The rapid market changes and strong product individualization create the need for great flexibility of manufacturing management on new foundations, such as the Industry 4.0 model. Digital manufacturing is the basis for Industry 4.0, that have the following dimensions: (i) digital manufacturing based on advanced digital-oriented technologies, (ii) smart products (advanced manufacturing mode and new characteristics), and (iii) smart supply - chain (procurement of raw materials and delivery of finished products). Bidirectional exchange of information in collaborative manufacturing, using it exchange also for digital platforms of design of the innovative products. In this paper we are showing developed model of one Serbian digital factory model for Quality 4.0 (Q 4.0) , especially in workshop as a part of ERP/MES model.

Digital technologies have been present in manufacturing and industry for a long time, and today they represent the basis for the application of the Industry 4.0 model in practice. The transformation marked by the Industry 4.0 model is based on the application of a large number of models of communication technologies, solutions for connecting entities (machines, parts, ...) in the factory within Internet of Things (IoT), as well as the possibility of big data analysis (BDA) throught the artificial intelligence tools (AI/ML). These technologies provide great opportunities to create more flexible and profitable manufacturing processes based on data, and using the Internet. As more and more factories and their equipment are IoT enabled, the volume of data will only grow, hence the application of digital technologies is inevitable. To truly pave the way forward to Industry 4.0 and beyond, manufacturing must evolve into cognitive manufacturing, which is the base model of smart manufacturing. This paper provides an overview of the development of the concept of digital manufacturing, from different aspects, with examples of application in real manufacturing. The goal is to create a good practice of digital manufacturing with the elements of Industry 4.0, as a basis for the development and application of the smart manufacturing (SM) model.

Purpose of review The forest industry has deployed ICTs to improve productivity of forest supply chains. In this sense, digitalization of data flows significantly impacts wood supply chains, from forest to mills, which must react flexibly to market fluctuations of forest products. The goal of this study was to conduct a literature review on data flow implementations in management systems of wood supply chains and to evaluate their applicability in supply chains to analyze the opportunities for improving them in practice. Recent findings We utilized the ScienceDirect database, Scopus and Web of Science in order to document data flows in systems and actual applications. Due to ongoing outsourcing, the wood procurement chains, and the wood supply chains were identified in the forest industry. Several different technologies can be implemented also in wood supply chains to digitalize the data flow depending on the specific needs of organizations. In this regard, digitalization of Big Data causes significant changes in elements of supply models that should be integrated and standardized in the optimization and simulation systems. Summary A modelling guide is suggested for accurate dynamic solution of problems of forest logistics. Then, the opportunities in digitalization for connecting different data flows, technologies, and stakeholders are discussed thoroughly. We conclude that data-driven dynamic optimization, and locally implemented digitalization contributes to wood supply modeling in the establishment of standards for cloud-based platforms at time-related and sequential measures which will ensure successful forest logistics through the planning and the monitoring of wood supply chains.

Event-related potentials (ERPs) are estimated by averaging time-locked single trial electroencephalography (EEG) signals in response to specific events or stimuli. Classifying ERPs accurately is a challenge because (a) single trials have poor signal-to-noise-ratios (SNRs) and (b) it is difficult to collect large single trial ensembles to generate high SNR ERPs for classifier training and testing. The m-subsample averaging (m-SA) strategy which generates small-sample ERPs by repeated averaging of a small number of single trials drawn without replacement, has been proposed as a solution to the two problems. An ERP formed by averaging m single trials is referred to as an m-ERP where m is referred to as the averaging parameter. In this study, we conduct thorough analyses of m-SA and focus on issues not addressed in previous studies to better understand the beneficial properties of m-SA and to further support its application for ERP classification. Specifically, we (a) analyze the improvement in SNR as a function of m using the mean-root-mean-square SNR and visual analyses of m-ERP plots with confidence intervals, (b) analyze the improvement in interclass separation as a function of m, (c) determine how the SNR and interclass separation analyses can help to select the averaging parameter m, (d) determine the number of distinct m-ERPs that can be drawn from a single-trial ensemble, and (e) determine several probabilities related to the generation of distinct m-ERPs. Furthermore, an extensive set of experiments are designed to analyze the performance of support vector machine and convolution neural network classifiers employing m-SA with various combinations of the averaging parameters used for generating the training and test sets. The results confirm that ERPs can be classified accurately using small subsample averaging. Most importantly, it is concluded that m-SA can be deployed in practice to accurately classify ERPs in brain activity research and in clinical applications without having to collect a prohibitively large number of single trials.

In this case series, we examine the impact of the Titan-iR Pro® deep spectrum biotherapeutic device on brain function in healthy individuals. Here we present 15 participants, aged 26-68 years, who enrolled in this open-label study, designed to assess the effect of a single 15-minute course of deep spectrum biotherapy on brain function. Although symptom-free upon enrollment, participants had diverse medical histories with some displaying early signs of cognitive decline. As part of the evaluation, all participants engaged in electroencephalogram (EEG) recording sessions that focused on assessing event-related potential (ERP) evaluations, including audio P300 amplitudes and latencies (speeds), neurocognitive testing, and physical reaction times. Following the Titan-iR Pro intervention an improvement in EEG-based cognitive markers was observed. These results underscore the importance of further investigation into the potential of advanced and deep spectrum photobiomodulation in the prevention and treatment of cognitive decline and other neurological disorders.

SHANK3 encodes scaffold protein involved in postsynaptic receptor density in glutamatergic synapses, including those in the parvalbumin (PV)+inhibitory neurons – the key players in generation of sensory gamma oscillations, such as 40-Hz auditory steady-state response(ASSR). Here we describe a clinical and neurophysiological phenotype of a 15-years old girl (SH01) with microduplication of 16389 bp in 22q13.33, affecting the SHANK3 gene in comparison to typically developing children (n=32). EEG were recorded during the binaurally presentation of 40-Hz clicks’ trains lasting for 500 ms with inter-trial intervals 500-800 ms. SH01 was diagnosed with mild mental retardation and learning disabilities(F70.88) and had problems with reading and writing, as well as smaller vocabulary than TD peers. Her clinical phenotype generally resembled the phenotype of previously described patients with 22q13.33 microduplication. SH01 had mild autistic symptoms but below the threshold for ASD diagnosis. No seizures or MRI abnormalities were reported. While SH01 had relatively preserved auditory event-related potential(ERP) with slightly attenuated P1, her 40-Hz ASSR was totally absent significantly deviating from TD’s ASSR. Absence of 40-Hz ASSR in patient with microduplication, affected SHANK3 gene, indicates deficient temporal resolution of the auditory system, that might underlie language problems, and represent neurophysiological biomarker of SHANK3 abnormalities.