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Industry 4.0 has become one of the most dominant research areas in industrial science today. Many industrial machinery units do not have modern standards that allow for the use of image analysis techniques in their commissioning. Intelligent material handling, sorting, and object recognition are not possible with the machinery we have. We therefore propose a novel deep learning approach for existing robotic devices that can be applied to future robots without modification. In the implementation, 3D CAD models of the PCB relay modules to be recognized are also designed for the implantation machine. Alternatively, we developed and manufactured parts for the assembly of aluminum profiles using FDM 3D printing technology, specifically for sorting purposes. We also apply deep learning algorithms based on the 3D CAD models to generate a dataset of objects for categorization using CGI rendering. We generate two datasets and apply image-to-image translation techniques to train deep learning algorithms. The synthesis achieved sufficient information content and quality in the synthesized images to train deep learning algorithms efficiently with them. As a result, we propose a dataset translation method that is suitable for situations in which regenerating the original dataset can be challenging. The results obtained are analyzed and evaluated for the dataset. [ABSTRACT FROM AUTHOR]

The efficiency of natural language processing has improved dramatically with the advent of machine learning models, particularly neural network-based solutions. However, some tasks are still challenging, especially when considering specific domains. This paper presents a model that can extract insights from customer reviews using machine learning methods integrated into a pipeline. For topic modeling, our composite model uses transformer-based neural networks designed for natural language processing, vector-embedding-based keyword extraction, and clustering. The elements of our model have been integrated and tailored to better meet the requirements of efficient information extraction and topic modeling of the extracted information for opinion mining. Our approach was validated and compared with other state-of-the-art methods using publicly available benchmark datasets. The results show that our system performs better than existing topic modeling and keyword extraction methods in this task. [ABSTRACT FROM AUTHOR]

The average yield of maize is significantly dependent on the meteorological conditions of the growing year. Both the most favorable weather conditions and the weather anomalies that tend to cause damage depend on the given phenophase. The aim of this research is to analyze the climatic changes that are important in maize production in the Hajdúság region. For the climatological study of the area, homogenized temperature and precipitation data from the Hungarian Meteorological Service was used for the Debrecen region, which are freely available for download from the data repository of the institution. Trend analysis was performed for the last 50-year (1973-2022) and 30-year (1993-2022) periods. In total, 40 meteorological data series matching the study objective were analyzed. Linear regression calculations were performed using the SPSS 27 statistical software. For the non-parametric procedure, the MAKESENS Excel application was used, based on the Mann- Kendall (MK) test and Sen's slope estimation. This research shows that the choice of the length of the study period affects the results of trend analysis. The numerical values of the trend slope for the 30-year vs. 50-year period differ, and for some parameters there are also substantial differences (e.g., trend sign). The results of the parametric and non-parametric trend analyses differed only marginally for the temperature variables included. Also, for precipitation data that do not follow a normal distribution (e.g., monthly), there were only a few significant differences. The trend in mean annual temperature shows an increase of 0.39 and 0.52 °C in 10 years, and an increase of around 2 °C in 50 years and 1.5 °C in 30 years. There is a significant warming in both the summer and winter half-years, with the summer half-year showing a steeper upward trend in the 50-year data series and the winter half-year in the 30-year data series. There is a clear pattern of large, highly significant warming in the summer months and less significant changes in the two spring and two autumn months that were observed. A negative, non-significant trend in annual precipitation is observed. The decreases of 17 mm and 24 mm/10 years obtained for the 50- and 30-year time series are not negligible from a practical point of view. For the summer half-year, the precipitation amount is decreasing, with a slope of -27 mm/10 years for the last 30 years, but even this value is not significant due to the high variability. There is no significant change in the amount of precipitation in the winter half-year over the last decades. Significant trends cannot be detected from monthly or even semi-annual or annual precipitation data. The Mann-Kendall test showed a trend decrease only in the 30-year April data series at the p=0.1 significance level. Overall, the changes are negative for maize production. It should be highlighted that the obvious warming, combined with a slight decrease in precipitation, is leading to a deterioration in crop water availability and a reduction in crop yields. The impact of the identified adverse climatic changes can be compensated to a significant extent by the proposed agrotechnical responses. [ABSTRACT FROM AUTHOR]

An important concern with regard to the ensembles of algorithms is that using the individually optimal parameter settings of the members does not necessarily maximize the performance of the ensemble itself. In this paper, we propose a novel evaluation method for simulated annealing that combines dataset sampling and image downscaling to accelerate the parameter optimization of medical image segmentation ensembles. The scaling levels and sample sizes required to maintain the convergence of the search are theoretically determined by adapting previous results for simulated annealing with imprecise energy measurements. To demonstrate the efficiency of the proposed method, we optimize the parameters of an ensemble for lung segmentation in CT scans. Our experimental results show that the proposed method can maintain the solution quality of the base method with significantly lower runtime. In our problem, optimization with simulated annealing yielded an F 1 score of 0.9397 and an associated M C C of 0.7757. Our proposed method maintained the solution quality with an F 1 score of 0.9395 and M C C of 0.7755 while exhibiting a 42.01% reduction in runtime. It was also shown that the proposed method is more efficient than simulated annealing with only sampling-based evaluation when the dataset size is below a problem-specific threshold. [ABSTRACT FROM AUTHOR]

Aim: The term "Cuproptosis" was coined to describe a novel type of cell death triggered by intracellular copper buildup that is fundamentally distinct from other recognized types such as autophagy, ferroptosis, and pyroptosis in recent days. As the underlying mechanism was newly identified, its potential connection to pancreatic adenocarcinoma (PAAD) is still an open issue. Methods: A set of machine learning algorithms was used to develop a Cuproptosis-related gene index (CRGI). Its immunological characteristics were studied by exploring its implications on the expression of the immunological checkpoints, prospective immunotherapy responses, etc. Moreover, the sensitivity to chemotherapeutic drugs was predicted. Unsupervised consensus clustering was performed to more precisely identify different CRGI-based molecular subtypes and investigate the immunotherapy and chemotherapy efficacy. The expression of DLAT, LIPT1 and LIAS were also investigated, through real-time quantitative polymerase chain reaction (RTqPCR), western blot, and immunofluorescence staining (IFS). Results: A novel CRGI was identified and validated. Additionally, correlation analysis revealed major changes in tumor immunology across the high- and low-CRGI groups. Through an in-depth study of each medication, it was determined that the predictive chemotherapeutic efficacy of 32 regularly used anticancer drugs differed between high- and low-CRGI groups. The results of the molecular subtyping provided more support for such theories. Expressional assays performed at transcriptomic and proteomic levels suggested that the aforementioned Cuproptosis-related genes might serve as reliable diagnostic biomarkers in PAAD. Significance: This is, to the best of our knowledge, the first study to examine prognostic prediction in PAAD from the standpoint of Cuproptosis. These findings may benefit future immunotherapy and chemotherapeutic therapies. [ABSTRACT FROM AUTHOR]