Real Industry Research Articles

Assessment of novel hybrid treatment wetlands as nature‐based solutions for pharmaceutical industry wastewater treatment

AbstractThis study investigated the use of nature‐based solutions for treating real pharmaceutical industry wastewater in Jordan. A pilot‐scale hybrid treatment wetland (TW) equipped with local zeolite was employed, comprising a tidal flow TW and a horizontal subsurface flow TW. This system was efficient in treating pharmaceutical wastewater with removal efficiencies of 61.4%, 52.6%, 60.1%, and 61.9% for chemical oxygen demand, total phosphorus, total nitrogen, and NH4+‐N, respectively. The final effluent met Jordanian standards for the reuse of treated wastewater in irrigation (Class B). Five pharmaceuticals, namely, enrofloxacin, ciprofloxacin, ofloxacin, lincomycin, and trimethoprim, demonstrated nearly completed removal (93.6–99.9%). Moderated removal performances (59.2–68.2%) were observed for two compounds, flumequine and sulfaquinoxaline. However, three pharmaceuticals, namely, carbamazepine, diclofenac, and sulfadimidine, showed limited removal performances (1.1–20.5%). This study supported the feasibility of using nature‐based solutions for treating pharmaceutical wastewater and highlighted that future studies are required to optimize this strategy for removing a broader range of pharmaceuticals.

Biodegradability enhancement and sequential treatment of real chemical industry wastewater by chemical coagulation and Electro-Fenton processes

A sequential treatment with chemical coagulation in the first step and Electro-Fenton (EF) in the second step was applied to chemical industry wastewater. By coagulation using Poly Aluminum Chloride (PAC) as a coagulant, 57.5% chemical oxygen demand (COD), 96.8% total suspended solids (TSS), and 90.5% color index (CI) removal were obtained. In the EF process applied after chemical coagulation, the design parameters were optimized by the central composite design. Two quadratic models of the four factors were established with the COD and CI as two responses. Optimized conditions of the EF were pH 2.5, Fe2+ dose 5 mM, applied current 1 A, and reaction time 60 min. Under these conditions, the COD removal from the validation experiments was 63.5%. In total, 84.5% COD removal was achieved with the two-step treatment. By extending the reaction time in EF to ensure the discharge limits, 89.5% COD removal was reached, and the COD concentration decreased to 193 mg/L after 240 min. At the same time, the 5-day biochemical oxygen demand (BOD5)/COD ratio increased from 0.1 to 0.57 after 240 min. Consequently, the discharge limit was provided and the biodegradability was improved.

New Approach for Process Capability Analysis Using Multivariate Quality Characteristics

The evaluation of manufacturing processes aims to ensure that the processes meet the desired requirements. Therefore, process capability indexes are used to measure the capability of a process to meet customer requirements and/or engineering specifications. However, most of the manufacturing products have more than one quality characteristic (QC), in which case, the multivariate QCs should be evaluated together using a single capability index. The research in this article proposes a methodology for estimating the multivariate process capability index (PCI). First, the dimensions of the multivariate QCs are reduced into a new single variable using the proportion of the process specification region, by comparing each variable datapoint to its specification limits. Moreover, nonnormal data are transformed to normality using a root transformation algorithm. Then, a large data sample is generated using the parameters of the new variable. The generated data are compared to the specification limits to estimate the percent of nonconforming (PNC). Finally, the capability index of a given process datapoints is estimated using the PNC. Accordingly, managerial insights for the implementation of the proposed methodology in real industry are presented. The methodology was assessed by well-known multivariate samples from four different distributions, in which an algorithm was developed for generating these samples with their given correlations. The results show the effectiveness of the proposed methodology for estimating multivariate PCIs. Also, the results from this research outperform the previous published results in most cases.

Data Augmentation Techniques for Machine Learning Applied to Optical Spectroscopy Datasets in Agrifood Applications: A Comprehensive Review.

Machine learning (ML) and deep learning (DL) have achieved great success in different tasks. These include computer vision, image segmentation, natural language processing, predicting classification, evaluating time series, and predicting values based on a series of variables. As artificial intelligence progresses, new techniques are being applied to areas like optical spectroscopy and its uses in specific fields, such as the agrifood industry. The performance of ML and DL techniques generally improves with the amount of data available. However, it is not always possible to obtain all the necessary data for creating a robust dataset. In the particular case of agrifood applications, dataset collection is generally constrained to specific periods. Weather conditions can also reduce the possibility to cover the entire range of classifications with the consequent generation of imbalanced datasets. To address this issue, data augmentation (DA) techniques are employed to expand the dataset by adding slightly modified copies of existing data. This leads to a dataset that includes values from laboratory tests, as well as a collection of synthetic data based on the real data. This review work will present the application of DA techniques to optical spectroscopy datasets obtained from real agrifood industry applications. The reviewed methods will describe the use of simple DA techniques, such as duplicating samples with slight changes, as well as the utilization of more complex algorithms based on deep learning generative adversarial networks (GANs), and semi-supervised generative adversarial networks (SGANs).

Gum arabic-assisted green synthesis of biocompatible MoS2 nanoparticles for methylene blue photodegradation.

The current work reports the gum arabic-mediated greener synthesis of MoS2 nanoparticles (NPs) and its utilization for the solar light-assisted degradation of methylene blue. Furthermore, the safety analyses were performed on human-beneficial gut bacterium, L. delbrueckii, and human blood cells to confirm the biocompatibility of NPs synthesized. Antioxidant and antimicrobial activities were done to explore their usefulness for biological applications. Sonication and microwave treatment were used to obtain spherical 10-12nm MoS2 NPs as characterized using high-resolution transmission electron microscopy. FT-IR characterization revealed the occurrence of gum arabic on the NPs surface. The MoS2 NPs exhibited ~ 98% MB degradation within 8h under direct sunlight exposure. Moreover, the reusability studies have also been evaluated and free radical trapping experiments indicated that superoxide (•O2-) is the dominant active species of the reaction system. Furthermore, 98.89% MB degradation efficiency was observed within 150min in the case of real textile industry MB effluent samples. Untreated MB inhibited the growth of L. delbrueckii on MRS agar plates, while growth was observed in the case of MoS2 NPs-treated MB samples indicating safety of current MB degradation approach. MoS2 NPs inhibited the growth of E. coli MTCC1698 and S. aureus MTCC 3160 with 26mm and 21mm zone of inhibition, respectively. Furthermore, MoS2 NPs have shown antioxidant properties, resulting in 82.3 ± 0.43% of DPPH scavenging activity which was comparable to ascorbic acid (81.6 ± 0.6%), a standard antioxidant molecule. The NPs have not shown any hemolytic activity at 0.0625 and 0.125mg/mL doses to human blood proving their biocompatible nature. Gum arabic-synthesized biocompatible MoS2 NPs have good potential to treat MB released as waste from the textile industry and other biological applications.

Dynamic factor copula-based modeling for market risk optimization with an application to the real industry in China

As finance returns to its fundamental purpose of serving the real economy, its connections with various industries are strengthening. Accurately depicting the interdependence among these industries and mitigating financial risks has become increasingly critical. The dependence among China's real industries is dynamic rather than static, which is particularly pronounced during the COVID-19 pandemic. In this paper, we propose a dynamic factor model to optimize the risk of high-dimensional portfolios. To describe the dependence structure, we employ the factor copula model, driven by a GAS (Generalized Autoregressive Score) model. By combining the dynamic factor model with a mean-ES (Expected Shortfall) model, we construct a dynamic factor copula-mean-ES model. Our empirical findings, based on an analysis of 24 industries in China, suggest that the dynamic heterogeneous factor copula model is the most suitable for describing portfolio risk. Furthermore, the mean-ES model ensures the lowest portfolio risk for a given expected return. Accurate return predictions enable leveraging market information to develop a "good knowledge" of dynamic copula and risk optimization. This "good knowledge" of dynamic copulas facilitates precise return prediction and effective risk optimization of portfolios, thereby addressing the relationship between risk prevention and sustainability. Moreover, it reveals the internal connection between China's real industry and the risk landscape of the financial market.

부동산 산업의 경제적 파급효과와 충격반응 분석

This study examines the effect of the real estate industry on other industries as input and output resources. We define the real estate industry based on the two- and three-digit industry classification system as the recent real estate industry expands into other interdisciplinary business sectors. We also aggregate the sub-sector of the real estate-related industry into the broad real estate-related industry. We analyze input-out tables to find the spillover effect and value chain to other related industries by utilizing a total of 17 annual industry input-output tables from the Bank of Korea. This study also extends the spillover effect of the real estate industry along with a fluctuation of macroeconomic conditions. We find that the magnitude of the economic effect of the real estate industry on other industries is large when we classify the real estate industry into the two-digit industry classification system except for the analysis of the additive value impact. We also examine the impulse response analysis (IRA) by utilizing vector autoregression analysis. IRA indicates that macroeconomic variables affect the real estate industry and drive the same directional spillover effect on other industries. Our analysis segregates the national input-output analysis into the regional input-output analysis to identify the economic effect of real estate market fluctuation of the Seoul Metropolitan Area on other regions.

Development of Real Estate Brokerage Services Using Chatbot

Chatbot is a program that, uses artificial intelligence technology to communicate with users through natural language processing. The chatbot provides, an interactive interface and may provide an appropriate response to a user's question or request. This can facilitate communication between users and businesses, and reduce, manpower and costs.
 First, the convenience of real estate brokerage services can be improved by utilizing artificial intelligence technology of chatbots. Second, a more professional real estate brokerage service can be provided by combining chatbots and real estate experts. Third, by using, artificial intelligence technology of chatbots, customers' preferences can be identified and customized services tailored to them can be provided.
 Recently, real estate brokerage services, using chatbot have, been rapidly developing. Among the various development measures, improving real estate brokerage services using artificial intelligence technology of chatbot is one of the most likely measures. Therefore, the real estate brokerage industry needs to actively introduce chatbot technology to provide more, advanced services.
 For this reason, the purpose of this paper is to promote the, safety of real estate brokerage services using chatbot and the community of users so that real estate brokerage services can be further developed.

Cross-technology Communication between Visible Light and Battery-free RFIDs

The ubiquity of illumination facilities enables the versatile development of Visible Light Communication (VLC). VLC-based research achieved high-speed wireless access and decimeter-level indoor localization with complex equipment. However, it is still unclear whether the VLC is applicable for widely-used battery-free Internet-of-Things nodes, e.g., passive RFIDs. This paper proposes LightSign, the first cross-technology system that enables passive RFID tags to receive visible light messages. LightSign is compatible with commercial protocols, transparent to routine RFID communications, and invisible to human eyes. We propose a pseudo-timing instruction to achieve microsecond-level light switching to modulate the VLC message. To make it perceptible to passive RFIDs, we design an augmented RFID tag and prove its effectiveness theoretically and experimentally. With only one reply from an augmented tag, LightSign can decode 100-bit-long VLC messages. We evaluate LightSign in real industry environments and test its performance with two use cases. The results show that LightSign achieves up to 99.2% decoding accuracy in varying scenarios.

Natural sunlight-driven enhanced abatement of real resin industry wastewater using sludge-derived photocatalyst: Potential to generate carbon credits

The resin manufacturing industry is one of the industrial sectors that consumes huge quantities of water and generates enormous quantities of toxic wastewater with a high level of chemical oxygen demand (COD). In the present study, the performance of an optimized copped-doped raw galvanic waste-derived (CRGW) photocatalyst was evaluated in a novel stirred tank photoreactor in batch and continuous configurations as a possible solution for the treatment of real resin industry wastewater (RIW) under natural sunlight. Under optimal operating conditions, a COD removal rate of 70.3% and 43.5% was observed in batch and continuous configurations within 4 h, respectively. The photoreactor in continuous mode was aided by a magnetic bed that anchored the photocatalyst to the reaction segment without subsequent retrieval from the treated effluent. The mixture of pollutants in the RIW underwent photodegradation due to a pronounced bathochromic shift in UV absorption. Moreover, the XRD and XPS results confirmed the crystallinity and successful charge migration in the synthesized CRGW. The applicability of the photocatalyst for degradation of RIW was demonstrated through an economic analysis, and its specific cost was determined to be 9.5 USD/m3 of treated wastewater. Furthermore, this study aims to be a step toward generating carbon credits by avoiding the release of 18 tons of CO2/yr into the atmosphere by utilizing natural sunlight for photodegradation. The reduction, avoidance, or removal of greenhouse gases is pivotal in today’s world, and carbon credits can accelerate this transformational journey towards a sustainable future.

Digital innovation through cybersecurity learning factories

Globally, the cybersecurity workforce changes tremendous challenges. Shortages in specialised cybersecurity staff members essentially puts organisations at risk. New graduates can face difficulties in entering the cybersecurity domain due to a lack of experience and knowledge. However, with the advent of newer techniques for knowledge development, we find that learning factories offer a fresh perspective. Learning factories provide a mechanism to remove the barriers in the field of cybersecurity and cultivate a nurturing training environment. This paper looks at the modernisation of traditional training by covering the application of learning factories in the cybersecurity field. It aims to show how knowledge can be geared into more practical schemes to empower participants and expose them to critical cybersecurity skills. Through the paper, it will be demonstrated that learning factories can be used for real-world learning and information sharing. Learning factories embody the principles of knowledge sharing and promotes more efficient knowledge management. With the use of nominated tools and technologies cyber security learning factories can help measure the effectiveness of worker training as well provide for consistent facilitated training. Overall, learning factories can help to transform training and build knowledge application.
 Learning factories may be set up to tackle real industry challenges and are particularly useful in the field of cybersecurity. Using learning factories there is an opportunity to advance multi-dimensional cybersecurity skills and develop innovation in the field. Due to the added advantages of Information and Communication Technology (ICT) being virtually accessible, there is the added benefits of agility, responsiveness and increased engagement. Using a variety of modes, cybersecurity learning factories can combine the techniques of gamification, videos, multi-media and simulation. All of this provided an augmented and enriched experience for participants. Learning factories are a low-cost solution to replicating working environments thus assisting in skills development. Through the application of learning factories, a skilled workforce can be developed and cultivated.

Change Point-based Spatio-temporal Process Modeling of Image Degradation for Manufacturing Process

As an advent of smart factory technology, data-driven condition-based maintenance (CBM) is developed to automatically control the production process in engineering field. CBM usually focuses on diagnosing the production status based on real-time data from the sensors. In general manufacturing field, the performance of production equipment gradually decreases due to the wear or deterioration of equipment. To determine if the process is in-control, degradation modeling of observed data from the equipment and its statistical inference is conducted. In this paper, we propose image-based degradation modeling and change-point detection using spatio-temporal process (STP). To describe the deteriorating patterns of image observation, degradation based on spatial and temporal relationship is conducted. At the same time, change-point is estimated to distinguish the degradation under normal and abnormal production status. Through the application to the image stream in real industry, the proposed monitoring scheme `vely conduct the bi-phase representation providing the change-point of manufacturing processes.

Reliable Sampling Mechanism for Takagi–Sugeno Fuzzy NCSs Under Deception Cyberattacks for the Application of the Inverted Pendulum System

This article investigates the stability problem of Takagi–Sugeno fuzzy networked control systems (TSNCSs) under deception cyberattacks via a reliable sampling mechanism, which has important research value for applications in network security. First, a fuzzy weight functional method is introduced, and a new Lyapunov–Krasovskii functional is developed, which better incorporates nonlinear problems in the model. Then, in order to reduce the initial constraints, improved time delay closed-loop functions are constructed that consider the delay information and the characteristics of the sampling time points. Furthermore, considering the reliability issues of controllers in real industry, we establish some sufficient conditions and implement a novel reliable sampling controller with deception attacks (DAs) to control the asymptotic stability of TSNCS. Finally, the correctness and feasibility of the proposed method are verified experimentally using an inverted pendulum system.

A new transfer entropy approach based on information granulation and clustering for root cause analysis

In complex industrial facilities, a fault may generate in a unit and easily propagate to other units, resulting in degraded performance of process operations and deteriorating the situation. Therefore, it is of great importance to trace the propagation of faults and locate the root causes. The non-parametric causality inference technique known as Transfer Entropy (TE) is successful in identifying cause–effect relations for both nonlinear and linear processes. Even though, TE has two drawbacks limiting its practical use in real industries: (1) It requires the studied process be stationary whereas the presence of faults may lead to nonstationary changes; (2) The computational complexity of TE is high while the real application could be sensitive to the computational cost. Motivated by these issues, this paper proposes a new transfer entropy approach based on the information granulation and clustering to identify the root causes of faults. The contributions of the proposed approach are twofold: (1) the Information Granulation based Transfer Entropy (IGTE) and Information Granulation based Direct Transfer Entropy (IGDTE) are proposed to infer causal and direct causal relations; (2) an Ordering Points To Identify the Clustering Structure (OPTICS) clustering based Probability Density Function (PDF) estimator is designed to estimate joint/conditional probabilities based on the information granule. Two case studies are used to illustrate the effectiveness of the proposed approach.

Causal Disentanglement: A Generalized Bearing Fault Diagnostic Framework in Continuous Degradation Mode.

In recent years, the identification of out-of-distribution faults has become a hot topic in the field of intelligent diagnosis. Existing researches usually adopt domain adaptation methods to complete the generalization of diagnostic knowledge with the aid of target domain data, but the acquisition of fault samples in real industries is extremely time-consuming and costly. Moreover, most researches focus on samples with fixed fault levels, ignoring the fact that system degradation is a continuous process. In response to the above intractable problems, this article proposed a causal disentanglement network (CDN) to realize cross-machine knowledge generalization and continuous degradation mode diagnosis. In CDN, multitask instance normalization and batch normalization structure was proposed to learn task-specific knowledge and enhance the informativeness of the extracted features. On this basis, a causal disentanglement loss was proposed, which minimized the mutual information of features between subtask structures and captured the causal invariant fault information for better generalization. The experimental results proved the superiority and generalization ability of CDN, and the visualization results proved the performance of CDN in causality mining.

Ethereum Smart Contract Deployment for a Real Estate Management System (REMS) Implemented in Blockchain

The real industry is crucial sector of the world wide financial system, with significant economic, social and environmental implications. However, real estate transactions are often slow, complex and costly and can be prone to fraud errors, which can lead to significant financial losses and legal disputes. This paper proposes the adoption of blockchain technology in real estate cadastral systems as a solution to the challenges encountered in managing property ownership and transactions. We have described the implementation of our prototype called REMS (Real Estate Management System) and we have presented the deployment of smart contracts in Ethereum platform. Based on measurement, benchmarks and other observation of the system, we have evaluated the server usage of the blockchain network and decided whether Blockchain-as-a-Service (BaaS) should be involved or not in our system. The study results demonstrate the successful implementation of a real estate management system (REMS) using blockchain technology and Ethereum's smart contracts. This study is important because it confirms that similar solutions can be implemented in other areas of public administration, where the structure of the work is similar, i.e. where we deal with issuing documents to citizens.

Densely connected semi-Bayesian network for machinery fault diagnosis with non-ideal data

Fault diagnosis techniques (FDT) face the challenge of implementing model learning in the presence of limited, imbalanced, or non-ideal data, which is a fundamental and crucial problem that hinders their applications in real industrial scenarios. In this paper, a novel deep neural network (DNN), densely-connected semi-Bayesian network (DSBNet) is proposed to implement feature learning of vibration signals for machinery fault diagnosis in non-ideal data scenarios. Firstly, deep Bayesian learning is embedded into the multi-scale semi-Bayesian block (MSBB) of DSBNet as a local feature extraction and enhancement module. The re-parameterization operations of Bayesian convolutional layers in MSBB perform uncertainty inference on the features by learning the mean and variance of the Gaussian convolution kernel, achieving local expansion and enhancement of network features. Furthermore, convolutional features are integrated into MSBB to generate multi-scale semi-Bayesian features. An adaptive selector based on the multi-class and multi-scale attention mechanism is proposed to enhance effective semi-Bayesian features and suppress redundant features. The proposed methodology facilitates the adaptive end-to-end training of DSBNet, which enables it to match the scale of the current dataset and achieve optimal performance. The effectiveness of DSBNet is verified on two testbeds and multiple in-service computing devices in real industry. The testing results illustrate that DSBNet outperforms other state-of-the-art DNNs, especially in the non-ideal training data scenarios.

Synergetic removal of hazardous pollutants from aqueous environment using lignocellulosic biosorbents

Water pollution with dyes that are non-biodegradable has become a very concerning environmental problem in the world today. Therefore, the aim of this research was to optimize an adsorption process for the removal of one of the most harmful dyes Eriochrome Black T (EBT). For the first time, native hazelnut shells were used as biosorbents in this regard. The biomaterial was analysed by infrared spectroscopy with Fourier transformation, scanning electron microscopy, electron dispersive spectroscopy, Boehm titrations, physicochemical analysis and by determination of zero charge point and cation exchange capacity. Dye concentration was established by UV–Vis spectrophotometer and heavy metals concentration by flame atomic absorption spectrometry (FAAS). Characterization techniques of EBT showed successful removal from wastewater due to the obvious changes in morphological and composition structure of the hazelnut shell. The highest capacity of adsorption (5.8 mg/g) and removal efficiency (75.3%) was found at pH 3, sorbent mass 500 mg, 80 mg/L EBT concentration, 90 min contact time and solution temperature 20 °C. The pseudo-second-order model described the best investigated process and maximum Langmuir’s adsorption capacity was 27.55 mg/g. The interfering effect of seven heavy metals increased removal efficiency to 98% showing a positive impact. Furthermore, the process was found feasible with negative values of Gibbs free energy and exothermic with negative values of enthalpy change (-35.26 kJ/mol). The utilized green and environmentally friendly sorbent under optimized conditions could be used in real textile industries for EBT and heavy metals removal.

The importance of wheat conditioning in the production of wholemeal flours

<p>The grinding technique evolved along with the development of mankind and today it has become a real industry. The need to diversify flour assortments has raised the performance of the milling industry and, more than that, the products obtained are of superior quality due to the new machines and techniques for conditioning and grinding. By conditioning we mean the treatment of wheat with water or water and heat. It can be said that, among the technological operations of preparing wheat for mills, conditioning is the one that most influences the technological process of mills, the degree of extraction, the content of mineral substances of the flour, the separation of the germs and implicitly the baking properties of the flour. The essential parameters taken into account in this stage are the moisture content and the degree of hardness of the wheat grain. When determining the degree of extraction, the hectoliter weight has the major impact. Normally, an extraction of over 95% is sought when we talk about wholemeal flours. With the latest generation machines and their control with the help of new technology, 100% wholemeal wheat flours are obtained. The degree of extraction also influences the physico-chemical parameters of the flour and, of course, the ash content. Enzymatic activity of flour depends on the degree of extraction,  and wholemeal flour is richer in enzymes than white flour. This difference is primarily due to the distribution of non-uniform enzymes in the wheat grain, starting from the core of the wheat grain to its periphery. Wholemeal flour contains significant amounts of bran and germs – the finer the bran, the darker the color of the flour – so it is important that the bran particles are larger and rarer for the flour obtained from the endosperm to express its characteristics in a meaningful way in the finished product.</p>

Risk spillover in China’s real estate industry chain: a DCC-EGARCH-ΔCoVaR model

Real estate’s role in the financial crisis has forced central banks and academics to focus on the real estate risk’s spillover effects. However, findings on this matter are erratic and could differ from country to country. Prior research mostly ignored risk contagion at the level of the real industry and instead concentrated on real estate and financial institutions. Therefore, to analyze the risk spillover of China’s real estate industry from a novel perspective of the industrial chain, a mixed model (DCC-EGARCH-CoVaR model) is proposed in this work. It fixes the flaw in existing models’ inability to account for asymmetries and the weakness of traditional methods in explaining the time-varying and nonlinear risk infection process. The findings demonstrate that China’s real estate industry has a noticeable risk spillover effect on upstream and downstream industries. The downstream industry is the one most affected by risk spillover from the real estate sector, followed by the upstream industry, and the banking sector is the least affected. The risk spillover effects of macro uncertainties like the COVID-19 outbreak, the financial crisis, and the stock market fall on real estate vary significantly. These findings are helpful for regulators to prevent systematic financial risks and for institutional investors to make timely strategic asset allocations and adjustments.