Latest Research

Article  |  2020-11-24

Thermal Hydrolysis of Wastewater Sludge Followed by Fungal Fermentation for Organic Recovery and Hyphae Fiber Production

Wastewater sludge creates a difficult environmental problem for many large cities. This study developed a three-phase innovative strategy for sludge treatment and reduction, including thermal hydrolysis, fungal fermentation, and anaerobic digestion. Increasing the temperature during the treatment from 140 to 180 °C significantly improved the sludge reduction and organic release efficiencies (p < 0.05, one-way analysis of variance (ANOVA) for the triplicate experiments at each temperature). After two cycles of thermal hydrolysis, the overall volatile solid (VS) reduction ratios of the sludge were 36.6%, 47.7%, and 58.5% for treatment at 140, 160, and 180 °C, respectively, and the total organic carbon (TOC) conversion efficiency reached 28.0%, 38.0%, and 45.1%, respectively. The highest concentrations of carbohydrates and proteins were obtained at 160 °C in sludge liquor, whereas the amount of humic substances significantly increased for the treatment at 180 °C (p < 0.05, one-way ANOVA for the triplicate experiments at each temperature) due to the Maillard reaction. Fungal fermentation of the hydrolyzed sludge liquor with Aspergillus niger converted the waste organics to valuable fiber materials. The biomass concentration of fungal hyphae reached 1.30 and 1.27 g·L−1 in the liquor of sludge treated at 140 and 160 °C, corresponding to organic conversion ratios of 24.6% and 24.0%, respectively. The fungal hyphae produced from the sludge liquor can be readily used for making papers or similar value-added fibrous products. The paper sheets made of hyphae fibers had a dense structure and strong strength with a tensile strength of 10.75 N·m·g−1. Combining fungal fermentation and anaerobic digestion, the overall organic utilization efficiency can exceed 75% for the liquor of sludge treated at 160 °C.

Jia-jin Liang ,   Bing Li   et al.

Review  |  2020-11-24

Engineered Hybrid Materials with Smart Surfaces for Effective Mitigation of Petroleum-originated Pollutants

The generation and controlled or uncontrolled release of hydrocarbon-contaminated industrial wastewater effluents to water matrices are a major environmental concern. The contaminated water comes to surface in the form of stable emulsions, which sometimes require different techniques to mitigate or separate effectively. Both the crude emulsions and hydrocarbon-contaminated wastewater effluents contain suspended solids, oil/grease, organic matter, toxic elements, salts, and recalcitrant chemicals. Suitable treatment of crude oil emulsions has been one of the most important challenges due to the complex nature and the substantial amount of generated waste. Moreover, the recovery of oil from waste will help meet the increasing demand for oil and its derivatives. In this context, functional nanostructured materials with smart surfaces and switchable wettability properties have gained increasing attention because of their excellent performance in the separation of oil–water emulsions. Recent improvements in the design, composition, morphology, and fine-tuning of polymeric nanostructured materials have resulted in enhanced demulsification functionalities. Herein, we reviewed the environmental impacts of crude oil emulsions and hydrocarbon-contaminated wastewater effluents. Their effective treatments by smart polymeric nanostructured materials with wettability properties have been stated with suitable examples. The fundamental mechanisms underpinning the efficient separation of oil–water emulsions are discussed with suitable examples along with the future perspectives of smart materials.

Nisar Ali ,   Muhammad Bilal   et al.

Article  |  2020-10-14

Artificial intelligence and wireless communications

The applications of (AI) and (ML) technologies in have drawn significant attention recently. AI has demonstrated real success in speech understanding, image identification, and natural language processing domains, thus exhibiting its great potential in solving problems that cannot be easily modeled. AI techniques have become an enabler in to fulfill the increasing and diverse requirements across a large range of application scenarios. In this paper, we elaborate on several typical wireless scenarios, such as channel modeling, channel decoding and signal detection, and channel coding design, in which AI plays an important role in . Then, AI and information theory are discussed from the viewpoint of the information bottleneck. Finally, we discuss some ideas about how AI techniques can be deeply integrated with wireless communication systems.

Jun Wang ,   Rong Li   et al.

Article  |  2020-10-14

Multi-dimensional optimization for approximate near-threshold computing

The demise of Dennard’s scaling has created both power and utilization wall challenges for computer systems. As transistors operating in the near-threshold region are able to obtain flexible trade-offs between power and , it is regarded as an alternative solution to the scaling challenge. A reduction in supply voltage will nevertheless generate significant reliability challenges, while maintaining an error-free system that generates high costs in both and consumption. The main purpose of research on computer architecture has therefore shifted from improvement to complex multi-objective optimization. In this paper, we propose a three-dimensional optimization approach which can effectively identify the best system configuration to establish a balance among , , and reliability. We use a dynamic programming algorithm to determine the proper voltage and approximate level based on three predictors: system , consumption, and output quality. We propose an which uses a hardware/software co-design fault injection platform to evaluate the impact of the error on output quality under (NTC). Evaluation results demonstrate that our approach can lead to a 28% improvement in output quality with a 10% drop in overall efficiency; this translates to an approximately 20% average improvement in accuracy, power, and .

Jing Wang ,   Wei-wei Liang   et al.

Article  |  2020-10-14

A low-overhead asynchronous consensus framework for distributed bundle adjustment

Generally, the (DBA) method uses multiple worker nodes to solve the bundle adjustment problems and overcomes the computation and memory storage limitations of a single computer. However, the performance considerably degrades owing to the introduced by the additional block partitioning step and synchronous waiting. Therefore, we propose a low- consensus framework. A based asynchronous method is proposed to early achieve consensus with respect to the faster worker nodes to avoid waiting for the slower ones. A scene summarization procedure is designed and integrated into the block partitioning step to ensure that clustering can be performed on the small summarized scene. Experiments conducted on public datasets show that our method can improve the worker node utilization rate and reduce the block partitioning time. Also, sample applications are demonstrated using our large-scale culture heritage datasets.

Zhuo-hao Liu ,   Chang-yu Diao   et al.

Article  |  2020-10-14

Automatic synthesis of advertising images according to a specified style

Images are widely used by companies to advertise their products and promote awareness of their brands. The automatic synthesis of advertising images is challenging because the advertising message must be clearly conveyed while complying with the style required for the product, brand, or target audience. In this study, we proposed a to capture individual design attributes and the relationships between elements in advertising images with the aim of automatically synthesizing the input of elements into an advertising image according to a specified style. To achieve this multi-format advertisement design, we created a dataset containing 13 280 advertising images with rich annotations that encompassed the outlines and colors of the elements, in addition to the classes and goals of the advertisements. Using our probabilistic models, users guided the style of synthesized advertisements via additional constraints (e.g., context-based keywords). We applied our method to a variety of design tasks, and the results were evaluated in several perceptual studies, which showed that our method improved users’ satisfaction by 7.1% compared to designs generated by nonprofessional students, and that more users preferred the coloring results of our designs to those generated by the color harmony model and Colormind.

Wei-tao You ,   Hao Jiang   et al.