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Recently, due to the practicability in several domains, generative adversarial network (GAN) has successfully been adopted in the field of natural language generation (NLG). The purpose of this paper focuses on improving the quality of text and generating sequences similar to human writing for several real applications.

A novel model, GAN², is developed based on a GAN with dual adversarial architecture. We train the generator by an internal discriminator with a beam search technique to improve the quality of generated sequences. Then, we enhance the generator with an external discriminator to optimize and strengthen the learning process of sequence generation.

The proposed GAN² model could be utilized in widespread applications, such as chatbots, machine translation and image description. By the proposed dual adversarial structure, we significantly improve the quality of the generated text. The average and top-1 metrics, such as NLL, BLEU and ROUGE, are used to measure the generated sentences from the GAN² model over all baselines. Several experiments are conducted to demonstrate the performance and superiority of the proposed model compared with the state-of-the-art methods on numerous evaluation metrics.

Generally, reward sparsity and mode collapse are two main challenging issues when adopt GAN to real NLG applications. In this study, GAN² exploits a dual adversarial architecture which facilitates the learning process in the early training stage for solving the problem of reward sparsity. The occurrence of mode collapse also could be reduced in the later training stage with the introduced comparative discriminator by avoiding high rewards for training in a specific mode. Furthermore, the proposed model is applied to several synthetic and real datasets to show the practicability and exhibit great generalization with all discussed metrics.

The objective is to ascertain and amalgamate the extant data about farmers' plans for climate change adaptation and mitigation. The chapter provides comprehensive insights to help farmers and policymakers make informed and relevant decisions.

We conducted five-step scoping review procedure with key databases: Scopus, ProQuest, Science Direct, Springer Link and PLOS ONE. The keywords including climate change AND Agriculture AND production AND rice AND farmer AND farming AND mitigation AND adaptation AND resilience AND Indonesia focusing on publications from 2013 to 2023. We employed PRISMA-Scr protocol and data were thematically extracted using NVivo 12 Plus tool.

The search yielded 878 records, of which 116 were screened in full text, and 37 were included in the review. Key findings indicate that adaptation strategies and management planning involving social capital and social networks are essential for anticipating and responding to climate change-induced disasters. The collaboration between farmers, farmer groups, government and NGOs is pivotal for climate change mitigation.

The research implications highlight the need for targeted policy interventions and capacity-building initiatives to enhance farmers' knowledge and skills in climate change adaptation, fostering collaboration among various stakeholders.

The originality and value of the paper lie in the comprehensive review of the adaptive measures and strategies employed by farmers in Indonesia to mitigate the impacts of climate change on agriculture. This contribution is significant as it not only addresses local challenges but also connects them to broader global discussions on food security and climate adaptation.

The purpose of this study is analysis of deformation and vibrations of turbine blades produced by high electrolyte pressure during electrochemical machining.

An experimental setup was designed, experiments were conducted and the obtained results were compared with the finite element results. The deformations were measured according to various flow rates of electrolyte. In finite element calculations, the pressure distribution created by the electrolyte on the blade surface was obtained in the ANSYS® (A finite element analysis software) Fluent software and transferred to the static structural where the deformation analysis was carried out. Three different parameters were examined, namely blade thickness, blade material and electrolyte pressure on blade disk caused by mass flow rate. The deformation results were compared with the gap distances between cathode and anode.

Large deformations were obtained at the free end of the blade and the most curved part of it. The appropriate pressure values for the electrolyte to be used in the production of blisk blades were proposed numerically. It has been determined that high pressure applications are not suitable for gap distance lower than 0.5 mm.

When the literature is examined, it is required that the high speed flow of the electrolyte is desired in order to remove the parts that are separated from the anode from the machining area during electrochemical machining. However, the electrolyte flowing at high speeds causes high pressure in the blisk blades, excessive deformation and vibration of the machined part, and as a result, contact of the anode with the cathode. This study provides important findings for smooth electro chemical machining at high electrolyte flows.

Cultural and creative industries (CCIs) have been argued to play an important role in achieving the sustainable development goals (SDGs). However, the mechanisms through which CCIs contribute to the attainment of SDGs remain underexplored. In this study, the authors adopt a network perspective to examine how the structure of a local CCI network relates to its local sustainability performance. By examining a database of 210,182 networked firms out of 1.34 million CCI firms across 294 cities in China, the authors conclude that both the scale and the density of a local CCI network improve the city’s performance in terms of attaining SDGs 8–12. The authors discuss the implications of these findings and propose future research avenues in international business.

The purpose of this study is to provide a framework to detect and prevent virtual property theft in virtual world environments. The issue of virtual property theft is a serious problem which has ramifications in both the real and virtual world. Virtual world users invest a considerable amount of time, effort and often money to collect virtual property, only to have them stolen by thieves. Many virtual property thefts go undetected and often only discovered after the incident has occurred.

This paper presents the design of an autonomic detection framework to identify virtual property theft at two key stages: account intrusion and virtual property trades. Account intrusion is an unauthorized user attempting to gain access to an account and unauthorized virtual property trades are trading of items between two users which exhibit theft characteristics.

Initial tests of this framework on a synthetic data set show an 80 per cent detection rate. This framework allows virtual world developers to tailor and extend it to suit their specific requirements. It provides an effective way of detecting virtual property theft while being low maintenance, user friendly and cost effective.

To the author’s knowledge, there is no detection framework, system or tool that works on virtual property theft detection in virtual world environments without access to authentic virtual world data or attack data (because of privacy issues and unwillingness of virtual world environments companies to collaborate). The topic of virtual property theft, lack of existing labelled data sets, user anonymity, size of virtual world environments data sets and privacy issues with virtual world companies and a number of other critical factors distinguish this paper from previous studies.

This paper aims to develop an architecture for 3D printers in an Industrial Internet of Things (IIoT) controlled automated manufacturing environment. An algorithm is proposed to estimate the electrical energy consumption of 3D printing jobs, which is used, 3D Printing, Sustainable Manufacturing, Industry 4.0, Electrical Energy Estimation, IIoT to schedule printing jobs on optimal electrical tariff rates.

An IIoT-enabled architecture with connected pools of 3D printers and an Electrical Energy Estimation System (EEES) are used to estimate the electrical energy requirement of 3D printing jobs. EEES applied the combination of Maximum Likelihood Estimation and a dynamic programming–based algorithm for estimating the electrical energy consumption of 3D printing jobs.

The proposed algorithm decently estimates the electrical energy required for 3D printing and able to obtain optimal accuracy measures. Experiment results show that the electrical energy usage pattern can be reconstructed with the EEES. It is observed that EEES architecture reduces the peak power demand by scheduling the manufacturing process on low electrical tariff rates.

Proposed algorithm is validated with limited number of experiments.

IIoT with 3D printers in large numbers is the future technology for the automated manufacturing process where controlling, monitoring and analyzing such mass numbers becomes a challenging task. This paper fulfills the need of an architecture for industries to effectively use 3D printers as the main manufacturing tool with the help of IoT. The electrical estimation algorithm helps to schedule manufacturing processes with right electrical tariff.

The purpose of this study is to investigate which of the two carbon allowance allocation methods (CAAMs), i.e. grandfathered system carbon allowance allocation (GCAA) and baseline system carbon allowance allocation (BCAA), is more beneficial to capital-constrained supply chains under the carbon emission allowance repurchase strategy (CEARS).

Adopting CEARS to ease the capital-constrained supply chains, this study develops two-period game models with manufacturers as leaders and retailers as followers from the perspective of profit and social welfare maximization under two CAAMs (GCAA and BCAA), where the first period produces normal products, and the second period produces low-carbon products.

First, higher carbon-saving can better use CEARS and achieve a higher supply chain profit under the two CAAMs. However, the higher the end-of-period carbon price is, the lower the social welfare is. Second, when carbon-saving is small, GCAA achieves both economic and environmental benefits; BCAA reduces carbon emissions at the expense of economic benefit. Third, the supply chain members gain higher profits and social welfare under GCAA, so the government and supply chain members are more inclined to choose GCAA.

By analyzing the profits and total carbon emissions of capital-constrained supply chains under GCAA and BCAA, this study provides theoretical references for retailers and capital-constrained manufacturers. In addition, by comparing the difference in social welfare under GCAA and BCAA, it provides a basis for the government to choose a reasonable CAAM.

Recent studies have shown that hyperuricemia is a predictor of non-communicable disease and an increment of mortality rate. Also, elevated serum uric acid may be associated with obesity in the adult population. This study aims to evaluate the association between serum uric acid levels with metabolic parameters and risk of obesity in the Iranian population.

The cross-sectional study was done on 550 participants, who were referred to a hospital for elective angiography in Rasht, Iran; anthropometric indices (waist circumference (WC) and body mass index (BMI)) and hematological factors were measured using the standard approaches. Based to the angiography results, the severity of atherosclerosis was defined.

The mean (SD) concentration of serum uric acid for all participants was 5.15 (1.37) mg/dl. Individuals who were at the highest tertile had higher mean (SD) of weight (p = 0.004), creatinine and blood urea nitrogen (p < 0.001) lower fasting blood sugar (FBS) (p = 0.000) and HbA1c (p = 0.016), and they were mostly men compared with those in the lowest tertile. After adjusting for confounders, FBS (ß = –0.145, p = 0.001) and HbA1c (%) (ß = –0.130, p = 0.019) had inverse and weight (ß = 0.156, p = 0.001) had direct association with serum uric acid. After adjustment for additionally potential confounders subjects in the highest tertile of serum uric acid had 92 per cent higher chance of obesity compared with subjects in the lowest tertile (OR 1.92; 95 per cent CI 1.13, 3.23).

The present study has concluded that increase serum uric acid related to high risk of obesity and low mean of FBS and HbA1c.

The purpose of this paper is to characterize pressure non‐uniformity in a wafer‐to‐wafer bond chamber using pressure sensitive paper.

Pressure non‐uniformity in a wafer‐to‐wafer bond chamber is characterized using pressure sensitive paper. The effect of poor pressure uniformity is discussed, and the non‐uniformity corrected for use in a eutectic Au/Sn based wafer‐to‐wafer bond.

Several types of under solder metallization were also investigated, with Nb/Au seed metal providing the best overall result with good solder compression, liquid proof seal and minimal solder spill‐out. Solder compression versus pressure applied was studied to achieve an excellent gap control (2‐3 μm) between the bonded substrates.

This paper shows that characterization of applied pressure measured directly at the substrate is an important aspect in the development of high yielding bond processes.

The purpose of this chapter is to ask and answer the question of what would happen if Genetically Modified Organism (GMO) plant materials were banned. We report on two studies – one with United States only ban and one with a global ban. We used a global computable general equilibrium (CGE) model, Global Trade Analysis Project (GTAP), for the analysis. This model has been used in hundreds of published papers on trade, energy, land use, and environmental issues. Our use of the model was to estimate the crop yield benefits for the major GMO crops, and then to convert this to a loss if the GMO traits were banned. We then shocked the GTAP model with the yield losses and estimate economic, land use, and greenhouse gas (GHG) emission impacts. We found that losing the GM technology would cause commodity and food prices to increase and also bring about a significant increase in GHG emissions. The increase in emissions is caused by the need to convert forest and pasture to compensate for the lost production. Another interesting conclusion of the global ban study is that economic well-being for the United States, the world’s largest GMO user, actually increases with a ban. Many regions that ban or use little GMO varieties like the European Union, India, China, and Japan all see economic well-being decrease. These counterintuitive results are driven mainly by trade patterns. Therefore GMO technology helps agriculture reduce its carbon footprint. Without this technology, agricultural land-use GHG emissions increase as do food prices. Some groups would like to see GMOs banned and also see GHG emissions fall. You cannot have it both ways.