Implementation Of Scheme Research Articles

Wastewater treatment facilities in the north of the Irkutsk Oblast: the current state

Operation of centralized wastewater disposal systems requires not only technical, but also organizational measures. The efficiency of these measures can be assessed by increasing the quality of wastewater treatment. The analysis of operation of facilities in the northern conditions and the zone of sharply continental climate demonstrates a number of issues that require urgent solution. According to the studies of biological wastewater treatment in such conditions, a comprehensive approach should be taken, considering climatic parameters and conditions of their operation, along with technological features of wastewater treatment plants. The paper considers the performance indicators of two water treatment plants located in the northern districts of Irkutsk Oblast. Insufficient wastewater treatment for some pollutants was revealed. A complex analysis was carried out, which identified a number of possible causes of exceeded concentrations of pollutants. Maximum permissible concentrations set for fishery water bodies were used as normative indicators. In order to provide a more complete and in-depth analysis, each stage of treatment should be evaluated according to the implemented scheme in accordance with the project and the developed technological regulations, and the optimal parameters and wastewater analyses should be specified step-by-step. A preliminary analysis resulted in a number of measures to improve the efficiency of wastewater treatment and to justify the introduction of a system for the quality control of wastewater discharged by consumers.

Semi-Active Suspension Design for an In-Wheel-Motor-Driven Electric Vehicle Using a Dynamic Vibration-Absorbing Structure and PID-Controlled Magnetorheological Damper

Abstract: The in-wheel motor (IWM) powertrain layout offers greater design flexibility and higher efficiency of an electric vehicle but has limited commercial success mainly due to the concerns of increased unsprung mass. This paper proposes a semi-active suspension system for in-wheel motors that combines both a dynamic vibration-absorbing structure (DVAS) and a PID-controlled MR damper, in order to achieve optimised comfort, handling and IWM vibration for a small car application. Whilst PID control and DVAS are not entirely new concepts, the usage of both optimisation techniques in a semi-active in-wheel motor suspension has seen limited implementation, which makes the current work novel and significant. The semi-active suspension operating both in passive fail-safe mode and full feedback control was compared to a conventional in-wheel motor passive suspension in terms of sprung mass acceleration, displacement, stator acceleration, tyre deflection and suspension travel for three different road profile inputs using MATLAB/Simulink. The implementation of a PID-controlled MR damper improved road comfort and road holding performance and decreased in-wheel motor vibration over the DVAS passive suspension mainly in terms of a maximum peak amplitude decrease of 40%, 35% and 32% for the sprung mass acceleration, tyre deflection and stator acceleration, respectively. The results are significant since they show that the use of a simple, easily implemented control scheme like PID control was able to significantly improve IWM suspension performance when paired with a DVAS. This study provides further confidence to manufacturers to commercially develop and implement the IWM layout as its major disadvantage can be reasonably addressed using a simple readily available control approach.

Multi-Label Diabetic Retinopathy Detection Using Transfer Learning Based Convolutional Neural Network

Retinopathy is a progressive and common retinal disease that most progressive diabetics suffer from and causes blood vessels in the retina to swell and leak blood and fluid. This condition requires timely diagnosis via medical experts to prevent causing visual loss among patients. To enhance the feasibility of checking many persons, diverse deep-learning schemes have recently been developed for diabetic retinopathy detection. In this paper, retinopathy image detection system based on diverse deep learning schemes (VGG-19, DenseNet-121, and EfficientNet-B6) has been presented. The implemented deep learning schemes with multi-label classification are trained and tested using the Asia Pacific Tele Ophthalmology Society (APTOS-2019) dataset, and the two combined datasets Indian Diabetic Retinopathy Image Dataset (IDRiD) and Messidor-2. The system outcomes of classification are exhibited as sensitivity, precision, F1Score, and accuracy measurements, and the system performance is compared with recently existing related systems. The attained outcomes indicate that the implemented EfficientNetB6 network outperforms peers’ schemes and related systems via realizing supreme accuracy using balanced multi-class retinopathy datasets.

NIOS II soft-core processor and ethernet controller solution for RPC-DAQ in INO ICAL

This paper introduces a high-performance Soft-Core Processor based data acquisition system designed for handling Resistive Plate Chambers (RPCs). The DAQ consist of FPGA-based hardware equipped with Soft-Core Processor and embedded hardwired Ethernet controllers named RPC-DAQ, offering a versatile and fast network-enabled data acquisition solution. A soft processor, NIOS, is instantiated within an Intel Cyclone IV FPGA, overseeing control, communication, and data transfer with remote processing units. These integrated RPC-DAQ units, in substantial numbers, connect to a limited set of high-end processing units via LAN switches. This paper provides a detailed account of the software implementation scheme for the NIOS processor in the RPC-DAQ system. A remarkable 28,800 RPC-DAQ units will be deployed in proximity to the RPCs, serving the proposed INO-ICAL experiment in Theni-Madurai, Tamil Nadu. The network-enabled RPC-DAQ units controlled by the soft processor offloads FPGA tasks including event data acquisition, periodic health monitoring of RPCs, command interfaces, high voltage control, and data transfer to back-end data concentrators. Communication and data transfer are executed efficiently via TCP and UDP protocols over a 100 Mbps Ethernet interface. This system provides innovative solutions to improve data acquisition and control in large-scale scientific experiments.

JOB SATISFACTION SCALE FOR TECH WORKERS: IMPLEMENTATION COMPASS

Tech workers are not just employees but ubiquitous architects and drivers of the imminent digitalized future. Tech worker’s wellbeing is, therefore, crucial for global prosperity – necessitating their continuous monitoring and management in the workplace. Although a cross-cultural job satisfaction scale has been specifically developed and validated to help gauge the wellbeing of tech workers, christened JSST, it lacked an implementation guideline. This lack of implementation guidelines for the JSST questionnaire can stifle its practicality and, by extension, the wellbeing of Tech workers. The aim of this study, therefore, is to design an implementation guideline for JSST. The implementation guideline was designed using descriptive statistics and algorithmic specification. Adopting a self-assessment online survey on the original Five-point Likert JSST Questionnaire, this study also demonstrated the implementation of the guideline. 276 valid Tech workers’ job satisfaction self-assessment data solicited globally using the JSST questionnaire was employed to demonstrate the guideline’s feasibility. Overall, the job satisfaction index of the global tech workers has been estimated to fall into the third quartile of satisfaction, Q3, which is low. This low satisfaction index may account for the high turnover and turn-away in the global Tech industry. A notable outcome of this study is the seamless JSST implementation scheme, which is not only effective but also highly adaptable. This scheme can be tailored for the implementation of any multi-faceted psychometric scale, making it a versatile tool for assessing employee wellbeing. The automation for web accessibility of the JSST implementation scheme is encouraged.

Reliability assessment of the thyristor module of high-voltage hybrid switchgears in reservation conditions

In the article, the authors proposed a methodology for evaluating the main indicators of the operational reliability of a high-voltage thyristor module, which is an element of electric power equipment, using the example of the design of such electrical devices as hybrid switches and converters in the structure of traction substations, power supply systems of metallurgical complexes, etc. The proposed method makes it possible to estimate the prob-ability of failure-free operation of the real design of the thyristor module, taking into account its design and implementation scheme, as well as the level and depth of redundancy of the semiconductor devices included in it. As initial data, the proposed method involves the use of electrical load parameters in terms of voltage and current and their redistribution as the reserve resource is exhausted, taking into account the design features of both the electrical device itself and the power supply network where it is located. is established. The proposed method makes it possible to evaluate the effectiveness of the influence of design factors (the number of semiconductor devices, the principle and depth of redundancy, the economic component, etc.) on the main indicators of the operational reliability of the corresponding types of high-voltage equipment, taking into account the electrical load and its changes. The proposed method was tested on the example of a real scheme of the thyristor module of the on-load tap-changer switching device, which was in industrial operation for a long time in the most unfavourable conditions in the power supply structure of the metallurgical industry, where there is high switching frequency, load fluctuations, etc. The implementation of this technique in the conditions of designing high-voltage switching and converting electrical equipment, as well as its operation in the power supply structure, allows to evaluate the influence of the structural features of the semiconductor part, taking into account the load mode, as well as the influence of external and operational factors. A more accurate determination of the effectiveness of the proposed methodology for predicting operational reliability indicators depending on the mode and depth of the load and the type of backup system can be achieved by conducting an additional series of experimental tests on the choice of the type of theoretical system. distribution law and determination of its parameters for specific designs of electrical devices. Thus, a conclusion was made about the possibility of using the obtained results to evaluate the parameters of the operational reliability of the above-mentioned high-voltage equipment, which is used as part of switchgear and power supply systems in both high and low voltage conditions.

Sensitivity Analysis of MTPA Control to Angle Errors for Synchronous Reluctance Machines

This study investigated the sensitivity of maximum torque per ampere (MTPA) control in synchronous reluctance machines (SynRMs) to angle errors, examining specifically how deviations in the reference control trajectory affected performance. Analytical and numerical methods were employed to analyze this sensitivity systematically, including the impact of magnetic saturation. Two MTPA control implementation schemes were evaluated, with torque and current amplitude as the reference variables, using a template SynRM from the open-source simulation tool SyR-e. The results indicated that performance sensitivity to angle errors was moderately low near the MTPA trajectory, allowing for significant angle deviations with minimal performance loss. Although magnetic saturation increased this sensitivity slightly, reducing the allowable error range by up to 25%, the maximum angle deviation for up to 1% of the performance decrease still corresponded to approximately ±3∘ around the MTPA trajectory. The findings of this study suggest potential for simplifying control implementations, reducing component costs through less precise position determination (sensor-based or sensorless), and achieving additional control objectives such as torque ripple reduction.

OPPORTUNITIES OF ADVANCED BIOMETHANE PRODUCTION FROM MICROALGAE GROWN ON BIOGAS PLANT DIGESTATE. Part 1.

The purpose of the work is to review technologies and analyze the possibility of introducing microalgae cultivation at biogas/biomethane plants while simultaneously obtaining of additional biomethane yield and utilization of the liquid fraction of the digestate. Modern technologies, types and equipment for growing microalgae on the liquid fraction of digestate of biogas plants were considered and analyzed. The current situation of biogas/biomethane production in Ukraine and disposal of the liquid digestate fraction is presented. The chemical composition of the digestate and the methods of its preparation for the cultivation of microalgae were analyzed. Recommendations and a block scheme for technology implementation at a biogas/biomethane plant have been developed. Today, one of the most economically profitable options for Ukraine is the production of biomethane. On the other hand, there is a clear need for innovative, low-cost, safe production of biomethane using alternative, high-yield biomass that can utilize waste feedstock and also sequester carbon. Microalgae is a promising biomass that can convert nutrient-rich digestate into valuable biomass for biofuel and biodiesel production and is considered as a double-counting feedstock (RED II Directive, Annex 9). Microalgae demonstrate high photosynthetic efficiency and productivity (almost twice that of terrestrial plants) and are promising for advanced biomethane production. Integrating microalgae harvesting into an anaerobic digestion plan is the most efficient and promising way to harvest microalgae, as most of the components required for microalgae harvesting are almost free under the following conditions: process heat, CO₂, nutrients, water, transport of microalgae to end use. Electricity alone for lighting is not free, but can be minimized by using solar energy, energy storage and biogas electricity. Algae biomass can potentially increase the annual production of biomethane at an existing plant by 9.4%, as was achieved for a similar project in Sweden [33].

Anti-jamming and Power Minimization Interference Nulling in Uplink MIMO-NOMA Technique

Non-orthogonal Multiple Access (NOMA) provides use of the power domain to boost system efficiency in the spectrum. This letter explores the use of a new transceiver design and non-orthogonal multiple access (NOMA) for MIMO uplinks. The overall energy use can be reduced while still meeting individual rate requirements by utilizing a new NOMA implementation scheme with group interference cancellation. Jamming attacks can target NOMA communication. MIMO technology is used to implement anti-jamming regulations in NOMA systems. While subsequent interference cancellation utilized to get rid of between groups interference, interference nulling at the transmitters and equalizers at the jointly designed receivers for improved power system efficiency. Where the transmitter is side, interference nulling techniques have been developed. By using the above technique, the total power consumption (dBm) which it required which it is less when compare to traditional technique like orthogonal multiple access (OMA). The outcomes of the simulation show that, in comparison to both signal alignment NOMA and orthogonal multiple-use communication, the proposed NOMA scheme typically requires less power.

USE OF MINING WASTE TO PRESERVE FIELD MOISTURE

Purpose. Justification of the effectiveness of the proposed method of retaining irrigation moisture in arable land due to the formation of filter inclusions. Research methodology. Computer modeling of filtration flows of irrigation water from the surface to the underlying soil layers. Results. In this article, the authors proposed a method to prevent irrigation water loss in agricultural fields by creating grooves on arable land that have a small open surface area. In order to prevent the walls of the groove from collapsing, the possibility of filling their cavity with small solid burnt mining wastes, which have high filtration properties, was considered. At the same time, filling the void of the groove with a mixture made on the basis of burned waste mass with the addition of bottom sediments raised from reservoirs will also act as a means of increasing productivity. The implementation scheme for arranging irrigation ditches filled with burnt rock in the field, as well as the initial and operational conditions , are substantiated. According to the results of computer and mathematical modeling, using the ANSYS WB program, in a non-stationary setting, qualitative indicators of the water distribution mechanism in the experimental soil fragment were obtained. The wave nature of water migration under the influence of gravitational and capillary forces has been established. The studies show that irrigation water is concentrated in the “groove with rock – moistened soil massif” system, which has the shape of an oval with a limited contact plane with the surface. This determines the low intensity of evaporation, and, accordingly, the effectiveness of the proposed method of retaining water in the arable land by adding solid burnt mining waste. Scientific novelty. The use of small solid mining waste to create irrigation ditches on arable land is proposed, and qualitative indicators of the water distribution mechanism in the experimental soil fragment are obtained. Practical significance. The use of small solid mining wastes, which have high filtration properties, to fill the cavity of irrigation ditches on arable land will allow reducing the loss of water to evaporation and improve the physical and mechanical properties of loamy soils. Key words: mining solid waste, irrigation water, ditch, arable land, loamy soil.

Research of local network reliability indicators during the implementation of the port aggregation scheme

The article is devoted to a relevant scientific and applied problem related to the improvement of potential solutions for increasing the reliability of local networks by means of link (port) aggregation on switches. The article presents a study aimed at a comparative quantitative analysis of the reliability level of a local network in which the mechanism of link (port) aggregation is implemented. The technological task is formulated in the form of a mathematical model for calculating the probability of network uptime with a port aggregation scheme. The boundary values of network reliability indicators are calculated depending on the level of reliability of individual links and the implemented port aggregation scheme, for which the code in MATLAB is written and the corresponding graphs are constructed. It is established that at the level of local networks, the implementation of the mechanism of link (port) aggregation is an effective means of increasing their performance and reliability. The conditions under which this scheme is most appropriate are substantiated. It is demonstrated that in these conditions when implementing the aggregation scheme, it was advisable to limit ourselves to four links. The study results have confirmed the effectiveness of the proposed solution for aggregating links (ports) and allowed us to formulate general recommendations related to the implementation of reliable routing in practice. Consequently, it is proposed to use four aggregated links, provided that the probability of link failure is between 0.5 and 0.7. If the probability of link failure exceeds 0.7, it is advisable to limit the number of aggregated links to two. Further increases in the number of aggregated links affect the cost and performance of the network but do not improve reliability.

Hybrid Heuristic‐Based Invariable Step‐Size Zero‐Attracting Modified Normalized Least Mean Square for Efficient Channel Estimation in Broadband Wireless Communication System

ABSTRACTThe stability of the broadband channel estimation capabilities is provided by the normalized least mean square (NLMS) approaches. When compared to compression sensing‐based algorithms, the NLMS is very effective to perform in the channel estimation model, and also, the computational complexity of these algorithms is very low. But the important drawback of these algorithms is the intrinsic sparsity of broadband communication channels is not effectively utilized in these systems. The channel sparsity is exploited using invariable step‐size zero‐attracting NLMS algorithms in the adaptive sparse channel estimation techniques. The best trade‐off between the computational complexity and the cost function cannot be obtained, and the convergence rate is low in the ISS‐ZA‐NLMS, and a new algorithm is developed with the help of existing ISS‐ZA‐NLMS. The objective of this research work is to develop a novel channel estimation mechanism for broadband wireless communication systems using the enhanced NLMS method with the usage of a hybrid heuristic strategy. This work designs and develops an efficient channel estimation technique named as hybrid heuristic‐based invariable step‐size zero‐attracting modified NLMS for enhancing efficiency. The heuristic algorithms like rat swarm optimizer and red deer algorithm are fused together to get hybrid rat red deer swarm optimization for estimating the channels with higher efficiency. The main scope of the implemented scheme is to offer better performance regarding the mean square error (MSE) measures. The simulation analysis is made finally to estimate the performance of the investigated HH‐ISS‐ZA‐MNLMS with the comparison of conventional approaches.

Discrete-time approximation for backward stochastic differential equations driven by G-Brownian motion

In this paper, we study the discrete-time approximation schemes for a class of backward stochastic differential equations driven by [Formula: see text]-Brownian motion ([Formula: see text]-BSDEs) which corresponds to the hedging pricing of European contingent claims. By introducing an auxiliary extended [Formula: see text]-expectation space, we propose a class of [Formula: see text]-schemes to discrete [Formula: see text]-BSDEs in this space. With the help of nonlinear stochastic analysis techniques and numerical analysis tools, we prove that our schemes admit half-order convergence for approximating [Formula: see text]-BSDE in the general case. In some special cases, our schemes can achieve a first-order convergence rate. Finally, we give an implementable numerical scheme for [Formula: see text]-BSDEs based on Peng’s central limit theorem and illustrate our convergence results with numerical examples.

(Invited) Heterogeneous 3D CFET with Ge Nanosheet Channel

Recently, complementary field-effect transistors (CFETs) have been seriously studied for next-generation device architectures to further improve PPA (power, performance, and area). However, there are still many challenges including process integration and structure optimization, decisions on implementation schemes (monolithic or sequential), etc. At the transistor level, unbalanced transport between n- and p-FET would be one of the most critical issues because CFETs inherently require the same width between n- and p-FETs. Here, we will discuss the opportunity of heterogeneous or hybrid channel design to mitigate these issues and its process flexibility to realize CFETs.

Stochastic analysis of dynamic fracture of concrete using CT-image based mesoscale models with a rate-dependent phase field method

Concrete structures are commonly exposed to dynamic loads spanning a wide range of strain rates, and the inherent mesoscale heterogeneities complicate stochastic dynamic fracture mechanisms even more. This work develops a numerical framework using mesoscale concrete models based on micro computed tomography (CT) images to investigate such mechanisms with meaningful stochastic analyses. A rate-dependent phase field model is proposed to characterise the dynamic initiation and propagation of cracks by incorporating both micro-viscosity and macroscopic viscoelasticity, which is described by two standard Maxwell elements with different relaxation times to consider a wide range of strain rates. Moreover, the viscoelastic constitutive relation is formulated in the full strain space, which allows for a spectral decomposition of the strain tensor to determine the effective damage driving force, thus effectively addressing the issue of compressive fracture. A numerical implementation scheme is developed by combining user-defined element and material subroutines in ABAQUS/Explicit solver. Extensive Monte Carlo simulations of dynamic tension up to a strain rate of 200 s−1 are performed with statistical analyses. This work reveals the intricate dynamics associated with mesoscale heterogeneities and identifies the critical transition state at 20 s−1. The transition is characterised by changing modes of fracture patterns, stress wave propagation, and load-carrying capacities. A new TDIF–strain rate–standard deviation relation is also proposed and aligns well with the increasing dispersion of experimental data. The relationship between void content and tensile strength reflects the formation characteristics of crack networks, with the void content exhibiting a positive correlation with the TDIF from 20 s−1 to 100 s−1.

Farmers’ Compliance with the Recommended Doses of Pesticides on Watermelon in Yobe State, Nigeria

The study investigated farmers’ compliance with the recommended doses of pesticides on watermelon in Yobe State, Nigeria. A multi-stage sampling procedure was adopted to select one hundred and seventy-three (173) respondents for the study. Primary data were collected using a structured questionnaire. Data were collected on socio-economic characteristics, training in pesticide application, awareness of hazards associated with pesticides, access to information on hazards associated with pesticides and compliance with recommended doses of pesticides. The results revealed that the mean age of the respondents was 43.19, 97.7% were married 73.4% did not have English language reading ability. Training in application of pesticides (0.18), awareness of hazards associated with pesticides (0.28) and access to information (0.43) were low. Most (91.9%) respondents did not comply with the recommended doses of pesticides. Awareness of hazards (B= 2.74, p = 0.01) and access to information (B= 1.45, p = 0.08) significantly influenced compliance with the recommended doses of pesticides. Stakeholders in the agriculture and food sectors should plan and implement schemes to train, inform and boost awareness of the farmers on hazards associated with pesticides to motivate compliance with recommended doses of pesticides, produce safe watermelon and sustain a healthy environment.

Re-Defining Youth and Women Empowerment: Interogating the NAFA Program Initiative in The Gambia

With the extreme poverty level and its widespread in the Gambia as of 2012 and government’s concern to counter it, empowerment became one of the strategies to tackle the problem through social protection program, gazette as a policy for reduction of the shocks on the marginalized and the vulnerable. This study centered on the NAFA project-a quick cash transfers project mechanism and its execution in the Barrow administration to needy households in tackling poverty and hunger in the Gambia and towards empowering the perceived vulnerable women and youths in the Gambian communities. The study examined the execution of the scheme and its effectiveness by the government and the extent to which it had been used to adequately improve the poor conditions of women and the youths under the program. A descriptive research design with analytical approaches was employed to explore the objectives of the scheme in the pandemic era that people found it most difficult to earn living due to COVID-19 outbreak. Findings confirmed that NAFA scheme was relatively successful amidst inadequacies. The research advised on further reforms for a sustainable implemented NAFA scheme.

An updated parameterization of the unstable atmospheric surface layer in the Weather Research and Forecasting (WRF) modeling system

Abstract. The accurate parameterization of atmospheric surface layer processes is crucial for weather forecasts using numerical weather prediction models. Here, an attempt has been made to improve the surface layer parameterization in the Weather Research and Forecasting model version 4.2.2 (WRFv4.2.2) by implementing similarity functions proposed by Kader and Yaglom (1990) to make it consistent in producing the transfer coefficient for momentum observed over the tropical region (Srivastava and Sharan, 2015). The surface layer module in WRFv4.2.2 is modified in such a way that it contains the commonly used similarity functions for momentum (φm) and heat (φh) under convective conditions instead of the existing single functional form. The updated module has various alternatives of φm and φh, which can be controlled by a flag introduced in the input file. The impacts of utilizing different functional forms have been evaluated using the bulk flux algorithm as well as real-case simulations with the WRFv4.2.2 model. The model-simulated variables have been evaluated with observational data from a flux tower at Ranchi (23.412° N, 85.440° E), India, and the ERA5-Land reanalysis dataset. The transfer coefficient for momentum simulated using the implemented scheme is found to agree well with its observed non-monotonic behavior in convective conditions (Srivastava and Sharan, 2021). The study suggests that the updated surface layer scheme performs well in simulating the surface transfer coefficients and could be potentially utilized for the parameterization of surface fluxes under convective conditions in the WRF model.

Novel adaptive control approach to fractal fractional order deforestation model and its impact on soil erosion

Deforestation exerts profound ramifications on soil quality and biodiversity, thereby exerting substantial economic repercussions. The depletion of organic matter and structural integrity of soil following tree removal for agricultural purposes underscores the severity of this issue. In elucidating the soil pollution stemming from deforestation, this research employs a sophisticated five-compartment SDIFR model integrating fractal dimension and fractional order dynamics. The rigorous analysis, including the application of Picard Lindelof’s fixed point theorem, establishes the existence and uniqueness of explicit solutions. Furthermore, the examination of local and global stability sheds light on the system’s behavior, delineating between pollution-free equilibrium and pollution-extinct equilibrium states. To regulate system behavior, an adaptive control framework grounded in fractal fractional order is proposed, leveraging the Adams-Bashforth numerical approximation scheme for implementation. Through numerical simulations, the study underscores the pivotal role of parameters, thus substantiating the significance of the proposed model in comprehensively addressing the complexities of soil pollution induced by deforestation.

Efficient Nonparametric Estimation of 3D Point Cloud Signals through Distributed Learning

Advancements in technology have elevated the prominence of 3D point cloud data, making its analysis increasingly vital across various applications. This need drives the demand for advanced statistical analytic approaches to handle challenges such as size, sparsity, and irregularity in 3D point clouds while ensuring accurate and efficient information extraction. This article introduces a novel nonparametric distributed (NPD) learning framework that uses trivariate spline smoothing over a triangulation of domain. The proposed NPD algorithm features a straightforward, scalable, and communication-efficient implementation scheme that can achieve near-linear speedup. In addition, we provide rigorous theoretical support for the NPD estimation framework and demonstrate that the NPD spline estimators attain the same convergence rate as the global spline estimators obtained using the entire dataset and achieve the optimal nonparametric convergence rate established by Stone under some regularity conditions. To evaluate the efficacy of the proposed NPD method, we conduct simulation studies comparing it with several global nonparametric estimation methods used to smooth the 3D data. The results demonstrate the superior performance of the NPD method in accurately and efficiently processing and learning from 3D point clouds, highlighting its potential to advance large and complex data analysis. Supplementary material, which contains related technical details, proofs of the theoretical results, and additional results in simulation studies, is available online.