Powerful Machine Research Articles

Образи Японії на сторінках катеринославської преси у роки Першої світової війни

The aim of the article is to analyze the reasons for the transformation of perceptions of Japan and the Japanese in Russian society from the image of an «enemy» during the Russo-Japanese War of 1904-1905 to an “ally” during the First World War and to deter- mine the components of these images based on the materials of the Katerinoslav press of 1914-1915. Results. As a result of the research, a number of articles from regional publications of the Katerinoslav region, stored in the collection of Dmytro Yavornitskiy Dnipropetrovsk National Historical Museum, and related to Russian-Japanese interactions during the First World War, were introduced into scientifi c circulation. Th e regional press of the Russian Empire, on the one hand, refl ected the government’s position, on the other hand, it was aimed at a reader who was less socially active and less inclined to analyze the geopolitical situation. Th e one who is the most interesting from the point of view of studying with the help of imagological tools, whom in the language of the time it was customary to call the term «everyman».Th e scientifi c novelty of this article lies in the fact that the analysis was based on a unique source complex of the provin- cial press, well preserved in the collection of the Dnipropetrovsk National Historical Museum named aft er D. I. Yavornytskyi. Th e provincial press is an excellent base for imagological intel- ligence regarding the generalized ideas of the most “average” representative of Russian society. Conclusions. Th e starting point for the formation of the image of Japan and the Japanese in Russian society during the First World War was the defeat of the Russian Empire in the Russo- Japanese War of the beginning of the 20th century, which was perceived very painfully, including by the people of Katerinoslav. Accordingly, Japan’s entry into the First World War on the side of the Entente (together with Russia) forced the Russian press to make a “180 degree turn” in rep- resentations of the Land of the Rising Sun. Japan appears in the provincial press of Katerinoslav as a proud country, a powerful military machine capable of opposing Germany in Tsingtao, a profi table trading partner and a supplier of weapons, which the Russian Empire desperately needed. In general, we can characterize the image of Japan through the prism of the Russian regional press as a benefi cial situational ally.

A Survey on Multi-Objective Based Parameter Optimization for Deep Learning

Deep learning models form one of the most powerful machine learning models for the extraction of important features. Most of the designs of deep neural models, i.e., the initialization of parameters, are still manually tuned. Hence, obtaining a model with high performance is exceedingly time-consuming and occasionally impossible. Optimizing the parameters of the deep networks, therefore, requires improved optimization algorithms with high convergence rates. The single objective-based optimization methods generally used are mostly time-consuming and do not guarantee optimum performance in allcases. Mathematical optimization problems containing multiple objective functions that must be optimized simultaneously fall under the category of multi-objective optimization sometimes referred to as Pareto optimization. Multi-objective optimization problems form one of the alternatives yet useful options for parameter optimization. However, this domain is a bit less explored. In this survey, we focus on exploring the effectiveness of multi-objective optimization strategies for parameter optimization in conjunction with deep neural networks. The case studies used in this study focus on how the twomethods are combined to provide valuable insights into the generation of predictions and analysis in multiple applications.

ScribbleDom: Using Scribble-Annotated Histology Images to Identify Domains in Spatial Transcriptomics Data.

Spatial domain identification is a very important problem in the field of Spatial Transcriptomics (ST). The state-of-the-art solutions to this problem focus on unsupervised methods, as there is lack of data for a supervised learning formulation. The results obtained from these methods highlight significant opportunities for improvement. In this paper, we propose a potential avenue for enhancement through the development of a semi-supervised convolutional neural network (CNN) based approach. Named ScribbleDom, our method leverages human expert's input as a form of semi-supervision, thereby seamlessly combines the cognitive abilities of human experts with the computational power of machines. ScribbleDom incorporates a loss function that integrates two crucial components: similarity in gene expression profiles and adherence to the valuable input of a human annotator through scribbles on histology images, providing prior knowledge about spot labels. The spatial continuity of the tissue domains is taken into account by extracting information on the spot micro-environment through convolution filters of varying sizes, in the form of Inception blocks. By leveraging this semi-supervised approach, ScribbleDom significantly improves the quality of spatial domains, yielding superior results both quantitatively and qualitatively. Our experiments on several benchmark datasets demonstrate the clear edge of ScribbleDom over state-of-the-art methods-between 1.82% to 169.38% improvements in Adjusted Rand Index (ARI) for 9 of the 12 Human DLPFC samples, and 15.54% improvement in the Melanoma cancer dataset. Notably, when the expert input is absent, ScribbleDom can still operate, in a fully unsupervised manner like the state-of-the-art methods, and produces results that remain competitive. Source code is available at Github (https://github.com/1alnoman/ScribbleDom) and Zenodo (https://zenodo.org/badge/latestdoi/681572669). Supplementary data are available at Bioinformatics online.

Power balance calculation of a mobile energy vehicle with a mounted adapter for harvesting bast crops

Bast crops are characterized by the content of strong bast fibers in the stems and a significant height of crops. The process of cleaning them involves the use of narrowly focused and powerful machines. The article considers the principle of distribution of the effective power of a self-propelled harvesting unit for bast crops. The evaluation of the components of the power balance will allow quantifying the power capabilities of the engine and the loading of the energy means in the unit with an adapter to perform the technological process for various cleaning conditions

A Hybrid Approach for Alluring Ads Phishing Attack Detection Using Machine Learning.

Phishing attacks are evolving with more sophisticated techniques, posing significant threats. Considering the potential of machine-learning-based approaches, our research presents a similar modern approach for web phishing detection by applying powerful machine learning algorithms. An efficient layered classification model is proposed to detect websites based on their URL structure, text, and image features. Previously, similar studies have used machine learning techniques for URL features with a limited dataset. In our research, we have used a large dataset of 20,000 website URLs, and 22 salient features from each URL are extracted to prepare a comprehensive dataset. Along with this, another dataset containing website text is also prepared for NLP-based text evaluation. It is seen that many phishing websites contain text as images, and to handle this, the text from images is extracted to classify it as spam or legitimate. The experimental evaluation demonstrated efficient and accurate phishing detection. Our layered classification model uses support vector machine (SVM), XGBoost, random forest, multilayer perceptron, linear regression, decision tree, naïve Bayes, and SVC algorithms. The performance evaluation revealed that the XGBoost algorithm outperformed other applied models with maximum accuracy and precision of 94% in the training phase and 91% in the testing phase. Multilayer perceptron also worked well with an accuracy of 91% in the testing phase. The accuracy results for random forest and decision tree were 91% and 90%, respectively. Logistic regression and SVM algorithms were used in the text-based classification, and the accuracy was found to be 87% and 88%, respectively. With these precision values, the models classified phishing and legitimate websites very well, based on URL, text, and image features. This research contributes to early detection of sophisticated phishing attacks, enhancing internet user security.

Estimated volume of eucalyptus plantations through ALOS satellite images

Estimates of wood volume, either in even or uneven-aged forests, is necessary for planning forest management, studies concerning carbon sequestration and natural resources conservation, as well as energy planning in regions where wood is the primary fuel for power generation. Such purposes mainly involve analysis of large areas, which leads to challenges that can be solved through remote sensing. Using multispectral and microwave data, respectively, from the AVNIR-2 and PALSAR sensors, both on board in the ALOS satellite, the wood volume of a commercial eucalyptus plantation located in the state of Minas Gerais, Brazil was estimated using artificial neural. Neural networks emerged as a powerful machine learning technique, capable of modeling intrinsically nonlinear relationships that are present in the data. Thus, the obtained estimates for wood volume in the eucalyptus plantations showed a correlation coefficient with the corresponding observed volumes (forest inventory) of 0.99, a square root of the quadratic error (RQEM) of 0.3% and errors with amplitude between -1 to 1%. The efficiency of artificial neural networks for estimating wood volume in homogeneous plantations has been proven. The multilayer perceptron neural network was efficient in modeling the complex relationships that exist between multispectral, microwave and wood volume data, producing highly accurate estimates. Only a small amount of plots was sufficient to train the artificial neural networks in order to estimate volume. In addition, the fast convergence obtained by the BFGS algorithm allowed a comprehensive data analysis.

Exposure-response relation for vibration-induced white finger: effect of different methods for predicting prevalence.

A pooled analysis of vibration-induced white finger (VWF) in population groups of workers has been performed using the results of a published meta-analysis as source material (Nilsson T, Wahlström J, Burström L. Hand-arm vibration and the risk of vascular and neurological diseases a systematic review and meta-analysis. PLoS One. 2017:12(7):e0180795. https://doi.org/10.1371/journal.pone.0180795). The methods of data selection follow those described previously by Scholz et al. (in Scholz MF, Brammer AJ, Marburg S. Exposure-response relation for vibration-induced white finger: inferences from a published meta-analysis of population groups. Int Arch Occup Environ Health. 2023a:96(5):757-770. https://doi.org/10.1007/s00420-023-01965-w) to enable comparison with the results of the present work. The analyzed epidemiologic studies contain different prevalences of VWF observed after different durations of employment involving exposure to the vibration of power tools and machines. These prevalences are transformed to 10% prevalence by either linear or polynomial (i.e. "S"-shaped curvilinear) interpolation in order to compare with the exposure-response relation contained in the relevant international standard (ISO 5349-1:2001). An exposure-response relation is constructed using regression analysis for the time (in years) to reach 10% prevalence in a population group, when subjected to a daily vibration exposure calculated according to the procedures specified in the standard, A(8). Good fits to the data are obtained when polynomial and linear prevalence interpolation is used. The 95-percentile confidence intervals (CIs) of the exposure-response relation predicted by polynomial prevalence interpolation lie at somewhat larger lifetime exposures than those obtained by linear prevalence interpolation. Uncertainty in the precision of polynomial prevalence interpolation is mitigated by giving equal weight to linear interpolation when interpreting the results. When the 95-percentile CIs of the exposure-response models obtained by linear and polynomial prevalence interpolation are used to define the most probable exposure-response relation, the resulting common range of values includes the ISO exposure-response relation. It is proposed that an exposure-response relation for the onset of VWF derived from a regression analysis is specified in terms of the lower limit of its CI. Hence, when exposure measures are constructed according to the ISO standard and equal weight is given to the results of the 2 methods for interpolating prevalence described here, the ISO exposure-response relation would be considered to provide a conservative estimate for a 10% prevalence of VWF to develop in a population group, at least for A(8) > 4 m/s2. It thus remains the relation to use for assessing exposure to hand-transmitted vibration in the workplace. Additional research is needed to resolve inconsistencies in the ISO method for calculating daily exposures.

Quantum and Quantum-Inspired Stereographic K Nearest-Neighbour Clustering.

Nearest-neighbour clustering is a simple yet powerful machine learning algorithm that finds natural application in the decoding of signals in classical optical-fibre communication systems. Quantum k-means clustering promises a speed-up over the classical k-means algorithm; however, it has been shown to not currently provide this speed-up for decoding optical-fibre signals due to the embedding of classical data, which introduces inaccuracies and slowdowns. Although still not achieving an exponential speed-up for NISQ implementations, this work proposes the generalised inverse stereographic projection as an improved embedding into the Bloch sphere for quantum distance estimation in k-nearest-neighbour clustering, which allows us to get closer to the classical performance. We also use the generalised inverse stereographic projection to develop an analogous classical clustering algorithm and benchmark its accuracy, runtime and convergence for decoding real-world experimental optical-fibre communication data. This proposed 'quantum-inspired' algorithm provides an improvement in both the accuracy and convergence rate with respect to the k-means algorithm. Hence, this work presents two main contributions. Firstly, we propose the general inverse stereographic projection into the Bloch sphere as a better embedding for quantum machine learning algorithms; here, we use the problem of clustering quadrature amplitude modulated optical-fibre signals as an example. Secondly, as a purely classical contribution inspired by the first contribution, we propose and benchmark the use of the general inverse stereographic projection and spherical centroid for clustering optical-fibre signals, showing that optimizing the radius yields a consistent improvement in accuracy and convergence rate.

Quantifying narrowing partition thickness in a vertical pneumatic separation channel for soybean seed cleaning

The paper highlights that the study of the soybean seed separation processes in a vertical deep aspirating channel contributes to the improvement of technical and economic indicators in grain cleaning equipment. (Research purpose) The research aims to substantiate the thickness of narrowing partitions in the vertical deep aspirating channel. (Materials and methods) A prototype of a vertical deep aspirating channel with three sections of varying height, was used for cleaning soybean seeds. The optimal thickness of the narrowing partitions in the channel has been justified. Impurity extraction completeness and seed separation efficiency within a vertical aspirating channel were assessed for soybean seed cleaning under maximum specific grain load conditions. (Results and discussion) The following parameters have been determined: the optimal thickness for the narrowing partitions is 70 millimeters under a maximum specific grain load of 5 kilograms per square centimeter per hour. Seed losses do not exceed 3 percent in the aspirating channel with narrowing partitions, with impurity extraction completeness of 74 percent for the «Waste» fraction. The efficiency of the separation process stands at 71 percent, and the purity level of the «Seeds» fraction reaches 81 percent in a single pass during the preliminary cleaning stage. (Conclusions) It has been established that the use of narrowing partitions in a vertical aspirating channel during soybean seed cleaning leads to an increase in the specific grain load up to 5 kilograms per square centimeter per hour, compared to 2 kilograms per square centimeter per hour achieved by traditional pneumatic separation channels. The proposed technical solution reduces soybean seed losses to 3 percent (compared to the initial requirements of no more than 10 percent) in a single pass and reduces machine power consumption from 4.5 to 2.25 kilowatt-hours.

Development and Validation of an XGBoost-Algorithm-Powered Survival Model for Predicting In-Hospital Mortality Based on 545,388 Isolated Severe Traumatic Brain Injury Patients from the TQIP Database.

Traumatic brain injury (TBI) represents a significant global health issue; the traditional tools such as the Glasgow Coma Scale (GCS) and Abbreviated Injury Scale (AIS) which have been used for injury severity grading, struggle to capture outcomes after TBI. This paper aims to implement extreme gradient boosting (XGBoost), a powerful machine learning algorithm that combines the predictions of multiple weak models to create a strong predictive model with high accuracy and efficiency, in order to develop and validate a predictive model for in-hospital mortality in patients with isolated severe traumatic brain injury and to identify the most influential predictors. In total, 545,388 patients from the 2013-2021 American College of Surgeons Trauma Quality Improvement Program (TQIP) database were included in the current study, with 80% of the patients used for model training and 20% of the patients for the final model test. The primary outcome of the study was in-hospital mortality. Predictors were patients' demographics, admission status, as well as comorbidities, and clinical characteristics. Penalized Cox regression models were used to investigate the associations between the survival outcomes and the predictors and select the best predictors. An extreme gradient boosting (XGBoost)-powered Cox regression model was then used to predict the survival outcome. The performance of the models was evaluated using the Harrell's concordance index (C-index). The time-dependent area under the receiver operating characteristic curve (AUC) was used to evaluate the dynamic cumulative performance of the models. The importance of the predictors in the final prediction model was evaluated using the Shapley additive explanations (SHAP) value. On average, the final XGBoost-powered Cox regression model performed at an acceptable level for patients with a length of stay up to 250 days (mean time-dependent AUC = 0.713) in the test dataset. However, for patients with a length of stay between 20 and 213 days, the performance of the model was relatively poor (time-dependent AUC < 0.7). When limited to patients with a length of stay ≤20 days, which accounts for 95.4% of all the patients, the model achieved an excellent performance (mean time-dependent AUC = 0.813). When further limited to patients with a length of stay ≤5 days, which accounts for two-thirds of all the patients, the model achieved an outstanding performance (mean time-dependent AUC = 0.917). The XGBoost-powered Cox regression model can achieve an outstanding predictive ability for in-hospital mortality during the first 5 days, primarily based on the severity of the injury, the GCS on admission, and the patient's age. These variables continue to demonstrate an excellent predictive ability up to 20 days after admission, a period of care that accounts for over 95% of severe TBI patients. Past 20 days of care, other factors appear to be the primary drivers of in-hospital mortality, indicating a potential window of opportunity for improving outcomes.

Enhancing LAN Failure Predictions with Decision Trees and SVMs: Methodology and Implementation

Predicting Local Area Network (LAN) equipment failure is of utmost importance to ensure the uninterrupted operation of modern communication networks. This study explores the use of machine learning algorithms to enhance the accuracy of equipment failure prediction in LAN environments. Using these algorithms to enhance LAN failure predictions involves collecting and analyzing network data, such as packet loss rates and latency, to identify patterns and anomalies. These algorithms can then predict potential LAN failures by recognizing early warning signs and deviations from normal network behavior. By leveraging machine learning, network administrators can proactively address issues, reduce downtime, and improve overall network reliability. In our study, two powerful machine learning algorithms—decision tree and support vector machine (SVM)—are used. To evaluate the effectiveness of the proposed models, a comprehensive dataset comprising various LAN equipment parameters and corresponding failure instances is utilized. The dataset is pre-processed to handle missing values and normalize features, ensuring the algorithms’ optimal performance. Performance metrics, such as accuracy, precision, recall, and F1-score, are employed to assess the predictive capabilities of the models. The excremental results of our study lead to more reliable and stable network operations by allowing early detection of potential issues and preventive maintenance. This leads to reduced downtime, improved network performance, and enhanced overall user satisfaction. They demonstrate the efficacy of both decision tree and SVM algorithms in accurately predicting LAN equipment failure.

Application of machine learning approaches in predicting clinical outcomes in older adults – a systematic review and meta-analysis

BackgroundMachine learning-based prediction models have the potential to have a considerable positive impact on geriatric care.DesignSystematic review and meta-analyses.ParticipantsOlder adults (≥ 65 years) in any setting.InterventionMachine learning models for predicting clinical outcomes in older adults were evaluated. A random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months.Outcome measuresStudies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric.ResultsThirty-seven studies that satisfied the systematic review criteria were appraised, and eight studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 – 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 – 0.86) for mortality over 6 months, signifying good discriminatory power.ConclusionThe meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes.

Study on hydroturbine power trend prediction based on machine learning

The application of computational fluid dynamics combined with 3D modeling of the hydraulic model was discussed in this paper. Through the complex calculation of the internal low field, dynamic simulation was used to predict the characteristics of water energy index. Based on the collection of a large number of different types of turbine operation data in different working conditions, the improved multi-layer neural network power prediction model based on the adaptive inverse normalization strategy and the improved grey wolf algorithm were used to optimize the least squares support vector machine power prediction model. Rlue activation function was used in the improved multi-layer neural network model uses to improve the computation speed and prevent gradient disappearance. Combined with L2 regularization to prevent data overfitting, Adam gradient optimizer was applied to achieve fast convergence of network output. The simulation results shown that the method can realize the high precision prediction of turbine output power.

AI Ethics Beyond the Anglo-Analytic Approach

That artificial intelligence (AI), algorithms, and related technologies could use a few good booster shots of “humanism” is widely apparent. In both program code and implementation, AI and algorithms have been accused of harbouring deep-seated flaws that conflict with human values. They are prime examples of the skew towards white, Western, men and demonstrate the bankruptcy in the face of neoliberalist, profit- and market-oriented social paradigms that this special issue seeks to address. Currently, computer scientists and AI researchers who are looking to remedy these problems are often in favour of more data, more powerful machines, more complex algorithms—in short, that we should fix problems with AI by building better AI. In this view human beings and the world can be modelled in code—our lives, interactions, society, and our very selves can be broken down into data points which can be assessed by highly advanced technologies. When these scientists and researchers seek to broaden their approach they often look to philosophy. However, the philosophy they look to is overwhelmingly Anglo-analytic, which views the world in extremely similar ways. Both AI and Anglo-analytic philosophy argue for solutions to humanistic problems which are essentially mathematical. They share in seeing important concepts, such as persons, emotions, agency, and ethics, as mechanistic, atomistic, and calculable. In this paper I will argue that Classical Chinese philosophy offer insightful resources for addressing the humanist problems in AI. Rather than arguing for mathematical solutions, or envisioning persons, emotions, agency, and ethics, as other rigid, atomistic, and mechanistic approaches, Chinese philosophy emphasizes transformation, interrelatedness, and correlative developments. Accordingly, it offers tools for appreciating the world, society, and ourselves as spontaneous, complex, and full of tension. AI can be programmed and used in ways that do not reduce the complexity and conflict in the world, but provide us instead with tools to make sense of it—tools that are humanistic in nature. To this end, Chinese philosophy can be a helpful collaborative partner.

Quantitative analysis of high-frequency activity in neonatal EEG

ObjectiveTo determine the presence and potential utility of independent high-frequency activity recorded from scalp electrodes in the electroencephalogram (EEG) of newborns. MethodsWe compare interburst intervals and continuous activity at different frequencies for EEGs retrospectively recorded at 256 Hz from 4 newborn groups: 1) 36 preterms (<32 weeks' gestational age, GA); 2) 12 preterms (32–37 weeks' GA); 3) 91 healthy full terms; 4) 15 full terms with hypoxic-ischemic encephalopathy (HIE). At 4 standard frequency bands (delta, 0.5–3 Hz; theta, 3–8 Hz; alpha, 8–15 Hz; beta, 15–30 Hz) and 3 higher-frequency bands (gamma1, 30–48 Hz; gamma2, 52–99 Hz; gamma3, 107–127 Hz), we compared power spectral densities (PSDs), quantitative features, and machine learning model performance. Feature selection and further machine learning methods were performed on one cohort. ResultsWe found significant (P < 0.01) differences in PSDs, quantitative analysis, and machine learning modelling at the higher-frequency bands. Machine learning models using only high-frequency features performed best in preterm groups 1 and 2 with a median (95% confidence interval, CI) Matthews correlation coefficient (MCC) of 0.71 (0.12–0.88) and 0.66 (0.36–0.76) respectively. Interburst interval-detector models using both high- and standard-bandwidths produced the highest median MCCs in all four groups.High-frequency features were largely independent of standard-bandwidth features, with only 11/84 (13.1%) of correlations statistically significant. Feature selection methods produced 7 to 9 high-frequency features in the top 20 feature set. ConclusionsThis is the first study to identify independent high-frequency activity in newborn EEG using in-depth quantitative analysis. Expanding the EEG bandwidths of analysis has the potential to improve both quantitative and machine-learning analysis, particularly in preterm EEG.

Navigating the Challenges and Opportunities of Literary Translation in the Age of AI: Striking a Balance Between Human Expertise and Machine Power

This paper explores the evolving role of teachers and literary translators in the era of artificial intelligence and machine translation and addresses the challenges encountered in literary translation. It focuses on achieving a balance between human expertise and machine power to ensure faithful translations of literary works while preserving their inherent artistic value. The study analyses four translations of an excerpt from the short story “Lake Como” by Serbian writer Srđan Valjarević. These translations, from Serbian to English, were conducted in groups by third-year Anglistics students at the Faculty of Philology, Sinergija University in Bijeljina, whose native language is Serbian. The quality and accuracy of the translations were assessed, accompanied by detailed justifications of our preference judgments. A comparison was made between our assessments and those generated by Chat GPT-3, aiming to provide insights into the advancements made by AI in the realm of literary translation. This comprehensive evaluation of translations, encompassing both human translators and AI-powered language models (LLMs), offers a deeper understanding of the specific strengths and weaknesses exhibited by LLMs in the context of literary translation. Through this human evaluation process, we strive to shed light on the specific areas where LLM translators excel and identify the challenges they are still facing. The findings contribute to a deeper understanding of AI's impact on literary translation and pave the way for future research and advancements in this area.

Analysis of Results of Experimental Study of Working Processes of a Positive-Displacement Piston Hybrid Power Machine Having Two Suction Valves with Change of Compressed Gas Discharge Pressure

Analysis of Results of Experimental Study of Working Processes of a Positive-Displacement Piston Hybrid Power Machine Having Two Suction Valves with Change of Compressed Gas Discharge Pressure

Reducing the Fuel Consumption of an Hybrid Electric Vehicle With the Use of Model Predictive Control-Case Study

The article presents operational aspects related <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">to energy management strategy (EMS) - model predictive control (MPC). The mathematical model showing the synergy of working machines of the hybrid electric vehicle (HEV) was presented. Then the results of road tests have been shown. They were based on the factory control of the above-mentioned system. The results of the operating parameters of the system according to the factory control and the results of the operating parameters according to the MPC with different linear quadratic tracking (LQT) curves were compared. Application of thirteen reference LQT trajectories results in lower fuel consumption of the HEV. It led to changes in the power and the battery state of charge (SOC): for factory control - from 50.1% (the beginning) to 56.1% (the end of course) and for MPC from 50.1% (the beginning) to 59.9% (the end of the course). The applied MPC with 13 reference trajectories (LQT) of power machines of the series-parallel HEV allowed for fuel savings on the level of 4%.

XGBoost Analysis based on Consumer Behavior

With the rapid development of the Internet and e-commerce, a large amount of consumer data has become available, which allows us to better understand consumer preferences and purchasing trends. XGBoost (Extreme Gradient Boosting) is a powerful machine learning algorithm that performs well when dealing with large data sets and complex features. This article first introduces the basic principles of the XGBoost algorithm, and then discusses in detail how to apply it to consumer behavior analysis. This paper uses real consumer data set, including multi-dimensional information such as customer identification, product identification and customer purchasing behavior. By building the XGBoost model, we are able to identify important features, predict consumer purchase intentions, and provide personalized recommendations. In addition, the performance evaluation and optimization methods of the model are discussed to ensure its accuracy and practicability. Finally, we summarize the main findings of this study, highlighting the potential applications of XGBoost analytics based on consumer behavior in marketing and business decisions. By digging deeper into consumer behavior data, businesses can better meet customer needs, improve sales efficiency, and achieve sustainable competitive advantage, and this research provides strong support for the use of machine learning techniques to optimize market strategies.

Calculation of Cooling Process for Walls in a Working Chamber of Positive Displacement Hybrid Reciprocating Power Machine with Regenerative Heat Exchange Based Using Heat Balance Equation

Calculation of Cooling Process for Walls in a Working Chamber of Positive Displacement Hybrid Reciprocating Power Machine with Regenerative Heat Exchange Based Using Heat Balance Equation