scholarly journals Recent Automation Trends in Portugal: Implications on Industrial Productivity and Employment in Automotive Sector

Societies ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 101
Author(s):  
Nuno Boavida ◽  
Marta Candeias
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
Organizational Design ◽  
Work Organization ◽  
Automotive Sector ◽  
Negative Effects ◽  
Short Term ◽  
Machine Age ◽  
Recent Developments ◽  
Industrial Productivity

Recent developments in automation and artificial intelligence (AI) are leading to a wave of innovation in organizational design and changes in the workplace. Techno-optimists even named it the “second machine age,” arguing that it now involves the substitution of the human brain. Other authors see this as just a continuation of previous ICT developments. Potentially, automation and AI can have significant technical, economic, and social implications in firms. This paper will answer the following question: What are the implications on industrial productivity and employment in the automotive sector with the recent automation trends, including AI, in Portugal? Our approach used mixed methods to conduct statistical analyses of relevant databases and interviews with experts on R&D projects related to automation and AI implementation. Results suggest that automation can have widespread adoption in the short term in the automotive sector, but AI technologies will take more time to be adopted. The findings show that adoption of automation and AI increases productivity in firms and is dephased in time with employment implications. Investments in automation are not substituting operators but rather changing work organization. Thus, negative effects of technology and unemployment were not substantiated by our results.

scholarly journals Recent automation trends in Portugal: implications on industrial productivity and employment in automotive sector

2021 ◽  
Author(s):  
Nuno Boavida ◽  
Marta Candeias
Keyword(s):  
Manufacturing Industry ◽  
Organizational Design ◽  
Autonomous Systems ◽  
Automotive Sector ◽  
Learning Collaborative ◽  
Human Machine Interaction ◽  
Machine Age ◽  
Structural Funds ◽  
Collaborative Tools ◽  
Industrial Productivity

Artificial Intelligence (AI) is an automation mechanism that runs in a computer system performing tasks that normally require human intelligence, such as visual perception, speech recognition, decision making or translation [1]. Some authors argue that recent developments in AI are leading to a wave of innovation in organizational design and changes to institutionalized norms of the workplace [2]. Techno-optimists even named this present phase the ‘second machine age’, arguing that it now involves the substitution of the human brain (Brynjolfsson and McAfee 2014). Potentially, the ability to apply AI in a generalized way can produce significant technical, economic and social effects in firms. But how many of these AI applications are ready and how far can they be from reaching the manufacturing industry market? The paper will answer the question: what are the implications on industrial productivity and employment in the automotive sector with the recent automation trends in Portugal? We will focus on AI as the most relevant emergent technology to understand the development of automation in areas related to robotics, software, and data communications in Europe (Moniz 2018). R&D investments in industrial processes in general may reflect productivity improvements derived from the increased automation process. Our results will be based on case studies from the automotive and components sector combined with database search by keywords that signal intelligence automation developments and AI applications selected from national R&D projects (on robotics, machine learning, collaborative tools, human-machine interaction, autonomous systems, etc) supported by European structural funds. The implications on industrial productivity and employment will be discussed in relation to automation trends in the automotive sector.

scholarly journals Long- and Short-Term Conductance Control of Artificial Polymer Wire Synapses

Polymers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 312
Author(s):  
Naruki Hagiwara ◽  
Shoma Sekizaki ◽  
Yuji Kuwahara ◽  
Tetsuya Asai ◽  
Megumi Akai-Kasaya
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
High Speed ◽  
Conductive Polymer ◽  
Long Term Potentiation ◽  
Short Term ◽  
Synaptic Functions ◽  
Term Potentiation ◽  
Artificial Brain ◽  
External Stimuli

Networks in the human brain are extremely complex and sophisticated. The abstract model of the human brain has been used in software development, specifically in artificial intelligence. Despite the remarkable outcomes achieved using artificial intelligence, the approach consumes a huge amount of computational resources. A possible solution to this issue is the development of processing circuits that physically resemble an artificial brain, which can offer low-energy loss and high-speed processing. This study demonstrated the synaptic functions of conductive polymer wires linking arbitrary electrodes in solution. By controlling the conductance of the wires, synaptic functions such as long-term potentiation and short-term plasticity were achieved, which are similar to the manner in which a synapse changes the strength of its connections. This novel organic artificial synapse can be used to construct information-processing circuits by wiring from scratch and learning efficiently in response to external stimuli.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

scholarly journals Subway Gearbox Fault Diagnosis Algorithm Based on Adaptive Spline Impact Suppression

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 660
Author(s):  
Zhongshuo Hu ◽  
Jianwei Yang ◽  
Dechen Yao ◽  
Jinhai Wang ◽  
Yongliang Bai
Keyword(s):  
Fault Diagnosis ◽  
Spline Interpolation ◽  
Reference Value ◽  
High Amplitude ◽  
Engineering Practice ◽  
Negative Effects ◽  
Short Term ◽  
Time Domain Signal ◽  
Gearbox Vibration ◽  
Long Time

In the signal processing of real subway vehicles, impacts between wheelsets and rail joint gaps have significant negative effects on the spectrum. This introduces great difficulties for the fault diagnosis of gearboxes. To solve this problem, this paper proposes an adaptive time-domain signal segmentation method that envelopes the original signal using a cubic spline interpolation. The peak values of the rail joint gap impacts are extracted to realize the adaptive segmentation of gearbox fault signals when the vehicle was moving at a uniform speed. A long-time and unsteady signal affected by wheel–rail impacts is segmented into multiple short-term, steady-state signals, which can suppress the high amplitude of the shock response signal. Finally, on this basis, multiple short-term sample signals are analyzed by time- and frequency-domain analyses and compared with the nonfaulty results. The results showed that the method can efficiently suppress the high-amplitude components of subway gearbox vibration signals and effectively extract the characteristics of weak faults due to uniform wear of the gearbox in the time and frequency domains. This provides reference value for the gearbox fault diagnosis in engineering practice.

scholarly journals Catchment water quality in the year preceding and immediately following restoration of a drained afforested blanket bog

2021 ◽  
Author(s):  
Paul P. J. Gaffney ◽  
Mark H. Hancock ◽  
Mark A. Taggart ◽  
Roxane Andersen
Keyword(s):  
Water Quality ◽  
Nutrient Release ◽  
Pass Rate ◽  
Future Research ◽  
Negative Effects ◽  
Short Term ◽  
Peatland Restoration ◽  
Blanket Bog ◽  
Quality Threshold ◽  
Policy Incentives

AbstractThe restoration of drained afforested peatlands, through drain blocking and tree removal, is increasing in response to peatland restoration targets and policy incentives. In the short term, these intensive restoration operations may affect receiving watercourses and the biota that depend upon them. This study assessed the immediate effect of ‘forest-to-bog’ restoration by measuring stream and river water quality for a 15 month period pre- and post-restoration, in the Flow Country peatlands of northern Scotland. We found that the chemistry of streams draining restoration areas differed from that of control streams following restoration, with phosphate concentrations significantly higher (1.7–6.2 fold, mean 4.4) in restoration streams compared to the pre-restoration period. This led to a decrease in the pass rate (from 100 to 75%) for the target “good” quality threshold (based on EU Water Framework Directive guidelines) in rivers in this immediate post-restoration period, when compared to unaffected river baseline sites (which fell from 100 to 90% post-restoration). While overall increases in turbidity, dissolved organic carbon, iron, potassium and manganese were not significant post-restoration, they exhibited an exaggerated seasonal cycle, peaking in summer months in restoration streams. We attribute these relatively limited, minor short-term impacts to the fact that relatively small percentages of the catchment area (3–23%), in our study catchments were felled, and that drain blocking and silt traps, put in place as part of restoration management, were likely effective in mitigating negative effects. Looking ahead, we suggest that future research should investigate longer term water quality effects and compare different ways of potentially controlling nutrient release.

scholarly journals Characterization of Short-Term Heat Stress in Holstein Dairy Cows Using Altered Indicators of Metabolomics, Blood Parameters, Milk MicroRNA-216 and Characteristics

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 722
Author(s):  
Jang-Hoon Jo ◽  
Jalil Ghassemi Nejad ◽  
Dong-Qiao Peng ◽  
Hye-Ran Kim ◽  
Sang-Ho Kim ◽  
...  
Keyword(s):  
Heat Stress ◽  
Dairy Cows ◽  
Milk Yield ◽  
Milk Fat ◽  
Blood Parameters ◽  
Negative Effects ◽  
Short Term ◽  
Lactating Cows ◽  
Humidity Index ◽  
Holstein Dairy Cows

This study aims to characterize the influence of short-term heat stress (HS; 4 day) in early lactating Holstein dairy cows, in terms of triggering blood metabolomics and parameters, milk yield and composition, and milk microRNA expression. Eight cows (milk yield = 30 ± 1.5 kg/day, parity = 1.09 ± 0.05) were homogeneously housed in environmentally controlled chambers, assigned into two groups with respect to the temperature humidity index (THI) at two distinct levels: approximately ~71 (low-temperature, low-humidity; LTLH) and ~86 (high-temperature, high-humidity; HTHH). Average feed intake (FI) dropped about 10 kg in the HTHH group, compared with the LTLH group (p = 0.001), whereas water intake was only numerically higher (p = 0.183) in the HTHH group than in the LTLH group. Physiological parameters, including rectal temperature (p = 0.001) and heart rate (p = 0.038), were significantly higher in the HTHH group than in the LTLH group. Plasma cortisol and haptoglobin were higher (p < 0.05) in the HTHH group, compared to the LTLH group. Milk yield, milk fat yield, 3.5% fat-corrected milk (FCM), and energy-corrected milk (ECM) were lower (p < 0.05) in the HTHH group than in the LTLH group. Higher relative expression of milk miRNA-216 was observed in the HTHH group (p < 0.05). Valine, isoleucine, methionine, phenylalanine, tyrosine, tryptophan, lactic acid, 3-phenylpropionic acid, 1,5-anhydro-D-sorbitol, myo-inositol, and urea were decreased (p < 0.05). These results suggest that early lactating cows are more vulnerable to short-term (4 day) high THI levels—that is, HTHH conditions—compared with LTLH, considering the enormous negative effects observed in measured blood metabolomics and parameters, milk yield and compositions, and milk miRNA-216 expression.

Review of Recent Developments in Surface Nanocrystallization of Metallic Biomaterials

Nanoscale ◽  
2021 ◽  
Author(s):  
Srijan Acharya ◽  
Satyam Suwas ◽  
Kaushik Chatterjee
Keyword(s):  
Human Body ◽  
Surface Characteristics ◽  
Metallic Materials ◽  
Short Term ◽  
Surface Nanocrystallization ◽  
Metallic Biomaterials ◽  
Recent Developments

Metallic materials are widely used to prepare implants for both short-term and long-term use in the human body. The performance of these implants is greatly influenced by their surface characteristics,...

Sign in / Sign up

Export Citation Format

Share Document