Precision Demands Research Articles

Revolutionizing Retail: Machine Learning Applications for Enhanced Customer Experience and Operational Efficiency

In today's highly competitive retail landscape, businesses are constantly seeking innovative ways to enhance customer experience while optimizing operational efficiency. This paper explores the transformative potential of machine learning (ML) applications in addressing this dual challenge. Leveraging diverse datasets encompassing customer behaviours, inventory management, and supply chain operations, our research employs state-of-the-art ML algorithms to develop predictive models and personalized recommendation systems. Our methodology demonstrates the ability of ML to analyze vast amounts of data in real-time, leading to precise demand forecasting, dynamic pricing strategies, and tailored customer experiences. Through a comprehensive evaluation, we reveal significant improvements in revenue generation and cost reduction. Moreover, the discussion highlights the ethical considerations and challenges associated with ML adoption in retail. In conclusion, this study underscores the pivotal role of machine learning in redefining the retail landscape, offering a competitive edge through data-driven insights, and ultimately revolutionizing the industry.

Machine Learning Fusion and Data Analytics Models for Demand Forecasting in the Automotive Industry: A Comparative Study

Demand forecasting is a crucial aspect of managing the supply chain, as it helps companies optimize inventory levels and minimize expenses related to inventory shortages. In recent years, machine learning (ML) algorithms have gained popularity for demand forecasting, as they can handle large and complex datasets and provide accurate predictions. Precise demand prediction for car brands is vital for companies to minimize costs and prevent inventory shortages. The demand for distributing cars is a critical component of inventory management. However, estimating demand for new car sales is difficult due to its continuous nature. To address this challenge, a study was conducted to train, test, and compare the performance of five machine learning algorithms (Random Forest, Multiple Linear Regression, k-Nearest Neighbors, Extreme Gradient Boosting, and Support Vector Machine) using a benchmark dataset. Among all the experiments, the Support Vector Machine algorithm achieved the highest accuracy score of 71.42%. Moreover, Multiple Linear Regression performed well, with an accuracy score of 66.66%. On the other hand, the Extreme Gradient Boosting algorithm had the lowest accuracy score of 42.85%. All experiments used a train-test split of 7525.

Analysis of Various Metals in Comparison using Additive Manufacturing and 3D Printing Technologies

Abstract: Now a day’s as additive manufacturing (or 3D printing) technology has advanced, several forming technologies now have unique traits and classification methods. The main forming methods for metal parts include, SLM, WAAM, EBM and DMLS technologies, depending on the various deposition states of the forming materials. It is clear from a comparison of several metal additive manufacturing processes that none of the technologies discussed above have yet been able to satisfy the demands for high precision, superior mechanical characteristics, and affordability. Each procedure' benefits and drawbacks are examined, and the application fields are heavily introduced

Analysis of Various Metals in Comparison Using Additive Manufacturing and 3D Printing Technologies

Abstract: Now a day’s as additive manufacturing (or 3D printing) technology has advanced, several forming technologies now have unique traits and classification methods. The main forming methods for metal or non-metal (polymer) parts include SLA, SLS, SLM, WAAM, and EBM technologies, depending on the various deposition states of the forming materials. It is clear from a comparison of several metal additive manufacturing processes that none of the technologies discussed above have yet been able to satisfy the demands for high precision, superior mechanical characteristics, and affordability. Each procedure' benefits and drawbacks are examined, and the application fields are heavily introduced.

Plasma noise in TianQin time-delay interferometry

TianQin is a proposed geocentric space-based gravitational wave observatory mission, which requires time-delay interferometry (TDI) to cancel laser frequency noise. With high demands for precision, solar-wind plasma environment at $\sim 10^5$ km above the Earth may constitute a non-negligible noise source to laser interferometric measurements between satellites, as charged particles perturb the refractivity along light paths. In this paper, we first assess the plasma noises along single links from space-weather models and numerical orbits, and analyze the time and frequency domain characteristics. Particularly, to capture the plasma noise in the entire measurement band of $10^{-4} - 1$ Hz, we have performed additional space-weather magnetohydrodynamic simulations in finer spatial and temporal resolutions and utilized Kolmogorov spectra in high-frequency data generation. Then we evaluate the residual plasma noises of the first- and second-generation TDI combinations. Both analytical and numerical estimations have shown that under normal solar conditions the plasma noise after TDI is less than the secondary noise requirement. Moreover, TDI is shown to exhibit moderate suppression on the plasma noise below $\sim 10^{-2}$ Hz due to noise correlation between different arms, when compared with the secondary noise before and after TDI.

An efficient method for train–track–substructure dynamic interaction analysis by implicit-explicit integration and multi-time-step solution

In this work, a method is put forward to obtain the dynamic solution efficiently and accurately for a large-scale train–track–substructure (TTS) system. It is called implicit-explicit integration and multi-time-step solution method (abbreviated as mI-nE-MTS method). The TTS system is divided into train–track subsystem and substructure subsystem. Considering that the root cause of low efficiency of obtaining TTS solution lies in solving the algebraic equation of the substructures, the high-efficient Zhai method, an explicit integration scheme, can be introduced to avoid matrix inversion process. The train–track system is solved by implicitly Park method. Moreover, it is known that the requirement of time step size differs for different sub-systems, integration methods and structural frequency response characteristics. A multi-time-step solution is proposed, in which time step size for the train–track subsystem and the substructure subsystem can be arbitrarily chosen once satisfying stability and precision demand, namely the time spent for m implicit integral steps is equal to n explicit integral steps, i.e., mI = nE as mentioned above. The numerical examples show the accuracy, efficiency, and engineering practicality of the proposed method.

Formation optimization of various spacing configurations for a fleet of unmanned surface vehicles based on a hydrodynamic energy-saving strategy

The purpose of this study is to optimize an energy-saving formation of a fleet of unmanned surface vehicles (USVs) for minimum energy consumption where the wave relies on its own hydrodynamic behavior. This work also provides new insights into the mechanisms of energy-saving formations based on the spacing configurations in terms of longitudinal offset and transverse separation. The numerical model is validated to meet precision demands with a prototype experimental test using a fleet of USVs. The echelon formation is constructed within the aft wedge wave pattern region considering the flow expansion of the Kelvin wave. A body force model is applied to replicate the impact of the propeller, and a mesh progressive refinement is executed to divide the functional zones of the computational domain. Energy consumption equations of formations are proposed to quantify the energy profit relationship between individual vehicles and fleets. The results highlight that the resistance component greatly contributes to the energy consumption, and that the hydrodynamic behavior mechanics impact the energy-saving capacity of the fleet. To achieve minimum energy consumption, the formation configurations are optimized based on the spacing partition to achieve energy-savings for individual vehicles and the whole fleet. These findings can be utilized for future energy-savings when using USV's for a variety of purposes, such as hydrographic surveying, data collection, reconnaissance, and general surveillance.

Melatonin signalling in Schwann cells during neuroregeneration.

It has widely been thought that in the process of nerve regeneration Schwann cells populate the injury site with myelinating, non–myelinating, phagocytic, repair, and mesenchyme–like phenotypes. It is now clear that the Schwann cells modify their shape and basal lamina as to accommodate re–growing axons, at the same time clear myelin debris generated upon injury, and regulate expression of extracellular matrix proteins at and around the lesion site. Such a remarkable plasticity may follow an intrinsic functional rhythm or a systemic circadian clock matching the demands of accurate timing and precision of signalling cascades in the regenerating nervous system. Schwann cells react to changes in the external circadian clock clues and to the Zeitgeber hormone melatonin by altering their plasticity. This raises the question of whether melatonin regulates Schwann cell activity during neurorepair and if circadian control and rhythmicity of Schwann cell functions are vital aspects of neuroregeneration. Here, we have focused on different schools of thought and emerging concepts of melatonin–mediated signalling in Schwann cells underlying peripheral nerve regeneration and discuss circadian rhythmicity as a possible component of neurorepair.

Online Unified Solution for Selective Harmonic Elimination Based on Stochastic Configuration Network and Levenberg–Marquardt Algorithm

In this article, an online unified solution based on stochastic configuration network (SCN) and Levenberg– Marquardt (LM) algorithm is proposed to generate optimal switching angles for selective harmonic elimination (SHE) in both the symmetric and asymmetric cascaded H-bridge (CHB) multilevel inverters (MLIs). Different from the traditional neural network framework for SHE, this unified solution uses SCN to only generate initial values of the switching angles online, which significantly reduce the precision demand on training SCN and avoids the local optima problem of gradient descent algorithm. After obtaining the initial values from SCN online, the LM algorithm can be used to rapidly solve the exact switching angles for SHE, which guarantees the solving efficiency and precision of the final solutions. Moreover, the stability of the proposed method is proved via Newton-like convergence theory. Compared with intelligent search algorithms and look-up table method, the proposed solution can not only online generate optimal theoretical switching angles but with much fewer data storage space. Experimental results of both symmetric and asymmetric MLI cases are presented to validate the correctness and online applicability of the proposed solution.

Resource-demand Estimation for Edge Tensor Processing Units

Machine learning has shown tremendous success in a large variety of applications. The evolution of machine-learning applications from cloud-based systems to mobile and embedded devices has shifted the focus from only quality-related aspects towards the resource demand of machine learning. For embedded systems, dedicated accelerator hardware promises the energy-efficient execution of neural network inferences. Their precise resource demand in terms of execution time and power demand, however, is undocumented. Developers, therefore, face the challenge to fine-tune their neural networks such that their resource demand matches the available budgets. This article presents Precious , a comprehensive approach to estimate the resource demand of an embedded neural network accelerator. We generate randomised neural networks, analyse them statically, execute them on an embedded accelerator while measuring their actual power draw and execution time, and train estimators that map the statically analysed neural network properties to the measured resource demand. In addition, this article provides an in-depth analysis of the neural networks’ resource demands and the responsible network properties. We demonstrate that the estimation error of Precious can be below 1.5% for both power draw and execution time. Furthermore, we discuss what estimator accuracy is practically achievable and how much effort is required to achieve sufficient accuracy.

Echo-Signal De-Noising of CO2-DIAL Based on the Ensemble Empirical Mode Decomposition

The carbon dioxide (CO2) differential absorption lidar echo signal is susceptible to noise and must satisfy the high demand for signal-retrieval precision. Thus, a proper de-noising method should be selected to improve the inversion result. In this paper, we simultaneously decompose three signal pairs into different intrinsic mode functions (IMFs) using the method of ensemble empirical mode decomposition (EEMD). Further, the correlation coefficients of the IMFs with the same temporal scale are regarded as the criterion to determine the components that need removal. This method not only retains the useful information effectively but also removes the noise component. A significant improvement in the R2 of the differential absorption optical depth (DAOD) of the de-noised signals is obtained. The results of the simulated and observed analysis signal demonstrated improvement both in the SNR and in the retrieval precision.

Design and Development of Array POS for Airborne Remote Sensing Motion Compensation

Multi-antenna airborne remote sensing systems have received more attention recently because they can realize high-resolution three-dimensional (3-D) imaging, such as array Synthetic Aperture Radar (SAR). Their high-precision imaging needs multi-antenna motion and relative motion between antennas. However, the existing facility and technology hardly meet the motion measurement precision demand of array SAR. To solve this problem, an array Position and Orientation System (POS) for airborne remote sensing motion compensation is designed and developed. It is composed of a high-precision POS, several small-size Inertial Measurement Units (IMU), and a 6-D deformation measurement system based on Fiber Bragg Grating (FBG) sensors. Firstly, the transfer alignment method based on 6-D deformation is used to measure the relative motion between array POS. Then, the motion conversion method from array POS to array SAR is presented to obtain the multi-antenna motion and relative motion between antennas. Finally, the ground experiment results identify that the accuracies of multi-antenna position, multi-antenna attitude, and flexible baseline length between antennas are superior to 3 cm, 0.01°, and 0.1 mm, respectively, which can meet the motion measurement precision demand of array SAR.

Modeling and analysis of reluctance motion system with asymmetrical air gaps.

To achieve high throughput and efficiency, semiconductor photolithography machines need an actuation system that can meet high acceleration and precision demands on the nanoscale. One available solution is the reluctance actuator, which provides higher acceleration and force output than the standard Lorentz actuator. A floating stage with air-bearings is used to eliminate friction in the photolithography process; however, vibration transfer is not entirely eliminated, leading to potential misalignment and asymmetries between the actuator elements. With asymmetrical offsets between mover elements, the output force can be greatly affected. This paper shows a method for estimating the force of various asymmetrical cases for the C-core reluctance actuator. Analytical models are developed and further improved through polynomial curve fitting using precomputed finite element simulation results from Comsol Multiphysics (COMSOL) to achieve more optimal solutions. An experiment verified the results of the force estimation equations, which were within ∼11% for different cases of asymmetric air gaps. This contribution will lead to a design for a control system that will overcome the issue of asymmetries or other altered states.

Adolescent Ruthlessness and the Transitioning Of The Analyst’s Mind

Features of ruthlessness may come into play in the encounter between adolescents who come into the therapeutic space with clear and precise demands to be supported through their gender transition. Winnicott's concept of ruthlessness, extended from the infant-mother matrix to the emotional situation of the clinic, allows a consideration of the conditions under which the analyst can think about adolescent demands. These conditions involve the desire of both patient and analyst for certitude and the analyst's urgency to respond, as well as the adolescent's contradictory desires to both destroy and create gender. The work of Sally Swartz, who brought to Winnicott's conception a notion of ruthlessness in protest, helps us consider the qualities of ruthlessness in constituting gender. A snippet of work with a nonbinary patient shows how questions of gender cannot be understood apart from the intersubjective transferential field. Tying ruthlessness to the enigma of desire illuminates the emotional situation of the clinical encounter between the nonbinary or trans patient and the analyst, a situation that is also libidinal. An analytic move from the question of gender identity to the realm of an emotional situation allows the analyst to meet adolescent ruthlessness. This meeting is an ethical attempt to understand the other, but also reveals one's resistance to giving up something in order to understand.

Evaluating the Abnormality of Bilateral Motor Cortex Activity in Subacute Stroke Patients Executing a Unimanual Motor Task With Increasing Demand on Precision

Abnormal contralesional M1 activity is consistently reported in patients with compromised upper limb and hand function after stroke. The underlying mechanisms and functional implications of this activity are not clear, which hampers the development of treatment strategies targeting this brain area. The goal of the present study was to determine the extent to which contralesional M1 activity can be explained by the demand of a motor task, given recent evidence for increasing ipsilateral M1 activity with increasing demand in healthy age-matched controls. We hypothesized that higher activity in contralesional M1 is related to greater demand on precision in a hand motor task. fMRI data were collected from 19 patients with ischemic stroke affecting hand function in the subacute recovery phase and 31 healthy, right-handed, age-matched controls. The hand motor task was designed to parametrically modulate the demand on movement precision. Electromyography data confirmed strictly unilateral task performance by all participants. Patients showed significant impairment relative to controls in their ability to perform the task in the fMRI scanner. However, patients and controls responded similarly to an increase in demand for precision, with better performance for larger targets and poorer performance for smaller targets. Patients did not show evidence of elevated ipsilesional or contralesional M1 blood oxygenation level-dependent (BOLD) activation relative to healthy controls and mean BOLD activation levels were not elevated for patients with poorer performance relative to patients with better task performance. While both patients and healthy controls showed demand-dependent increases in BOLD activation in both ipsilesional/contralateral and contralesional/ipsilateral hemispheres, patients with stroke were less likely to show evidence of a linear relationship between the demand on precision and BOLD activation in contralesional M1 than healthy controls. Taken together, the findings suggest that task demand affects the BOLD response in contralesional M1 in patients with stroke, though perhaps less strongly than in healthy controls. This has implications for the interpretation of reported abnormal bilateral M1 activation in patients with stroke because in addition to contralesional M1 reorganization processes it could be partially related to a response to the relatively higher demand of a motor task when completed by patients rather than by healthy controls.

Machine learning predicts electrospray particle size

Electrospraying (ES) is a state-of-the-art processing technique with the promise of achieving key nanotechnology and contemporary manufacturing needs. As a versatile technique, ES can produce particles with different sizes, morphologies, and porosities by tuning a list of experiment parameters. However, this level of precision demands an exhaustive trial-and-error approach, at high costs and heavily relies on processing expertise. The present study demonstrates how machine learning (ML) can expedite the optimization process by accurately predicting particle diameter, for both nano- and micron-sized particles. This was achieved by constructing an informative electrospraying database containing 445 records from the literature, followed by the development of predictive ML models. Feature engineering techniques were explored, where ultimately it was found that solvent physiochemical properties as the molecular representation and data with imputation provided models the highest performance. The top two models were XGBoost and Random Forest (RF), which yielded root-mean-squared errors (RMSE) of 3.91 μm and 6.19 μm evaluated by 5-fold cross-validation (CV), respectively. These models were experimentally validated in-house with different combinations of experiment parameters, where RMSE between the predicted and actual particle size was found to be 1.30 μm for the XGBoost model and 1.62 μm for the RF model. Therefore, it was concluded that data generated by the ES literature, in addition to being both cost- and material-free, can yield high-performing ML models for predicting particle size. The ML models were also consulted to determine the key processing parameters that govern particle size, where it was concluded that the models learnt similar attributes identified by scaling laws.

A novel artificial neural network model for forecasting electricity demand enhanced with population-weighted temperature mean and the unemployment rate

Precise electricity demand forecasting has principal significance in the energy production planning of the developing countries. Especially during the last decade, numerous recent methods have been utilized to predict the forthcoming electricity demand in different time resolutions accurately. This contribution presents a novel approach, which improves the forecasting of Turkey’s electricity demand in monthly time resolution. An artificial neural network model has been proposed with appropriate input features. Yearly-based gross demand shows approximately linear increment due to population increase and economic growth, while monthly-based gross demand indicates an oscillation due to the effect of seasonal temperature fluctuations. However, there is no clear linear relation between electricity demand and temperature; for the ideal case, it is the V-shaped curve around balance point temperature. Since temperature levels in each region of the country demonstrate a high variance even in the same time period, weighted average temperature point was calculated with respect to the population weights of the selected regions of Turkey. In order to fit a function for monthly oscillations, a linear function according to weighted average temperature point was created. Unemployment data was added to the training data set as an indicator of economic fluctuations. The mean absolute percentage errors of the model were calculated for training, validation, and testing as 3.77 %, 2.02 %, and 1.95 % respectively.

Husserl on Significance at the Core of Meaning

I reconstruct the notion of significance [Sinnhaftigkeit] in the later Husserl, with attention to his conceptions of judgment and transcendental logic. My analysis is motivated by the idea that an account of significance can help to connect analytic, Anglo-American conceptions of meaning as a precise, law-governed phenomenon investigated via linguistic analysis and Continental European conceptions of meaning in a broader “existential” sense. I argue that Husserl’s later work points to a transcendental-logical conception of a founding level of significance [Sinnhaftigkeit] prior to language, and that this conception meets characteristically analytic demands for precision and governance by logical constraints. At the same time, since it is based in descriptions of perceptual intentionality at the level of essential possibility, it leaves room for an account of meaning as a partially undetermined phenomenon of lived experience, and not just of our language and concepts, and thereby meets the characteristically Continental demand to take at face value meaning’s vagueness and indeterminacy in everyday human life.

Tourism demand forecasting with spatiotemporal features

Tourism demand forecasting is a crucial prerequisite for effective and efficient tourism management. This study develops a novel model based on deep learning methods for precise demand forecasting, namely, spatial-temporal fused graph convolutional network (ST-FGCN). ST-FGCN generates forecasts based on spatial effects extracted using graph convolutional network and temporal dependency captured through long short-term memory. A data-driven spatial matrix is used in our model to strengthen forecasting performance further. Two markedly different forecasting experiments verify the effectiveness of our model. Empirical results suggest that incorporating spatial effects can remarkably reduce forecasting errors. Furthermore, our model shows good applicability for data with different time granularity and different periods: before and during the COVID-19 pandemic.

Multi‐hour ahead forecasting of building energy through a new integrated model

AbstractThe increase in electricity demand requires improved energy planning programs, which involve better energy distribution. Therefore, precise energy demand forecasting is of great importance for optimizing energy distribution. In this respect, a new hybrid forecasting model was proposed in this study for multi‐hour forecasting of total energy requirement (cooling and heating load of buildings) in three different regions in Algeria with different climate conditions. The proposed models are based on two main steps: In the first step, time‐series data were decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) into a different intrinsic function (IMFs), and then extreme learning machine (ELM) is employed as essence predictors. The proposed CEEMDAN‐ELM model uses the decomposed IMFs as input data, and the total energy concern as a desired output. The integrated CEEMDAN‐ELM model was evaluated and validated on three different databases each of which had 2 years of measurement on an hourly scale. Experimental results show that the hybridization mechanism CEEMDAN‐ELM outperform the stand‐alone model (ELM) in terms of forecasting errors over the entire forecasting horizon. Forecasting results of CEEMDAN‐ELM led to a normalized root mean square error (nRMSE) in the range of [0.71–4.66] for all studied regions and horizons whereas conventional ELM provides an nRMSE in the range of [0.93–16.89].