Abstract This paper exploits India's rapid, comprehensive, and externally imposed trade reform to establish a causal link between changes in tariffs and firm productivity. Pro-competitive forces, resulting from lower tariffs on final goods, as well as access to better inputs, due to lower input tariffs, both appear to have increased firm-level productivity, with input tariffs having a larger impact. The effect was strongest in import-competing industries and industries not subject to excessive domestic regulation. While we find no evidence of a differential impact according to state-level characteristics, we observe complementarities between trade liberalization and additional industrial policy reforms.
Purpose: The aim of this study is to examine the existence and differentiation of the impact of innovation on economic growth across country groups through a panel data analysis covering 93 countries between 2011 and 2023. The research investigates whether innovation affects economic growth and whether this effect varies according to country income groups.Methodology: Data from the World Bank and the Global Innovation Index (GII) were used in the study, and empirical analyses were conducted using Ordinary Least Squares (OLS) and Fully Modified Ordinary Least Squares (FMOLS) methods. The preference for panel data analysis allowed for the consideration of structural differences between countries and changes over time.Findings: The findings indicate that innovation has a significant and positive effect on economic growth. Furthermore, it was determined that the impact of innovation on growth is higher in low- and middle-income countries, while it is relatively lower in high-income countries.Originality: This study provides an original contribution to literature by comparatively analyzing the effect of innovation on economic growth across country groups. The results reveal that innovation should be at the center of development policies to achieve sustainable growth goals.
Abstract Food insecurity in Nigeria has persisted for decades, resisting various agricultural policies and programmes since the 1970s. This study investigates the impacts of traditional agriculture (TA), sustainable agriculture (SA), and industrial agriculture (IA) on food security in Nigeria from 2000 to 2022, using a system dynamics modelling approach supported by optimization and linear programming techniques. The model also accounts for climate change and land resource dynamics, integrating data from Nigeria and nine comparable countries to enhance robustness. Results show that while crop yield increased over time, food security fluctuated due to factors including land degradation and uneven food distribution. The findings highlight that improving agricultural productivity alone is insufficient; sustainable practices, climate adaptation, and improved distribution systems are essential for long-term food security in Nigeria.
Nutritional diseases. Deficiency diseases, Public aspects of medicine
Collaborative robots have lately been extremely relevant to the domain of production and manufacturing industry after the arrival of Fourth Industrial Revolution or Industry 4.0 (IR 4.0). Collaborative robots have evolved as one of the key drivers in Industry 4.0 and they have advanced substantially within the last couple of decades. In comparison to the industrial robots, collaborative robots offer increased productivity, flexibility, versatility and safety. Collaborative robots are designed to execute tasks alongside the human workforce while sharing the same working space as colleagues, offering greater mobility and flexibility. Collaborative robots allow for a physical interaction with humans in a shared workspace to execute production, manufacturing and assembly tasks. These machines are designed to be reprogrammed easily, even by personnel without any programming background. Human-Robot Interaction (HRI) between humans and collaborative robots provides promising methods to achieve increased productivity with reduced production costs by combining the decision-making ability of humans along with the repeatability and strength of robots. This article reviews the significance of the collaborative robots today and also presents an insight into their future potential.
In industry, the networking and automation of machines through the Internet of Things (IoT) continues to increase, leading to greater digitalization of production processes. Traditionally, business and production processes are controlled, optimized and monitored using business process management methods that require process discovery. However, these methods cannot be fully applied to industrial production processes. Nevertheless, processes in the industry must also be monitored and discovered for this purpose. The aim of this paper is to develop an approach for process discovery methods and to adapt existing process discovery methods for application to industrial processes. The adaptations of classic discovery methods are presented as universally applicable guidelines specifically for the Industrial Internet of Things (IIoT). In order to create an optimal process model based on process evaluation, different methods are combined into a standardized discovery approach that is both efficient and cost-effective.
This paper presents a study on transforming a traditional human-operated vehicle into a fully autonomous device. By leveraging previous research and state-of-the-art technologies, the study addresses autonomy, safety, and operational efficiency in industrial environments. Motivated by the demand for automation in hazardous and complex industries, the autonomous system integrates sensors, actuators, advanced control algorithms, and communication systems to enhance safety, streamline processes, and improve productivity. The paper covers system requirements, hardware architecture, software framework and preliminary results. This research offers insights into designing and implementing autonomous capabilities in human-operated vehicles, with implications for improving safety and efficiency in various industrial sectors.
Tomislav Duricic, Peter Müllner, Nicole Weidinger
et al.
Many industrial sectors rely on well-trained employees that are able to operate complex machinery. In this work, we demonstrate an AI-powered immersive assistance system that supports users in performing complex tasks in industrial environments. Specifically, our system leverages a VR environment that resembles a juice mixer setup. This digital twin of a physical setup simulates complex industrial machinery used to mix preparations or liquids (e.g., similar to the pharmaceutical industry) and includes various containers, sensors, pumps, and flow controllers. This setup demonstrates our system's capabilities in a controlled environment while acting as a proof-of-concept for broader industrial applications. The core components of our multimodal AI assistant are a large language model and a speech-to-text model that process a video and audio recording of an expert performing the task in a VR environment. The video and speech input extracted from the expert's video enables it to provide step-by-step guidance to support users in executing complex tasks. This demonstration showcases the potential of our AI-powered assistant to reduce cognitive load, increase productivity, and enhance safety in industrial environments.
Across the technology industry, many companies have expressed their commitments to AI ethics and created dedicated roles responsible for translating high-level ethics principles into product. Yet it is unclear how effective this has been in leading to meaningful product changes. Through semi-structured interviews with 26 professionals working on AI ethics in industry, we uncover challenges and strategies of institutionalizing ethics work along with translation into product impact. We ultimately find that AI ethics professionals are highly agile and opportunistic, as they attempt to create standardized and reusable processes and tools in a corporate environment in which they have little traditional power. In negotiations with product teams, they face challenges rooted in their lack of authority and ownership over product, but can push forward ethics work by leveraging narratives of regulatory response and ethics as product quality assurance. However, this strategy leaves us with a minimum viable ethics, a narrowly scoped industry AI ethics that is limited in its capacity to address normative issues separate from compliance or product quality. Potential future regulation may help bridge this gap.
Video anomaly detection (VAD) is a challenging task aiming to recognize anomalies in video frames, and existing large-scale VAD researches primarily focus on road traffic and human activity scenes. In industrial scenes, there are often a variety of unpredictable anomalies, and the VAD method can play a significant role in these scenarios. However, there is a lack of applicable datasets and methods specifically tailored for industrial production scenarios due to concerns regarding privacy and security. To bridge this gap, we propose a new dataset, IPAD, specifically designed for VAD in industrial scenarios. The industrial processes in our dataset are chosen through on-site factory research and discussions with engineers. This dataset covers 16 different industrial devices and contains over 6 hours of both synthetic and real-world video footage. Moreover, we annotate the key feature of the industrial process, ie, periodicity. Based on the proposed dataset, we introduce a period memory module and a sliding window inspection mechanism to effectively investigate the periodic information in a basic reconstruction model. Our framework leverages LoRA adapter to explore the effective migration of pretrained models, which are initially trained using synthetic data, into real-world scenarios. Our proposed dataset and method will fill the gap in the field of industrial video anomaly detection and drive the process of video understanding tasks as well as smart factory deployment.
The identification of “industrial soot” or “vehicle exhaust” pollution facilitates developing proper measures for the mitigation of regional air pollution. In order to identify the pollution types at a regional level, this paper applies the Luenberger productivity indicator to decompose air pollutant emissions performance. Furthermore, we simultaneously consider pollution rates and the productivity change. Thus, we propose a new modeling framework allowing for the variable-specific decomposition of the environmental performance along time and quantity dimensions to identify the underlying patterns. The panel data for 30 provinces and autonomous regions are then applied to identify regional atmospheric pollution type. The results show that SO2 emission from industrial soot and NOx emissions from vehicle exhaust constitute an important source of regional atmospheric environmental inefficiency, though the former seems to be more decisive. The southeast coastal provinces showed generally lower levels of inefficiency, compared to the northwest inland area. During the period of the 11th Five-Year Plan of China, industrial SO2 emission performance contributed to the increase in the atmospheric environmental productivity, while traffic NOx emissions acted as a negative factor in this regard. Therefore, the government should seek to increase the intensity of environmental regulation in transportation sector. At the country level, technical progress associated with both types of pollutions was positive and thus exceed the negative efficiency change for the same variables. In particular, in Beijing-Tianjin-Hebei region, the productivity changes in industrial SO2 emissions and traffic NOx emissions indicate a “stably advancing” type. The results further indicate that there are 18 provinces of China which have experienced mixed-type pollution. Jilin and Hainan were classified as provinces experiencing vehicle exhaust gas pollution, whereas Guizhou was defined as that subject to industrial soot pollution. The government should formulate and implement diversified support and regulation policies to govern SO2 and NOx pollution at the regional level.
Abstract Conventional solar still owns poor efficiency and low distillate output. Many investigators improved the performance of solar still by varying the design of its components. This paper evaluates the effect of several design modifications on the performance of solar still to investigate the most excellent design suitable for industrial and domestic application. From this review, it is found that for single-basin single-slope passive solar still, still integrated with the Fresnel lens with 638% improvement in productivity appears to be a superior design. For single-basin single-slope active solar still, stepped still coupled to solar air heater is the most suitable design with 112% improvement in productivity. For double-basin single-slope solar still, still with reflectors, flat plate collector and the mini solar pond is the finest design with productivity improvement of 127.65%. For a single-basin multiple-slope solar still, hybrid solar still with PV/T collector is the productive design with 370% increase in freshwater yield. Out of all innovative energy-efficient design of solar stills, the most significant improvement in productivity is 676% delivered by tubular design consisting of a semicircular trough filled by a black cloth and coupled to an external reflector. From this investigation, it is found that for domestic application, single-basin single-slope cascade solar still is the most suitable and economical design; however, for industrial application, tubular still with the wick is the most appropriate design.
Abstract This paper investigates the impact of industrial robot adoption on inclusive growth based on labour market evidence from a cross-country panel dataset of 74 economies between 2004 and 2016. It finds that the adoption of industrial robots is associated with significant gains in labour productivity and total employment in developed economies, while such effects are insignificant in developing countries. Increased robot adoption is related to a significantly lower labour share of GDP in developing economies but not in developed countries. Overall, in both developed and developing economies, increased robot adoption is linked with significantly higher income inequality, although there is no evidence of technological unemployment. Furthermore, the employment of both male and female workers is positively associated with the adoption of industrial robots in developed economies, although females benefit slightly more. In developing countries, however, only those with middle or advanced levels of education benefit from the diffusion of robots.
ChatGPT is an Artificial Intelligence (AI)-powered Natural Language Processing (NLP) tool that comprehends and produces text in response to given commands. It can be adopted for various requirements, like answering our inquiries, assisting us with content creation, translating languages, and more. The fourth industrial revolution, called ''Industry 4.0,'' denotes a new production age focused on automation, digitalisation, and real-time connectivity of production systems. ChatGPT can help Industry 4.0 in a variety of ways. ChatGPT and AI-driven process optimisation is poised to revolutionise Industry 4.0 by enhancing productivity, quality assurance, and efficiency. For developing this paper, various articles on ChatGPT/ AI for Industry 4.0 were identified through Scopus, ScienceDirect, Google Scholar and ResearchGate. Industry 4.0 progresses due to the incorporation of cutting-edge technology like AI, Machine Learning (ML), and NLP and Manufacturing operations are changing. The ChatGPT language model is becoming well-known for daily use because of its promising applications. In the framework of Industry 4.0, it promises to revolutionise processes to assist advancement in boosting business productivity and efficiency. This paper studies the major need for ChatGPT for Industry 4.0. Various associated features, traits and versatile competencies of ChatGPT for Industry 4.0 are identified and briefed. Finally, it identifies and discusses the significant applications of ChatGPT for Industry 4.0. ChatGPT is a very flexible and efficient method for creating human-machine interfaces and automatically generating text, which provides proper knowledge and guidance to the employee. Applications for ChatGPT include chatbots, virtual assistants, automated customer care, language translation, and content production. In future, it will become an effective tool for enhancing communication and automating processes in Industry 4.0.
Electronic computers. Computer science, Economics as a science
An important channel through which less developed European countries have grown over the past twenty years is through the industrial transformation of their economies from low to higher value-added activities. The aim of the paper is to address the role of industrial transformation on regional imbalances, by analysing the different components of industrial productivity dynamics, namely the industrial composition, competition and reallocation effects. Based on both a shift-share analysis and a simulation analysis, the paper shows that the reallocation towards higher value-added sectors in Central and Eastern European (CEE) countries could in fact lead to higher regional inequalities. Our empirical results lead us to claim that short-term normative interventions should go in the direction of supporting “modern and technologically advanced traditional sectors” rather than necessarily pushing CEE countries towards a high value-added industry specialization.