Data-Driven Insights through Industrial Retrofitting: An Anonymized Dataset with Machine Learning Use Cases

Small and medium-sized enterprises (SMEs) often encounter practical challenges and limitations when extracting valuable insights from the data of retrofitted or brownfield equipment. The existing literature fails to reflect the full reality and potential of data-driven analysis in current SME environments. In this paper, we provide an anonymized dataset obtained from two medium-sized companies leveraging a non-invasive and scalable data-collection procedure. The dataset comprises mainly power consumption machine data collected over a period of 7 months and 1 year from two medium-sized companies. Using this dataset, we demonstrate how machine learning (ML) techniques can enable SMEs to extract useful information even in the short term, even from a small variety of data types. We develop several ML models to address various tasks, such as power consumption forecasting, item classification, next machine state prediction, and item production count forecasting. By providing this anonymized dataset and showcasing its application through various ML use cases, our paper aims to provide practical insights for SMEs seeking to leverage ML techniques with their limited data resources. The findings contribute to a better understanding of how ML can be effectively utilized in extracting actionable insights from limited datasets, offering valuable implications for SMEs in practical settings.

Machine Learning for Industry 4.0: A Systematic Review Using Deep Learning-Based Topic Modelling

Machine learning (ML) has a well-established reputation for successfully enabling automation through its scalable predictive power. Industry 4.0 encapsulates a new stage of industrial processes and value chains driven by smart connection and automation. Large-scale problems within these industrial settings are a prime example of an environment that can benefit from ML. However, a clear view of how ML currently intersects with industry 4.0 is difficult to grasp without reading an infeasible number of papers. This systematic review strives to provide such a view by gathering a collection of 45,783 relevant papers from Scopus and Web of Science and analysing it with BERTopic. We analyse the key topics to understand what industry applications receive the most attention and which ML methods are used the most. Moreover, we manually reviewed 17 white papers of consulting firms to compare the academic landscape to an industry perspective. We found that security and predictive maintenance were the most common topics, CNNs were the most used ML method and industry companies, at the moment, generally focus more on enabling successful adoption rather than building better ML models. The academic topics are meaningful and relevant but technology focused on making ML adoption easier deserves more attention.

Industrial Needs in the Fields of Artificial Intelligence, Internet of Things and Edge Computing

Industry 4.0 corresponds to the Fourth Industrial Revolution, resulting from technological innovation and research multidisciplinary advances. Researchers aim to contribute to the digital transformation of the manufacturing ecosystem both in theory and mainly in practice by identifying the real problems that the industry faces. Researchers focus on providing practical solutions using technologies such as the Industrial Internet of Things (IoT), Artificial Intelligence (AI), and Edge Computing (EC). On the other hand, universities educate young engineers and researchers by formulating a curriculum that prepares graduates for the industrial market. This research aimed to investigate and identify the industry's current problems and needs from an educational perspective. The research methodology is based on preparing a focused questionnaire resulting from an extensive recent literature review used to interview representatives from 70 enterprises operating in 25 countries. The produced empirical data revealed (1) the kind of data and business management systems that companies have implemented to advance the digitalization of their processes, (2) the industries' main problems and what technologies (could be) implemented to address them, and (3) what are the primary industrial needs and how they can be met to facilitate their digitization. The main conclusion is that there is a need to develop a taxonomy that shall include industrial problems and their technological solutions. Moreover, the educational needs of engineers and researchers with current knowledge and advanced skills were underlined.

Role of Academics in Transferring Knowledge and Skills on Artificial Intelligence, Internet of Things and Edge Computing

Universities play an essential role in preparing human resources for the industry of the future. By providing the proper knowledge, they can ensure that graduates will be able to adapt to the ever-changing industrial sector. However, to achieve this, the courses provided by academia must cover the current and future industrial needs by considering the trends in scientific research and emerging technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and Edge Computing (EC). This work presents the survey results conducted among academics to assess the current state of university courses, regarding the level of knowledge and skills provided to students about the Internet of Things, Artificial Intelligence, and Edge Computing. The novelty of the work is that (a) the research was carried out in several European countries, (b) the current curricula of universities from different countries were analyzed, and (c) the results present the teachers' perspective. To conduct the research, the analysis of the relevant literature took place initially to explore the issues of the presented subject, which will increasingly concern the industry in the near future. Based on the literature review results and analysis of the universities' curricula involved in this study, a questionnaire was prepared and shared with academics. The outcomes of the analysis reveal the areas that require more attention from scholars and possibly modernization of curricula.

Compacting oblivious agents on dynamic rings

In this paper we investigate dynamic networks populated by autonomous mobile agents. Dynamic networks are networks whose topology can change continuously, at unpredictable locations and at unpredictable times. These changes are not considered to be faults, but rather an integral part of the nature of the system. The agents can autonomously move on the network, with the goal of solving cooperatively an assigned common task. Here, we focus on a specific network: the unoriented ring. More specifically, we study 1-interval connected dynamic rings (i.e., at any time, at most one of the edges might be missing). The agents move according to the widely used Look-Compute-Move life cycle, and can be homogenous (thus identical) or heterogenous (agents are assigned colors from a set of c > 1 colors). For identical agents, their goal is to form a compact segment, where agents occupy a continuous part of the ring and no two agents occupy the same node: we call this the Compact Configuration Problem. In the case of agents with colors, called the Colored Compact Configuration Problem, the goal is to group agents such that each group is formed by all agents having the same color, it occupies a continuous segment of the network, and groups of agents having different colors occupy distinct areas of the network. In this paper we determine the necessary conditions to solve both proposed problems. For all solvable cases, we provide algorithms for both the monochromatic and the colored version of the compact configuration problem. All our algorithms work even for the simplest model where agents have no persistent memory, no communication capabilities and do not agree on a common orientation within the network. To the best of our knowledge this is the first work on the compaction problem in a dynamic network.

The influence of dynamics and speech on understanding humanoid facial expressions

Human communication relies mostly on nonverbal signals expressed through body language. Facial expressions, in particular, convey emotional information that allows people involved in social interactions to mutually judge the emotional states and to adjust its behavior appropriately. First studies aimed at investigating the recognition of facial expressions were based on static stimuli. However, facial expressions are rarely static, especially in everyday social interactions. Therefore, it has been hypothesized that the dynamics inherent in a facial expression could be fundamental in understanding its meaning. In addition, it has been demonstrated that nonlinguistic and linguistic information can contribute to reinforce the meaning of a facial expression making it easier to be recognized. Nevertheless, few studies have been performed on realistic humanoid robots. This experimental work aimed at demonstrating the human-like expressive capability of a humanoid robot by examining whether the effect of motion and vocal content influenced the perception of its facial expressions. The first part of the experiment aimed at studying the recognition capability of two kinds of stimuli related to the six basic expressions (i.e. anger, disgust, fear, happiness, sadness, and surprise): static stimuli, that is, photographs, and dynamic stimuli, that is, video recordings. The second and third parts were focused on comparing the same six basic expressions performed by a virtual avatar and by a physical robot under three different conditions: (1) muted facial expressions, (2) facial expressions with nonlinguistic vocalizations, and (3) facial expressions with an emotionally neutral verbal sentence. The results show that static stimuli performed by a human being and by the robot were more ambiguous than the corresponding dynamic stimuli on which motion and vocalization were associated. This hypothesis has been also investigated with a 3-dimensional replica of the physical robot demonstrating that even in case of a virtual avatar, dynamic and vocalization improve the emotional conveying capability.

SEAI: Social Emotional Artificial Intelligence Based on Damasio's Theory of Mind

A socially intelligent robot must be capable to extract meaningful information in real time from the social environment and react accordingly with coherent human-like behavior. Moreover, it should be able to internalize this information, to reason on it at a higher level, build its own opinions independently, and then automatically bias the decision-making according to its unique experience. In the last decades, neuroscience research highlighted the link between the evolution of such complex behavior and the evolution of a certain level of consciousness, which cannot leave out of a body that feels emotions as discriminants and prompters. In order to develop cognitive systems for social robotics with greater human-likeliness, we used an "understanding by building" approach to model and implement a well-known theory of mind in the form of an artificial intelligence, and we tested it on a sophisticated robotic platform. The name of the presented system is SEAI (Social Emotional Artificial Intelligence), a cognitive system specifically conceived for social and emotional robots. It is designed as a bio-inspired, highly modular, hybrid system with emotion modeling and high-level reasoning capabilities. It follows the deliberative/reactive paradigm where a knowledge-based expert system is aimed at dealing with the high-level symbolic reasoning, while a more conventional reactive paradigm is deputed to the low-level processing and control. The SEAI system is also enriched by a model that simulates the Damasio's theory of consciousness and the theory of Somatic Markers. After a review of similar bio-inspired cognitive systems, we present the scientific foundations and their computational formalization at the basis of the SEAI framework. Then, a deeper technical description of the architecture is disclosed underlining the numerous parallelisms with the human cognitive system. Finally, the influence of artificial emotions and feelings, and their link with the robot's beliefs and decisions have been tested in a physical humanoid involved in Human-Robot Interaction (HRI).

Vasopressin receptor antagonists in patients with chronic heart failure

In this brief review, the pathophysiology of hyponatremia and its clinical significance in the course of chronic heart failure (CHF) are illustrated. Moreover, issues concerning the optimal treatment for hyponatremia during CHF are addressed and discussed. In addition, advantages and limitations resulting from the use of vasopressin receptor antagonists, drugs that have recently emerged as the best available resource against hyponatremia, are highlighted.

Open Biomedical Engineering education in Africa

Despite the virtual revolution, the mainstream academic community in most countries remains largely ignorant of the potential of web-based teaching resources and of the expansion of open source software, hardware and rapid prototyping. In the context of Biomedical Engineering (BME), where human safety and wellbeing is paramount, a high level of supervision and quality control is required before open source concepts can be embraced by universities and integrated into the curriculum. In the meantime, students, more than their teachers, have become attuned to continuous streams of digital information, and teaching methods need to adapt rapidly by giving them the skills to filter meaningful information and by supporting collaboration and co-construction of knowledge using open, cloud and crowd based technology. In this paper we present our experience in bringing these concepts to university education in Africa, as a way of enabling rapid development and self-sufficiency in health care. We describe the three summer schools held in sub-Saharan Africa where both students and teachers embraced the philosophy of open BME education with enthusiasm, and discuss the advantages and disadvantages of opening education in this way in the developing and developed world.

Can a Humanoid Face be Expressive? A Psychophysiological Investigation

Non-verbal signals expressed through body language play a crucial role in multi-modal human communication during social relations. Indeed, in all cultures, facial expressions are the most universal and direct signs to express innate emotional cues. A human face conveys important information in social interactions and helps us to better understand our social partners and establish empathic links. Latest researches show that humanoid and social robots are becoming increasingly similar to humans, both esthetically and expressively. However, their visual expressiveness is a crucial issue that must be improved to make these robots more realistic and intuitively perceivable by humans as not different from them. This study concerns the capability of a humanoid robot to exhibit emotions through facial expressions. More specifically, emotional signs performed by a humanoid robot have been compared with corresponding human facial expressions in terms of recognition rate and response time. The set of stimuli included standardized human expressions taken from an Ekman-based database and the same facial expressions performed by the robot. Furthermore, participants' psychophysiological responses have been explored to investigate whether there could be differences induced by interpreting robot or human emotional stimuli. Preliminary results show a trend to better recognize expressions performed by the robot than 2D photos or 3D models. Moreover, no significant differences in the subjects' psychophysiological state have been found during the discrimination of facial expressions performed by the robot in comparison with the same task performed with 2D photos and 3D models.

Inference of human affective states from psychophysiological measurements extracted under ecologically valid conditions

Compared to standard laboratory protocols, the measurement of psychophysiological signals in real world experiments poses technical and methodological challenges due to external factors that cannot be directly controlled. To address this problem, we propose a hybrid approach based on an immersive and human accessible space called the eXperience Induction Machine (XIM), that incorporates the advantages of a laboratory within a life-like setting. The XIM integrates unobtrusive wearable sensors for the acquisition of psychophysiological signals suitable for ambulatory emotion research. In this paper, we present results from two different studies conducted to validate the XIM as a general-purpose sensing infrastructure for the study of human affective states under ecologically valid conditions. In the first investigation, we recorded and classified signals from subjects exposed to pictorial stimuli corresponding to a range of arousal levels, while they were free to walk and gesticulate. In the second study, we designed an experiment that follows the classical conditioning paradigm, a well-known procedure in the behavioral sciences, with the additional feature that participants were free to move in the physical space, as opposed to similar studies measuring physiological signals in constrained laboratory settings. Our results indicate that, by using our sensing infrastructure, it is indeed possible to infer human event-elicited affective states through measurements of psychophysiological signals under ecological conditions.

Wearable wireless tactile display for virtual interactions with soft bodies

We describe here a wearable, wireless, compact, and lightweight tactile display, able to mechanically stimulate the fingertip of users, so as to simulate contact with soft bodies in virtual environments. The device was based on dielectric elastomer actuators, as high-performance electromechanically active polymers. The actuator was arranged at the user’s fingertip, integrated within a plastic case, which also hosted a compact high-voltage circuitry. A custom-made wireless control unit was arranged on the forearm and connected to the display via low-voltage leads. We present the structure of the device and a characterization of it, in terms of electromechanical response and stress relaxation. Furthermore, we present results of a psychophysical test aimed at assessing the ability of the system to generate different levels of force that can be perceived by users.

Suitability of partly destoned exhausted olive cake as by-product feed ingredient for lamb production

The effect of diets with different levels of partly destoned exhausted olive cake (PDEOC) on growth performance and carcass traits of Gentile di Puglia breed lambs was studied. Sixty lambs (16.5 ± 0.5 kg) at weaning were randomly allocated to 3 isocaloric and isonitrogenous diets for 50 d. Pelleted total mixed rations (TMR) were formulated to provide olive by-product at 3 different levels: 1) a control diet without olive by-product (PDEOC-0), 2) an experimental corn-based diet containing 10% by-product (PDEOC-10) replacing part of the oat hay and sunflower meal, and 3) an experimental corn-based diet containing 20% PDEOC (PDEOC-20) replacing part of the oat hay and soybean meal. To evaluate in vivo digestibility of the diets, adult rams (n = 3) were placed in metabolic cages, their individual feces and urine were collected, and differences were observed for DM and fiber fractions. Results from the growth trial of the lambs showed that performance was influenced by olive by-product inclusion in diet (P < 0.05). At the end of the feeding period, lambs were slaughtered, and none of the variables studied were influenced by dietary treatment except for cold carcass dressing (P = 0.027) and half-carcass weight (P = 0.019), which were improved in lambs fed the PDEOC-20 diet. As a result, the current study confirms that olive by-product can be used in lamb finishing rations, resulting in a valuable ingredient as replacement for conventional feeds, which could reduce feeding costs because of the lower cost of the olive by-product. Use of olive by-products as animal feed may become economically feasible for producers where the olive oil industries play an important economic role.

Milk quality, manufacturing properties and blood biochemical profile from dairy cows fed peas (&lt;i&gt;Pisum sativum&lt;/i&gt; L.) as dietary protein supplement

Abstract. Pea (Pisum sativum L.), a protein-rich legume seed well adapted to many climatic areas and widely used for feed and food, was fed to Italian Friesian breed cows for 16 weeks to assess its effects on milk yield and production, renneting properties and metabolic responses. Cows within each group were assigned to two isonitrogenous and isoenergetic concentrates based on corn plus soybean meal or peas. Individual milk samples were collected from two consecutive milkings, composited, and then analysed for fat, protein, casein and lactose contents and somatic cells count as well as blood and milk urea and milk technological characteristics. Cow blood samples were taken and plasma were analysed for metabolites, biological enzymes, β-hydroxybutyrate (BHBA) and non-esterified fatty acids (NEFA). Peas supplementation had no effects on metabolic blood profile as well as on milk composition traits and clotting aptitude. Milk and blood from cows fed peas indicated a reduction (P<0.05) of their urea concentrations compared to those fed soybean meal. Our findings indicate that peas can replace soybean meal as protein source in diet of dairy cows without unfavourable effects on milk quality and cheesemaking properties.

Prevalence of sleep disturbances in mild cognitive impairment and dementing disorders: a multicenter Italian clinical cross-sectional study on 431 patients

BACKGROUND/AIMS

Sleep disturbances are common in the elderly and in persons with cognitive decline. The aim of this study was to describe frequency and characteristics of insomnia, excessive daytime sleepiness, sleep-disordered breathing, REM behavior disorder and restless legs syndrome in a large cohort of persons with mild cognitive impairment or dementia.

METHODS

431 consecutive patients were enrolled in 10 Italian neurological centers: 204 had Alzheimer's disease, 138 mild cognitive impairment, 43 vascular dementia, 25 frontotemporal dementia and 21 Lewy body dementia or Parkinson's disease dementia. Sleep disorders were investigated with a battery of standardized questions and questionnaires.

RESULTS

Over 60% of persons had one or more sleep disturbances almost invariably associated one to another without any evident and specific pattern of co-occurrence. Persons with Alzheimer's disease and those with mild cognitive impairment had the same frequency of any sleep disorder. Sleep-disordered breathing was more frequent in vascular dementia. REM behavior disorder was more represented in Lewy body or Parkinson's disease dementia.

CONCLUSION

A careful clinical evaluation of sleep disorders should be performed routinely in the clinical setting of persons with cognitive decline. Instrumental supports should be used only in selected patients.

A flexible bioreactor system for constructing in vitro tissue and organ models

To develop in vitro models of cells, tissues and organs we have designed and realized a series of cell culture chambers. Each chamber is purpose designed to simulate a particular feature of the in vivo environment. The bioreactor system is user friendly, and the chambers are easy to produce, sterilize and assemble. In addition they can be connected together to simulate inter-organ or tissue cross-talk. Here we discuss the design philosophy of the bioreactor system and then describe its construction. Preliminary results of validation tests obtained with hepatocytes and endothelial cells are also reported. The results show that endothelial cells are extremely sensitive to small levels of shear stress and that the presence of heterotypic signals from endothelial cells enhances the endogenous metabolic function of hepatocytes.

Development and evaluation of a social robot platform for therapy in autism

People with ASD (Autism Spectrum Disorders) have difficulty in managing interpersonal relationships and common life social situations. A modular platform for Human Robot Interaction and Human Machine Interaction studies has been developed to manage and analyze therapeutic sessions in which subjects are driven by a psychologist through simulated social scenarios. This innovative therapeutic approach uses a humanoid robot called FACE capable of expressing and conveying emotions and empathy. Using FACE as a social interlocutor the psychologist can emulate real life scenarios where the emotional state of the interlocutor is adaptively adjusted through a semi closed loop control algorithm which uses the ASD subject's inferred "affective" state as input. Preliminary results demonstrate that the platform is well accepted by ASDs and can be consequently used as novel therapy for social skills training.

A low shear stress modular bioreactor for connected cell culture under high flow rates

A generic "system on a plate" modular multicompartmental bioreactor array which enables microwell protocols to be transferred directly to the bioreactor modules, without redesign of cell culture experiments or protocols is described. The modular bioreactors are simple to assemble and use and can be easily compared with standard controls since cell numbers and medium volumes are quite similar. Starting from fluid dynamic and mass transport considerations, a modular bioreactor chamber was first modeled and then fabricated using "milli-molding," a technique adapted from soft lithography. After confirming that the shear stress was extremely low in the system in the range of useful flow rates, the bioreactor chambers were tested using hepatocytes. The results show that the bioreactor chambers can increase or maintain cell viability and function when the flow rates are below 500 microL/min, corresponding to wall shear stresses of 10(-5) Pa or less at the cell culture surface.

The reversed clock drawing test phenomenon in Alzheimer's disease: A perfusion SPECT study

AIM

To unveil a brain single photon emission computed tomography (SPECT) pattern in Alzheimer's disease (AD) patients, showing a reversed clock drawing test (CDT) phenomenon.

PATIENTS AND METHODS

Among 1,005 consecutive subjects, 9 AD patients who drew a reversed CDT (AD-R) underwent SPECT, which was analysed (SPM2) versus a group of 10 AD patients performing the CDT correctly (AD+) and versus 15 controls (CTR). Brain SPECT in 11 AD patients who mistook the CDT in a common way (AD-) was compared with AD+ and CTR groups.

RESULTS

Relative hypoperfusion was found in AD-R versus CTR in right medial frontal, parahippocampal and subcallosal gyri, and in left insula and superior temporal gyrus. Hypoperfusion was found in AD-R versus AD+ in the right uncus, superior temporal and parahippocampal gyri. In the AD- versus CTR comparison, hypoperfusion was found in left hippocampus, parahippocampal gyrus and superior parietal lobule. In AD-R versus AD+ and CTR merged together, the analysis showed hypoperfusion in the right parahippocampus, medial frontal gyrus, superior temporal gyrus and uncus, in the left insula and superior temporal gyrus.

CONCLUSION

Fronto-temporal dysfunction, especially in the right hemisphere, plays a role in the reversed CDT phenomenon in AD patients, whereas matched AD patients mistaking the CDT in a common way show left posterior temporo-parietal hypoperfusion.

A renal abnormality as a possible cause of "essential" hypertension

The renal abnormality which causes hypertension in the Milan hypertensive strain of rats disappears as hypertension develops. Because of the many analogies between the condition in these rats and "essential" hypertension in man, the same pattern of change may occur if a renal abnormality is the cause of essential hypertension in man. This hypothesis was tested in two groups of young normotensive subjects matched for age, sex, and body-surface area; in the first group both parents were hypertensive, and in the second group both parents were normotensive. Renal plasma-flow, glomerular filtration-rate, plasma-volume, plasma-renin activity, plasma-concentrations of Na+, K+, and catecholamines, 24 h urinary excretion of Na+, K+, and aldosterone, and the cardiac index were measured so that renal function and the role of factors affecting blood-pressure regulation could be assessed. Renal plasma-flow was significantly higher (p less than 0.01) in the first group, whereas results of tests for all the other factors were almost the same in both groups. The hypothesis that a primary kidney abnormality causes hypertension in a proportion of patients with essential hypertension is proposed.