Stages Of Acquisition Research Articles

Land Acquisition for Infrastructure Development with the Mechanism of Government Cooperation with Business Entities (KPBU): Case Study of Lhok Guci KPBU Project

This research discusses land acquisition for infrastructure development through the mechanism of Government Cooperation with Business Entities (PPP) with a case study of the Lhok Guci PPP project. Land acquisition is a crucial stage in PPP that often faces various legal, social, and economic challenges. This research uses a normative juridical method to analyze relevant regulations and a conceptual approach to understand key concepts. The stages of land acquisition studied include planning, location determination, implementation, compensation, release of rights, granting land rights, and supervision. The Lhok Guci case study shows the importance of transparency and coordination between relevant parties for the success of PPP projects. The results of the study are expected to improve the efficiency of the land acquisition process in future infrastructure projects.

IMPLEMENTING NEUROSCIENTIFIC PRINCIPLES IN EARLY CHILDHOOD EDUCATION: A CASE STUDY OF TKIT UMAR BIN KHATHAB KUDUS

<p><em>An early childhood education teacher must possess adequate knowledge and skills in early childhood pedagogy to ensure that the learning process is effective and efficient. This study aims to reveal teaching techniques that incorporate neuroscientific insights to enhance children’s ability to absorb the information provided during lessons at TKIT Umar Bin Khathab Kudus. The research methodology employed is descriptive and qualitative, with data collection conducted through observation, interviews, and documentation. The study participants include a principal, two teachers, 30 students from class B, and two parents. After data collection, the study analyzes the data by relevant theories. The results indicate that the teaching at TKIT Umar Bin Khathab Kudus has adopted neuroscience principles by implementing center-based methods. Teachers’ skills in initiating lessons have been observed during the pre-exposure and preparation stages. Furthermore, teachers have conducted the stages of initiation, acquisition, elaboration, and memory incubation in explaining the learning material. Finally, in concluding the learning process, teachers have applied the stages of verification, comprehension checking, celebration, and integration. The teaching skills applied are reflected in the improvement of students’ learning outcomes on each indicator.</em></p>

L2 cognitive construal and morphosyntactic acquisition of pseudo-passive constructions

Abstract Discussion of the theoretical underpinning of pseudo-passive constructions abounds in generative linguistics and comparative typology. However, the cognitive construal of and morphosyntactic acquisition of (un)grammatical English pseudo-passive constructions by Chinese-speaking L2-English users with different proficiency levels have never been investigated. It is worth examining whether proficiency level modulates constructional acceptability and cognitive construal, which would allow us to infer L2-English users’ morphosyntactic acquisition stages of pseudo-passive constructions. To fill this research gap, the study recruited 120 L2-English speakers with different levels of proficiency and 40 native English speakers to complete a grammaticality judgment test with an acceptance judgment scale from −2 to 2 for 40 ungrammatical pseudo-passive sentences and 40 fillers. Proficiency level significantly correlates with a firmer rejection of ungrammatical pseudo-passive sentences and cognitive construal which is more target-like. Significantly asymmetrical acceptance levels between two types of target ungrammatical constructions are also revealed for low (p = 0.002) and medium proficiency (p = 0.000) groups, demonstrating the type effect on grammaticality judgment and morphosyntactic acquisition.

Carbon Footprint of Spent Ternary Lithium-Ion Battery Waste Recycling

Road transport is the primary source of greenhouse gas emissions in China's transportation field. As an important means to achieve the "double carbon" goal in the transportation field, the new energy automobile industry will face a large number of power battery scrapping in the future. In order to quantitatively assess the carbon emission reduction benefits generated by the spent ternary lithium-ion battery waste recycling industry, the carbon footprint accounting model of spent ternary lithium-ion battery waste recycling and utilization was constructed from the life cycle perspective. By optimizing the power structure and transportation structure, the carbon emission reduction potential of spent ternary lithium-ion battery waste recycling was predicted and evaluated. In addition, the uncertainty analysis was conducted using the propagation of uncertainty equation to ensure the reliability and effectiveness of the carbon footprint results. The results showed that the current carbon footprint of Chinese enterprises using wet technology to recover 1 kg waste lithium batteries was -2 760.90 g (directional recycling process) and -3 752.78 g (recycling process), and the uncertainty of the carbon footprint was 16 % (directional recycling process) and 15 % (recycling process), respectively. From the analysis of carbon emission contribution, the regenerated product stage was the primary source of carbon reduction in the wet recycling and utilization of waste ternary lithium batteries, whereas the battery acquisition, disassembly, and end treatment stages were the main sources of carbon increase. Compared to optimizing the transportation structure, optimizing the power structure could effectively achieve greater carbon emission reduction potential. Under the collaborative optimization scenario, compared to that before optimization, 14 %-19 % carbon emission reduction could be achieved. Compared with native products, the directional circulation process and recycling process could achieve 9 % and 11 % emission reduction potential, respectively.

Research on the Current Situation and Calculation Method of Carbon Emissions Assessment for Building Curtain Walls

Curtain wall systems stand out as a pivotal domain within the construction sector’s endeavors towards energy efficiency and carbon mitigation. To refine the evaluation framework for carbon emissions within this industry, this paper explores the calculation and assessment method for building curtain walls. The article first reviews the current research status regarding carbon emissions from materials and the impact of curtain walls on buildings in the operational stage. Based on lifecycle theory, the carbon emissions from building curtain walls are divided into six stages: material acquisition, processing and production, installation and construction, transportation, use and maintenance, and dismantling. On this basis, this paper proposes a method for calculating carbon emissions from building curtain walls. Following that, a case study is conducted using a specific glass curtain wall project for illustrative analysis. The results indicate that the carbon emissions from the material acquisition stage constitute approximately 90% of the total, serving as the primary source of carbon emissions for glass curtain walls. Furthermore, the scientific application of photovoltaics can significantly reduce the carbon emission levels of building curtain walls. Finally, an analysis was conducted on the current issues existing in the evaluation of carbon emissions.

Neuroanatomical, transcriptomic, and molecular correlates of math ability and their prognostic value for predicting learning outcomes.

Foundational mathematical abilities, acquired in early childhood, are essential for success in our technology-driven society. Yet, the neurobiological mechanisms underlying individual differences in children's mathematical abilities and learning outcomes remain largely unexplored. Leveraging one of the largest multicohort datasets from children at a pivotal stage of knowledge acquisition, we first establish a replicable mathematical ability-related imaging phenotype (MAIP). We then show that brain gene expression profiles enriched for candidate math ability-related genes, neuronal signaling, synaptic transmission, and voltage-gated potassium channel activity contributed to the MAIP. Furthermore, the similarity between MAIP gene expression signatures and brain structure, acquired before intervention, predicted learning outcomes in two independent math tutoring cohorts. These findings advance our knowledge of the interplay between neuroanatomical, transcriptomic, and molecular mechanisms underlying mathematical ability and reveal predictive biomarkers of learning. Our findings have implications for the development of personalized education and interventions.

Intonation words in initial intentional communication of Mandarin-speaking children.

Intonation words play a very important role in early childhood language development and serve as a crucial entry point for studying children's language acquisition. Utilizing a natural conversation corpus, this paper thoroughly examines the intentional communication scenes of five Mandarin-speaking children before the age of 1;05 (17 months). We found that children produced a limited yet high-frequency set of intonation words such as " [a], [æ], [ε], [ən], [ə], eng [əŋ], [o], and [i]." These intonation words do not express the children's emotional attitudes toward propositions or events; rather, they are utilized within the frameworks of imperative, declarative, and interrogative intents. The children employ non-verbal, multimodal means such as pointing, gesturing, and facial expressions to actively convey or receive commands, provide or receive information, and inquire or respond. The data suggests that the function of intonation words is essentially equivalent to holophrases, indicating the initial stage of syntactic acquisition, which is a milestone in early syntactic development. Based on the cross-linguistic universality of intonation word acquisition and its inherited relationship with pre-linguistic intentional vocalizations, this paper proposes that children's syntax is initiated by the prosodic features of intonation. The paper also contends that intonation words, as the initial form of human vocal language in individual development, naturally extend from early babbling, emotional vocalizations, or sound expressions for changing intentions. They do not originate from spontaneous gesturing, which seems to have no necessary evolutionary relationship with the body postures that chimpanzees use to change intentions, as suggested by existing research. Human vocal language and non-verbal multimodal means are two parallel and non-contradictory forms of communication, with no apparent evidence of the former inheriting from the latter.

Emergent bilingual children during the silent period: A scoping review of their communication strategies and classroom environments.

This scoping review aimed to investigate the communication strategies utilized by children who acquire a minority language (L1) and subsequently learn a community language (L2) during what is commonly referred to as the "silent period." Electronic database searches were conducted using keywords such as "silent period" and "bilingual children," resulting in the inclusion of 40 studies in the review. The findings revealed that emergent bilingual children utilize various communication strategies, including nonverbal communication, private speech, and their L1, to communicate within classroom environments. The findings shed light on the adaptability of emergent bilingual children during early stage of L2 acquisition. Furthermore, our review provides information about the classroom contexts such as teacher support and peer interactions where children develop their L2 skills. From a clinical perspective, recognizing these strategies and classroom contexts could significantly enhance the screening process for emergent bilingual children.

Predicting game-induced emotions using EEG, data mining and machine learning

BackgroundEmotion is a complex phenomenon that greatly affects human behavior and thinking in daily life. Electroencephalography (EEG), one of the human physiological signals, has been emphasized by most researchers in emotion recognition as its specific properties are closely associated with human emotion. However, the number of human emotion recognition studies using computer games as stimuli is still insufficient as there were no relevant publicly available datasets provided in the past decades. Most of the recent studies using the Gameemo public dataset have not clarified the relationship between the EEG signal’s changes and the emotion elicited using computer games. Thus, this paper is proposed to introduce the use of data mining techniques in investigating the relationships between the frequency changes of EEG signals and the human emotion elicited when playing different kinds of computer games. The data acquisition stage, data pre-processing, data annotation and feature extraction stage were designed and conducted in this paper to obtain and extract the EEG features from the Gameemo dataset. The cross-subject and subject-based experiments were conducted to evaluate the classifiers’ performance. The top 10 association rules generated by the RCAR classifier will be examined to determine the possible relationship between the EEG signal's frequency changes and game-induced emotions.ResultsThe RCAR classifier constructed for cross-subject experiment achieved highest accuracy, precision, recall and F1-score evaluated with over 90% in classifying the HAPV, HANV and LANV game-induced emotions. The 20 experiment cases’ results from subject-based experiments supported that the SVM classifier could accurately classify the 4 emotion states with a kappa value over 0.62, demonstrating the SVM-based algorithm’s capabilities in precisely determining the emotion label for each participant’s EEG features’ instance.ConclusionThe findings in this study fill the existing gap of game-induced emotion recognition field by providing an in-depth evaluation on the ruleset algorithm’s performance and feasibility of applying the generated rules on the game-induced EEG data for justifying the emotional state prediction result.

Statistical Insignificance is not wholesale transfer in L3 Acquisition: an approximate replication of Rothman (2011)

Abstract This study was an approximate replication of Rothman (2011),examining the determiner phrase syntax of a large sample (n = 211) of L3 learners of Portuguese who spoke English and Spanish. Rothman (2011) investigated whether L3 Italian or Brazilian Portuguese speakers are differently impacted by another known Romance Language, if it was their L1 or L2. The original study concluded that groups did not perform differently on experimental tasks on the basis of a null effect, and that the typological similarity of Spanish, Portuguese, or Italian predicts transfer in the initial stages of L3 acquisition. The present replication recreated all materials, which were unavailable, and examined the same population and questions. However, rather than examining L3 Italian and L3 Brazilian Portuguese, the present work maintained a constant L3 Portuguese. Learners were divided into two groups in a mirror-image design (n = 96 L1 English-L2 Spanish, n = 115 L1 Spanish-L2 English), and data were collected online. Like the original study, there was no main effect of group in any of the two-way analyses of variance. However, results show that it should not be assumed that experimental groups behave equivalently based on a null effect: Of the four total post hoc tests of equivalence, only two were significant when the equivalence bounds were set at a small effect size (d = $ \pm $ .4). Ultimately, it is argued that determining the smallest effect size of interest and subsequent equivalence testing are necessary to answer key questions in the field of L3 acquisition.

Latency Evaluation in the Image Acquisition System Based on MTCA.4 Architecture for Plasma Diagnostics

ITER diagnostic systems provide measurements to the Plasma Control System (PCS) in real-time. These measurements are used for plasma control and machine protection. Latency is an important parameter in the assessment of such systems. It is a time gap between capturing an external event by hardware and finishing the processing of acquired data. PCS requires the diagnostic systems to introduce a maximum total latency of 10 to 100 ms, therefore, the systems need to be tested if they meet the requirements. The system evaluated in this paper is a reference real-time image acquisition system developed as a base for ITER diagnostic systems. It consists of hardware based on the Micro Telecommunications Computing Architecture (MicroTCA) standard, developed firmware, and software. It supports cameras with various interfaces. In the paper, two cameras, with a Camera Link and 1 GigE Vision interfaces were selected to perform latency evaluation. The paper presents two methods of measuring the latency of image acquisition. The first one is based on precise time stamping consecutive stages of acquisition. This approach allows for determining which step of acquisition takes more or less time. In consequence, the software or hardware can be optimized. The other one uses LED to evaluate a particular camera, by checking the time of camera reaction to the trigger. A dedicated testing framework is developed to perform automated tests to evaluate latency. It supports collecting and analyzing the results of measurements. Besides that, a dedicated hardware is used to perform the latency tests using LED. The results and discussion of the measurements are presented in the manuscript. They show the latency evaluated using earlier proposed methods, comparing the cameras used in the image acquisition system.

Effect of Phase Shifting on Real-Time Detection and Classification of Power Quality Disturbances

Power quality improvement and Power quality disturbance (PQD) detection are two significant concerns that must be addressed to ensure an efficient power distribution within the utility grid. When the process to analyze PQD is migrated to real-time platforms, the possible occurrence of a phase mismatch can affect the algorithm’s accuracy; this paper evaluates phase shifting as an additional stage in signal acquisition for detecting and classifying eight types of single power quality disturbances. According to their mathematical models, a set of disturbances was generated using an arbitrary waveform generator BK Precision 4064. The acquisition, detection, and classification stages were embedded into a BeagleBone Black. The detection stage was performed using multiresolution analysis. The feature vectors of the acquired signals were obtained from the combination of Shannon entropy and log-energy entropy. For classification purposes, four types of classifiers were trained: multilayer perceptron, K-nearest neighbors, probabilistic neural network, and decision tree. The results show that incorporating a phase-shifting stage as a preprocessing stage significantly improves the classification accuracy in all cases.

REFLEXIVE AND ANTI-CAUSATIVE VERB PRODUCTION REVISITED

The aim of the present research was to reexamine the production of reflexive and anti-causative se-verbs in Serbian at different stages of language acquisition after a nine-month period from the first experiment (Ilić, 2019). The results of the first experiment suggest the following order of acquisition: lexical reflexive > true reflexive > anti-causative verbs. Moreover, they speak in favour of the Continuity Hypothesis (Snyder et al., 1995; Snyder & Hyams, 2015), since the children were able to produce reflexive forms correctly from the earliest tested age. The verbs which were tested in both experiments were true reflexive (e.g. kupati se ‘wash oneself’), lexical reflexive (e.g. vrteti se ‘spin’) and anti-causative se-verbs (e.g. otvoriti se ‘open’). A total of sixty subjects belonging to three age groups (40-51, 52-63, 65-77 months, twenty participants in each group) took part in the research. The data collection technique was a verb elicitation task, and the children were asked to name the activities presented in the visual stimuli. The number of tested verbs was the same for each verb type (six per group). The production of verbs across age groups improved in comparison with the first experiment. The production of lexical reflexive verbs was significantly better than the production of anti-causative verbs in the youngest group. Therefore, the results of the follow-up experiment have confirmed the suggested order of acquisition. However, no differences were found in the two older groups, which indicates that the production of more complex verb types improves considerably between the ages of four and five.

Improved extended empirical wavelet transform for accurate multivariate oscillation detection and characterisation in plant-wide industrial control loops

The conventional extended empirical wavelet transform (EEWT) proposed recently is intended to decompose multivariate signals with clear peaks in power spectra without considering the cases where the signals contain high noise levels. Even when dealing with signals with distinct peaks, the EEWT method can still encounter challenges in properly decomposing the signals. However, plant-wide data from industrial control loops, including controllers’ outputs, process variables, and manipulated variables, are commonly corrupted by high levels of noise, which can be introduced at various stages of data acquisition, transmission, and processing within the control system. To address these limitations and ensure the applicability of the EEWT to real-world industrial data with diverse and challenging characteristics, this paper presents an improved version called the improved extended empirical wavelet transform (IEEWT). The IEEWT incorporates noise-reduced power spectra and detrended fluctuation analysis techniques to enhance the decomposition. The proposed method demonstrates accurate multivariate data decomposition for both simulated and real data sets, surpassing the limitations associated with the conventional EEWT.

Analysis of the Carbon Footprint of Academic Gowns: A Case Study of Thai University

The apparel industry has a significant impact on climate change due to the substantial amount of greenhouse gas (GHG) emissions. Academic gowns commonly used in Thailand may be a significant contributor to high GHG levels due to apparel consumption. The objective of this study was to quantify the carbon footprint (CF) of an academic gown for bachelor degree students worn during the commencement ceremony at a private Thai university. The evaluation complies with the national guidelines on Carbon Footprint of Product (CFP) established by the Thailand Greenhouse Gas Management Organization (TGO) in line with ISO 14067:2018 Greenhouse gases - Carbon footprint of products - Requirements and guidelines for quantification. Considering only a single impact category: climate change, GHG emissions were calculated for the entire product life cycle, including raw material acquisition, production process, distribution, use, and the end-of-life (EoL) treatment, and relevant transportation/delivery. Data in this study were collected from the entrepreneur producing academic gowns for rental purposes. The numerical results revealed that CFP of an academic gown with a length of 40 inches, weighing 1,284.30 g, is 42.7 kgCO2-eq over its entire life cycle with 39.71% contributing from use phase and EoL treatment. The stages of raw material acquisition, use phase and production process caused most of the emissions at 41.08%, 33.69% and 18.49%, respectively. The carbon footprint of this gown serves as an important baseline data to enhance design development and the production process for emission reductions.

Non-Invasive Fetal Well-Being Monitoring Approaches: A Mini Review on Fetal Signal Separation Techniques

Fetal signal separation is vital in producing an accurate interpretation of the health condition of a fetal. In the context of a non-invasive fetal monitoring approach, the signals are acquired from the abdomen of pregnant women. As a result, a mix of maternal and fetal signals is obtained. These maternal and fetal signals are vague, as both signals are interchanged during the signal acquisition stage. Since the signals are overlapped, a signal separation technique must be employed to process the fetal signal for further analysis. This paper presents published studies on applying signal processing techniques involving fetal signal separation. These papers are obtained through a strategy known as the PRISMA technique. The online databases include ACM, Emerald Publishing, IEEE Explore Digital Library, Science Direct, Scopus, and Springer, with published years spanning from 2018 until 2022. Numerous separation techniques were found, such as adaptive filtering, blind source separation (BSS), and alternative approaches. Issues on the existing methods for fetal signal separation are discussed. In addition, the limitations and drawbacks of the research work involving existing fetal signal separation are reviewed in the paper. The potential direction of future research in this field is addressed as well. Based on this mini-review, it can be concluded that noise and ambiguity can still occur in the extracted fetal signals, even when signal processing techniques are applied. In the future, deep learning would be accommodating in improving the efficiency of extracting fetal signals obtained from the non-invasive fetal well-being monitoring technique. Meanwhile, apart from fetal heart rate (fHR) detection, fetal hypoxia can also be another important focus of study for improving fetal well-being monitoring.

How Does Increasing Contextual Interference in a Musical Practice Session Affect Acquisition and Retention?

The purpose of this study was to investigate the effect of three schedules of practice on high-level violinists’ learning. The contextual interference (CI) effect occurs when two or more tasks are practiced in an interleaved manner, which has been shown to impair initial learning but improve retention. How a musician alternates between tasks during practice can have a great impact on learning. This study was designed to explore how an increasing schedule of CI within a practice session would compare to the traditional blocked and random schedules. Sport research has shown a dynamically adaptive schedule to be advantageous, yet this area is relatively unexplored in music. Twenty advanced violinists at a music school practiced three sets of musical excerpts under three practice schedule conditions: blocked (pieces practiced one at a time), increasing contextual interference (blocked and then random), and interleaved (pieces randomly alternating). Recordings were taken at the stages of sight-reading, acquisition (immediately after practice sessions), and retention (24 hours later). Expert ratings of each musician’s recordings revealed no effect at the acquisition stage. However, at the delayed retention test, the blocked condition proved to be most effective. Fifty percent of participants perceived blocked practice to be the most effective.

Factor analysis of environmental effects in circular closed‐loop supply chain network design and modelling under uncertainty in the manufacturing industry

AbstractSustainable development and competitive advantage are impacted by strategic choices that maximize resource value and reduce waste. Numerous instances of thriving OEM remanufacturing can be observed, predominantly in the business‐to‐business domain. The significance of Closed‐Loop Supply Chain (CLSC) in generating value and managing the recovery process is widely acknowledged within the supply chain industry. Manufacturing companies now have to come up with green supply chain and process design strategies in response to recent changes in environmental regulations. This study designates the specific features of circular closed‐loop supply‐chain design considering end‐of‐life products. Uncertainty in various aspects, such as acquisition, processing, and market stages, is impeding the progress of circular economies and also sustainable development in closed‐loop supply chains (CLSCs). This has led to increased complexity in remanufacturing processes and decreased efficiency. To address this issue, the study proposes a comprehensive, integrative approach for establishing a sustainable CLSC network that adapts to fluctuating demand through a questionnaire analysis. Moreover, the study introduces a multi‐objective optimization model for a dual‐channel supply chain network, aiming to enhance the flow. This model considers both economic and environmental objectives to achieve a sustainable and efficient supply chain system. To determine the ideal circular closed‐loop supply chain (CLSC) network design, the research proposes linear programming model with mixed integer, where recovery can take place in one of three ways: through material recovery, component recovery, or product recovery. Numerous findings from a thorough analysis by data backup that inform CLSC managers of ways to improve product returns in terms of quantity and quality. To address the uncertainty problem, the research also developed the fuzzy credibility constraint technique with a Simulated Annealing algorithm. Using cutting‐edge methods, it is explored and compared how sensitivity analysis results, the impact of altering the problem parameters, and the performance of the suggested model respectively.

Middle Managers’ Relational Dynamics in the Context of Acquisitions: Balancing Strategic Interdependence and Organizational Autonomy

AbstractDuring acquisitions, middle managers (MMs) play a crucial role in facilitating the integration of two previously separate organizations, thereby facing the challenge of balancing competing needs for strategic interdependence and organizational autonomy. Drawing on interviews with acquiring and acquired MMs, this study explores the unfolding dynamics of framing the relationships between both groups. We introduce the concept of actionable intersubjectivity as a core ability of MMs to take into account the presence of the other while accomplishing integration tasks. We propose a grounded process model that traces how actionable intersubjectivity is developed to varying degrees throughout the MMs’ relational dynamics occurring in the various interaction spaces provided at each acquisition stage by getting a sense of the other (pre‐acquisition), by deciphering how the other think (approval), by experiencing how the other act and react (post‐acquisition I) and by putting oneself in the place of the other (post‐acquisition II). Our paper contributes to mergers and acquisitions research in two ways: (1) by unpacking the relational dynamics between MMs and introducing actionable intersubjectivity as a crucial ability in the context of acquisitions and (2) by extending our understanding of MMs’ role in balancing strategic interdependence and organizational autonomy during the acquisition process.

The effect of reward on motor learning: different stage, different effect.

Motor learning is a prominent and extensively studied subject in rehabilitation following various types of neurological disorders. Motor repair and rehabilitation often extend over months and years post-injury with a slow pace of recovery, particularly affecting the fine movements of the distal extremities. This extended period can diminish the motivation and persistence of patients, a facet that has historically been overlooked in motor learning until recent years. Reward, including monetary compensation, social praise, video gaming, music, and virtual reality, is currently garnering heightened attention for its potential to enhance motor motivation and improve function. Numerous studies have examined the effects and attempted to explore potential mechanisms in various motor paradigms, yet they have yielded inconsistent or even contradictory results and conclusions. A comprehensive review is necessary to summarize studies on the effects of rewards on motor learning and to deduce a central pattern from these existing studies. Therefore, in this review, we initially outline a framework of motor learning considering two major types, two major components, and three stages. Subsequently, we summarize the effects of rewards on different stages of motor learning within the mentioned framework and analyze the underlying mechanisms at the level of behavior or neural circuit. Reward accelerates learning speed and enhances the extent of learning during the acquisition and consolidation stages, possibly by regulating the balance between the direct and indirect pathways (activating more D1-MSN than D2-MSN) of the ventral striatum and by increasing motor dynamics and kinematics. However, the effect varies depending on several experimental conditions. During the retention stage, there is a consensus that reward enhances both short-term and long-term memory retention in both types of motor learning, attributed to the LTP learning mechanism mediated by the VTA-M1 dopaminergic projection. Reward is a promising enhancer to bolster waning confidence and motivation, thereby increasing the efficiency of motor learning and rehabilitation. Further exploration of the circuit and functional connections between reward and the motor loop may provide a novel target for neural modulation to promote motor behavior.