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The development of e-systems has given consumers and businesses access to a plethora of information, which has complicated the process of decision making. Document classification is one of the main decisions that any business adopts in their decision making to categorize documents into groups according to their structure. In this paper, we combined multi-agent systems (MASs), which is one of the IDSS systems, with Bayesian-based classification to filter out the specialization, collaboration, and privacy of distributed business sources to produce an efficient distributed classification system. Bayesian classification made use of MAS to eliminate distributed sources’ specialization and privacy. Therefore, incorporating the probabilities of various sources is a practical and swift solution to such a problem, where this method works the same when all the data are merged into a single source. Each intelligent agent can collaborate and ask for help from other intelligent agents in classifying cases that are difficult to classify locally. The results demonstrate that our proposed technique is more accurate than those of the non-communicated classification, where the results proved the ability of the utilized productive distributed classification system.

This study evaluated the efficacy of enzyme induced calcite precipitation (EICP) in restricting the mobility of heavy metals in soils. EICP is an environmentally friendly method that has wide ranging applications in the sustainable development of civil infrastructure. The study examined the desorption of three heavy metals from treated and untreated soils using ethylene diamine tetra-acetic acid (EDTA) and citric acid (C6H8O7) extractants under harsh conditions. Two natural soils spiked with cadmium (Cd), nickel (Ni), and lead (Pb) were studied in this research. The soils were treated with three types of enzyme solutions (ESs) to achieve EICP. A combination of urea of one molarity (M), 0.67 M calcium chloride, and urease enzyme (3 g/L) was mixed in deionized (DI) water to prepare enzyme solution 1 (ES1); non-fat milk powder (4 g/L) was added to ES1 to prepare enzyme solution 2 (ES2); and 0.37 M urea, 0.25 M calcium chloride, 0.85 g/L urease enzyme, and 4 g/L non-fat milk powder were mixed in DI water to prepare enzyme solution 3 (ES3). Ni, Cd, and Pb were added with load ratios of 50 and 100 mg/kg to both untreated and treated soils to study the effect of EICP on desorption rates of the heavy metals from soil. Desorption studies were performed after a curing period of 40 days. The curing period started after the soil samples were spiked with heavy metals. Soils treated with ESs were spiked with heavy metals after a curing period of 21 days and then further cured for 40 days. The amount of CaCO3 precipitated in the soil by the ESs was quantified using a gravimetric acid digestion test, which related the desorption of heavy metals to the amount of precipitated CaCO3. The order of desorption was as follows: Cd > Ni > Pb. It was observed that the average maximum removal efficiency of the untreated soil samples (irrespective of the load ratio and contaminants) was approximately 48% when extracted by EDTA and 46% when extracted by citric acid. The soil samples treated with ES2 exhibited average maximum removal efficiencies of 19% and 10% when extracted by EDTA and citric acid, respectively. It was observed that ES2 precipitated a maximum amount of calcium carbonate (CaCO3) when compared to ES1 and ES3 and retained the maximum amount of heavy metals in the soil by forming a CaCO3 shield on the heavy metals, thus decreasing their mobility. An approximate improvement of 30% in the retention of heavy metal ions was observed in soils treated with ESs when compared to untreated soil samples. Therefore, the study suggests that ESs can be an effective alternative in the remediation of soils contaminated with heavy metal ions.

The wireless power transfer (WPT) system holds potential as a viable solution for charging electric vehicles (EVs) owing to its benefits including safety, automated operation, efficiency, and simplicity. Among the WPT technologies, inductive power transfer (IPT) stands out as particularly well-suited for charging EV batteries. This is primarily due to its capability to transmit high power across considerable air gap distances, accommodating the ground clearance requirements of most EVs, operating automatically without driver involvement, ensuring safety and convenience even in challenging conditions such as snow, rain, and dust, and offering maintenance-free operation by eliminating the need for plug-in connections. This manuscript provides a comprehensive exploration and analysis of the progress made in IPT technology. The manuscript introduces the operational principle of the IPT system and highlights the benefits of its components. Additionally, it discusses the transmitter and receiver architectures, outlines the characteristics of various charging pads, in case of both stationary and in-motion charging scenarios. Furthermore, it delves into different compensation circuit topologies and various WPT designs based on compensating structures associated with the IPT system. It also categorizes the converter topologies utilized in the system and presents the operating technique for each one. In addition, the ongoing research and development (R&D) endeavors pertaining to each technology are discussed, addressing challenges, existing gaps, and offering recommendations for further advancements in both stationary and in-motion charging applications.

In the present work, a novel hybrid solar-based smart building energy system is introduced and studied. The system comprises innovative photovoltaic-thermal-cooling (PVTC) panels integrated with hot and cold storages with two-way interaction with electricity, heat, and cooling networks (if any). The proposed system is compared with PV-based systems integrated with battery and heat pump for a case study complex building in Aarhus, Denmark. The comparison is conducted by evaluating the performance and economic indicators and investigating the effect of significant parameters on each scenario via a parametric study. Furthermore, the optimal operating conditions and sizing of the proposed system are determined using the genetic algorithm method considering initial cost and traded energy with local energy networks as the objective functions. The comparison results show that the proposed solution is the most cost-effective scenario with the lowest initial cost of about 457,000 $ and a payback period of 6.6 years. This is mainly due to the simultaneous interaction with electricity/heat/cooling networks as well as the elimination of the battery and the heat pump, which are offered by the proposed scenario. It is shown that, in comparison to PV panels, the PVTC can produce 328.7 MWh and 125.6 MWh extra heat and cooling annually. The scatter distribution of significant parameters shows that the panel area and heat storage capacity are not sensitive parameters, and keeping the cold storage capacity at the lower bound is a techno-economically better option.

A fixed Filter-Thyristor Controlled Reactor (FF-TCR) type of compensator is proposed to improve the power factor of a single phase thyristor-controlled inductive load and it is shown that this compensator gives better power factor improvement than the more generally used FC-TCR type of compensator. Analysis is carried out for the exact equivalent circuit of an FF-TCR compensator containing two filter branches tuned to the third and the fifth harmonics respectively. It is shown that with an FF-TCR compensator the power factor improvement is much better when the source impedance is large. However, even with a small source impedance, a reasonable improvement in power factor is realised. It is further shown that even when the source impedance is large the distortion in the terminal voltage waveform is kept within reasonable limits. The analytical results are verified experimentally.

AbstractThis work reports the synthesis, characterization, photophysical, and photovoltaic properties of five new thieno[3,2‐b][1]benzothiophene isoindigo (TBTI)‐containing low bandgap donor–acceptor conjugated polymers with a series of comonomers and different side chains. When TBTI is combined with different electron‐rich moieties, even small structural variations can have significant impact on thin film morphology of the polymer:phenyl C70 butyric acid methyl ester (PCBM) blends. More importantly, high‐resolution electron energy loss spectroscopy is used to investigate the phase‐separated bulk heterojunction domains, which can be accurately and precisely resolved, enabling an enhanced correlation between polymer chemical structure, photovoltaic device performance, and morphology.

Abstract In this paper, we examine the time-varying causal relationship between green bonds and other assets including US conventional bonds, WilderHill clean energy (equity) index, and CO2 emission allowances price during the period spanning from 30 July 2014 to 10 February 2020. We apply the novel time-varying Granger causality test (Shi et al. 2018) based on the recursive evolving algorithm introduced by Phillips et al. (2015a, 2015b) for controlling financial bubbles to detect real–time causality, detecting possible changes in the causal direction and dating financial turbulences, The study based on this algorithm reveals a significant causality running from the US 10-year Treasury bond index to green bonds starting from the end of the year 2016 until the end of the sample period. Besides, we find that the link CO2 emission allowances price causing green bonds is significant from the beginning of the sample period to the end of the year 2015. Furthermore, by using the recursive-evolving causality algorithm of the Shi et al. (2018) test, we find that the causality running from the clean energy index to green bonds is very limited to the year 2019. On the other hand, there is no significant causality running from green bonds to all considered assets, indicating no predictive power for this asset in its proper domain, which is not yet examined in the literature.

The availability of food in a country and the capacity of its citizens to access, acquire, and receive enough food are both referred to as having food security. A crucial component of food security is ensuring and maintaining safe and high-quality goods, which the supply chain process should take into due deliberation. To enhance the food supply chain, organic and wholesome food items should be encouraged. Although packaged goods are evaluated and approved by legal authorities, there is no mechanism in place for testing and assessing the market’s available supply on a regular basis. As a result, food manufacturers are compelled to provide nutritious and healthy products. In this research, we propose an explainable artificial intelligence-based faster regions with convolutional neural networks (XAI-based Faster RCNN) model to evaluate the contents of the food items through user-friendly web-based front-end design and QR code. To validate each communication token in the network, an elliptic curve integrated encrypted scheme (ECIES) based on blockchain technology is utilized. Additionally, artificial rabbit optimization (ARO) is used to register each user and assign him a key. The user will gain a deeper understanding of machine learning (ML) and AI applications using the XAI technique. An EAI-based Faster RCNN model is proposed to help digitize information about food products, rapidly retrieve the information, and discover any hidden information in the quick response (QR) code that could have impacted the safety and quality of the food. The results of the experiments indicated that the proposed method requires less response time than other existing methods with the increase of payload and users. The Shapley additive explanation is used to obtain a legal plea for the laboratory test based on the nutritional information present in the QR code. The benefits provided by ECIES-based blockchain technology assist policymakers, manufacturers, and merchants in efficient decision-making, minimizing public health hazards, and improving welfare. This paper also shows that the accuracy achieved by the proposed method reached 99.53%, with the lowest processing time.