Wide Network Research Articles

Establishing reward systems for excellence in teaching – the experience of academics pioneering a reward system

ABSTRACT Higher education institutions are struggling to elevate the value and status of academic teaching. In this endeavour, rewards for excellence in teaching are becoming a common measure. This study reports on the experience of the first academic teachers who were given the status as rewarded teachers in new reward systems. We explore rewarded teachers’ potential to influence teaching and learning culture through a socio-cultural perspective, where influence is assumed to materialise through teachers’ networks and cultural change is linked to a widening of significant networks. Interviews with 13 rewarded teachers from three universities were analysed using thematic analysis. We find that rewarded teachers maintain their positions in existing networks and gain visibility and influence in wider networks. This widening of their teaching and learning network is a first step, that over time can become a wider significant network potentially important in influencing culture. We suggest that a productive measure to support rewarded teachers is to provide support for expanding their significant networks further, bridging the boundaries between teaching cultures. This study adds to our knowledge about how reward impacts networks, and the potential role rewarded teachers play in cultural change, a perspective that is underexplored in research on reward systems.

The Geometry of Feature Space in Deep Learning Models: A Holistic Perspective and Comprehensive Review

As the field of deep learning experiences a meteoric rise, the urgency to decipher the complex geometric properties of feature spaces, which underlie the effectiveness of diverse learning algorithms and optimization techniques, has become paramount. In this scholarly review, a comprehensive, holistic outlook on the geometry of feature spaces in deep learning models is provided in order to thoroughly probe the interconnections between feature spaces and a multitude of influential factors such as activation functions, normalization methods, and model architectures. The exploration commences with an all-encompassing examination of deep learning models, followed by a rigorous dissection of feature space geometry, delving into manifold structures, curvature, wide neural networks and Gaussian processes, critical points and loss landscapes, singular value spectra, and adversarial robustness, among other notable topics. Moreover, transfer learning and disentangled representations in feature space are illuminated, accentuating the progress and challenges in these areas. In conclusion, the challenges and future research directions in the domain of feature space geometry are outlined, emphasizing the significance of comprehending overparameterized models, unsupervised and semi-supervised learning, interpretable feature space geometry, topological analysis, and multimodal and multi-task learning. Embracing a holistic perspective, this review aspires to serve as an exhaustive guide for researchers and practitioners alike, clarifying the intricacies of the geometry of feature spaces in deep learning models and mapping the trajectory for future advancements in this enigmatic and enthralling domain.

WideCaps: a wide attention-based capsule network for image classification

WideCaps: a wide attention-based capsule network for image classification

Investigating the Impact of Key Factors on Electric/Electric-Vehicle Charging Station Adoption in Indonesia

The utilization of energy in transportation sector is a major contributor to carbon dioxide emissions, and the adoption of electric vehicles (EVs) is seen as a solution to reduce this impact. However, the utilization of electric charging stations (ECS) and EV charging stations (EVCS) in Indonesia is not optimal, as evidenced by low usage levels. This study aimed to understand the characteristics of ECS and EVCS users in Indonesia in order to identify determinant factors for ECS and EVCS utilization. Using descriptive quantitative analysis, exploratory factor analysis, and binary logistic regression, the study found that potential EVCS users are adults from upper-middle-class backgrounds, with four family members, who own a motorcycle and live in suburban areas. Furthermore, a wide social network, knowledge of technology, and ease of access, convenience, and price were identified as key factors for choosing ECS/EVCS. However, an attitude of caring for the environment had a negative effect on ECS/EVCS usage, and respondents who charge their electronic devices at home overnight were less likely to use ECS/EVCS. The study also found that the number of sockets in a respondent's home, previous family members' experience, and duration of work experience and self-identity were also reasons for choosing to use ECS/EVCS. Overall, this study provides a deeper understanding of the factors that influence the utilization of ECS and EVCS in Indonesia.

THIP: A trajectory history‐based indoor positioning algorithm using stress‐free floor plan for patient monitoring

SummaryThe Internet of Things (IoT) is a novel paradigm that consists of a wide network of machines and intelligent devices with the capability of communication and interaction with each other. One of the significant issues for the IoT is its healthcare applications. The positioning systems based on their environment can be classified into two main categories: Indoor and outdoor. Recently, IoT indoor positioning attracts too much attention among researchers for patient monitoring. Hence, in this paper, a stress‐free floor plan indoor positioning algorithm for patient monitoring is proposed, called THIP. The history of the patient's movement is stored in a trajectory history, and this information is employed to estimate the patient's more accurate position. The proposed THIP algorithm is simulated in MATLAB software. Several experiments are conducted to evaluate the performance of the proposed algorithm. Simulation results show that the proposed THIP algorithm raises patient positioning accuracy and declines the distance error cumulative compared to LiFS by about 4.68% and 60.6 m, respectively. Moreover, it can identify the floor level of the moving user.

A novel parallel merge neural network with streams of spiking neural network and artificial neural network

Some neuroscientific studies have demonstrated that the human brain is a complex integrated spatiotemporal system. The human brain supports a wide range of models, algorithms and coding schemes. Inspired by this point, we propose a novel neural network model, the parallel merge neural network (PMNN), which processes spatial and temporal information in parallel with two streams: a spiking neural network (SNN) and an artificial neural network (ANN). The information learned through these two streams is finally merged and flows to the final output layer. Unlike existing wide networks that process a single type of information, the PMNN can learn from the rich spatial, temporal and spatiotemporal information underlying training samples in a manner that is similar to that of the human brain. The experimental results with five UCI datasets and the MNIST dataset demonstrate that the PMNN is very efficient in terms of convergence and generalizable in terms of accuracy, generation, macro-recall, macro-precision and macro-F1.

Taoist Behavior, an Approach to Create of Social Capital in Organization: a Case Study at Najaf International Airport

Purpose: The aim of this study is to explore the relationship between Taoist behavior and social capital as one of the important types of positive behaviors in organizations. Theoretical Framework: The study's main goal is to identify how Najaf international airport contributes to obtaining all information through the wide social networks of personal contacts by the managers of the organizations. Design/methodology/approach-The study collected data by designing a questionnaire distributed to (248) employees at Najaf International Airport, and the hypotheses that were developed through the literature related to the research variables were tested. Findings- The results revealed the existence of a correlation and impact relationship between Taoist behavior and the social capital of the organization, and this indicates the importance of social capital for workers and the extent of its contribution to achieving success through appropriate behavior for work, and the method of effective performance in light of competition and unexpected environmental conditions. As the Taoist behavior of the employees is based on two elements: balance and flexibility, and this is in line with the requirements of the social capital of a successful organization. Research, Practical & Social implications: The study is advocate for improving the basis and practices of Taoist conduct in the workplace by pushing individuals to balance their requirements with the demands of their work environment and the demands of their employer. Originality/Value: This paper develops and tests a model that explores the impact of Taoist behavior on social capital.

BUILDING ENTREPRENEURSHIP SPIRIT THROUGH DIGITAL TECHNOLOGY BUSINESS

Students are a very potential component of nation-building. With a young age range, strong staff, a wide enough network, and the ability to think and have high creativity, it is the foundation and basic capital for the country's progress. BPS data as of February 2020 released Indonesia's employment conditions showing the unemployment rate fell to 5.01 percent or decreased by 50 thousand people over the past year. Although the aggregate unemployment rate has decreased, judging by the level of education, more and more university graduates are unemployed. The high level of educated unemployment is because many universities graduates target that after graduation, they have to get a job armed with the knowledge they have obtained through their courses. Even though they are university graduates, it is not easy for someone to be accepted into a job, increasing the number of unemployed. By seeing these problems, it is appropriate for the government and related parties to develop a strategy to direct university graduates to become job creators, not job seekers. This underlies the importance of efforts to build an entrepreneurial spirit among students. The rise of digital technology today can be said to be a form of change management in the business world; by utilizing digital technology in the business sector, it is hoped that an entrepreneurial spirit will form among students. But unfortunately, the current conditions are where students can't take advantage of sophisticated digital technology opportunities to seize entrepreneurship opportunities and make them a livelihood. The study aimed to describe: (1) supporting and inhibiting factors in building student entrepreneurship; and (2) strategies to build student entrepreneurship through digital technology businesses. This research is a qualitative descriptive study located at STKIP PGRI Jombang. The data sources used are primary and secondary. The research subjects were entrepreneurship lecturers and students of the 2019 economic education study program who had taken theory and practice entrepreneurship courses. Data collection techniques using observation, interviews, and documentation. Data analysis techniques use data reduction, data display, and conclusion. We are checking the research's validity using source and technique triangulation. The research results show that: (1) Supporting factors in building an entrepreneurial spirit for students of the STKIP PGRI Jombang Economic Education Study Program are (a) collaboration between universities and the business world by conducting entrepreneurial training and internships, (b) self-confidence, which is one of the capitals for opening a business, and ( c) an organization which is a forum for channeling interests and honing student skills; (2) The inhibiting factors in building the entrepreneurship spirit of students of the Economic Education Study Program STKIP PGRI Jombang are (a) the lack of a student entrepreneurship spirit which results in students choosing less to do entrepreneurship, (b) the lack of capital. In the minds of students, to start a business, they must have a large capital, and it is this thinking that must be changed by every tertiary institution, (c) the lack of entrepreneurship training in tertiary institutions; and (3) Strategies to build an entrepreneurial spirit through digital technology business for students of the Economic Education Study Program STKIP PGRI Jombang are (a) holding entrepreneurship seminars that aim to change the mindset of students, (b) holding digital technology training for students

Early prediction of MODS interventions in the intensive care unit using machine learning

BackgroundMultiple organ dysfunction syndrome (MODS) is one of the leading causes of death in critically ill patients. MODS is the result of a dysregulated inflammatory response that can be triggered by various causes. Owing to the lack of an effective treatment for patients with MODS, early identification and intervention are the most effective strategies. Therefore, we have developed a variety of early warning models whose prediction results can be interpreted by Kernel SHapley Additive exPlanations (Kernel-SHAP) and reversed by diverse counterfactual explanations (DiCE). So we can predict the probability of MODS 12 h in advance, quantify the risk factors, and automatically recommend relevant interventions.MethodsWe used various machine learning algorithms to complete the early risk assessment of MODS, and used a stacked ensemble to improve the prediction performance. The kernel-SHAP algorithm was used to quantify the positive and minus factors corresponding to the individual prediction results, and finally, the DiCE method was used to automatically recommend interventions. We completed the model training and testing based on the MIMIC-III and MIMIC-IV databases, in which the sample features in the model training included the patients’ vital signs, laboratory test results, test reports, and data related to the use of ventilators.ResultsThe customizable model called SuperLearner, which integrated multiple machine learning algorithms, had the highest authenticity of screening, and its Yordon index (YI), sensitivity, accuracy, and utility_score on the MIMIC-IV test set were 0.813, 0.884, 0.893, and 0.763, respectively, which were all maximum values of eleven models. The area under the curve of the deep–wide neural network (DWNN) model on the MIMIC-IV test set was 0.960, and the specificity was 0.935, which were both the maximum values of all these models. The Kernel-SHAP algorithm combined with SuperLearner was used to determine the minimum value of glasgow coma scale (GCS) in the current hour (OR = 0.609, 95% CI 0.606–0.612), maximum value of MODS score corresponding to GCS in the past 24 h (OR = 2.632, 95% CI 2.588–2.676), and maximum score of MODS corresponding to creatinine in the past 24 h (OR = 3.281, 95% CI 3.267–3.295) were generally the most influential factors.ConclusionThe MODS early warning model based on machine learning algorithms has considerable application value, and the prediction efficiency of SuperLearner is superior to those of SubSuperLearner, DWNN, and other eight common machine learning models. Considering that the attribution analysis of Kernel-SHAP is a static analysis of the prediction results, we introduce the DiCE algorithm to automatically recommend counterfactuals to reverse the prediction results, which will be an important step towards the practical application of automatic MODS early intervention.

Knowledge of autism gained by learning from people through a local UK Autism Champion Network: A health and social care professional perspective.

The Autism Act 10 Years On found few autistic adults thought health and social care professionals had a good understanding of autism. Autism training has been made law in the United Kingdom for health and social care staff to tackle health inequality. The county wide Autism Champion Network evaluated here is an equal partnership of interested staff across sectors (Autism Champions) and autistic experts by virtue of lived experience (Autism Advisory Panel). With knowledge flowing both ways, the Autism Champions take learning back to teams to support continuous development of services to meet autistic need. Seven health and social sector professionals from the Network participated in semi-structured interviews on sharing knowledge of autism gained with their teams. All participants provide care and support for autistic people, some working in specialist positions. Results showed that developing new relationships with people outside their own team to signpost to, answer questions and share resources, and informal learning from autistic people, was more valued and used in practice than information gained from presentations. These results have implications in developing learning for those who need above a basic knowledge of autism and may be useful for others considering setting up an Autism Champion Network.

Wide and deep learning based approaches for classification of Alzheimer's disease using genome-wide association studies.

The increasing incidence of Alzheimer's disease (AD) has been leading towards a significant growth in socioeconomic challenges. A reliable prediction of AD might be useful to mitigate or at-least slow down its progression for which, identification of the factors affecting the AD and its accurate diagnoses, are vital. In this study, we use Genome-Wide Association Studies (GWAS) dataset which comprises significant genetic markers of complex diseases. The original dataset contains large number of attributes (620901) for which we propose a hybrid feature selection approach based on association test, principal component analysis, and the Boruta algorithm, to identify the most promising predictors of AD. The selected features are then forwarded to a wide and deep neural network models to classify the AD cases and healthy controls. The experimental outcomes indicate that our approach outperformed the existing methods when evaluated on standard dataset, producing an accuracy and f1-score of 99%. The outcomes from this study are impactful particularly, the identified features comprising AD-associated genes and a reliable classification model that might be useful for other chronic diseases.

Issue Information

Journal of NeurochemistryVolume 165, Issue 4 p. 455-456 ISSUE INFORMATIONFree Access Issue Information First published: 15 May 2023 https://doi.org/10.1111/jnc.15632 Read the full article: ‘A critical update on the leptin-melanocortin system’ by O. Lavoie, N. J. Michael and A. Caron (J. Neurochem. 2023, vol. 165 (4), pp. 467– 486) on doi:10.1111/jnc.15765 AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Graphical Abstract Front cover The race is ongoing to identify the still unknown GABAergic neurons that mediate the direct effects of leptin on energy homeostasis. However, does that one population really exist? Or is it the combination of many neurons, acting in a wide network to differentially control the various effects of leptin? Image content This RNA fluorescence in situ hybridization of the vesicular inhibitory amino acid transporter (VGAT, Slc32a1) shows the vast distribution of GABAergic neurons within the mouse nervous system. Read the full article ‘A critical update on the leptin-melanocortin system’ by O. Lavoie, N. J. Michael and A. Caron (J. Neurochem. 2023, vol. 165 (4), pp. 467– 486) on doi:10.1111/jnc.15765 Volume165, Issue4May 2023Pages 455-456 RelatedInformation

Sensing Scale in Experimental Gardens: Un-Lawning with Silphium Civic Science

Gardening experiments are timely in the context of what many now call the Anthropocene, an era that highlights questions of how humans collectively relate to the larger Earth systems in which we are embedded. In Ecocriticism on the Edge: The Anthropocene as a Threshold Concept, Timothy Clark reflects on the “unreadability” of the Anthropocene. He invites ecocritics to address this challenge by practicing “scale framing,” reading texts in variable and increasingly broad scales, and engaging the contradictions that emerge. We applied a scale framing approach to a story of relationships with Silphium integrifolium in an experimental gardening project. Silphium is a native North American perennial prairie plant being domesticated as a future oilseed crop. We are researchers and participants in a civic science project, in which individual garden sites are designed to collect data on and conserve silphium ecotypes while being linked into a wider network. In particular, we analyzed a civic science video story created by Ellie Irons called “Un-Lawning with Silphium.” Through our ecocritical analysis, we generated a framework to visualize nested and cross-scalar relationships in gardening projects. This framework could help inform the design and assessment of experimental gardening projects that feature the arts and humanities (e.g., digital narratives, ecocriticism, and pedagogy) and connect them with the natural and social sciences (e.g., plant breeding, botany, geography, and ecology) through transdisciplinary and participatory research methodologies for public engagement (e.g., civic science). We found that civic science gardening with silphium, and other gardening experiments in the Anthropocene, can guide public sensory engagements with scale, help spark recognition and investigation of contradictory scale effects, and motivate us to imagine and build relationships of caring responsibility.

Percutaneous irreversible electroporation in locally advanced pancreatic cancer – a review and a case report

Background. The results of local destruction methods in locally advanced pancreatic cancer (LAPCa) are contradictory. Radiation therapy is the most commonly used. Other methods are used much less frequently, irreversible electroporation (IRE) is one of them. Most authors indicate an acceptable level of complications and mortality, but without an improvement in long-term results. The results of two meta-analyses have been published, the authors indicate the possibility of using the IRE in selected patients. The authors also point out that minimally invasive methods of using the IRE be preferred. Some experience has been gained in the use of percutaneous access for IRE in LAPCa. Computed tomography, ultrasound guidance can be used for navigation. The level of complications can reach 50 %. Mortality with percutaneous access, as a rule, is absent or does not exceed 5 %. Long-term results are the same with the results of open IRE.Aim. To share authors experience of using percutaneous irreversible electroporation in pancreatic cancer, because there are no references to the use of percutaneous IRE in LAPCa in Russia.Materials and methods. The IRE was performed for 53-year female patient with LAPCa after successful induction therapy. A step-by-step pulse effect of electrodes installed under ultrasound control on the tumor infiltrate was carried out. Magnetic resonance imaging, computed tomography and other types of studies were used for diagnostic purposes.Results. The involvement of the common hepatic artery and portal vein remained after the induction therapy, which did not allow performing pancreatoduodenal resection. Ultrasonic navigation and flat-detector computed tomography allowed to install the electrodes adequatly and safely. The impact zone almost completely blocked the infiltrate zone, a more optimal location of the electrodes was limited by the wide network of venous collaterals. Magnetic resonance imaging data performed before and after the procedure showed no progression of the disease within more than three months after the procedure, including in the affected area. Tumor shrinkage was noted as a partial response.Conclusion. The first experience confirmed the safety and the absence of subsequent complications when using the percutaneous access method of IRE for LAPCa. Follow-up monitoring of the patient will allow to say more correctly about the possibility of the method to provide long-term local control.

A communication-efficient and privacy-aware distributed algorithm for sparse PCA

Sparse principal component analysis (PCA) improves interpretability of the classic PCA by introducing sparsity into the dimension-reduction process. Optimization models for sparse PCA, however, are generally non-convex, non-smooth and more difficult to solve, especially on large-scale datasets requiring distributed computation over a wide network. In this paper, we develop a distributed and centralized algorithm called DSSAL1 for sparse PCA that aims to achieve low communication overheads by adapting a newly proposed subspace-splitting strategy to accelerate convergence. Theoretically, convergence to stationary points is established for DSSAL1. Extensive numerical results show that DSSAL1 requires far fewer rounds of communication than state-of-the-art peer methods. In addition, we make the case that since messages exchanged in DSSAL1 are well-masked, the possibility of private-data leakage in DSSAL1 is much lower than in some other distributed algorithms.

Correction: The Association of a Geographically Wide Social Media Network on Depression: County-Level Ecological Analysis

[This corrects the article DOI: 10.2196/43623.].

Analytic Theory for the Dynamics of Wide Quantum Neural Networks.

Parametrized quantum circuits can be used as quantum neural networks and have the potential to outperform their classical counterparts when trained for addressing learning problems. To date, much of the results on their performance on practical problems are heuristic in nature. In particular, the convergence rate for the training of quantum neural networks is not fully understood. Here, we analyze the dynamics of gradient descent for the training error of a class of variational quantum machine learning models. We define wide quantum neural networks as parametrized quantum circuits in the limit of a large number of qubits and variational parameters. Then, we find a simple analytic formula that captures the average behavior of their loss function and discuss the consequences of our findings. For example, for random quantum circuits, we predict and characterize an exponential decay of the residual training error as a function of the parameters of the system. Finally, we validate our analytic results with numerical experiments.

Educational everyday life of students of the Yelabuga Teacher’s Institute in 1939–1941

The article is devoted to the study of the educational everyday life of students of the Yelabuga Teacher's Institute during the formation of the system of higher pedagogical education in the USSR. According to the Decree of the Council of People’s Commissars of the RSFSR in 1939, 48 pedagogical schools were transformed into teachers' institutes. Within a few years, a wide network of such institutions was created in the country. Only in the RSFSR in 1940 there were 130 teacher’s institutes, and by 1945 there were already 187 of them. By the decree of the Government of the USSR in Yelabuga, by the beginning of the 1939–1940 academic year, a teacher’s institute of republican significance was created on the basis of a pedagogical school. The study was carried out on the example of the Yelabuga Teachers’ Institute based on the analysis of archival sources and materials from the periodical press.
 The study analyzes the main structural components of the educational everyday life of students of the teacher’s institute: temporal characteristics associated with educational activities; organization of the educational process at the institute; observance of generally accepted norms and rules of behavior of students, methods of spending leisure and recreation, culture of food and life. The housing and material and financial conditions of the life of student youth in the conditions of Soviet reality in the pre-war period were studied.

Consular Reports as a Source of Information for Periodicals of the Ministry of Finance of Russia at the Beginning of the Twentieth Century

The author examines the reports of Russian consular services located in different countries, which served as important sources of various commercial and political information for periodicals issued by the Ministry of Finance of Imperial Russia at the beginning of the twentieth century. The subject of the study is the information policy of one of the leading government departments, which was one of the first to deploy a wide correspondent network around the world in order to obtain information about the most important events in the world economy and politics, analyze them and bring them to the main consumer – the domestic business class, who was extremely interested in promptly obtaining this information. The scientific novelty of the publication lies in the fact that the reports of the Russian consuls, revealed during the analysis of archival documents containing correspondence between the Trade Department of the Ministry of Finance and the Editorial Office of the time-based publications of this ministry, have not yet been reflected in the scientific literature. Meanwhile, market, industrial, stock exchange, banking, socio-political information, which regularly appeared in the "Bulletin of Finance, Industry and Trade" and the daily "Trade and Industrial Newspaper", played an important role for the orientation of business circles not only in the processes on world markets, but also for the political development of the domestic bourgeoisie, understanding its role in the socio-economic and political life of Russia during the period under review. And the correspondent network of these publications, the use of technological innovations became the basis for the creation of first a Trade and Telegraph Agency under the Ministry of Finance, and then a government agency.The St. Petersburg Telegraph Agency, which initiated the formation of the state information policy.

Prism adaptation in patients with unilateral lesion of the parietal or cerebellar cortex: A pilot study on two single cases using a concurrent exposure procedure

Neuroimaging studies showed that prism adaptation (PA), a widely used tool for the rehabilitation of neglect, involves a wide network of brain regions including the parietal cortex and the cerebellum. In particular, the parietal cortex has been suggested to mediate the initial stage of PA through conscious compensatory mechanisms as a reaction to the deviation induced by PA. The cerebellum, on the other side, intervenes in sensory errors prediction to update internal models in later stages. It has been suggested that two mechanisms may underlie PA effects: recalibration, a strategic cognitive process occurring in the initial stages of PA, and realignment, a fully automatic reorganization of spatial maps emerging later and more slowly in time. The parietal lobe has been proposed to be involved mainly in the recalibration whereas the realignment would be carried over by the cerebellum. Previous studies have investigated the effects of a lesion involving either the cerebellum or the parietal lobe in PA taking into account both realignment and recalibration processes. Conversely, no studies have compared the performance of a patient with a cerebellar lesion to that of a patient with a parietal lesion. In the present study, we used a recently developed technique for digital PA to test for differences in visuomotor learning after a single session of PA in a patient with parietal and a patient with cerebellar lesions, respectively. The PA procedure, in this case, includes a digital pointing task based on a concurrent exposure technique, which allows patients to fully see their arm during the pointing task. This procedure has been shown to be as effective as the terminal exposure condition in neglect rehabilitation albeit different processes take place during concurrent exposure condition compared to the most used terminal exposure (allowing to see only the final part of the movement). Patients’ performances were compared to that of a control group. A single session of PA was administered to 1) a patient (BC) with left parieto-occipital lesion involving superior parietal lobe (SPL) and inferior parietal lobe (IPL), 2) a patient (TGM) with a stroke in the territory sub-served by the superior cerebellar artery (SCA) , and 3) 14 healthy controls (HC).The task included three conditions: before wearing prismatic goggles (pre-exposure), while wearing prisms (exposure) and after removing the goggles (post-exposure). Mean deviations were calculated for the following phases: pre-exposure, early-exposure, late-exposure, post-exposure. The presence of after-effect was calculated as the difference between pre-exposure and post-exposure conditions. For each of these conditions, patients’ performance was compared to that of the control group by using a modified Crawford t-test. We found that the patient with the parietal lesion had a significantly different performance in the late-exposure and in the post-exposure compared to both HC and the patient with the cerebellar lesion. Conversely, no differences were observed between TGM and HC across all the conditions.Our results show an increase in the magnitude of the adaptation during the late stage of PA in the patient with the parietal lesion whereas no differences in the performance between the cerebellar patient and the controls were found. These results confirm previous studies suggesting that the parietal cortex is an important node of a wider network involved in PA effect. Furthermore, results concerning the cerebellar patient suggest that visuomotor learning is not affected by lesions of the SCA territory when a concurrent exposure is used as, in such case, it less relies on sensory errors prediction to update internal models. Results are discussed considering the novelty of the applied PA technique.