetooth low energy (BLE). The results show that CWGO consistently outperforms the other algorithms, especially in larger node configurations, demonstrating its scalability and robustness in static and dynamic environments. Moreover, the unified metric reveals significant performance gaps with EECHIGWO, which underperforms all wireless technologies. DUCISCA and DE-SEP show moderate and fluctuating results, underscoring their limitations in larger networks. While E-CERP performs competitively, it generally lags behind CWGO. The unified metric offers a holistic view of algorithm performance, conveying clearer comparisons across multiple factors. This study emphasized the importance of a unified evaluation approach for WSN algorithms and positions CWGO as a superior solution for efficient cluster head selection and routing optimization in diverse WSN scenarios. While CWGO demonstrates superior performance in simulation, future research should validate these findings in real-world deployments, accounting for hardware limitations and in a highly dynamic environment. Further optimization of the unified metrics’ computational efficiency could enhance its real-time applicability in larger, energy-resource-constrained WSNs.

Influence of information flow topology and maximum platoon size on mixed traffic stability

Beyond platooning with pure connected and automated vehicles (CAVs), mixed platooning with both CAVs and human-driven vehicles (HDVs) emerges as a practical formation pattern in mixed traffic flow. This paper investigates how the two types of information flow topology (IFT) as “looking ahead” or “looking behind”, alongside the maximum size of mixed platoons, influence the stability of the entire traffic flow. Precisely, the mixed traffic system is partitioned into three subsystems: independent HDVs, independent CAVs, and mixed platoons, and a dynamical modeling framework is established under two types of specific IFT: Multi-Predecessor Following (MPF) for “looking ahead” and Multi-Successor Leading (MSL) for “looking behind”. Then, a unified string stability evaluation method is proposed, which captures the role of IFT and maximum platoon size, the latter being associated with the emergence probability of different subsystems and CAV penetration rates. Practical considerations, such as the heterogeneity of vehicle dynamics and the absence of HDV communication capabilities, are also incorporated. Extensive numerical analysis and nonlinear traffic simulations reveal that “looking behind” shows superior performance in mitigating traffic perturbations, especially at low penetration rates, compared with the prevailing choice of “looking ahead”. In addition, increasing the platoon size could further enhance traffic flow stability. These results provide practical insights into the design of IFT and platoon size for CAV cooperation in future mixed traffic environments.

Evaluation Method for Anti-Chaff Jamming Performance of Typical Radio Fuze

Abstract Chaff jamming is one of the common countermeasures in electronic warfare against radio fuze. Currently, there is no unified and effective evaluation criterion and method for the anti-chaff jamming capability of radio fuze. An evaluation method for the anti-chaff jamming performance of radio fuze during the missile-target encounter trajectory phase is proposed. A dynamic echo model of chaff clouds was established. The echo signals of the chaff cloud and the target were calculated and the fuze actuation were simulated and analyzed. The results show that this evaluation method can effectively assess the anti-chaff jamming performance of typical radio fuze.

Research Status and Progress on Technologies for Exploration of Dry Hot Rock Resources in China

Abstract Hot Dry Rock (HDR) represents a clean and renewable geothermal energy source, playing a crucial role in global energy transition and climate change mitigation. China possesses abundant HDR resources. However, compared to the international advanced level, domestic exploration technology is still in its early stages. This paper analyzes the distribution of HDR resources in China and the achievements in exploration projects. It delves into the principles, applications, limitations, and development trends of key technologies in the main processes of HDR resource exploration. The paper emphasizes the necessity of improving exploration precision, enhancing high-temperature drilling fluid development, advancing logging technology, and establishing unified evaluation standards. Furthermore, it highlights the potential of artificial intelligence and big data technologies in improving exploration efficiency and reducing development risks, aiming to provide scientific technical guidance and practical references for the exploration and utilization of HDR resources in China.

Class-Incremental Learning: A Survey.

Deep models, e.g., CNNs and Vision Transformers, have achieved impressive achievements in many vision tasks in the closed world. However, novel classes emerge from time to time in our ever-changing world, requiring a learning system to acquire new knowledge continually. Class-Incremental Learning (CIL) enables the learner to incorporate the knowledge of new classes incrementally and build a universal classifier among all seen classes. Correspondingly, when directly training the model with new class instances, a fatal problem occurs - the model tends to catastrophically forget the characteristics of former ones, and its performance drastically degrades. There have been numerous efforts to tackle catastrophic forgetting in the machine learning community. In this paper, we survey comprehensively recent advances in class-incremental learning and summarize these methods from several aspects. We also provide a rigorous and unified evaluation of 17 methods in benchmark image classification tasks to find out the characteristics of different algorithms empirically. Furthermore, we notice that the current comparison protocol ignores the influence of memory budget in model storage, which may result in unfair comparison and biased results. Hence, we advocate fair comparison by aligning the memory budget in evaluation, as well as several memory-agnostic performance measures.

Enhancing comparability in early child development assessment with the D-score

The lack of a valid and interpretable score to track early child development over time is a primary reason for neglecting child development in policymaking. Many instruments exist, but there is no accepted method for comparing their scores across different ages, samples, and instruments. This paper aims (1) to enhance the Development Score (D-score), a unidimensional scale for early child development, to compare measurements across ages, samples, and instruments, (2) to develop a conversion key that enables the transformation of measurements obtained from existing instruments into a D-score, and (3) to investigate two new measures designed to optimize the quantification of the D-score. Study 1 gathered data from 51 sources in 32 countries among 66,075 children using 18 instruments with 2,211 items. Subject matter experts used the output of the Study-1 true score equating model to create the Global Scales for Early Development Short Form (GSED SF) and Long Form (GSED LF). Study 2 collected additional data on the GSED LF and GSED SF in three countries among 4,374 children. The Study-2 model enables the conversion of measurements into a D-score for 20 different instruments. We propose the D-score as a unifying evaluation unit to reduce fragmentation, simplify measurement, and enhance comparability.

Evaluating Synthetic Data Generation from User Generated Text

Abstract User-generated content provides a rich resource to study social and behavioral phenomena. Although its application potential is currently limited by the paucity of expert labels and the privacy risks inherent in personal data, synthetic data can help mitigate this bottleneck. In this work, we introduce an evaluation framework to facilitate research on synthetic language data generation for user-generated text. We define a set of aspects for assessing data quality, namely, style preservation, meaning preservation, and divergence, as a proxy for privacy. We introduce metrics corresponding to each aspect. Moreover, through a set of generation strategies and representative tasks and baselines across domains, we demonstrate the relation between the quality aspects of synthetic user generated content, generation strategies, metrics, and downstream performance. To our knowledge, our work is the first unified evaluation framework for user-generated text in relation to the specified aspects, offering both intrinsic and extrinsic evaluation. We envisage it will facilitate developments towards shareable, high-quality synthetic language data.

Gait planning of a 4–5R rolling mechanism based on the planar 6R single-loop chain

Abstract. This paper proposes a 4–5R rolling mechanism based on the spatial extension design of a planar 6R single-loop chain. By analyzing the locomotion of the planar equivalent form, a modular gait theory integrating different modes of gait with high efficiency, low energy consumption, and high speed is established. A unified kinematic strategy expression, encapsulated in the form of the gait period table, is tailored for the kinematic chain's gait on the flat terrain. A contrast gait is conducted to ascertain its velocity parameters and volatility of the center of mass (CM). By optimizing the corresponding indicators, two distinct gait patterns are achieved: a faster speed gait that prioritizes increased speed and a steady gait that emphasizes stability with reduced CM volatility. Drawing from the mobility analysis and simulation outcomes of the planar 6R single-loop kinematic chain, a theory of locomotion for a closed-chain linkage mechanism in space is proposed. A locomotion strategy on the flat ground is derived, and a unified evaluation index is proposed. Finally, the feasibility of the two working modes is verified using a physical prototype. The theoretical work in this paper simplifies the design process of closed-chain linkage robots and improves the mobility performance of closed-chain linkage robots. It lays the foundation for researching new types of closed-chain linkage robots.

Novel Seizure Assessment Tool for Unified Seizure Evaluation: Correspondence.

Novel Seizure Assessment Tool for Unified Seizure Evaluation: Correspondence.

Resilience in maritime logistics: Theoretical framework, research methodology, and indicator system

Maritime logistics, essential for global trade, faces increasing vulnerability due to operational complexity and disruptions such as extreme weather, geopolitical conflicts, and pandemics. Although maritime logistics resilience (MLR) has garnered growing attention, the field still lacks a unified theoretical framework and standardized evaluation indicator system, exposing a significant research gap. This study seeks to fill this gap by conducting a systematic review of 163 papers from Scopus, Web of Science, and Google Scholar, covering the period from 2009 to August 2023. The study defines maritime logistics resilience, identifies its four key attributes—robustness, vulnerability, redundancy, and rapidity—and explores their interrelationships, establishing a theoretical framework. Based on this foundation, an innovative evaluation indicator system is proposed for the first time, designed to assess resilience across the three main clusters of the maritime logistics system: ports, shipping networks, and the maritime supply chain. The proposed framework and indicator system provide a novel, comprehensive approach to evaluating MLR, offering both theoretical contributions and practical tools for industry practitioners. This research fills a critical gap in the field and lays the groundwork for future empirical studies and policy development.

Revisiting streaming anomaly detection: benchmark and evaluation

Anomaly detection in streaming data is an important task for many real-world applications, such as network security, fraud detection, and system monitoring. However, streaming data often exhibit concept drift, which means that the data distribution changes over time. This poses a significant challenge for many anomaly detection algorithms, as they need to adapt to the evolving data to maintain high detection accuracy. Existing streaming anomaly detection algorithms lack a unified evaluation framework that validly assesses their performance and robustness under different types of concept drifts and anomalies. In this paper, we conduct a systematic technical review of the state-of-the-art methods for anomaly detection in streaming data. We propose a new data generator, called SCAR (Streaming data generator with Customizable Anomalies and concept dRifts), that can synthesize streaming data based on synthetic and real-world datasets from different domains. Furthermore, we adapt four static anomaly detection models to the streaming setting using a generic reconstruction strategy as baselines, and then compare them systematically with 9 existing streaming anomaly detection algorithms on 76 synthesized datasets that have various types of anomalies and concept drifts. The challenges and future research directions for anomaly detection in streaming data are also presented.

Research on Driver Emotion Monitoring and Regulation System in Intelligent Assisted Driving

Abstract. With the rapid development of smart connected vehicles, the influence, recognition and regulation of driver's emotions are crucial for intelligent cockpits with "emotional interaction". This review aims to provide a comprehensive analysis of driver emotion monitoring systems in intelligent assisted driving. By summarizing and analyzing related literature, the structure and application of emotion monitoring systems are introduced, their principles and evaluation criteria are discussed in depth, and the advantages and limitations of the three sub-systems, namely, detection, decision-making and regulation, are analyzed in detail. The results show that although emotion monitoring and modulation technologies perform well in enhancing driving safety and comfort, their practical applications still face challenges in terms of technology maturity, cost control, and privacy protection. In addition, the reliability and accuracy of current emotion monitoring systems in complex driving environments have yet to be improved. Future research should focus on interdisciplinary cooperation to integrate the latest achievements in psychology, computer science and engineering. At the same time, it is necessary to develop unified technical standards and evaluation guidelines to promote compatibility and interoperability between different systems and enhance research on privacy protection measures to ensure the security and confidentiality of user data. Through these efforts, the field of intelligent driving is expected to achieve comprehensive development and wide application, further enhancing driver safety and user experience.

A comparative analysis of the evaluation system for comprehensive reform of agricultural water prices in China

Water is a fundamental natural resource that affects human and social development, and also plays an important role in modern agricultural production and food security. In China, agricultural water consumption accounts for the majority of the total water consumption. For a long time, China has not fully implemented water pricing and payment for agricultural water, and the lower agricultural water cost have led to the waste of water resource and the rapid conversion of agricultural water to other industries. In order to avoid agricultural water waste and achieve the goal of agricultural sustainable development, the Chinese government has launched a comprehensive reform of agricultural water prices nationwide since 2016. According to the plan, nowadays, the reform will faces overall evaluation and acceptance in 2024 and 2025. However, there is a lack of a relatively unified and flexible nationwide evaluation index system and acceptance measures. In this study, a comparative research of the evaluation system for comprehensive reform of agricultural water prices was developed. Based on existing evaluation and acceptance measures issued by four typical provincial regions, this study compared and analyzed the compliance and similarities and differences of these acceptance measures with national reform policies and requirements. From the aspects of evaluation procedure and index system, a systematic comparative analysis was conducted on the integrity and differences in the four regions. At the same time, suggestions were given for policy-making of national evaluation process and standard. The comparative analysis results showed that the evaluation indices in the four provincial regions have met all the key points of national reform policies and have a complete system, but there were significant differences in the structure of the evaluation index system in different provincial regions. It is recommended to establish a simple, relatively unified and practical national evaluation and acceptance system as soon as possible. In addition, evaluation criteria for individual specific indices should be determined by region based on the differences in economic capacity and agricultural water conservancy facilities conditions.

RAP chunks produced in cold milling operation of asphalt pavement: Evaluation, mechanism, and engineering investigation in China

Cold milling is a widely used method for rehabilitating asphalt pavement, generating reclaimed asphalt pavement (RAP) chunks. Within this process, aggregates within the asphalt pavement will be crushed, forming RAP agglomerates and aggregate breakdown. However, the mechanism of these phenomena has remained unclear, and a unified evaluation method has yet to be established. In this study, RAP agglomeration and aggregate fragmentation were characterized, five distinct methods were systematically assessed, and the mechanism of RAP agglomeration and breakdown was analyzed by discrete element method (DEM) simulation based on setting different particle contact parameters, then followed by a mechanical analysis, and demonstrated in engineering. The results revealed that both agglomeration and aggregate breakdown occur within RAP particles of various sizes, with the five methods showing similar trends in quantifying these effects. Through DEM simulations and mechanical analyses, the aggregate breakdown predominantly occurs at the cutter's motion trajectory of the cutter and during crack propagation, while agglomeration was mainly related to the sliding surface's area. The milling speed and depth positively impact RAP agglomeration, while negatively affecting aggregate breakdown, and milling drum speed exerts minimal influence on these phenomena. RAP agglomeration varies considerably in different engineering projects, and cold milling parameters should be determined based on the material composition of the asphalt pavement and design requirements to control agglomeration and breakdown rates of RAP.

County-Level Cultivated Land Quality Evaluation Using Multi-Temporal Remote Sensing and Machine Learning Models: From the Perspective of National Standard

Scientific evaluation of cultivated land quality (CLQ) is necessary for promoting rational utilization of cultivated land and achieving one of the Sustainable Development Goals (SDGs): Zero Hunger. However, the CLQ evaluation system proposed in previous studies was diversified, and the methods were inefficient. In this study, based on China’s first national standard “Cultivated Land Quality Grade” (GB/T 33469-2016), we constructed a unified county-level CLQ evaluation system by selecting 15 indicators from five aspects—site condition, environmental condition, physicochemical property, nutrient status and field management—and used the Delphi method to calculate the membership degree of the indicators. Taking Jimo district of Shandong Province, China, as a case study, we compared the performance of three machine learning models, including random forest, AdaBoost, and support vector regression, to evaluate CLQ using multi-temporal remote sensing data. The comprehensive index method was used to reveal the spatial distribution of CLQ. The results showed that the CLQ evaluation based on multi-temporal remote sensing data and machine learning model was efficient and reliable, and the evaluation results had a significant positive correlation with crop yield (r was 0.44, p < 0.001). The proportions of cultivated land of high-, medium- and poor-quality were 27.43%, 59.37% and 13.20%, respectively. The CLQ in the western part of the study area was better, while it was worse in the eastern and central parts. The main limiting factors include irrigation capacity and texture configuration. Accordingly, a series of targeted measures and policies were suggested, such as strengthening the construction of farmland water conservancy facilities, deep tillage of soil and continuing to construct well-facilitated farmland. This study proposed a fast and reliable method for evaluating CLQ, and the results are helpful to promote the protection of cultivated land and ensure food security.

Enhancing robustness of community structure in networks against attacks with gray information

In recent years, network science has received significant attention and application. As an important mesoscale structure of networks, community structure reveals the inherent modular structure and potential functionality of networks. Therefore, maintaining the community structure during attacks is crucial. Existing studies on community robustness primarily focus on two attack behaviors: malicious attacks and random failures. However, little is known on the community robustness in the more practical situations where the attackers have limited ability to access precise network information, leading to gray information attack behaviors. Hence, in this paper, we investigate the robustness of network communities under gray information attacks and the enhancing algorithms. Following the community robustness measure for the sequential attacks, we establish a unified evaluation framework for attacks with gray information by introducing a gray attack coefficient, which treats the usual random failures and malicious attacks as the two special situations in the proposed framework. Several enhancing algorithms with local search strategy are exquisitely devised. The efficacy of our framework and algorithms which significantly improve the community robustness against gray attacks is demonstrated by the experimental results on a variety of real-world networks. Our findings have important implications not only in enhancing the community robustness of existing networks but also in designing robust ones from the scratch, which paves the way to further understand and seek strategies and solutions for network community robustness with gray information.

A 3D visible space index for evaluating urban openness based on the digital urban model

The evaluation of urban open spaces plays an important role in landscape design, urban management and public security. However, few three-dimensional openness approaches with high accuracy and applicability to multiple data models have been developed. A method has been developed to measure the openness of an arbitrary location in an urban space accurately. This method is based mainly on the geometric subdivision of 3D space. First, a complete visual sphere was constructed with the viewpoint as the centre. The visual sphere was subsequently uniformly subdivided by multiple lines of sight at certain horizontal and vertical angle intervals, generating multiple discrete visual difference elements (VDEs). Finally, the specific visible volumes in each VDE were calculated separately to accumulate the all-visible volumes. The visible space index (VSI) was defined as the ratio of all visible space volumes to the visual sphere. The results indicate that the proposed method can accurately calculate the volume of visible space at any location, and the corresponding VSI reflects the difference of visible space in urban environments under different terrain conditions. The proposed method is expected to provide technical support for large-scale urban openness mapping, consequently contributing to the unified evaluation of urban space.

Land-Based Carbon Effects and Human Well-Being Nexus

In light of international climate agreements and the Sustainable Development Goals (SDGs), there is a growing need to enhance the understanding of the linkages among land use/cover change (LUCC) and its carbon effects (CEs), as well as human well-being (HW). While existing studies have primarily focused on the impacts of LUCC on CEs or ecosystem services, there remains a gap in systematically elucidating the complex relationships among LUCC, CEs, and HW. This paper presents a comprehensive review of the nexus between land-based CEs and HW, examining: (1) the correlation between LUCC and CEs, encompassing methodologies for investigating LUCC CEs; (2) the association between CEs and HW, introducing the concept of “low-carbon human well-being” and evaluation framework; and (3) the proposed framework of “LUCC-CEs-HW,” which delves into the intricate connections among three elements. The study identifies research gaps and outlines potential future directions, including assessments of LUCC CEs and low-carbon HW, exploration of the “LUCC-CEs-HW” nexus, and the development of standardized measurement approaches. Key opportunities for further investigation include establishing a unified evaluation index system and developing scalable methods. This paper elucidates the relationships among LUCC, CEs, and HW, offering insights for future works.