Showing papers on "Quality (business) published in 2022"

Business Innovation based on artificial intelligence and Blockchain technology

Abstract: The growing business evolution and the latest Artificial Intelligence (AI) make the different business practices to be enhanced by the ability to create new means of collaboration. Such growing technology helps to deliver brand services and even some new kinds of corporate interactions with customers and staff. AI digitization simultaneously emphasized businesses to focus on the existing strategies and regularly and early pursue new market opportunities. While digital technology research in the framework of business innovation is gaining greater interest and the privacy of data can be maintained by Blockchain technology. Therefore in this paper, Business Innovation based on artificial intelligence and Blockchain technology (BI-AIBT) has been proposed to enhance the business practices and maintain the secured interaction among the various clients. The collection of qualitative empirical data is made up of few primary respondents from two distinct business sectors. BI-AIBT has been evaluated by undertaking and exploring the difference and similarities between digitalization's impact on value development, proposal, and business capture. Besides, organizational capacities and staff skills interaction issues can be improved by BT. The experimental result suggests that digital transformation is usually regarded as essential and improves business innovation strategies. The numerical result proposed BI-AIBT improves the demand prediction ratio (97.1%), product quality ratio (98.3%), Business development ratio (98.9%), customer behavior analysis ratio (96.3%), and customer satisfaction ratio (97.2%).

Trustworthy Target Tracking With Collaborative Deep Reinforcement Learning in EdgeAI-Aided IoT

Abstract: Mobile target tracking with artificial intelligence (AI) approaches such as deep reinforcement learning (DRL) in edge-assisted Internet of Things (Edge-IoT) platform can be promising. In this article, we propose DRLTrack , a framework for target tracking with a collaborative DRL called C-DRL in Edge-IoT with the aim to obtain two major objectives: high quality of tracking (QoT) and resource-efficient network performance. In DRLTrack , a huge number of IoT devices are employed to collect data about a target of interest. One or two edge devices in the network coordinate with a group of IoT devices and collaboratively detect the target by using the C-DRL approach and form an area around the target by the group of IoT devices. To maintain such an area during the tracking time, we employ a deep Q-network to track the target from one group to another. An EdgeAI sitting on the top of the edge devices has the control of the C-DRL approach during tracking and can identify a sequence of tracks. DRLTrack is said to be trustworthy as it shows trustworthy performance in terms of QoT, dynamic environments, and even under certain cyberattacks. We validate the performance of DRLTrack considering the objectives through simulations and it demonstrates superior performance compared with existing work.

Implications of social isolation, separation, and loss during the COVID-19 pandemic for couples' relationships.

Abstract: The broad isolation, separation, and loss resulting from the COVID-19 pandemic raise risks for couples' relationship quality and stability. Guided by the vulnerability-stress-adaptation model, we suggest that how pandemic-related loss, isolation, and separation impact couples' relationships will vary depending on the amount and severity of pandemic-related stress, together with enduring personal vulnerabilities (e.g. attachment insecurity), both of which can disrupt adaptive dyadic responses to these challenges. A review of emerging research examining relationship functioning before and during the initial stages of the pandemic offers support for this framework. We draw on additional research to suggest pathways for mitigating relationship disruptions and promoting resilience.

Sequential Gaussian simulation for geosystems modeling: A machine learning approach

Abstract: Sequential Gaussian Simulation (SGSIM) as a stochastic method has been developed to avoid the smoothing effect produced in deterministic methods by generating various stochastic realizations. One of the main issues of this technique is, however, an intensive computation related to the inverse operation in solving the Kriging system, which significantly limits its application when several realizations need to be produced for uncertainty quantification. In this paper, a physics-informed machine learning (PIML) model is proposed to improve the computational efficiency of the SGSIM. To this end, only a small amount of data produced by SGSIM are used as the training dataset based on which the model can discover the spatial correlations between available data and unsampled points. To achieve this, the governing equations of the SGSIM algorithm are incorporated into our proposed network. The quality of realizations produced by the PIML model is compared for both 2D and 3D cases, visually and quantitatively. Furthermore, computational performance is evaluated on different grid sizes. Our results demonstrate that the proposed PIML model can reduce the computational time of SGSIM by several orders of magnitude while similar results can be produced in a matter of seconds.

Mapping the postharvest life of imported fruits from packhouse to retail stores using physics-based digital twins

Abstract: Controlling the hygrothermal conditions around fresh fruit and vegetables is vital for their preservation. Therefore, cold chain stakeholders often measure temperature along the supply chain of fresh produce. However, the temperature is typically monitored only in one segment of the entire cold chain, namely from the supplier until the distribution center. Besides, such measured data are rarely used for decision-making because they are not translated into the impact on the quality of the products. We provide a solution by extending the monitoring until the retail stores and upcycling these thermal data into actionable metrics. To do so, we use physics-based digital twins, namely virtual representations of the food products. This study focuses on 331 cold chain shipments of cucumber, eggplant, strawberry, and raspberry imported from Spain to Switzerland. We followed these fruits through pre-cooling, thermally stable conditions at the distribution center, and the temperature ramp-up phase before arriving at the retail store. The temperature performance of each carrier and flow analysis of the shipment enabled us to map the complex logistic system better. The digital twins detected that the fruits lost 43 - 85% of their quality before being displayed at the retail store. This quality loss remains invisible to the retailer. Additionally, we found a strong correlation between fruit quality and shipment duration (i.e., for cucumber r = -0.95 (P

Expanding the collection portfolio of plastic packaging: Impact on quantity and quality of sorted plastic waste fractions

Abstract: Expanding the curbside collection system for household plastic packaging could help in accomplishing the ambitious recycling targets set by the European Union. In this study, the effects of such expansion in terms of quality and quantity of sorted plastic waste fractions were investigated. By applying a validated sorting model, the flows of packages in Belgium from their use phase until the output of the sorting plant and the quantity and composition of sorted waste fractions were simulated. These data were subsequently used to calculate existing performance indicators, such as grade and recovery, as well as new indicators, such as the Simpson's Diversity Index (SDI), which is a measure for the compositional diversity of sorted waste fractions. The highest SDI was calculated for the fraction named ‘other films’, namely 82%, showing that this fraction comprised the most diverse polymer mixture. Results indicated that certain sorted fractions achieve high product grade levels, e.g. more than 99% for the PS rigid fraction, whereas others had significantly lower product grades of approximately 80% for the PE films. Material Flow Analysis showed that by expanding the collection portfolio the collection rate of plastic packaging increased from 33.6 to 64.4 m%, of which 77.3% is simulated to be separated in the targeted waste fractions. However, this will be insufficient to meet the recycling target of 50% by 2025 as in total only 49.8% of plastic packaging is sorted correctly. Hence, additional improvements in both recycling technologies and packaging design are needed to further increase plastic recycling rates.

Improving the Efficiency of Production Processes of Enterprises of the Aviation Industry

Abstract: The article discusses the application of the mechanism for selecting alternatives from the semantic network of conditional statements when managing product quality parameters at the stages of the life cycle. This will make it possible to implement the timely improvement of quality management processes for aviation products based on the use of information technology to support the product life cycle in real time.

The Influence of YouTube Videos on the Learning Experience of Disabled People During the COVID-19 Outbreak

Abstract: A record number of individuals are not attending educational institutions during the Covid-19 outbreak. This situation is highly thought-provoking, yet; the use of new media technology can overcome this challenge. In this regard, this chapter investigated the influence of YouTube usage as an educational tool on the learning process of disabled people during the COVID-19 pandemic. The researchers selected n = 60 individuals working as disability specialists and used structural equation modeling to examine the proposed study model. The results revealed that there is a positive relationship between YouTube videos and e-Learning among disable individuals. Moreover, the quality, ease of use, and texts in the video also contribute to improving the disabled people’s learning experiences. Thus, the results highly supported technology acceptance and usage during the global healthcare crisis. Moreover, the researchers also recommended evaluating other aspects of YouTube Videos that can influence people with disabilities about their social media usage, especially during an emergency like Covid-19.

Harshness-aware sentiment mining framework for product review

Abstract: Sentiment mining has been a helpful mechanism that targets to understand the market feedback on certain commodities by utilizing user comments. In general, the process of yielding each comment is essentially associated with his/her criteria for rating (i.e., the degree of harshness) , which makes users provide biased comments. For instance, for a tolerant user, although the user is extremely dissatisfied with the product, harshness still makes her yield a neutral comment which cannot indicate the product quality. Existing work straightforwardly removes the comments of harsh users and those of tolerant ones, which is not the best strategy. To this end, we propose a harshness-aware sentiment analysis framework for product review. First, we depict the process of providing comments from users as a probabilistic graphical model in which the harshness is incorporated. Second, we employ a Bayesian-based inference for sentiment mining. Extensive experimental evaluations have shown that the results of the proposed method are more consistent with the expert evaluations than those of the state-of-the-art methods, and even outperform the method which infers the final evaluations with the ground truth of comments without considering users’ harshness.

Risks and Threats to Economic Security of Enterprises in the Construction Industry Under Pandemic Conditions

Abstract: The paper actualises the problem of identification of risks and threats to economic security of enterprises in the construction industry under pandemic conditions. It proves that ensuring timely identification of potential or real threats to economic security of enterprises in the construction industry is essential to develop a set of preventing measures for preventing or minimising the causes of their manifestation. A statistical analysis indicates the impact of COVID-19 pandemic and the quarantine measures upon the development of national economics in general and construction industry in particular. The paper describes the negative tendencies in the Ukrainian construction industry market. It proves that an efficient tool for preventing and effective overcoming the negative consequences of pandemic is timely identification of risks and threats to enterprise security in the construction industry. It suggests an improvement to multi-criteria classification of risks and threats to enterprises in the construction industry, which enables detailing the preconditions of threats and their sources, and rather more systematic approach to their predicting, forecasting their negative impact, identifying the quality structure of risks as well as taking into consideration the possibility of system risks development. The authors developed a structure model that enables identifying the predominant factors and preconditions of threats to appear, which allows removing the threats in an early stage of their development, predicting the probability of threats to appear, and taking preventive measures for their timely removing. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Relationships between software architecture and source code in practice: An exploratory survey and interview

Abstract: Context Software Architecture (SA) and Source Code (SC) are two intertwined artefacts that represent the interdependent design decisions made at different levels of abstractions - High-Level (HL) and Low-Level (LL). An understanding of the relationships between SA and SC is expected to bridge the gap between SA and SC for supporting maintenance and evolution of software systems. Objective We aimed at exploring practitioners’ understanding about the relationships between SA and SC. Method We used a mixed-method that combines an online survey with 87 respondents and an interview with 8 participants to collect the views of practitioners from 37 countries about the relationships between SA and SC. Results Our results reveal that: practitioners mainly discuss five features of relationships between SA and SC; a few practitioners have adopted dedicated approaches and tools in the literature for identifying and analyzing the relationships between SA and SC despite recognizing the importance of such information for improving a system's quality attributes, especially maintainability and reliability. It is felt that cost and effort are the major impediments that prevent practitioners from identifying, analyzing, and using the relationships between SA and SC. Conclusions The results have empirically identified five features of relationships between SA and SC reported in the literature from the perspective of practitioners and a systematic framework to manage the five features of relationships should be developed with dedicated approaches and tools considering the cost and benefit of maintaining the relationships.