Optimal Integration Research Articles

Benchmarking algorithms for single-cell multi-omics prediction and integration.

The development of single-cell multi-omics technology has greatly enhanced our understanding of biology, and in parallel, numerous algorithms have been proposed to predict the protein abundance and/or chromatin accessibility of cells from single-cell transcriptomic information and to integrate various types of single-cell multi-omics data. However, few studies have systematically compared and evaluated the performance of these algorithms. Here, we present a benchmark study of 14 protein abundance/chromatin accessibility prediction algorithms and 18 single-cell multi-omics integration algorithms using 47 single-cell multi-omics datasets. Our benchmark study showed overall totalVI and scArches outperformed the other algorithms for predicting protein abundance, and LS_Lab was the top-performing algorithm for the prediction of chromatin accessibility in most cases. Seurat, MOJITOO and scAI emerge as leading algorithms for vertical integration, whereas totalVI and UINMF excel beyond their counterparts in both horizontal and mosaic integration scenarios. Additionally, we provide a pipeline to assist researchers in selecting the optimal multi-omics prediction and integration algorithm.

Toward a new model for the successful implementation of information and communication technologies in education

The design of our Information and Communication Technologies for Education (ICT4E) implementation model highlights its adaptability to local realities, considering available resources, existing technological infrastructures, and the specific needs of learners. However, it is crucial to recognize that the inherent variability of educational contexts and resource constraints can present challenges to a generalized application of the model. In addition, the rapid pace of technological change can have an impact on the ongoing training of teachers, requiring constant vigilance to maintain the relevance of their pedagogical skills. Despite these challenges, our model stands out for its adaptable approach, encouraging ongoing adjustments to respond effectively to the diversity of educational environments. In addition, the prospect of research and innovation, the promotion of interdisciplinary collaboration and the adoption of holistic evaluation are essential strategies for strengthening the robustness of our model. However, it is imperative to remain attentive to ethical considerations, the issue of equity and inclusion, and the long-term impact of integrating ICT into education. Ultimately, our model aspires to be a balanced solution, aware of the challenges and open to the developments needed to ensure the harmonious and optimal integration of ICT into today’s educational landscape.

Polyvinyl alcohol-chitosan-polyethylene glycol-glycerol incorporated with Peganum harmala loaded in lipid nanocapsules as an elastic nanocomposite surgical sealant to control bleeding

Uncontrolled hemorrhage remains a critical threat in trauma and surgery. This study developed a novel hemostatic composite by encapsulating Peganum harmala L. seed extract (PH) with known hemostatic properties into lipid nanocapsules (PH-LNCs) and then embedding them within a polyvinyl alcohol-chitosan-polyethylene glycol-glycerol (PVA-CS-PEG-G) matrix. The composite was physically crosslinked via the dual processes of freezing-thawing and thermal crosslinking and exhibited robust mechanical properties reaching 0.434 ± 0.014 MPa and elasticity of 40.685 % ± 4.04. It also demonstrated excellent biocompatibility, surface morphology, physical stability, and ex-vivo skin deposition/permeation were assessed. The characterization of PH-LNCs revealed optimal PH-LNC formation and successful integration into the composite with particle size, zeta potential, and PDI were approximately 45.45 ± 24 nm, −16.3 ± 1.4 mV, and 0.374 ± 0.1, respectively. In vitro studies highlighted enhanced blood clotting and platelet adhesion, while in vivo experiments confirmed superior hemostatic efficacy in a mouse tail amputation model. The composite's soft texture, conformability, and mechanical strength make it a promising candidate for effective traumatic wound management.

Hybrid Model Integrating Locally Linear Embedding and LightGBM for Predicting Sulfur Content in Electroslag Remelting

This study proposes a model for predicting the sulfur content in the electroslag remelting (ESR) process, by integrating locally linear embedding (LLE) and the LightGBM algorithm. The LLE dimensionality reduction method is employed to preprocess the factors influencing the sulfur content, preserving the local relationships between samples while reducing the data dimensionality. Subsequently, five models are established, including LLE‐LightGBM, LLE‐RR, LLE‐RF, LLE‐XGBoost, and LLE‐CatBoost. Bayesian optimization is used to tune the hyperparameters of these models, which are then trained and validated using production data. The results demonstrate that the integration of LLE and Bayesian optimization improved the performance of all five models. The incorporation of LLE enhanced the models’ understanding and predictive capabilities of the data, thereby improving their performance and robustness. Among the five models, the LLE‐LightGBM model exhibits the best performance, with the following metrics: MSE = 0, = 0.9894, MAE = 0, MEDAE = 0, EVS = 0.9895, and ME = 1.8 × 10−5. Therefore, the LLE‐LightGBM model can accurately predict the sulfur content in the ESR process, providing valuable guidance for end‐point control and decision‐making, and offering a new approach toward the intelligent automation of the ESR process.

Enhancing DC microgrids cluster performance with distributed event-triggered consensus protocols

Microgrid (MG) clusters offer solutions to the challenges faced by individual MGs in extreme conditions but require precise coordination to manage generation-load imbalances effectively. This paper presents a hierarchical control strategy that combines time-based and event-based consensus protocols (ETC) in the overarching control layer. Traditional time-based protocols require constant sampling, leading to unnecessary communication and computational burdens. To address this, we introduce a mixed time-state dependent distributed event-triggered consensus protocol (MDETC), which reduces communication needs while maintaining MG performance. A set-time consensus algorithm is used at the secondary control level to synchronize currents and voltages quickly. Additionally, the integration of Particle Swarm Optimization (PSO) in primary control ensures efficient current distribution and voltage regulation among neighboring MGs. The study also explores automatic reconfiguration within the cluster to support Plug-N-Play functionality, ensuring seamless MG operation. The effectiveness of this control approach is demonstrated through MATLAB simulations of a four-DC MG cluster, showing improved efficiency in reducing triggering instances and achieving faster current and voltage stabilization under various conditions.

Investigating different scenarios of integrating solar-assisted carbon capture with combined cycle power plant and water desalination system

In this work, an optimal algorithm-based integration of solar thermal energy with carbon capture and desalination processes were proposed. The stripper columns, identified as the most energy-consuming equipment, were designed with constant capacities to ensure practicality and applicability. Results show that, without affecting the power plant's efficiency, using a solvent tank as an energy storage system (scenario-2) increases total CO2 capture from 45 % to 98 % compared to a part-time solar power system (scenario-1). Utilizing two solvent tanks reduces waste energy from 87 % to 67 % (scenario-3), while achieving 100 % CO2 capture. Using three different stripper columns (scenario-4) lowers the levelized cost of energy by 20 %. Incorporating an additional multiple effect distillation system (scenario-5) reduces levelized cost of energy and energy loss by 26 % and 74 %, respectively. Approximately 28 % of the total solar energy collected (4.64 × 106 MWh) was used to capture 1.57 × 106 ton.year−1 of CO2. The remaining energy was used in desalination to produce 9.4 × 106 m³.year−1 of fresh water, increasing overall energy efficiency from 28 % to 81 %, and reducing waste energy to 0.88 × 106 MWh, just 26 % of previous scenarios' waste.

A Systematic Review of the Use and Effect of Virtual Reality, Augmented Reality and Mixed Reality in Physical Education

New technologies are tools that are present in daily life on a regular basis. In order to improve the didactic process, education must take into account these new technologies. In the field of physical education, the significance of these technologies is reflected in the existence of applications that can be carried out within the field, both for educational purposes and for physical fitness and health. This is due to the potential presented by virtual reality, augmented reality and mixed reality. The objective of this study was to examine the utilisation and impact of AR, VR and MR technologies in physical education at the compulsory stage. In order to achieve this objective, a design based on the PRISMA methodology for conducting systematic reviews was employed. The databases of WOS, Scopus, PubMed and Google Scholar were subjected to analysis. The results indicate that there has been a notable increase in research activity in this field in recent years. The analysis yielded four principal areas of focus, namely the utilisation of pedagogical methodologies, the enhancement of motor and health-related competencies, and moreover, the facilitation of optimal integration of students in physical education. The utilisation and consequences of novel technologies represent a suitable instrument for enhancing the educational experience of students enrolled in physical education programmes.

Shape and topology optimization method with generalized topological derivatives

This paper introduces a novel method for shape and topology optimization based on a generalized approach for evaluating topological derivatives, which are essential for the integration of shape and topology optimization. Traditionally, evaluating these derivatives presents significant mathematical challenges due to the discontinuity introduced by the insertion of a hole within the domain of interest. To overcome this issue, the study employs Helmholtz-type partial differential equations (PDEs) to construct a filtered objective functional. This approach ensures differentiability across the material and void phases and continuity over the fixed design domain while maintaining the same evaluation value as the original objective functional. By considering differentiability, continuity conditions, and the relationship between shape and topological derivatives during asymptotic analysis, generalized topological derivatives are obtained through established mathematical procedures. These topological derivatives exhibit a direct correlation with the PDE solutions and demonstrate satisfactory smoothness, thereby facilitating refined shapes in optimization strategies. Furthermore, an effective shape update algorithm is proposed, which directly integrates topological derivatives into structural optimization problems, simplifying their implementation and improving efficiency. Finally, the efficacy of the proposed methodology is demonstrated through its application to various optimal design problems, including stiffness maximization, compliant mechanisms, and eigenfrequency maximization. Verification results further highlight its potential to enhance existing methods for addressing more practical and complex optimization challenges.

An efficient optimization approach with mobility management for enhanced QoS and secure communication in flying adhoc networks

In biomedical Applications, Network plays a vital role to transmit the health-related information from patient to doctors which helps to save the human lives. During the data transmission, there is the possibility of data loss, delayed delivery, malicious attack and that leads to huge loss in human lives. To overcome this issue, a new technique Smart & secure Flying Adhoc Networks (FANET) architecture was proposed for the Quality-of-Service (QoS) improvement in biomedical applications. This research work helps society to save human lives during any emergency. With the integration of optimization and clustering techniques, the design configuration of the proposed architecture includes mobility management and secure transmission. The first phase includes sensing the patient's information by using Arduino IDE hardware. The sensed data will be transmitted through different types of networks including mobile ad-hoc networks, vehicular ad-hoc networks and flying ad-hoc networks. The routing protocol concentrates on clustering formation, mobility management, secure transmission and optimization. When compared to other type of networks, the proposed Smart & secure Flying Adhoc Networks (FANET) improves the network performance. The quality-of-service was achieved using the parameters such as network throughput, latency, delivery rate, routing overhead and control overhead.

The Impact of Cross-Border Banking on Financial Stability

The globalization of financial markets has ushered in an era of interconnectedness, where financial institutions increasingly operate across national borders. This phenomenon, known as cross-border banking, has both potential benefits and risks for financial stability. While it can enhance risk diversification and promote economic growth, it also poses challenges such as contagion effects and regulatory coordination issues. This study aims to comprehensively investigate the impact of cross-border banking on financial stability, particularly within the European Union (EU). By examining the costs and benefits, developing metrics to measure optimal integration, and applying these metrics to EU countries, this research seeks to provide policymakers and regulators with valuable insights into the management of cross-border banking activities. The study begins by analysing the costs and benefits of cross-border banking. While it can offer advantages like risk spreading and increased credit stability, it also introduces risks such as contagion and regulatory challenges. The research explores the interplay between these factors and their implications for financial stability. To quantify the optimal level of cross-border banking integration, the study develops forward-looking indicators. These metrics aim to measure the extent to which a country's banking system is integrated into international markets while minimizing risks. By applying these indicators to EU countries, the research identifies which countries are more susceptible to systemic risks and which ones demonstrate optimal practices. Furthermore, the study investigates the systemic risks associated with cross-border banking, particularly the risk of financial contagion. It examines how the interconnectedness of financial institutions can facilitate the spread of shocks across borders and explores the role of regulatory coordination in mitigating these risks. The research also evaluates the trade-off between cross-border banking integration and local financial stability. While integration can provide diversification benefits, it can also increase vulnerability to external shocks. The study seeks to determine the optimal level of integration that allows countries to benefit from international markets without compromising domestic stability. Finally, the study provides policy recommendations based on its findings. By identifying countries with imbalances or low diversification, the research offers guidance on how to improve financial stability through targeted interventions. The recommendations aim to enhance the resilience of cross-border banking systems and contribute to a more stable and efficient global financial landscape.

Nonlinear Ensemble Deep Learning Model for Energy Consumption Prediction with Bayesian Optimization

Accurate prediction of electric energy consumption is crucial for efficient load dispatching, energy utilization, and grid operation. Traditional statistical and classical machine learning methods struggle with the nonlinear nature of energy consumption data, often leading to higher prediction errors. Additionally, deep learning models using a single approach face challenges such as convergence to local minima and poor generalization. This paper proposes a nonlinear ensemble deep learning model for residential energy consumption prediction, incorporating Bayesian optimization for hyperparameter tuning. The model combines Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and 1D Convolutional Neural Networks (1D-CNN), leveraging their powerful nonlinear feature learning capabilities. A k-means clustering approach is used to preprocess and reduce variability in the data, enhancing the ensemble model's performance. The ensemble model was tested on real energy consumption data from two districts in Addis Ababa, showing significant improvements in prediction accuracy with lower MAE, RMSE, and MAPE values compared to single models and un-clustered data. The integration of clustering and Bayesian optimization further enhanced model generalizability and minimized overfitting, demonstrating the effectiveness of a nonlinear approach in capturing complex energy consumption patterns.

Investigating the Effects of Tilt Angle and Fill Perforation on the Performance of Forced Draft Wet Cooling Tower

Cooling towers are critical components of the industrial sector, serving a crucial function in dispersing surplus heat to ensure optimal operational efficiency and protect equipment integrity. This study examines the impact of variations in the tilt angle and fill perforations on the performance of forced draft wet cooling towers. The study specifically examines two variables: the tilt angle of fill at θ1= 15°, θ2 = 20°, and θ3 =25°, and the perforation ratios labelled as RP1 = 2.6%, RP2 = 3%, and RP3= 3.6%. The experimental trials involved varying the airflow rates of 0.0203, 0.0263, 0.0299, 0.0377, and 0.0426 kg/s while maintaining a constant water flow rate of 0.0917 kg/s. The results indicate that a tilt angle of θ1° greatly improves the thermal and operational efficiency of the tower. In addition, greater perforation ratios, particularly RP3, significantly enhance the cooling tower's performance at different tilt angles compared to RP1 and RP2.

Chernobyl Disaster Optimizer-Based Optimal Integration of Hybrid Photovoltaic Systems and Network Reconfiguration for Reliable and Quality Power Supply to Nuclear Research Reactors

Chernobyl Disaster Optimizer-Based Optimal Integration of Hybrid Photovoltaic Systems and Network Reconfiguration for Reliable and Quality Power Supply to Nuclear Research Reactors

Empowering E-mobility: Day-ahead dynamic time of use tariff for electric vehicle charging

The greenhouse gases and preservation of the environment have been the most prioritized concern in many countries and international organizations, leading to set aims and initiatives for lowering the dependency on fossil fuels and minimizing carbon emissions. As a part of many initiatives, great emphasis is given to promoting electric vehicles (EVs) over their traditional carbon fuel-based counterpart. To support large-scale EV adoption, a dynamic tariff scheme is required that addresses complex issues like user comfort, savings, and utility revenue. In this research work, a dynamic pricing scheme is modeled for the centralized EV charging stations (EVCS) on a residential feeder. This pricing scheme integrates the existing static time of use (ToU) pricing to an hourly dynamic ToU tariff that varies according to the demand level in the residential feeder. The proposed tariff plan provides a day-ahead dynamic structure with a probable price variation range to aid the EV owners and the day-ahead charging mechanism to schedule the EVs beforehand. The developed scheme calculates the returns and adjusts the tariff including the demand charge and the recovery component on a daily, monthly, and yearly basis for the unsatisfactory investment revenue. Also, user convenience is ensured by setting the minimum possible tariff as well as satisfying the minimum revenue target with the integration of particle swarm optimization (PSO) in designing the pricing structure. The simulation results demonstrate the effectiveness of the proposed dynamic pricing scheme maintaining a trade-off between the interests of all parties involved. Moreover, the simulation outcome of the test case indicates that EV owners can save more than 32 % on charging bills if they plan correctly. On the other hand, the utility can earn more than 2.4 times the existing rate during the peak time of the residential feeder.

Low‐Dispersity Polymers via Free Radical Alternating Copolymerization: Effects of Charge‐Transfer‐Complexes

AbstractAlternating copolymers are crucial for diverse applications. While dispersity (Ð, also known as molecular weight distribution, MWD) influences the properties of polymers, achieving low dispersities in alternating copolymers poses a notable challenge via free radical polymerizations (FRPs). In this work, we demonstrated an unexpected discovery that dispersities are affected by the participation of charge transfer complexes (CTCs) formed between monomer pairs during free radical alternating copolymerization, which have inspired the successful synthesis of various alternating copolymers with low dispersities (>30 examples, Ð=1.13–1.39) under visible‐light irradiation. The synthetic method is compatible with binary, ternary and quaternary alternating copolymerizations and is expandable for both fluorinated and non‐fluorinated monomer pairs. DFT calculations combined with model experiments indicated that CTC‐absent reaction exhibits higher propagation rates and affords fewer radical terminations, which could contribute to low dispersities. Based on the integration of Monte Carlo simulation and Bayesian optimization, we established the relationship map between FRP parameter space and dispersity, further suggested the correlation between low dispersities and higher propagation rates. Our research sheds light on dispersity control via FRPs and creates a novel platform to investigate polymer dispersity through machine learning.

Network reconfiguration and integration of distributed energy resources in distribution network by novel optimization techniques

Nowadays, electrical load demand in radial distribution networks (RDN) is continuously growing, therefore RDNs are facing some serious challenges like voltage violation and line losses. Since modern RDNs have reconfiguration capabilities, optimal network reconfiguration is preferred over the costly construction of new lines and cables. In addition, the optimal integration of distributed energy resources (DER) helps to decrease above mentioned network challenges. This paper presents an improved version of the radiality maintenance algorithm (IRMA) to solve the optimal reconfiguration problem using a meta-heuristics algorithm. It also proposes two different schemes of algorithms by the blending of a Genetic algorithm (GA) and Teaching learning-based optimization (TLBO) to solve the optimal DER integration problem, where blending enhances the search space of each scheme which helps to find global minima in less iterations and time, while existing algorithms get stuck in local minima with same number of searching agents. The proposed problems are solved by minimizing active power loss by the single objective function and the sum of active power loss, reactive power loss, and voltage deviation index by multi-objective function using the weighted sum method where all objectives are equally dealt with, and their sum is used as single fitness function for the optimization algorithm. Four case studies are simulated using different DER technologies to improve each objective function. The efficiency of the proposed IRMA and two newly developed schemes of optimization algorithms, named GA-TLBO and TLBO-GA, are tested on two IEEE benchmark RDN of 33 and 69 buses. The results validate the efficiency of proposed algorithms that remarkably minimize a single objective from 210.9800 kW in the base case to 58.8768 kW, whereas existing algorithms like GWO and HHO were able to only reduce it to 72.7861 kW and 72.900 kW for IEEE 33 bus RDN, respectively. Similarly, for IEEE 69 bus system, single objective is reduced from base case of 224.9917 kW to 36.2543 kW, while existing algorithms like QOTLBO and QOSIMBO were only able to reduce it to 71.6250 kW and 71 kW, respectively. Furthermore, in multi-objective functions, reactive power losses and voltage deviation are also significantly improved from their base values. After that, the results of improved algorithms are compared with those of existing algorithms in the literature review for a comprehensive evaluation, which proves that proposed algorithm schemes are much more efficient and stable for the proposed problem as well as for standard benchmark optimization functions.

Centering The Voices of People Living with Multimorbity to Develop Visual Dashboards to Facilitate Structured Medication Reviews: The Role of Patient and Public Involvement and Engagement in the DynAIRx Study

The use of Artificial Intelligence in healthcare has the potential to enhance the experiences of both patients and healthcare practitioners (Rajpurker et al., 2022). In an effort to streamline Structured Medication Reviews (SMRs) and to support medication optimization, The Artificial Intelligence (AI) for dynamic prescribing optimization and care integration in multimorbidity (DynAIRx) is a project which seeks to apply AI extracted from clinical records to improve the lives of people who are living with multimorbidity. SMRs were introduced at the primary care level to holistically support complex multimorbid patients’ health journeys and who are considered to be polypharmacy (at least five medications daily). However, there has been poor integration of health records across systems, and limited guidance on how to identify people who need yearly SMR. Without appropriate monitoring a patient’s medication, their polypharmacy may worsen (NICE, 2017); negatively impacting a patient’s overall health. Using AI and statistical approaches to predict the risk of poor outcomes, DynAIRx will produce visual dashboards for healthcare providers to use to facilitate reviews of medications for patients requiring SMRs. A Public Advisory Steering Group, comprised of individuals with lived multimorbidity experiences or carers of people with multimorbid conditions sits at the heart of DynAIRx. The steering group is designed to ensure co-production is implemented throughout each work package. This presentation presents DynAIRx as a case study on how to develop and implement PPIE as user/partnership-centered approach to AI research in healthcare and potential implications of including the public’s perspectives on the larger product.

Timely data on the dynamic drug supply is crucial in addressing the ongoing drug poisoning crisis. The team is exploring ways to share information with emergency responders, healthcare providers, government officials, and pwSUD.

ObjectiveTransitioning from hospitals to primary care brings significant challenges, including increased readmission rates, mortality, and costs due to information gaps. Alberta, Canada, addressed this by integrating 1000+ health systems and implementing Connect Care (CC), a Clinical Information System (CIS), to enhance patient safety and care coordination. In 2020, the Primary Health Care Integration Network (PHCIN) released the Home to Hospital to Home (H2H2H) Transitions Guideline and metrics to improve patient outcomes and system integration. ApproachThis study examines provincial data on H2H2H transitions measures from acute care hospitals using CC between April 1, 2022, and October 31, 2023, to assess transitions and support improvement efforts. CC data assesses transition components, including confirming primary care providers at hospital discharge, utilizing the LACE Readmission Risk Index, and ensuring timely discharge summary (DS) signoffs. It links with administrative data to evaluate post-discharge outcomes like primary care physician follow-up, unplanned readmissions, and Emergency Department (ED) visits. Results47 CC-implemented sites show nearly 80% discharges identified a primary care provider; less than 5% incorporated the LACE index in DS. About 90-93% of DS were signed within 24-72 hours. Approximately 62% of moderate-risk and 55% of high-risk discharges received timely follow-up. Readmission and ED visit rates within 7-30 days varied from 2-8% and 5-11% respectively. ConclusionIn conclusion, adopting CC and H2H2H transition metrics facilitates integrated care measurement, highlighting the need for risk index inclusion and high-risk discharge follow-up for improved patient transitions. Sustained initiatives are vital for optimal outcomes and system integration in Alberta.

Lactiplantibacillus argentoratensis AGMB00912 protects weaning mice from ETEC infection and enhances gut health.

Maintaining a healthy intestinal environment, optimal epithelial barrier integrity, and balanced gut microbiota composition are essential for the growth performance of weaning pigs. We identified Lactiplantibacillus argentoratensis AGMB00912 (LA) in healthy porcine feces as having antimicrobial activity against pathogens and enhanced short-chain fatty acid (SCFA) production. Herein, we assess the protective role of LA using a weaning mouse model with enterotoxigenic Escherichia coli (ETEC) infection. LA treatment improves feed intake and weight gain and alleviates colon shortening. Furthermore, LA inhibits intestinal damage, increases the small intestine villus height compared with the ETEC group, and enhances SCFA production. Using the Kyoto Encyclopedia of Genes and Genomes and other bioinformatic tools, including InterProScan and COGNIZER, we validated the presence of SCFA-producing pathways of LA and Lactiplantibacillus after whole genome sequencing. LA mitigates ETEC-induced shifts in the gut microbiota, decreasing the proportion of Escherichia and Enterococcus and increasing SCFA-producing bacteria, including Kineothrix, Lachnoclostridium, Roseuburia, Lacrimispora, Jutongia, and Blautia. Metabolic functional prediction analysis revealed enhanced functions linked to carbohydrate, amino acid, and vitamin biosynthesis, along with decreased functions associated with infectious bacterial diseases compared to the ETEC group. LA mitigates the adverse effects of ETEC infection in weaning mice, enhances growth performance and intestinal integrity, rebalances gut microbiota, and promotes beneficial metabolic functions. These findings validate the functionality of LA in a small animal model, supporting its potential application in improving the health and growth performance of weaning pigs.

Optimality of multisensory integration while compensating for uncertain visual target information with artificial vibrotactile cues during reach planning

BackgroundPlanning and executing movements requires the integration of different sensory modalities, such as vision and proprioception. However, neurological diseases like stroke can lead to full or partial loss of proprioception, resulting in impaired movements. Recent advances focused on providing additional sensory feedback to patients to compensate for the sensory loss, proving vibrotactile stimulation to be a viable option as it is inexpensive and easy to implement. Here, we test how such vibrotactile information can be integrated with visual signals to estimate the spatial location of a reach target.MethodsWe used a center-out reach paradigm with 31 healthy human participants to investigate how artificial vibrotactile stimulation can be integrated with visual-spatial cues indicating target location. Specifically, we provided multisite vibrotactile stimulation to the moving dominant arm using eccentric rotating mass (ERM) motors. As the integration of inputs across multiple sensory modalities becomes especially relevant when one of them is uncertain, we additionally modulated the reliability of visual cues. We then compared the weighing of vibrotactile and visual inputs as a function of visual uncertainty to predictions from the maximum likelihood estimation (MLE) framework to decide if participants achieve quasi-optimal integration.ResultsOur results show that participants could estimate target locations based on vibrotactile instructions. After short training, combined visual and vibrotactile cues led to higher hit rates and reduced reach errors when visual cues were uncertain. Additionally, we observed lower reaction times in trials with low visual uncertainty when vibrotactile stimulation was present. Using MLE predictions, we found that integration of vibrotactile and visual cues followed optimal integration when vibrotactile cues required the detection of one or two active motors. However, if estimating the location of a target required discriminating the intensities of two cues, integration violated MLE predictions.ConclusionWe conclude that participants can quickly learn to integrate visual and artificial vibrotactile information. Therefore, using additional vibrotactile stimulation may serve as a promising way to improve rehabilitation or the control of prosthetic devices by patients suffering loss of proprioception.