Learning Capacity Research Articles

Critical juncture, learning and state capacity-in-motion: pathway cases in Asia

ABSTRACT Precisely because public health crises are uncertain, complex and have multiple dimensions, deeper institutional state capacity is needed. This article develops an approach to thinking about state capacity by incorporating time as a key component of a refurbished idea of state capacity. This reorientation helps us understand the specific mechanisms, especially of learning – that were crucial for the success of East Asian cases in managing the COVID-19 epidemic. Previous exposure to a health crisis – a critical juncture – activates learning capacity via two distinct mechanisms, one a political route of that restores public trust and renews the social contract, and the second through a functional route leading to administrative capacity building necessitated by a complex and multiplex crisis that crosses a variety of boundaries. Learning that builds coordinating state capacity is therefore needed. This article applies these ideas to understanding the successful pathway cases of South Korea and Taiwan.

Impact of Shared Leadership and Entrepreneurial Orientation on Organizational Learning in Banking Industry

This study aims to explore the impact of shared leadership on entrepreneurial orientation in Dubai commercial banks, and whether organizational learning ability intercedes the impact of shared leadership on entrepreneurial orientation. Embracing a quantitative research structure, information was gathered by methods for a poll based study of workers in Dubai commercial banks. In view of 330 usable reactions, the outcomes uncovered the critical impact of shared leadership and two of its measurements (uplifting inspiration and scholarly incitement) on entrepreneurial orientation. Two other measurements (admired impact and individualized thought) didn't add to entrepreneurial orientation. Moreover, the outcomes demonstrated that shared leadership significantly affects organizational learning ability, and that organizational learning capacity thus influences entrepreneurial orientation. The discoveries affirm that organizational learning capacity completely intervenes the impact of shared leadership on entrepreneurial orientation. Various suggestions are propelled, the generally significant of which is that banks ought to improve and build up administrators' shared traits by preparing them on the best way to manage workers so as to expand their entrepreneurial orientation. Banks ought to additionally think about improving their learning ability, as this assumes a huge role in upgrading and supporting the impact of shared leadership characteristics on entrepreneurial orientation.

Spaced Retrieval Effects on Learning Capacity in Patients With Mild-to-Moderate Cognitive Impairment

Abstract: Spaced retrieval (SR) improves the learning capacity of patients with memory deficits, while evidence on its long-term and generalization to other untrained measures is quite inconsistent. This systematic review was to analyze evidence on the SR effectiveness on recall performance, follow-up, and generalization measures in patients with cognitive impairment. A systematic search on PUBMED, MEDLINE, Web of Science, PubPsych, and ProQuest was performed between November 2020 and January 2021. The benefit effect of SR on direct, generalization, and long-term measures did not significantly differ from other learning techniques and the effect sizes even increased when SR was combined with another method. Effects on generalization depended on the similarity between the trained and untrained material. Existing evidence on SR remains quite scarce, mainly based on studies with moderate methodological quality giving rise to very heterogeneous results. Further investigation is still needed to overcome previous methodological limits and extend evidence to the immediate and long-term effects of the simultaneous application of different learning methods.

Trelagliptin relieved cognitive impairment of diabetes mellitus rats: Involvement of PI3K/Akt/GSK-3β and inflammation pathway

Cognitive impairment frequently coexists with diabetes. Trelagliptin is a once-weekly taking selective dipeptidyl peptidase-4 (DPP-4) inhibitor and a long-term effective hypoglycemic medicine; nonetheless, its effects for the treatment of diabetes-related cognitive impairment have only sometimes been explored. In this study, a DM model was built using streptozotocin (STZ) and a high-fat diet (HFD). The morris water maze test on DM rats revealed a considerably reduced capacity for spatial learning and memory, but trelagliptin was able to restore function. Trelagliptin could lower the mRNA expression of inflammatory factors such IL-1β, TNF-α, and IL-6 in DM rats. It could also reduce the ratio of p-IKKα/IKKα, and the immunofluorescence result of NF-κB also demonstrated a drop. Trelagliptin partially restored dendritic spines and prevented the loss or shrinkage of neurons, respectively, according to the results of Nissl's staining and golgi staining. Furthermore, PI3K/Akt/GSK-3β has been activated, and synaptic plasticity has been modified during this process. In conclusion, trelagliptin improved the cognitive lesion in DM rats by suppressing the activation of the inflammatory route and by activating the PI3K/Akt/GSK-3β pathway at the same time, as well as interacting with the pathways that protect neurons, which still need further research.

Human Capital Development and Performance in Garment Industries in Nigeria: Business Owners and Employees Simultaneous Development Perspectives

The development of firm employees may not be sufficient without simultaneous development of business founders’ intellectual capital because of the role business founders play in business strategy decisions and their increasing involvement in business operations. Prior studies seem to have ignored this important role of business founders in ascertaining the effects of human capital development in corporate performance by focusing solely on firm’s employees. This study proposed and tested human capital development from both business founders’ and employees’ perspective and determined how such simultaneous intellectual development can enhance firms’ productivity in garment industry in Nigeria. A survey research design method was employed and the data collected from 100 individuals who either owned or belonged to the top management team of Garment Industries in Nigeria was empirically tested using regression analysis. The result indicated that human capital development of business founders and employees positively affect firms’ productivity suggesting that firms that engage in simultaneous training of employees and founders performed better than those focusing solely on employees. Simultaneous training, educating, mentoring and learning capacity of both business founders and their employees can generate substantial returns to garment firms that can revitalize the industry while making them internationally competitive. The positive effect associated with simultaneous human capital development of founders and employees depends on good management efficiency as training the founders requires effective human development strategy. Generally, this effect suggests that human capital development should be given adequate attention and may not be value adding if business owners are not connected in the model of organizational human capital development.

Awareness on Augmentative Alternative Communication among Special Educators in Kerala

Abstract: AAC system permits individuals with communication disorder to communicate for various purposes. The rewarding part of AAC is the cooperation of professionals, such as speech-language pathologists, special educators, and other professionals, which results in enhancing the child's capacity for learning and communication. The aim of the current study was to investigate awareness in Special Educators regarding AAC for children with Communication Disabilities using a EQuestionnaire was developed in English/ Malayalam language, in Kottayam district, Kerala. The results of the current study revealed that moderate awareness was noticed among 30 special educators who underwent the study. It can be concluded that special educator’s effective knowledge and ongoing training allowed them to work with other professionals including speech language pathologists to improve the language output of children and adults with communication disorders.

DIAGNOSE EYES DISEASES USING VARIOUS FEATURES EXTRACTION APPROACHES AND MACHINE LEARNING ALGORITHMS

Ophthalmic diseases like glaucoma, diabetic retinopathy, and cataracts are the main cause of visual impairment worldwide. With the use of the fundus images, it could be difficult for a clinician to detect eye diseases early enough. By other hand, the diagnoses of eye disease are prone to errors, challenging and labor-intensive. Thus, for the purpose of identifying various eye problems with the use of the fundus images, a system of automated ocular disease detection with computer-assisted tools is needed. Due to machine learning (ML) algorithms' advanced skills for image classification, this kind of system is feasible. An essential area of artificial intelligence)AI (is machine learning. Ophthalmologists will soon be able to deliver accurate diagnoses and support individualized healthcare thanks to the general capacity of machine learning to automatically identify, find, and grade pathological aspects in ocular disorders. This work presents a ML-based method for targeted ocular detection. The Ocular Disease Intelligent Recognition (ODIR) dataset, which includes 5,000 images of 8 different fundus types, was classified using machine learning methods. Various ocular diseases are represented by these classes. In this study, the dataset was divided into 70% training data and 30% test data, and preprocessing operations were performed on all images starting from color image conversion to grayscale, histogram equalization, BLUR, and resizing operation. The feature extraction represents the next phase in this study ,two algorithms are applied to perform the extraction of features which includes: SIFT(Scale-invariant feature transform) and GLCM(Gray Level Co-occurrence Matrix), ODIR dataset is then subjected to the classification techniques Naïve Bayes, Decision Tree, Random Forest, and K-nearest Neighbor. This study achieved the highest accuracy for binary classification (abnormal and normal) which is 75% (NB algorithm), 62% (RF algorithm), 53% (KNN algorithm), 51% (DT algorithm) and achieved the highest accuracy for multiclass classification (types of eye diseases) which is 88% (RF algorithm), 61% (KNN algorithm) 42% (NB algorithm), and 39% (DT algorithm).

Teknik Pembelajaran Matematika di Sekolah Luar Biasa Berbasis Kooperatif Tipe Think Pair Share

The learning capacity of students with special needs must be maximized in learning mathematics in SLB in an efficient and inclusive way. Think pair share, a cooperative learning strategy that promotes collaboration, active involvement, and contact between students, is one strategy that can be applied. In this study, we will examine how think-pair-share cooperative learning is used to teach mathematics in special schools and how it affects student learning outcomes. By using literature study methodology, this research method is qualitative. Reading and categorizing books and various scientific journal articles is a data collection method. The results of the study indicate that there is a beneficial effect of exposure to cooperative learning based on think pair share on student learning outcomes in special schools. Through collaboration in groups, students with special needs can support each other and gain a better understanding of mathematics. cooperative learning type think pair share also increases student participation in the learning process, develops individual thinking power, and increases their self-confidence. The application of cooperative learning-based mathematics learning techniques of the think pair share type requires the active role of the teacher as a facilitator and learning director. Teachers need to plan activities according to students' needs, provide appropriate support, and manage interactions between students. In order for the learning process to be successful, it is also crucial to pay attention to the qualities of study groups. This study significantly advances inclusive mathematics teaching strategies in special education settings. The results of this study are supposed to serve as a guide for math educators and teachers as they develop and implement engaging and effective math programs for students with special needs.

Developing Basic English Module for Junior High School to Promote Students’ Cognitive Development

A module is a supplementary tool for existing teaching materials, allowing students to engage in independent learning by exploring the concepts presented within the module. This approach can enhance students' competence and foster their enthusiasm for the subject. The research aims to ascertain the English material requirements of seventh-grade students at SMPN 4 Bandar Lampung and create a comprehensive English curriculum for this grade level at the abovementioned school. The ADDIE model was utilized to achieve the stated objectives. The need analysis was carried out before the creation of the material. Based on the expert judgment of the material, it has been determined that the average score (X) is 3.6. The score is classified as good due to its placement within the 3.5 < X < 3.75 interval. However, there are two aspects that experts believe require further improvement. These aspects include enhancing the accessibility of the module's materials for students to comprehend and ensuring that the illustrations align with the content presented in the module. To enhance the comprehensibility of the module's content for students, the expert recommended providing more explicit instructions for each step of the activities students must undertake.Regarding the illustrations provided per the module's content, experts have recommended incorporating additional illustrations to enhance the module's level of engagement. Based on the expert judgment and evaluation of the presentation, it has been determined that the average score (X) is 3.75. Based on the obtained score, the categorization can be deemed satisfactory, as the value of X falls within the interval of 3.5 < X < 3.75. The expert has recommended an enhancement of the references or supporting materials. The expert recommended incorporating additional references to enhance the comprehensiveness of the module. Based on the expert judgment of the material, it has been determined that the average score (X) is 3.75. Based on the obtained score, it can be classified as satisfactory, as the X value falls within the 3.5 < X < 3.75 range. The expert recommended one aspect for improvement, specifically regarding the alignment of language usage with the student's cognitive development stage. The expert posited that certain languages may present a level of complexity that surpasses students' cognitive abilities. Consequently, it is recommended that these languages be modified or adapted to better align with the student's learning capacities.

Enhancing biosecurity against virus disease threats to Australian grain crops: current situation and future prospects

Australia is a major grain exporter, and this trade makes an important contribution to its economy. Fortunately, it remains free of many damaging virus diseases and virus vectors found elsewhere. However, its crop biosecurity is under increasing pressure from global ecological, climatic, and demographic challenges. Stringent biosecurity and plant health programs safeguard Australian grain production from damaging virus and virus vector incursions entering via different pathways. These programs formerly relied upon traditional testing procedures (indicator hosts, serology, PCRs) to intercept incoming virus-contaminated plant material. Recently, the integration of rapid genomic diagnostics innovation involving High Throughput Sequencing (HTS) smart tools into sample testing schedules is under exploration to improve virus testing accuracy, efficiency, and cost effectiveness under diverse circumstances. This process includes evaluating deployment of Illumina and Oxford Nanopore Technology shotgun sequencing. It also includes evaluating targeted viral genome HTS and virus vector metabarcoding approaches. In addition, using machine learning and deep learning capacities for big data analyses and remote sensing technologies will improve virus surveillance. Tracking damaging virus variants will be improved by surveillance networks which combine virus genomic-surveillance systems with an interoperable virus database. Sequencing Australian virus specimen collections will help ensure the accuracy of virus identifications based solely on genetic information. Enhancing routine diagnosis and data collection using these innovations will improve post entry virus interception and background virus and vector surveillance. This will help reduce the frequency of new incursions, improve virus management during eradication, containment and other plant health activities, and achieve more profitable Australian grain production.

Improving completeness and accuracy of 3D point clouds by using deep learning for applications of digital twins to civil structures

In the Architecture, Engineering, and Construction (AEC) sector, digital twins rely on precise 3D models to convey digital information about physical structures in a virtual space. However, due to the vulnerability to measurement errors in weak-textured regions, 3D point clouds generated by conventional photometric consistency or deep learning-based Multi-View Stereo (MVS) algorithms are often incomplete or inaccurate. Therefore, this paper integrates the consistency constraint across multiple views and the inferential capacity of deep learning to propose a novel approach for refining the missing regions of the depth maps generated by photo-consistency based MVS algorithms. The proposed solution involves a cost volume pyramid-based depth completion (CVP-DC) network with three multi-level pyramid structures, which sequentially estimates and completes depth maps in a coarse-to-fine manner. A dataset that consists of input images and the corresponding depth maps generated by photo-consistency based MVS algorithms, along with output ground truth depth maps, is developed using an open DTU MVS dataset. CVP-DC demonstrates competitive performance when tested on the public DTU MVS dataset, outperforming existing MVS algorithms in terms of both completeness and accuracy. Additionally, experimental studies are conducted utilizing UAV-collected RTK (Real-Time Kinematic) images of an outdoor bridge pier to reconstruct point clouds with absolute scales. Experimental validations demonstrate the effectiveness and applicability of the proposed approach in filling uneven and incomplete depth maps, thereby enhancing the completeness of the generated point clouds. The proposed approach holds promise for establishing precise 3D models for the digital twin of the AEC sector.

The impact of OFDI on firm pro-environmental behavior: Evidence from China

Outward Foreign Direct Investment (OFDI) is a crucial decision in the internationalization strategy of firms and they confront diversified environmental policies and practices of host countries, when engaging in cross-border investments. However, there is a lack of research to investigate whether foreign direct investment (OFDI) is an important factor affecting the environmental commitment of firms. This study aims to empirically analyze the impact and mechanism by which firm outward foreign direct investment (OFDI) influences pro-environmental commitments, utilizing data collected from Chinese listed companies between 2010 and 2020. The results show: 1) there is a significant positive relationship between firm OFDI and pro-environmental commitments; 2) mechanistic tests show that the OFDI can promote pro-environmental commitments through two channels: public attention and technological learning capacity; 3) heterogeneity analysis reveals that the green effects of OFDI are more pronounced firms with institutional investors and state ownership, as well as in firms situated in host countries with more stringent environmental regulations; 4) further analysis reveals OFDI-induced greater green commitments generated real environmental and economic outcomes: firms invested more in environmental protection and reduced CO2 emissions. In the meantime, they experienced lower excessive debt risk and higher innovative performance. The findings have positive implications for the promotion of firms’ pro-environmental behavior and sustainable development.

Enhancing Pediatric Adnexal Torsion Diagnosis: Prediction Method Utilizing Machine Learning Techniques.

This study systematically examines pediatric adnexal torsion, proposing a diagnostic approach using machine learning techniques to distinguish it from acute appendicitis. Our retrospective analysis involved 41 female pediatric patients divided into two groups: 21 with adnexal torsion (group 1) and 20 with acute appendicitis (group 2). In group 1, the average age was 10 ± 2.6 years, while in group 2, it was 9.8 ± 21.9 years. Our analysis found no statistically significant age distinctions between these two groups. Despite acute lower abdominal pain being a common factor, group 1 displayed shorter pain duration (28.9 h vs. 46.8 h, p < 0.05), less vomiting (28% vs. 50%, p < 0.05), lower fever incidence (4.7% vs. 50%, p < 0.05), reduced leukocytosis (57% vs. 75%, p < 0.05), and CRP elevation (30% vs. 80%, p < 0.05) compared to group 2. Machine learning techniques, specifically support vector classifiers, were employed using clinical presentation, pain duration, white blood cell counts, and ultrasound findings as features. The classifier consistently demonstrated an average predictive accuracy of 87% to 97% in distinguishing adnexal torsion from appendicitis, as confirmed across various SVM models employing different kernels. Our findings emphasize the capacity of support vector machines (SVMs) and machine learning as a whole to augment diagnostic accuracy when distinguishing between adnexal torsion and acute appendicitis. Nevertheless, it is imperative to validate these results through more extensive investigations and explore alternative machine learning models for a comprehensive understanding of their diagnostic capabilities.

Diverse pathways for climate resilience in marine fishery systems

AbstractBoth the ecological and social dimensions of fisheries are being affected by climate change. As a result, policymakers, managers, scientists and fishing communities are seeking guidance on how to holistically build resilience to climate change. Numerous studies have highlighted key attributes of resilience in fisheries, yet concrete examples that explicitly link these attributes to social‐ecological outcomes are lacking. To better understand climate resilience, we assembled 18 case studies spanning ecological, socio‐economic, governance and geographic contexts. Using a novel framework for evaluating 38 resilience attributes, the case studies were systematically assessed to understand how attributes enable or inhibit resilience to a given climate stressor. We found population abundance, learning capacity, and responsive governance were the most important attributes for conferring resilience, with ecosystem connectivity, place attachment, and accountable governance scoring the strongest across the climate‐resilient fisheries. We used these responses to develop an attribute typology that describes robust sources of resilience, actionable priority attributes and attributes that are case specific or require research. We identified five fishery archetypes to guide stakeholders as they set long‐term goals and prioritize actions to improve resilience. Lastly, we found evidence for two pathways to resilience: (1) building ecological assets and strengthening communities, which we observed in rural and small‐scale fisheries, and (2) building economic assets and improving effective governance, which was demonstrated in urban and wealthy fisheries. Our synthesis presents a novel framework that can be directly applied to identify approaches, pathways and actionable levers for improving climate resilience in fishery systems.

Associative learning in the box jellyfish Tripedalia cystophora

Associative learning, such as classical or operant conditioning, has never been unequivocally associated with animals outside bilatarians, e.g., vertebrates, arthropods, or mollusks. Learning modulates behavior and is imperative for survival in the vast majority of animals. Obstacle avoidance is one of several visually guided behaviors in the box jellyfish, Tripedalia cystophora Conant, 1897 (Cnidaria: Cubozoa), and it is intimately associated with foraging between prop roots in their mangrove habitat. The obstacle avoidance behavior (OAB) is a species-specific defense reaction (SSDR) for T.cystophora, so identifying such SSDR is essential for testing the learning capacity of a given animal. Using the OAB, we show that box jellyfish performed associative learning (operant conditioning). We found that the rhopalial nervous system is the learning center and that T.cystophora combines visual and mechanical stimuli during operant conditioning. Since T.cystophora has a dispersed central nervous system lacking a conventional centralized brain, ourwork challenges the notion that associative learning requires complex neuronal circuitry. Moreover, since Cnidaria is the sister group to Bilateria, it suggests the intriguing possibility that advanced neuronal processes, like operant conditioning, are a fundamental property of all nervous systems.

Trained teacher: Who is good at teaching

Knowledge distillation is an emerging method for acquiring efficient, small-scale networks. The main idea is to transfer knowledge from a complex teacher model with high learning capacity to a simple student model. To this end, various approaches to knowledge distillation have been proposed in the past few years, focusing mainly on modifications to student learning styles and less on changes to teacher teaching styles. Therefore, our new approach to knowledge distillation teacher training involves adapting the trained teachers to the knowledge distillation model in order to minimize the gap between the student model and the teacher model. We introduced the idea of a “Trained Teacher”: Our approach involves using a specially trained teacher network that, by incorporating knowledge distillation constraints during its own training, adapts to the teaching model in advance and performs nearly identically to a typical teacher network. This allows students to absorb the teacher's knowledge more effectively, thereby increasing their competence. In addition, the methods of mainstream knowledge distillation currently in use are equally appropriate to our educated teachers. Extensive tests on numerous datasets reveal that our technique outperforms the original knowledge distillation in accuracy on standard KD by 2%. Our code and pre-trained models can be found at https://github.com/JSJ515-Group/Trained_teacher.

Logical gates in ensembles of proteinoid microspheres.

Proteinoids are thermal proteins which swell into microspheres in aqueous solution. Ensembles of proteinoids produce electrical spiking activity similar to that of neurons. We introduce a novel method for implementing logical gates in the ensembles of proteinoid microspheres using chronoamperometry. Chronoamperometry is a technique that involves applying a voltage pulse to proteinoid microspheres and measuring their current response. We have observed that proteinoids exhibit distinct current patterns that align with various logical outputs. We identify four types of logical gates: AND, OR, XOR, and NAND. These gates are determined by the current response of proteinoid microspheres. Additionally, we demonstrate that proteinoid microspheres have the ability to modify their current response over time, which is influenced by their previous exposure to voltage. This indicates that they possess a capacity for learning and are capable of adapting to their environment. Our research showcases the ability of proteinoid microspheres to perform logical operations and computations through their inherent electrical properties.

Efficient Wheat Lodging Detection Using UAV Remote Sensing Images and an Innovative Multi-Branch Classification Framework

Wheat lodging has a significant impact on yields and quality, necessitating the accurate acquisition of lodging information for effective disaster assessment and damage evaluation. This study presents a novel approach for wheat lodging detection in large and heterogeneous fields using UAV remote sensing images. A comprehensive dataset spanning an area of 2.3117 km2 was meticulously collected and labeled, constituting a valuable resource for this study. Through a comprehensive comparison of algorithmic models, remote sensing data types, and model frameworks, this study demonstrates that the Deeplabv3+ model outperforms various other models, including U-net, Bisenetv2, FastSCN, RTFormer, Bisenetv2, and HRNet, achieving a noteworthy F1 score of 90.22% for detecting wheat lodging. Intriguingly, by leveraging RGB image data alone, the current model achieves high-accuracy rates in wheat lodging detection compared to models trained with multispectral datasets at the same resolution. Moreover, we introduce an innovative multi-branch binary classification framework that surpasses the traditional single-branch multi-classification framework. The proposed framework yielded an outstanding F1 score of 90.30% for detecting wheat lodging and an accuracy of 86.94% for area extraction of wheat lodging, surpassing the single-branch multi-classification framework by an improvement of 7.22%. Significantly, the present comprehensive experimental results showcase the capacity of UAVs and deep learning to detect wheat lodging in expansive areas, demonstrating high efficiency and cost-effectiveness under heterogeneous field conditions. This study offers valuable insights for leveraging UAV remote sensing technology to identify post-disaster damage areas and assess the extent of the damage.

Individual differences in preservice teachers’ online self-regulated learning capacity: A multilevel analysis

We examined the relationships between preservice teachers' use of six types of online self-regulated learning (SRL) strategies (goal-setting, time management, environmental structuring, task strategies, help-seeking, and self-evaluation) and their characteristics (gender, technology integration self-efficacy, prior online learning experience, and program of study), and how these relationships vary across teacher education programs. A total of 325 preservice teachers from 14 teacher education programs participated in the study. Data were analyzed using cross-sectional two-level multilevel models. Results revealed that, except for environmental structuring and self-evaluation strategies, preservice teachers' use of all specific SRL strategies significantly varied across teacher education programs. Females demonstrated higher use of goal-setting strategies compared to males. Technology integration self-efficacy was a significant positive predictor of all SRL strategies, except for task strategies. Prior online learning experience was a significant negative predictor of goal-setting and self-evaluation strategies. This study's findings inform teacher education by highlighting variations in online SRL among preservice teachers, influenced by gender, technology integration self-efficacy, and prior online learning experience. Study limitations and recommendations for future research are discussed.

Total saponins from Trillium tschonoskii Maxim promote neurological recovery in model rats with post-stroke cognitive impairment.

Total saponins from Trillium tschonoskii Maxim (TSTT), a bioactive component of local natural herbs in the Enshi area, China, have been demonstrated to have functions of restoring cognitive capacity and promoting axonal regeneration post-stroke, but the mechanism of this process remains unclear. The hippocampus is a critical tissue for controlling learning and memory capacity, and the sonic hedgehog (Shh) signaling pathway plays a major role in the patterning and synaptic plasticity of hippocampal neural circuits. Therefore, we aimed to investigate whether TSTT could restore learning and cognitive functions by modulating the Shh pathway in rats with post-stroke cognitive impairment (PSCI). The ischemia model was established by permanent middle cerebral artery occlusion (MCAO) in 100 Sprague-Dawley (SD) rats, and the model rats were administered using TSTT (100mg/kg) or donepezil hydrochloride as the positive control (daily 0.45mg/kg, DON) for 4weeks after the operation. As assessed by the Morris water maze test, the cognitive function of PSCI rats was significantly improved upon TSTT treatment. Meanwhile, the cerebral infarct volume reduced with TSTT, as shown by HE and TTC staining, and the number of Nissl bodies and dendritic spine density were significantly increased, as shown by Nissl and Golgi staining. In addition, TSTT upregulated PSD-95, SYN, and GAP-43, and inhibited neuronal apoptosis, as evidenced by increased Bcl-2 levels along with decreased Bax and caspase-3 expression. TSTT could also significantly upregulate Shh, Ptch1, Smo, and Gli1 proteins, indicating the activation of the Shh signaling pathway. Therefore, TSTT can protect PSCI rats by inhibiting apoptosis and promoting neuronal synaptic remodeling. The Shh pathway is also involved.