Evaluation Function Research Articles

Analysis of passenger perception heterogeneity and differentiated service strategy for air-rail intermodal travel

Air-rail intermodal services (ARISs) represent a highly promising multimodal solution within the transportation sector. Nonetheless, various uncertainties and challenges persist across multiple dimensions of air-rail interline travel, with discrepancies in passenger perceptions being a notable aspect. In an effort to pinpoint the pivotal factors contributing to these disparities among distinct passenger profiles, this study employs the Structural Equation Modeling-Multiple Indicator Multiple Cause-Artificial Neural Network (SEM-MIMIC-ANN) methodology. This approach explores the impact of numerous attributes on passenger perceptions in the context of air-rail intermodal travel, leveraging questionnaire data gathered from Shijiazhuang multimodal passengers. Furthermore, the study utilizes the Classification and Regression Tree (CART) decision tree algorithm to categorize actual passengers into distinct characteristic groups. Subsequently, the perception levels of these diverse passenger groups are quantified through the calculation of comprehensive evaluation function values. In conclusion, taking into account the real-world conditions of air-rail interline travel, this research formulates a tailored service strategy aimed at enhancing the overall passenger experience.

Optimization of Predefined-Time Agent-Scheduling Strategy Based on PPO

In this paper, we introduce an agent rescue scheduling approach grounded in proximal policy optimization, coupled with a singularity-free predefined-time control strategy. The primary objective of this methodology is to bolster the efficiency and precision of rescue missions. Firstly, we have designed an evaluation function closely related to the average flying distance of agents, which provides a quantitative benchmark for assessing different scheduling schemes and assists in optimizing the allocation of rescue resources. Secondly, we have developed a scheduling strategy optimization method using the Proximal Policy Optimization (PPO) algorithm. This method can automatically learn and adjust scheduling strategies to adapt to complex rescue environments and varying task demands. The evaluation function provides crucial feedback signals for the PPO algorithm, ensuring that the algorithm can precisely adjust the scheduling strategies to achieve optimal results. Thirdly, aiming to attain stability and precision in agent navigation to designated positions, we formulate a singularity-free predefined-time fuzzy adaptive tracking control strategy. This approach dynamically modulates control parameters in reaction to external disturbances and uncertainties, thus ensuring the precise arrival of agents at their destinations within the predefined time. Finally, to substantiate the validity of our proposed approach, we crafted a simulation environment in Python 3.7, engaging in a comparative analysis between the PPO and the other optimization method, Deep Q-network (DQN), utilizing the variation in reward values as the benchmark for evaluation.

Hepatic Hydatid cyst in children: An experience from East of Turkey

İntoduction: Hydatid cyst is a parasitic disease caused by the infection of Echinococcus granulosus and rarely Echinococcus alveolaris eggs, which are endemic in our country. Current treatment options for liver hydatid cysts; They can be listed as medical treatment, surgical treatment, percutaneous drainage and clinical follow-up only. In this study, the effectiveness of the WHO-IWGE protocol in terms of diagnosis and treatment strategies in our pediatric cases of liver hydatid cyst in a health institution in eastern Turkey was evaluated in the light of the literature. Material Method: Pediatric patients between the ages of 0-16 who were diagnosed with KcKH and followed up and treated were evaluated retrospectively. In pre-operative evaluation, lung radiography, abdominal ultrasonography, serological tests, hemogram and liver function tests were routinely performed. Results: 250 child patients between the ages of 0 and 16, whose data were fully available, were included in the study. 118 of the cases were girls and 132 were boys. The average age was 11.2 years. Most of the KcKH patients were admitted to the hospital for another reason and the cyst was detected incidentally. Of the 65 patients requiring surgery, 17 were patients who initially underwent primary intervention with PAIR. Patients who underwent PAIR were observed for one night and discharged in an average of 18 hours. Patients with free drainage were discharged in 2-4 days. The average length of stay for patients who underwent open surgery was found to be 9 days. In conclusion; The Gharbi classification updated by WHO is effective and reliable in determining the KcKH treatment strategy. In cases who are receiving chemotherapy and an intervention decision is made, a final USG is performed just before the procedure; It can provide both a change in treatment management and more patients benefiting from medical treatment.

GFedKG: GNN-based federated embedding model for knowledge graph completion

Knowledge Graph (KG) completion through reasoning over KGs is one of the most concerned issue in research around knowledge graphs. Current work focuses more on studying individual KG, while a series of related multi-source KGs are more common in real applications. In order to effectively exert their complementary roles in completing each KG under the premise of ensuring data privacy, federated learning becomes the focus of attention. However, existing methods ignore the explicit complementarity among KGs in terms of structural information. And they generally align embeddings of overlapping instances across clients, which constrains flexibility of each client model. Based on this, we propose the first GNN-based federated embedding framework for multiple knowledge graph completion, GFedKG. It focuses on the complementary role among KGs in topology structures and adaptively generates different aggregated neighboring features for entities in distinct clients. Such treatment achieves some consistency among clients while retaining the flexibility of each client model to characterize respective KG. Moreover, entity embeddings that represent the features of entities are better privacy-preserved since neighboring embeddings are transmitted instead of them. And with the aim of realizing more refined and accurate aggregation of neighboring features from multiple clients, we innovatively propose a context-aware graph neural network, along with the context-enhanced evaluation function. Homophily neighbors are also taken into account to cope with the low-homophily aspect of KGs. Extensive experiments demonstrate the advantages of GFedKG.

Unmasking Malicious Stance Indicators and Attitudinal Priming: An ‘Evaluative Textbite’ Approach to Identity Attacks in Violent Extremist Discourse

The article explores the patterning and functioning of attitude semantics in the practice of identity attacks within terrorist communications. Positioned in facework and stance-taking research (e.g. Tracy & Tracy, 1998, 2008, 2017), it introduces the concepts of ‘evaluative textbites’ and ‘attitudinal priming’ to linguistic examinations, advocating a functional approach to unravelling identity attacks, drawing on corpus analysis methods (e.g. word frequency, and concordance-line qualitative analysis) and the Appraisal framework (Martin & White, 2005). Findings reveal that, linked with stance-taking activity, attitudinal priming offers insights into how specific ideational targets are primed for particular attitudinal, evaluative functions. Evaluative textbites provide linguistic evidence of an author’s encoded hostile attitude and the nuanced patterning and functioning of ‘ideation-attitude’ co-occurrences in these attacks. Identity attacks are a rhetorical tool, normative and valuation-based, targeting individuals’ or out-groups’ immoral behaviours and devaluing victims by reference to their personal traits, power-distance relationships, interactional roles, and master identities. This article offers implications for future study of identity attacks in hate crimes, genocidal rhetoric, and defamation texts, and strengthens counter-extremism efforts by illuminating the investigative value of identity work.

PREDICTION OF THE INTERACTION MECHANISMS OF QUERCETIN, Α-CYPERMETHRIN AND ITS DERIVATIVES WITH THE Α-ESTROGEN RECEPTOR (IN SILICO STUDY)

Introduction. Excessive use of pesticides leads to environmental pollution and poses a danger to human and animal health. Pyethroid insecticides (pyrethroids) are the most commonly used pesticides because they are less toxic to non-target species. It is well-known that prolonged exposure to pyrethroids can result in the endocrine function of the gonads being disrupted. Cypermethrin is a very common pyrethroid, the toxicity of which is widely studied, but little is known about the endocrine-disrupting effect of its stereoisomer, α-cypermethrin. In our opinion, quercetin, a flavonoid that has estrogen-like and antioxidant properties, can be an effective means of protection against the complications of pyrethroid intoxication. Aim. In silico studies of the binding mechanisms of quercetin, α-cypermethrin and its derivatives with the estrogen receptor α, features of pharmacokinetics and toxicity profile of these compounds. Methods. The molecular docking procedure of quercetin, α-cypermethrin, 3-PВA and DCCA was carried out using the crystal structures of the ligand-binding domain of ERα: 3ERT (reference ligand – 4-hydroxytamoxifen), 1ERR (reference ligand – raloxifene), 1GWR and 1ERE (reference ligand – estradiol) from the PDB database of biological macromolecules. Three different docking software packages were used: Glide, AutoDock, and AutoDock Vina. The location of the reference ligand was used to determine the automatic detection of the binding site. The pkCSM platform, admetSAR online server and SwissADME were used for in silico investigation of pharmacokinetic properties and toxicity of quercetin and α-cypermethrin. The results. AutoDock predicts that α-cypermethrin has the lowest binding energy with 1ERE (–10.4 kcal/mol), 1GWR (–10.0 kcal/mol), and 1ERR. This may indicate an agonistic effect of this insecticide on ERα. The predicted Gscore values for α-cypermethrin reflect a stronger interaction with 3ERT: –9.29 kcal/mol. The binding characteristics of α-cypermethrin, its metabolites (3-PBA and DCCA) and 4-hydroxytamoxifen with 3ERT are similar, which may indicate an antagonistic effect of the pyrethroid for ERα. The opposite nature of the interaction of α-cypermethrin with ERα can be explained by the difference in evaluation functions and methods of optimization of protein structures and ligands depending on the software used. The results predicted by both AutoDock, Vina and Schrödinger Maestro Glide show for quercetin the lowest binding energy values with 1ERR, 1GWR and 1ERE, which is a sign of agonistic action. Quercetin binds to the ligand-binding domains of ERα more strongly than 3-PBA and DCCA. Quercetin has toxicity and pharmacokinetic parameters corresponding to the norm and meets all the requirements for drug-like properties. Based on the data obtained in silico, it is possible to assert the expediency of using quercetin as a component of the prevention of complications of the endocrinotoxic action of α-cypermethrin. Conclusions. The toxic effect of α-cypermethrin, which is aimed at disrupting the endocrine function of the gonads, may be associated with the high affinity of this compound and its metabolites for ERα, accompanied by both agonistic and antagonistic effects according to various evaluation criteria. Quercetin, a flavonoid with pronounced ER agonistic properties, can be used to prevent complications of α-cypermethrin intoxication based on its pharmacokinetic and drug-like parameters.

Precedent texts as linguistic mechanisms for creating an attractive newspaper headline in the Internet media discourse

The article examines newspaper headlines produced using precedent phenomena, and thereby they act as effective linguistic mechanisms for creating media text attractiveness. The research material is comprised of newspaper headlines obtained from the online versions of "Rossiiskaya Gazeta" texts for 2023 and 2024. We used cognitive discourse analysis in conjunction with a comprehensive methodology for examining active processes in language. We assume that headlines convey certain information about the text content and can perform text-forming and evaluative functions. The study analysed the sources of headlines based on precedent phenomena, types of precedent text transformation, functions of using precedent texts to create newspaper headlines in the modern Internet media language. The research shows that precedent text sources in the headings are well-known works of literature and art, phraseological units and proverbs, the so-called catchphrases, other set expressions and clich  es. Structural-semantic, semantic proper, and pragma-semantic models for transforming original precedent texts were identified. The paper also characterises such functions of using precedent text in headlines as the informative, text-forming, persuasive, expressive, evaluative, and attraction functions. We conclude that the considerable persuasive, attractive, and evaluative potential of precedent phenomena in headlines is most often realised in transformed precedent texts. This is associated with the foregrounding of cultural information known to readers due to its link to a specific precedent phenomenon. New information relevant to a particular author in a particular text is superimposed on the familiar cultural information in a figurative, playful form.

A new strategy for weed detection in maize fields

Timely determination of weed distributions in fields is crucial for the precise spraying of herbicides. This facilitates weed control while saving costs and protecting the environment. Existing weed detection strategies often rely on the utilization of numerous weed samples to train detection models directly, which presents challenges in situations involving limited weed samples. To address this issue, a novel weed detection strategy was proposed in this study to identify weeds accurately in fields with varying coverage levels. For this purpose, red–green–blue (RGB) images of maize fields with different weed coverage levels were captured via a vertical take-off and landing fixed-wing unmanned aerial vehicle (UAV). The UAV images were first mosaicked, and a new weed detection strategy was developed and assessed. In this process, the MeanShift segmentation method, coupled with the local variance (LV) segmentation evaluation function and the Otsu automatic classification method, was initially employed to extract vegetation areas. The you-only-look-once (YOLO) v5n model was subsequently improved and used to detect maize plants. Finally, weed mapping was achieved by removing the identified maize plants from the vegetation through overlay analysis. The evaluation of the proposed method via an external dataset yielded favorable weed detection results, with an R2 value of 0.96 and a root mean square error (RMSE) value of 3.08 % under the different weed coverage levels. Specifically, in addition to adjusting the activation function and the nonmaximum suppression method, the impacts of integrating various attention modules at different positions on the performance of the YOLO v5n model for maize plant detection were analyzed. Improving the YOLO v5n model by incorporating the efficient channel attention (ECA) module into the backbone of the original model and utilizing the Hardswish activation function is recommended. Overall, this study offers support for precise weed control.

Dentists who can auscultate: Microphone-based toothbrushing quality monitoring system for electronic toothbrush

Tooth brushing is a widely recognized and effective method for daily oral care. However, the lack of real-time feedback for monitoring the brushing process can hinder individuals in maintaining their oral hygiene. Currently, only high-end electric toothbrushes offer evaluation functions based on built-in sensors, making them unaffordable for the majority of people. In this paper, we propose a novel and cost-effective wearable acoustic-based system (both hardware and software) for monitoring the quality of daily tooth brushing. Our system combines a throat-microphone and a Bluetooth earphone to capture brushing noises, which are unique acoustical signals generated from the air and the human body during tooth brushing, respectively. By leveraging the distinctive characteristics of brushing sounds in these two different conductive media, we introduce an intelligent detection algorithm based on Single-Input Multiple-Output (SIMO) modeling and frequency response functions (FRFs) to detect the corresponding brushing area. Through experimentation, we have found that the divergence of FRFs provides more stable features compared to general statistical features used in previous studies. After comparing various machine learning algorithms, we have selected XGBoost as the final detection model. Additionally, we utilize the Symmetric Nearest Neighbor (SNN) filter in the post-processing stage to further reduce the jitter noise in continuous detection. The final system achieves an average precision of 98.46% when tested on tooth brushing data collected from different volunteers. To enhance user experience, we have developed an Android App with a user-friendly interface that incorporates the detection system. With a total wearable hardware cost of less than 10 dollars, our system shows great potential for integration into future smart wearable devices.

Optimization Study of Steady-State Aerial-Towed Cable Circling Strategy Based on BP Neural Network Prediction

This paper presents models for UAV aerial-towed cables in free-end and fixed-end configurations, crucial for tasks like communication and aerial charging. By establishing a quasi steady-state model, computational results on cable shapes are obtained. To accelerate computations, a backpropagation (BP) neural network prediction model is trained, significantly reducing the computation time. An evaluation function has been developed that integrates both aircraft performance and cable shape considerations to evaluate circling parameters across various states. This function integrates techniques such as BP neural networks and particle swarm optimization (PSO) to refine parameters such as velocities and bank angles for both free-end and fixed-end cables. The results show that the BP neural network accurately predicts cable shapes, achieving a maximum error of 5% in towing force and verticality. Additionally, PSO efficiently optimizes circling parameters, thereby enhancing the effectiveness of the evaluation function in identifying optimal solutions. This approach significantly improves the efficiency of determining optimal circling parameters for UAV aerial-towed cables, thereby contributing to their operational efficacy.

Research on Formation Rendezvous for Unmanned Surface Vehicles Considering Arbitrary Initial Positions and Headings

The formation of unmanned surface vehicles (USVs) stands as a paramount concern in USV formation control research. In addressing this, this paper introduces a novel formation strategy for USVs, which accounts for their arbitrary initial positions and headings. Firstly, given the random distribution of the USVs’ initial positions, to enhance the efficiency of formation assembly, the point set of the USVs is matched with basic formations using the Hausdorff distance to establish the formation. Building upon this foundation and acknowledging the potential hindrance posed by the arbitrary initial headings of the USVs, which could hinder their ability to reach the designated formation target points, a cost matrix is constructed based on the designed distance–azimuth evaluation function. Subsequently, the Hungarian algorithm is utilized to efficiently assign formation target points for the USVs. Ultimately, the effectiveness of the proposed strategy is validated through simulation experiments.

Research on Path Planning Method for Autonomous Patrol Robots

For autonomous patrol robots, how to complete multi-point path planning efficiently is a crucial challenge. To address this issue, this work proposes a practical and efficient path planning method for patrol robots. Firstly, the evaluation function of the traditional A* method is improved to ensure that the planned path maintains a safe distance from the obstacles. Secondly, a Dubins curve is used to optimize the planned path to minimize the number of turning points while adhering to kinematic constraints. Thirdly, a trajectory-preserving strategy is proposed to preserve the continuous trajectory, linking multi-points for future inspection tasks. Finally, the proposed method is validated through both simulation and real-world experiments. Experimental results demonstrate that our proposed method performs exceptionally well in terms of safety, actual trajectory distance, and total execution efficiency.

Accurate and fast autofocusing in off-axis digital holography based on step reduction search and particle swarm optimization

The accurate reconstruction distance is one of the important parameters in digital holography. Typical autofocusing methods have been proposed for digital holography to determine the focusing plane. In these methods, a series of holograms at fixed-step intervals always needed to be reconstructed and then locate the focal plane by using an evaluation function, a procedure that is very time-consuming. In this paper, an accurate and fast autofocusing method for off-axis digital holograms is proposed. The method first employs the step reduction search algorithm and the integral amplitude modulus (AMP) evaluation function with low computational effort to iteratively reduce the searching range of the focal plane. Then the particle swarm optimization (PSO) algorithm and a difference-in-amplitude (DIF) evaluation function with high sensitivity are used to accurately locate the focal plane. Numerical and experimental results show that the proposed method significantly improves the focusing plane detection accuracy and computational efficiency compared with the typical autofocusing methods.

MANAJEMEN KEWIRAUSAHAAN DI DAYAH SERAMBI ACEH

Abstract Etrepreneurship management, especially in an educational organization, is the creative and innovative use of economic potential with the courage to face risks to gain profits that are useful for the success of the program in the educational organization. One of the Dayah that develops entrepreneurial management is Dayah Serambi Aceh. There are several types of businesses run by Dayah Serambi Aceh, namely grocery stores, bookstores and hardware store. This research aims to determine the entrepreneurship management carried out by Dayah Serambi Aceh which includes the functions of planning, implementation, supervision and evaluation as well as to determine the obstacles faced by managers in carrying out entrepreneurial activities. This research used a qualitative approach with seven research subjects. The data collection techniques are through interviews, observation and documentation. The results of the research show that the entrepreneurship planning carried out by Dayah Serambi Aceh includes planning the type of business, the amount of business capital, the location of the business and the schedule for implementing the business. Then implementation is carried out by establishing a work contract for managers for each business with an incentive distribution of 60% of profits for Dayah Serambi Aceh and 40% of profits for managers calculated from annual sales profits. Furthermore, supervision is carried out by the leadership of Dayah Serambi Aceh by making regular visits to each business. And evaluation is carried out by assessing business performance, determining business development prospects and fixing things that are still obstacles in carrying out entrepreneurial activities. The obstacles faced by business managers are insufficient knowledge and expertise in the field of entrepreneurship and marketing reach which is still narrow in scope.

Towards Design Automation of Microfluidic Mixers: Leveraging Reinforcement Learning and Artificial Neural Networks.

Microfluidic mixers, a pivotal application of microfluidic technology, are primarily utilized for the rapid amalgamation of diverse samples within microscale devices. Given the intricacy of their design processes and the substantial expertise required from designers, the intelligent automation of microfluidic mixer design has garnered significant attention. This paper discusses an approach that integrates artificial neural networks (ANNs) with reinforcement learning techniques to automate the dimensional parameter design of microfluidic mixers. In this study, we selected two typical microfluidic mixer structures for testing and trained two neural network models, both highly precise and cost-efficient, as alternatives to traditional, time-consuming finite-element simulations using up to 10,000 sets of COMSOL simulation data. By defining effective state evaluation functions for the reinforcement learning agents, we utilized the trained agents to successfully validate the automated design of dimensional parameters for these mixer structures. The tests demonstrated that the first mixer model could be automatically optimized in just 0.129 s, and the second in 0.169 s, significantly reducing the time compared to manual design. The simulation results validated the potential of reinforcement learning techniques in the automated design of microfluidic mixers, offering a new solution in this field.

Research on the Coupled and Coordinated Development of Tourism and Transport in Chongqing Municipality

By examining the current status of tourism and transport research both domestically and internationally, and analyzing the development trends of Chongqing’s tourism and transport industry, this study evaluates the weight indices and performs a coupling and coordination analysis of various indicators of Chongqing’s tourism and transport industry from 2011 to 2019. The following conclusions are drawn: (1) The value of the comprehensive evaluation function for tourism is higher than that of the transport industry, indicating that Chongqing’s tourism industry is in a period of vigorous development; (2) The degree of coupling coordination development between Chongqing’s tourism and transportation industries from 2011 to 2019 is rising. As the coupling coordination stage continues to improve, the level of coupling coordination also improves, with the overall trend showing a progression from mild dysfunction to primary coordination. Based on these conclusions, corresponding countermeasures are proposed to promote the coupling coordination between tourism and transport in Chongqing, and suggestions are made for the overall improvement of the transport industry and the sustainable development of the tourism industry.

Design Method of Freeform Surface Optical Systems with Low Coupling Position Error Sensitivity.

Freeform off-axis reflective systems are significantly more difficult to align and assemble owing to their asymmetric surface shapes and system structures. In this study, a freeform surface system design method with low coupling position error sensitivity (FCPESM) was proposed. First, we established a mathematical model of a reflective system when it was perturbed by coupling position errors and used the clustering-microelement method to establish the coupling error sensitivity evaluation function. The evaluation function was then applied to the design process of a freeform surface off-axis three-mirror optical system. The results showed that the FCPESM optical design method can significantly relax the assembly tolerance requirements of optical systems on the basis of ensuring image performance. In this study, the reflective system was perturbed by tilt and decenter simultaneously, and the disturbance mechanism of position errors on optical systems was further improved. Through this research, freeform surface systems with both image performance and error sensitivity can be obtained, which makes freeform off-axis reflective systems with better engineering realizability.

Compound Attitude Control Strategy for Reusable Launch Vehicle Based on Improved Particle Swarm Optimization Algorithm

This study introduces an advanced dual-mode compound attitude control framework for reusable launch vehicles (RLVs), underpinned by an enhanced particle swarm optimization (PSO) algorithm. This innovative strategy is tailored to meet the stringent demands for precision and robust anti-interference capabilities across the entire flight envelope of RLVs. The research commences with the formulation of a comprehensive attitude dynamics model and diverse heterogeneous actuator representations, meticulously crafted to reflect the distinct phases of RLV flight. Building upon this foundation, a synergistic control paradigm is engineered, integrating PID and fuzzy PID controllers and dynamically adjusting the inertia weights and learning factors of the PSO algorithm to achieve the balance between global and local optimization performance, complemented by a refined fitness evaluation function. The crux of the study is the application of an upgraded PSO algorithm to fine-tune the controllers’ weighting coefficients, culminating in an optimized dual-mode compound attitude control system. A series of comparative simulation analyses are thoroughly executed to appraise the system’s responsiveness, stability, precision, and resilience to interference. The simulation outcomes demonstrate an average reduction of 42.21% in step response overshoot, an 18.52% decrease in settling time, a 53.18% decline in steady-state error, a 56.80% drop in the maximum deviation value, a 55.82% improvement in recovery speed, and a 75.61% enhancement in tracking precision for the proposed controller. The findings clearly verify the superior performance of the proposed control system, affirming its contribution to the advancement of RLV attitude control. The proposed controller holds promising potential for real application in attitude control systems and is poised to augment the reliability and mission success rate of RLVs under intricate flight scenarios.

Deep Learning-Based Dynamic Region of Interest Autofocus Method for Grayscale Image.

In the field of autofocus for optical systems, although passive focusing methods are widely used due to their cost-effectiveness, fixed focusing windows and evaluation functions in certain scenarios can still lead to focusing failures. Additionally, the lack of datasets limits the extensive research of deep learning methods. In this work, we propose a neural network autofocus method with the capability of dynamically selecting the region of interest (ROI). Our main work is as follows: first, we construct a dataset for automatic focusing of grayscale images; second, we transform the autofocus issue into an ordinal regression problem and propose two focusing strategies: full-stack search and single-frame prediction; and third, we construct a MobileViT network with a linear self-attention mechanism to achieve automatic focusing on dynamic regions of interest. The effectiveness of the proposed focusing method is verified through experiments, and the results show that the focusing MAE of the full-stack search can be as low as 0.094, with a focusing time of 27.8 ms, and the focusing MAE of the single-frame prediction can be as low as 0.142, with a focusing time of 27.5 ms.

Pengembangan Evaluasi Teknik Non Tes

The teaching and learning process is the core of education with teachers and learners playing the main roles. Evaluation is a key aspect of the educational process, ensuring the achievement of curriculum objectives. Evaluation not only measures student learning outcomes, but also helps understand the learning process and provides information for further improvement. Evaluation can be either test or non-test, and the use of both methods depends on the purpose of the assessment and the aspects of learning to be assessed. This research uses a literature review to explore the concepts and methods of non-test evaluation in education. Non-test evaluation includes various techniques such as performance assessment, project/product assessment, attitude assessment and others. The development of non-test evaluation is important as it allows for a comprehensive assessment of students' affective and psychomotor aspects, which cannot always be measured by written tests. The functions of non-test evaluation in education include measuring affective and psychomotor aspects, providing a holistic picture of students' abilities, supporting individualized learning, motivating students, and identifying strengths and weaknesses in learning. As such, non-test evaluation is an integral component of the learning process that helps maximize overall educational outcomes.