Initial System Research Articles

Adaptive practical prescribed-time formation tracking of networked nonlinear multiagent systems with quantized inter-agent communication

This paper presents an adaptive practical prescribed-time (PPT) control design for distributed time-varying formation tracking of networked uncertain strict-feedback nonlinear systems with quantized inter-agent communication under a directed network. The primary contribution of this study is to develop a time-varying formation tracking strategy to resolve the quantized inter-agent communication problem in the prescribed-time control field. A practical finite-time function is introduced to design an adaptive PPT formation tracker using quantized output information of neighbors, which can be used continuously after a prescribed time. A neural-network-based design methodology that utilizes the quantization-based distributed errors is presented to deal with the unknown virtual and actual control coefficient functions in the command-filtered backstepping design. Distributed adaptive compensating signals are constructed using the practical finite-time function to compensate for command-filter errors, unknown nonlinearities, and quantization errors in the PPT tracking framework. We prove that despite the presence of quantized inter-agent communication, the time-varying formation tracking errors converge to a compact set including the origin within the pre-assigned convergence time, where the compact set can be adjusted by choosing a design parameter of the practical finite-time function, and the prescribed settling time is independent of the initial system conditions and design parameters. The effectiveness of the proposed theoretical approach is confirmed through two simulation examples.

Gum Arabic-based three-dimensional printed hydrogel for customizable sensors

Most three-dimensional (3D) printed hydrogel exhibit non-idealized rheological properties in the process of direct ink writing and complicated curing. Therefore, accurate writability and convenient curing for 3D printed hydrogel remain a challenge. In this paper, we developed a typical 3D printed hydrogel which realized direct ink writing (DIW) at temperatures similar to human body. Silicon dioxide (SiO2) and Gum Arabic (GA) formed the Bingham fluid to ensure shape stability. The rapid initiation system of potassium persulfat (KPS) and N,N,N′,N′ -tetramethylethylenediamine (TMEDA) allowed the 3D printed hydrogel precursor solution to transiently form a hydrophobic conjoined cross-linking network structure of acrylamide (AAM) and lauryl methacrylate (LMA) after printing, resulting in preferable mechanical properties. Hydrogel precursor solution showed better rheological properties with the nature of Bingham fluids, and achieved transient cross-linking at 30 °C for 10 s in the rheological test. A variety of 3D printed hydrogel with individual strain sensing properties are prepared as customizable sensor that could monitor significant strain signals within 0–20 % strain with high sensitivity. Moreover, they were discovered excellent temperature sensitivity over a wide operating range (0–80 °C). The 3D printing hydrogel sensors were expected to have broad application prospects in flexible wearable devices and medical monitoring.

Application of liquid-based colorimetric method for high throughput screening of bioplastic-degrading strains using esterase assay

To alleviate environmental problems caused by using conventional plastics, bioplastics have garnered significant interest as alternatives to petroleum-based plastics. Despite possessing better degradability traits compared to traditional plastics, the degradation of bioplastics still demands a longer duration than initially anticipated. This necessitates the utilization of degradation strains or enzymes to enhance degradation efficiency, ensuring timely degradation. In this study, a novel screening method to identify bioplastic degraders faster was suggested to circumvent the time-consuming and laborious characteristics of solid-based plate assays. This liquid-based colorimetric method confirmed the extracellular esterase activity with p-nitrophenyl esters. It eliminated the needs to prepare plastic emulsion plates at the initial screening system, shortening the time for the overall screening process and providing more quantitative data. p-nitrophenyl hexanoate (C6) was considered the best substrate among the various p-nitrophenyl esters as substrates. The screening was performed in liquid-based 96-well plates, resulting in the discovery of a novel strain, Bacillus sp. SH09, with a similarity of 97.4% with Bacillus licheniformis. Furthermore, clear zone assays, degradation investigations, scanning electron microscopy, and gel permeation chromatography were conducted to characterize the biodegradation capabilities of the new strain, the liquid-based approach offered a swift and less labor-intensive option during the initial stages.

Ethyl-2-(tosylmethyl)acrylate: A promising chain transfer agent for the development of low-shrinkage dental composites.

To evaluate the potential of ethyl-2-(tosylmethyl)acrylate (ASEE) as chain transfer agent for the development of low-shrinkage photopolymerizable dental composites. Composites containing 10, 20 and 30mol% of ASEE in their organic matrix were formulated. Camphorquinone (CQ)/ethyl 4-(dimethylamino)benzoate (EDAB) (0.33wt%/0.60wt%), CQ/EDAB/Ivocerin® (0.33wt%/0.60wt%/0.10, 0.25 or 0.50wt%), CQ/EDAB/SpeedCure 938 (SC-938) (0.33wt%/0.60wt%/0.30, 0.50 or 1.00wt%) and Ivocerin® (0.50wt%) were used as photoinitiator systems. The glass transition temperature (Tg) and the crosslink density were determined by DMTA measurements. The flexural strength/modulus and ambient light working time were assessed according to ISO 4049. The shrinkage force was evaluated using a universal testing machine. The double bond conversion (DBC) was determined by NIR spectroscopy. DBC, flexural strength and modulus were measured after the storage of the specimens in deionized water at 37°C for 24h. The DBC, flexural strength and modulus data were analyzed by one-way ANOVA with p=0.05 as significance level. ASEE-based composites containing the classical initiator system CQ/EDAB exhibited low mechanical properties (flexural strength/modulus) and DBC. The screening of various photoinitiator systems showed that composites based on CQ/EDAB/Ivocerin® (0.33wt%/0.60wt%/0.50wt%), Ivocerin® (0.50wt%) or CQ/EDAB/SC-938 (0.33wt%/0.60wt%/1.00wt%) were particularly attractive. Indeed, the use of these photoinitiator systems enabled the formulation of composites containing up to 30mol% ASEE exhibiting excellent mechanical properties, high DBC, good network homogeneity and low shrinkage force values. Interestingly, the addition of SC-938 did not impair the ambient light working time of the uncured composites, whereas the incorporation of 0.50wt% Ivocerin® resulted in a strong decrease of this value. The addition of the allyl sulfone ASEE in combination with the initiator system CQ/EDAB/SC-938 (0.33wt%/ 0.60wt%/ 1.00wt%) is a promising strategy to develop low-shrinkage dental composites which exhibit excellent mechanical properties, low shrinkage force, high DBC and suitable ambient light working time.

Approximate time optimal control by deep neural networks trained with numerically obtained optimal trajectories

AbstractThis paper focuses on online time optimal control of nonlinear systems. This is achieved by approximating the results of time optimal control problems (TOCP) with deep neural networks (DNN) depending on the initial and terminal system state. In general, solving a TOCP for nonlinear systems is a computationally challenging task. Especially in the context of time optimal nonlinear model predictive control (TMPC) with hard real time constraints successful termination of a TOCP within sample times suitable for controlling mechanical systems cannot be guaranteed. Therefore, our approach is to train three DNNs with different aspects of numerical solutions of TOCPs with random initial and terminal state. These networks can then be used to approximate the TMPC by a one step model predictive control scheme with a significantly simpler structure and decreased calculation time. In order to verify this procedure by simulation, it is applied to a prove of concept example as well as the model of an industrial robot.

Utilizing hot-stage polarized microscopy and ATR-FTIR for ramipril co-crystal screening, supported by principal component analysis and cluster analysis

Context: Co-crystal formation, a method for enhancing the physicochemical properties of active pharmaceutical ingredients (APIs), has gained traction in pharmaceutical research. However, the current landscape lacks comprehensive and dependable co-crystal screening methods. Aims: To implement and assess a comprehensive methodology for co-crystal screening. This methodology combines hot-stage polarized microscopy (HSPM) and attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy, along with principal component analysis (PCA) and cluster analysis (CA). Methods: Three binary compounds containing ramipril, an API that had not previously been co-crystallized, and three pharmaceutical coformers were investigated. The Kofler mixed fusion method was initially employed for initial co-crystal system identification. Subsequently, potential co-crystals were produced in a solid state via a procedure involving the gradual evaporation of the solvent. PCA and CA were applied to ATR-FTIR spectral data to identify patterns indicative of co-crystal formation. Results: Our analysis revealed characteristic ATR-FTIR bands indicative of the formation of hydrogen bonds between ramipril and its coformers, signifying the successful formation of co-crystals. Differential scanning calorimetry (DSC) and powder X-ray diffraction (PXRD) measurements confirmed these findings. Experiments revealed two potential co-crystals, ramipril-vanillin, and ramipril-anthranilic acid. The study discusses the intricate HSPM images, spectra, thermogram of DSC, and X-ray diffraction properties of these systems in depth. Conclusions: Our findings validate the proposed methodology as a prospective tool for co-crystal screening, as ramipril co-crystals were successfully identified and characterized. This integrated method simplifies co-crystal screening and has the potential to substantially advance pharmaceutical research.

A PSO-TVAC for optimal installation of multiple distributed generations in a radial distribution system

<span>The integration of distributed generations (DGs) into distribution system networks has seen a significant increase owing to the depletion of conventional energy resources and the growing power demand. However, a high penetration of DGs can adversely affect the stability of the distribution networks due to the intermittent nature of their generation capabilities. Hence, it is crucial to design DGs optimally to support grid voltage regulation and improve distribution networks performance. This study utilizes a particle swarm optimization (PSO) with time-varying acceleration coefficients (PSO-TVAC) technique to optimize the location and size of various distributed generation units while minimizing the total active power loss. The initial system power loss was determined using a distribution load flow analysis based on the backward-forward technique. The PSO-TVAC algorithm was then employed to identify the optimal placement and sizing of DGs within the standard IEEE 33-bus radial distribution network. To assess the proposed algorithm's effectiveness, photovoltaic (PV) and wind turbine (WT) were considered as the DGs. In comparison with other algorithms, PSO-TVAC achieved the lowest power loss, measuring 72.79 [kW] and 12.14 [kW] for <br /> 3-PV and 3-WT installations, respectively. Furthermore, the optimal installation of 3-PV and 3-WT improved the distribution system performance by 65.49% and 94.25%, respectively.</span>

Rheological Properties of Gel Systems Based on Gamma-Irradiated Lightly Crosslinked Polyacrylic Acid

The effect of γ-irradiation of the gelling agent Carbomer 141 G, which is lightly crosslinked polyacrylic acid, on the rheological properties of gels based on the carbomer has been studied. It has been found that an increase in the dose of γ-irradiation in air has a destructive effect on the polymer, leading to a decrease in rheological parameters, such as viscosity, yield strength, and hysteresis area. Irradiation of the carbomer in air with doses greater than 300 kGy leads to a significant decrease or the complete loss of its gel-forming properties. At the same time, irradiation of the carbomer with the same dose in vacuum causes as small a decline in viscosity as 2% compared to the initial systems. It has been established that the optimal gelling agent for obtaining stable antibacterial gels is the carbomer irradiated with a dose of 30 kGy.

Preparation and characterization of high molecular weight vinyl-containing poly[(3,3,3-trifluoropropyl)methylsiloxane

Prior to crosslinking and vulcanization, fluorosilicone rubber is a linear polymer. This linear polymer contains 3,3,3,-trifluoropropyl methyl siloxane links, a few methyl vinyl siloxane links, and is formed by co-polymerization of 1,3,5-trimethyl-1,3,5-tris(3,3,3-trifluoropropyl) cyclotrisiloxane (D3F) with 2,4,6,8-tetramethyl-2,4,6,8-tetravinylcyclotetrasiloxane (V4) under alkaline conditions. To improve the performance of fluorosilicone rubber, three key points should be considered during the synthesis of vinyl-containing high-molecular-weight linear fluorosilicone polymers (fluorosilicone raw rubber): first, avoid the generation of low molecular weight equilibrium by-products; second, eliminate the influence of impurities; and third, increase the copolymerization participation rate of monomer V4. From the three aspects above, this study optimized the reaction conditions for the synthesis of high-molecular-weight linear fluorosilicone polymers containing vinyl. Various factors influencing polymerization were thoroughly investigated. These factors include the initiation system, accelerator, equilibrium reaction, feeding ratio, feeding sequence, neutralization mode, impurity content, etc.

Stabilization With Prescribed Instant for High-Order Integrator Systems.

This article develops a new controller design approach to stabilize system states onto the equilibrium at an arbitrarily selected time instant irrespective of the initial system states and parameters. By the stabilization approach, the actual convergence time (not the bound of actual convergence time) is independent of the initial value of system states. This feature differentiates our proposed prescribed-instant stability from conventional fixed, predefined, and prescribed time stability. In this work, we propose the controller design method for the prescribed-instant stability of n-order integrator systems. The proposed control is bounded and can gradually go to zero at an arbitrarily selected time instant, at which the system states reach zero simultaneously. This special stability of the controlled system is analyzed by reduction to absurdity. In simulations, an example of comparison with frequently used prescribed-time control is presented to show the difference. Moreover, the proposed stabilization method is validated by a magnetic suspension system with matched disturbances.

업사이클링 소셜벤처의 성공요인 분석

This study aims to identify success factors by applying the ERIS model to analyze the process of social ventures increasing the sustainable value of companies through upcycling businesses. To this end, a case analysis was conducted by applying an ERIS model that analyzes the performance of start-ups by selecting social ventures that conduct upcycling businesses using materials such as natural leather seats, seat belts, and airbags collected from automobile production processes and end-of-life automobiles. As a result of the analysis, it was confirmed that the entrepreneur's ability to identify market opportunities and establish an initial system in entrepreneurial factors, build a consistent brand image by securing tangible and intangible resources as resource factors, adapt to competitive industrial environment based on competitiveness, and finally, create value by pursuing sustainability in all corporate activities in strategic factors. Conclusions and implications were drawn based on the above results.

Analysis and treatments of primary support failure in large-span tunnel under a soft-hard interbedded surrounding rock mass

The construction of large-span tunnels inevitably involves traversing uneven strata. Due to the influence of the flatness of the large-span tunnel structure and asymmetric surrounding rock loads, concrete cracking and excessive deformation of steel arches occur frequently. Taking a large-span highway tunnel with uneven rock strata as an example, the mechanical behavior, failure causes, failure mechanisms, and treatment techniques of the tunnel's initial support structure were studied. Firstly, field monitoring and systematic analysis of typical sections revealed that the displacement of tunnels on the soft rock side and the contact pressure between the surrounding rock and the support were significantly greater than on the hard rock side. Then, combined with the field test results, a similar model test was designed based on the tunnel structure performance testing platform developed by the research team to study the deformation and failure modes of conventional initial support systems for large-span tunnels under geological unbalanced pressure. Finally, an innovative technique is proposed to adjust external loads and optimize internal forces of the structure by locally adding long anchor rods or cables, and the treatment effects of various schemes are evaluated and compared. The results show that the initial support system exhibits a state of biased pressure in the presence of weak surrounding rock on one side. The maximum bending moment of the soft rock side spandrel bearing structure, the spandrel and vault on the soft rock side are the most vulnerable positions of the structure. By implementing local long anchor or bolts, the stress distribution within the initial support structure of a large-span tunnel can be effectively enhanced. This method offers significant practical value and flexibility, addressing the issue of initial support failure caused by a soft-hard interbedded surrounding rock mass in large-span tunnels.

Psychological well-being of small enterprise employees: a multi-theoretical perspective

PurposeThis paper aims to suggest a multi-theoretical explanation using a success story to explain psychological well-being (PWB) among employees of K.C, a small enterprise found in Uganda, a developing country in Africa.Design/methodology/approachThe study used qualitative methodology. Based on in-depth interviews with K.C employees, a story was developed describing the practical experience, focusing on the context, actions, results and lessons learnt. Regarding the sample size, the saturation point was attained on the seventh participant.FindingsFindings reveal that employees that possess psychological capital set targets and generate avenues that allow them to achieve set goals, with personal initiative that makes them proactive to accomplish work tasks and individual adaptability that enables them to adjust their emotions and behavior to fit in a complex working environment, which makes them to think, feel and act positively. Furthermore, several theories, including broaden and build, personal initiative and complex adaptive systems theory, explain the manifestations of PWB of employees in small enterprises.Research limitations/implicationsThe study was limited by focusing on the context of a small enterprise. Future research may investigate other study contexts whose findings might be different. In addition, the study being hypothetical lacked statistical testing. It would be a meaningful effort if future studies statistically tested the suggested model. Irrespective of the limitations, the findings of this study remain significant.Practical implicationsIn practice, employees may replicate these findings to nurture PWB which eventually contributes to enterprises’ success. This could provide answers to the psychological challenges experienced by employees of small enterprises, especially in the African developing countries like Uganda where this is a major challenge. Specifically, the workers of K.C enterprise may depend on their PWB to deal with workplace challenges and sustain the enterprise’s performance.Social implicationsSocially, there is need to embrace positive social relationships among employees at the work place which will translate into well-being of society.Originality/valueThis paper is exceptional because it uses a success story showing practical experiences of how PWB of employees in small enterprises is nurtured in Uganda. In addition, a multi-theoretical perspective is used to explain the manifestations in the story, which is the greatest contribution of this paper. Further, a conceptual model is still proposed, depicting psychological capital, personal initiative and individual adaptability as antecedents of PWB.

Living anionic polymerization of vinyl monomers using benzyltrimethylsilane‐metal alkoxide initiator systems

AbstractWell controlled living anionic polymerization of vinyl monomers was achieved by using the initiator system composed of desilylation of benzyltrimethylsilane (BnTMS) by potassium tert‐butoxide (t‐BuOK) in tetrahydrofuran and trapping the resulting benzyl anion with α‐methylstyrene or 1,1‐diphenylethylene in the initiator system in order to control the desilylation and polymerization reaction. This method led to high initiation efficiency and strict control of molecular weights of the obtained polymers. Furthermore, the initiator system could be applied to the synthesis of a block copolymer of PMMA‐b‐PtBMA. These results showed that living nature of the polymer obtained using BnTMS/t‐BuOK initiator system in the presence of the appropriate additives.

The Implementation of Semantic Annotation Recognizing Technique in the Scraper Engine on the E-Publishing Website of the National Research and Innovation Agency (BRIN) Indonesia

The increasing need for swift information dissemination in line with modern technological advancements has emphasized the importance and significant impact of data analysis and processing as relevant academic disciplines. These processes encompass data acquisition from various sources, either through direct collection or extraction methods. Among the most crucial and widely utilized techniques for extracting data from the internet is web scraping, particularly when gathering data for research maintenance during the consolidation of multiple institutions into BRIN (National Research and Innovation Agency). Challenges emerge in effectively integrating existing research into a unified system without proper upkeep, as neglecting maintenance can lead to system degradation and hinder access to stored research. Successful maintenance necessitates centralized repositories for researchers' work data. The implementation of semantic annotation recognizing techniques within the web scraping feature of the E-Publishing website holds the potential to expedite this process. The use of web scraping promises to significantly simplify research data collection, while semantic annotation recognizing techniques are poised to streamline implementation, particularly due to the XML data foundation within the Open Archives Initiative (OAI) system. In the context of institution merging and research sustainability, technologies like web scraping and semantic annotation recognizing play pivotal roles in addressing these challenges.

Managing high cardiovascular disease risk among adults in Argentina using a multicomponent strategy linking key aspects of care: A two-arm cluster-randomized clinical trial (PRIMECare) protocol

BackgroundCardiovascular disease (CVD) imposes a significant burden on the Argentinian population. Management of its leading risk factors can significantly reduce the CVD burden in high-resource settings, but there is insufficient evidence for effective implementation of evidence-based interventions in lower-resource settings like Argentina. MethodsIn this two-arm cluster-randomized trial we seek to compare the effective implementation, of a multicomponent intervention, versus usual care, to improve the management of high CVD risk across the care continuum in three provinces of Argentina. The multicomponent intervention strategy links five primary components of the CVD care continuum to improve its management: (1) a data management system linking a digital mHealth (mobile health) screening tool used by community health workers (CHWs), (2) an electronic appointment scheduler that is integrated with the primary care center electronic appointment system, (3) point of care testing for lipid profiles, (4) a clinical decision support (CDS) system for medication initiation, and (5) a text message (SMS) reminder system to improve treatment adherence and life-style changes. The primary outcome is the mean change in Framingham laboratory-based, 10-year absolute CVD risk score between the study arms from baseline to twelve months after enrollment. ConclusionsThis protocol describes the development of a multicomponent intervention to implement effective management of CVD, developed with partners at the National and provincial Departments of Health in Argentina, with the goal of understanding its effective implementation in a primary health care system strengthened by universal health coverage, provision of free health care services, and provision of free medication.

Antisaturation fixed-time attitude tracking control based low-computation learning for uncertain quadrotor UAVs with external disturbances

External disturbances, uncertain parameters, asymmetric saturation input, and high computational burden can significantly damage the attitude tracking control performance of uncertain quadrotor unmanned aerial vehicles (UAVs). To accomplish the high-precision attitude tracking control, this study proposes an antisaturation fixed-time attitude tracking control based low-computation learning. Firstly, a fixed-time state observer is constructed to estimate the system states in fixed time. Secondly, by developing the fast fixed-time stable system with the time-varying gain function, a nonsingular fast fixed-time sliding mode surface is designed to improve the convergence speed and avoid the singularity problem. Thirdly, to solve the problem of asymmetric input saturation, an auxiliary compensation system is integrated to regulate the control inputs. Subsequently, an adaptive neural network (NN) technology is used to overcome the negative effects of external disturbances and uncertain parameters, where the designed adaptive mechanism is to adjust a virtual parameter online instead of the weight vector of the NN, which has the characteristics of low computational burden and simple structure. The Lyapunov-based stability analysis concludes that the closed-loop system is practical fixed-time stable and the tracking errors can converge to bounded regions around the origin in fixed time independently of the initial system states. Finally, comparative results are given to demonstrate that compared with the existing controllers, the controller developed in this study can achieve stronger robustness, faster convergence, and saturation elimination with lower error-index values.

Research on quality evaluation of online teaching platform based on RS-BP Neural Network

Online teaching platforms have developed into mainstream knowledge learning and exchange platform. The research on the quality evaluation of online teaching platforms and the construction of an applicable and scientific evaluation index system model can help explore the key factors affecting the quality of online teaching platforms and provide some references for evaluating online teaching platforms and improving online teaching quality. This study combines the rough set theory (RS) with the BP (Back Propagation) neural networks to build an RS-BP neural network model to evaluate the quality of online teaching platforms. Firstly, an initial online teaching platform quality evaluation index system is constructed based on knowledge transfer theory from four aspects: course content, knowledge transmitter, knowledge receiver and teaching platform. Then, 12 core evaluation indicators were generated by attribute reduction using rough set theory, and the weights of each core indication were determined. The normalized data input was then trained, validated, and tested to generate a rough set neural network quality evaluation model for online teaching platforms. After that, three representative online education platforms of content, interaction and compatibility are selected for empirical research. The accuracy of the model is first tested by the error between the simulated and output values, after which the core metric scores and the overall scores are calculated for the three types of platforms. The empirical results demonstrate that the model has certain advantages in terms of index simplification and adaptive training when evaluating online teaching platforms, as well as strong operability and practicality. The evaluation results show that the content online teaching platform has the highest comprehensive score, followed by the compatible and interactive online teaching platforms. According to the index scores, the quality of the course content, stage assessments, and contact between professors and students were identified as major elements influencing the quality of the online teaching platform. Finally, suggestions for optimization for each of the three types of online teaching platforms were made based on the core indicators and their weights, as well as the scores and characteristics of the three types of online teaching platforms, with the goal of improving the quality of online teaching platforms.

Battery thermal management system optimization using Deep reinforced learning algorithm

The temperature of the battery plays a critical role in the working conditions and a suitable temperature environment can help the battery extend its capacity. Air cooling and water cooling are prevalent as positive heat dissipation methods used in research. Phase change material (PCM) has a good heat dissipation capacity because of its latent heat property. Therefore, PCM is chosen as the battery thermal management method in this study. Adding two water channels in the center of the pack strengthens the heat dissipation capacity. Deep reinforcement learning (DRL) is used to explore the multi-optimal solution of the battery thermal arrangement, and its results are compared with the classical methods such as NSGA-ii and MOPSO. Max temperature, temperature difference of the battery, and average temperature of PCM are selected as the optimization targets. A comparison between DRL optimization and the initial system shows that the increased max temperature decreased by 0.33 ℃ (2.1%), and the average temperature of PCM decreased by 0.3 ℃ (2.3%). The temperature difference of DRL optimization decreased by 1.3% compared with that of NSGA-ii and MOPSO. The max temperature decreased by 0.2% and the average temperature of PCM decreased by 0.3% compared with those of NSGA-ii. In summary, DRL has the potential application in battery thermal management system (BTMS) optimization. The most important contribution of this work is utilizing DRL in the optimization process, which shows better results than existing optimization methods.

A social robot connected with chatGPT to improve cognitive functioning in ASD subjects.

Neurodevelopmental Disorders (NDDs) represent a significant healthcare and economic burden for families and society. Technology, including AI and digital technologies, offers potential solutions for the assessment, monitoring, and treatment of NDDs. However, further research is needed to determine the effectiveness, feasibility, and acceptability of these technologies in NDDs, and to address the challenges associated with their implementation. In this work, we present the application of social robotics using a Pepper robot connected to the OpenAI system (Chat-GPT) for real-time dialogue initiation with the robot. After describing the general architecture of the system, we present two possible simulated interaction scenarios of a subject with Autism Spectrum Disorder in two different situations. Limitations and future implementations are also provided to provide an overview of the potential developments of interconnected systems that could greatly contribute to technological advancements for Neurodevelopmental Disorders (NDD).