Recently, one critical issue looms large in the field of recommender systems - there are no effective benchmarks for rigorous evaluation - which consequently leads to unreproducible evaluation and unfair comparison. We, therefore, conduct studies from the perspectives of practical theory and experiments, aiming at benchmarking recommendation for rigorous evaluation. Regarding the theoretical study, a series of hyper-factors affecting recommendation performance throughout the whole evaluation chain are systematically summarized and analyzed via an exhaustive review on 141 papers published at eight top-tier conferences within 2017-2020. We then classify them into model-independent and model-dependent hyper-factors, and different modes of rigorous evaluation are defined and discussed in-depth accordingly. For the experimental study, we release DaisyRec 2.0 library by integrating these hyper-factors to perform rigorous evaluation, whereby a holistic empirical study is conducted to unveil the impacts of different hyper-factors on recommendation performance. Supported by the theoretical and experimental studies, we finally create benchmarks for rigorous evaluation by proposing standardized procedures and providing performance of ten state-of-the-arts across six evaluation metrics on six datasets as a reference for later study. Overall, our work sheds light on the issues in recommendation evaluation, provides potential solutions for rigorous evaluation, and lays foundation for further investigation.

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We propose a manager-worker framework (the implementation of our model is publically available at: https://github.com/zcaicaros/manager-worker-mtsptwr) based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), i.e. multiple-vehicle TSP with time window and rejections (mTSPTWR), where customers who cannot be served before the deadline are subject to rejections. Particularly, in the proposed framework, a manager agent learns to divide mTSPTWR into sub-routing tasks by assigning customers to each vehicle via a Graph Isomorphism Network (GIN) based policy network. A worker agent learns to solve sub-routing tasks by minimizing the cost in terms of both tour length and rejection rate for each vehicle, the maximum of which is then fed back to the manager agent to learn better assignments. Experimental results demonstrate that the proposed framework outperforms strong baselines in terms of higher solution quality and shorter computation time. More importantly, the trained agents also achieve competitive performance for solving unseen larger instances.

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Bisphenols are important industrial materials for example for the production of plastics, but are also well known for their adverse health effects, in particular bisphenol A (BPA) is an endocrine disruptor. The widespread use of plastics has raised concerns. Therefore, the removal of bisphenols from wastewater has sparked the interest of the scientific community. This work introduces a novel hybrid technique of micro-nano bubbles assisted laccase (MNB-Lac) to degrade bisphenols in water. The feasibility of MNB-Lac using BPA as a model contaminant was evaluated by comparing with MNB, Lac, ultrasound (UL), UL-Lac, and UL-MNB-Lac. Comprehensive investigations were carried out to understand the specific influences of key process parameters including the initial pollutant concentration, temperature, air intake, pH, outlet pipe length, and Lac concentration on BPA degradation. The alkaline environment and extended length of outlet pipe could improve the degradation efficiency further. MNB-Lac exhibited 2.3–6.2 folds higher BPA degradation and less time than the other above process under the optimal parameters. The mechanism of MNB-Lac revealed that the generation of hydroxyl radical, high O₂ solubility, and high mass transfer efficiency induced by MNB play important roles on enhancing the degradation catalyzed by Lac. MNB-Lac was successfully used for treating bisphenol B, bisphenol C, and the mixture of three bisphenols with high removal efficiency. Subsequently, these degradation products were analyzed by GC–MS. MNB-Lac potentially represents an innovative technology with considerable advantages in contaminant cleanup and time efficiency for treating phenolic contaminated water. Furthermore, the findings provide new insights into the enhancement of the performance of an oxidizing enzyme by introducing MNB technology.

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Cross-Entropy Method (CEM) is a gradient-free direct policy search method, which has greater stability and is insensitive to hyperparameter tuning. CEM bears similarity to population-based evolutionary methods, but, rather than using a population it uses a distribution over candidate solutions (policies in our case). Usually, a natural exponential family distribution such as multivariate Gaussian is used to parameterize the policy distribution. Using a multivariate Gaussian limits the quality of CEM policies as the search becomes confined to a less representative subspace. We address this drawback by using an adversarially-trained hypernetwork, enabling a richer and complex representation of the policy distribution. To achieve better training stability and faster convergence, we use a multivariate Gaussian CEM policy to guide our adversarial training process. Experiments demonstrate that our approach outperforms state-of-the-art CEM-based methods by 15.8% in terms of rewards while achieving faster convergence. Results also show that our approach is less sensitive to hyper-parameters than other deep-RL methods such as REINFORCE, DDPG and DQN. @en

Lignin valorization may offer a sustainable approach to achieve a chemical industry that is not completely dependent on fossil resources for the production of aromatics. However, lignin is a recalcitrant, heterogeneous, and complex polymeric compound for which only very few catalysts can act in a predictable and reproducible manner. Laccase is one of those catalysts and has often been referred to as an ideal “green” catalyst, as it is able to oxidize various linkages within lignin to release aromatic products, with the use of molecular oxygen and formation of water as the only side product. The extent and rate of laccase-catalyzed lignin conversion were measured using the label-free analytical technique isothermal titration calorimetry (ITC). IITC provides the molar enthalpy of the reaction, which reflects the extent of conversion and the time-dependent power trace, which reflects the rate of the reaction. Calorimetric assessment of the lignin conversion brought about by various fungal and bacterial laccases in the absence of mediators showed marked differences in the extent and rate of conversion for the different enzymes. Kraft lignin conversion by Trametes versicolor laccase followed Michaelis–Menten kinetics and was characterized by the following thermodynamic and kinetic parameters ΔH_ITC = −(2.06 ± 0.06)·10³ kJ mol⁻¹, K_M = 6.6 ± 1.2 μM and V_max = 0.30 ± 0.02 U/mg at 25°C and pH 6.5. We envision calorimetric techniques as important tools for the development of enzymatic lignin valorization strategies.

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For hearing-impaired listeners, both ambient noise suppression and binaural cues preservation of directional sources are required, such that a complete spatial awareness of the acoustic scene can be obtained. It was shown that the binaural multichannel Wiener filter (MWF) with partial noise preservation can achieve joint noise reduction and binaural cues preservation and incorporating an external microphone signal improves the performance of binaural MWFs. Motivated by this, we propose a binaural MWF incorporating external wireless devices in this paper. First, we theoretically analyze the performance of the MWF in terms of output signal-to-noise ratio (SNR) and binaural cues preservation errors. As in practice the external devices are power driven with a limited amount of battery resource and the power consumption heavily depends on the transmission rate, given an expected noise reduction performance we then optimize the bit-rate for a single external microphone. Further, we consider to minimize the total power consumption over multiple external devices under a constraint on the output SNR, which turns out to be a rate distribution problem. The proposed rate-distributed binaural MWF is evaluated using a hearing-aid setup with various dynamics. It is shown that the proposed method can obtain a desired SNR at a much lower bit-rate, and an expected trade-off between SNR gain and binaural cues preservation accuracy can be obtained by optimizing the bit-rates. Increasing the bit-rates improves both instrumental speech quality and speech intelligibility.@en

Laccase is a versatile multicopper oxidase that holds great promise for many biotechnological applications. For such applications, it is essential to explore good biocatalytic systems for high activity and recyclability. The feasibility of membrane enclosed enzymatic catalysis (MEEC) for enzyme recycling with laccase was evaluated. The dialysis membrane enclosed laccase catalysis (DMELC) was tested for the conversion of the non-phenolic model substrate 2,2′-Azino-bis(3-ethylbenzthiazoline-6-sulfonate) (ABTS). Trametes versicolor laccase was found to be completely retained by the dialysis membrane during the process. The ABTS total conversion after DMELC reached the same values as the batch reaction of the enzyme in solution. The efficiency of DMELC conversion of ABTS under different process conditions including shaking speed, temperature, ABTS concentration and pH was investigated. The repetitive dialysis minimally affected the activity and the protein content of the enclosed laccase. DMELC retained 70.3 ± 0.8% of its initial conversion after 5 cycles. The usefulness of MEEC extends to other enzymes with the benefit of superior activity of an enzyme in solution and the recyclability which is normally only obtained with immobilized enzymes.[Figure not available: see fulltext.]

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Hypericin is a naturally occurring compound synthesized by certain species of the genus Hypericum, with various pharmacological effects. It is used as a natural photosensitizing agent with great potential in photodynamic therapy. This review discusses the latest results about the biosynthetic pathways and chemical synthetic routes to obtain hypericin. Although many analysis methods can be used for the determination of hypericin purity, HPLC has become the method of choice due to its fast and sensitive analyses. The extraction and purification of hypericin are also described. Hypericin can be used as a photosensitizer due to a large and active π-electron conjugated system in its structure. Medical applications of hypericin are not easy due to several unsolved practical problems, which include hypericin phototoxicity, poor solubility in water, and extreme sensitivity to light, heat, and pH.

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Anaerobic fermentation for volatile fatty acids (VFAs) production is a promising technology for sewage sludge treatment and reutilization. The fundamental aspects and actual developments of VFA production from sewage sludge by anaerobic fermentation are critically reviewed. Based on fermentation mechanisms and microbiological analysis, controlling strategies for VFA production from sewage sludge were concluded into three aspects, namely methanogenesis inhibition, hydrolysis acceleration, and product optimization. At present, the preliminary process for VFA production has been determined, but research in this field is still full of challenges, as, for instance, the concentration and purity of VFAs are too low for further commercial applications. Though current attempts of pretreatment, cofermentation, high-solid fermentation, and liquid fermentation have shown great potentials in overcoming those bottlenecks, including low conversion rate of sludge organics, inappropriate compositions of substrates, low concentration of VFAs, and high cost of operation, there are still some possible difficulties for full-scale applications in both economic benefits and technical feasibility. Therefore, the application of VFAs from sewage sludge on externally added carbon for nitrogen and phosphorus removals in wastewater plants seems more practical recently. Finally, based on the difficult situations, most important perspectives need to be seriously considered before extensively preceding large-scale application of this technology.@en

With tremendous amount of recommendation algorithms proposed every year, one critical issue has attracted a considerable amount of attention: there are no effective benchmarks for evaluation, which leads to two major concerns, i.e., unreproducible evaluation and unfair comparison. This paper aims to conduct rigorous (i.e., reproducible and fair) evaluation for implicit-feedback based top-N recommendation algorithms. We first systematically review 85 recommendation papers published at eight top-tier conferences (e.g., RecSys, SIGIR) to summarize important evaluation factors, e.g., data splitting and parameter tuning strategies, etc. Through a holistic empirical study, the impacts of different factors on recommendation performance are then analyzed in-depth. Following that, we create benchmarks with standardized procedures and provide the performance of seven well-tuned state-of-the-arts across six metrics on six widely-used datasets as a reference for later study. Additionally, we release a user-friendly Python toolkit, which differs from existing ones in addressing the broad scope of rigorous evaluation for recommendation. Overall, our work sheds light on the issues in recommendation evaluation and lays the foundation for further investigation. Our code and datasets are available at GitHub (https://github.com/AmazingDD/daisyRec).

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Multi-microphone speech enhancement methods typically require a reference position with respect to which the target signal is estimated. Often, this reference position is arbitrarily chosen as one of the reference microphones. However, it has been shown that the choice of the reference microphone can have a significant impact on the final noise reduction performance. In this paper, we therefore theoretically analyze the impact of selecting a reference on the noise reduction performance with near-end noise being taken into account. Following the generalized eigenvalue decomposition (GEVD) based optimal variable span filtering framework, we find that for any linear beamformer, the output signal-to-noise ratio (SNR) taking both the near-end and far-end noise into account is reference dependent. Only when the near-end noise is neglected, the output SNR of rank-1 beamformers does not depend on the reference position. However, in general for rank-r beamformers with r>1 (e.g., the multichannel Wiener filter) the performance does depend on the reference position. Based on these, we propose an optimal algorithm for microphone reference selection that maximizes the output SNR. In addition, we propose a lower-complexity algorithm that is still optimal for rank-1 beamformers, but sub-optimal for the general rank-r beamformers. Experiments using a simulated microphone array validate the effectiveness of both proposed methods and show that in terms of quality, several dB can be gained by selecting the proper reference microphone.

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Hypericin is considered to be the most biologically active substance in the crude extract of Hypericum perforatum L. (also known as St. John's wort) and has a wide range of pharmacological effects. In this study, a high resolution high performance liquid chromatography method for determining hypericin was established by comparing different chromatographic conditions. Xylanase-assisted extraction and microwave-assisted extraction can improve the extraction yield of hypericin significantly. And the coupling strategy between two methods resulted in a significant difference on the extraction yield. Microwave-assisted extraction after xylanase-assisted extraction was found to be the most efficient strategy for extracting hypericin. The yield was 0.319 ± 0.006 mg g ⁻¹ , which was a 209.7% increase over unassisted extraction. The combination of enzyme assisted extraction followed by microwave assisted extraction can be more commonly applied to improve extraction efficiency of bioactive compounds from plants.

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We report the use of commercial laundry powder as a biocatalyst for a range of lipase-catalysed reactions including (trans)esterification, ester hydrolysis and chemoenzymatic epoxidation reactions. The enzymatic laundry powder exhibited excellent stability and recyclability, making it a readily available and cheap biocatalyst for chemical transformations.

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Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed to leverage side information of users or items (e.g., social network and item category), demonstrating a high degree of effectiveness in improving recommendation performance. This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information. Specifically, we provide an overview of state-of-the-art recommendation algorithms with side information from two orthogonal perspectives. One involves the different methodologies of recommendation: the memory-based methods, latent factor, representation learning and deep learning models. The others cover different representations of side information, including structural data (flat, network, and hierarchical features, and knowledge graphs); and non-structural data (text, image and video features). Finally, we discuss challenges and provide new potential directions in recommendation, along with the conclusion of this survey.@en

We designed a fast procedure to detect the nitrogen oxides (NO_x) sources in the China North Plain and to estimate their NO_x emissions through a two-dimensional Gaussian fitting method applied to averaged Ozone Monitoring Instrument (OMI) observations of nitrogen dioxide (NO₂) column concentration. The Northern China Plain is a region that has one of the highest densities of anthropogenic NO_x sources in the world and therefore the sources are difficult to distinguish. With our procedure we still found 94 individual NO_x emission sources. Of these sources Tangshan city has the strongest NOx emission rate (92 Gg N year^—1), while the weakest that we are still able to detect is Zhangjiakou city, with a NO_x emission rate of 0.4 Gg N year^—1. Using the fitting results, we reconstruct the NO₂ column concentration distribution map, which matches the OMI observations with an R² = 0.85 and a slope of 0.78. The derived NO_x emission rates for cities and provinces level show good agreement with former studies.

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Capturing the temporal dynamics of user preferences over items is important for recommendation. Existing methods mainly assume that all time steps in user-item interaction history are equally relevant to recommendation, which however does not apply in real-world scenarios where user-item interactions can often happen accidentally. More importantly, they learn user and item dynamics separately, thus failing to capture their joint effects on user-item interactions. To better model user and item dynamics, we present the Interacting Attention-gated Recurrent Network (IARN) which adopts the attention model to measure the relevance of each time step. In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions. By doing so, IARN can selectively memorize different time steps of a user's history when predicting her preferences over different items. Our model can therefore provide meaningful interpretations for recommendation results, which could be further enhanced by auxiliary features. Extensive validation on real-world datasets shows that IARN consistently outperforms state-of-the-art methods.@en

Representation learning (RL) has recently proven to be effective in capturing local item relationships by modeling item co-occurrence in individual user's interaction record. However, the value of RL for recommendation has not reached the full potential due to two major drawbacks: 1) recommendation is modeled as a rating prediction problem but should essentially be a personalized ranking one; 2) multi-level organizations of items are neglected for fine-grained item relationships. We design a unified Bayesian framework MRLR to learn user and item embeddings from a multi-level item organization, thus benefiting from RL as well as achieving the goal of personalized ranking. Extensive validation on real-world datasets shows that MRLR consistently outperforms state-of-the-art algorithms.

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Feature hierarchy (FH) has proven to be effective to improve recommendation accuracy. Prior work mainly focuses on the influence of vertically affiliated features (i.e. child-parent) on user-item interactions. The relationships of horizontally organized features (i.e. siblings and cousins) in the hierarchy, however, has only been little investigated. We show in real-world datasets that feature relationships in horizontal dimension can help explain and further model user-item interactions. To fully exploit FH, we propose a unified recommendation framework that seamlessly incorporates both vertical and horizontal dimensions for effective recommendation. Our model further considers two types of semanti-cally rich feature relationships in horizontal dimension, i.e. complementary and alternative relationships. Extensive validation on four real-world datasets demonstrates the superiority of our approach against the state of the art. An additional benefit of our model is to provide better interpretations of the generated recommendations.

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Existing feature-based recommendation methods incorporate auxiliary features about users and/or items to address data sparsity and cold start issues. They mainly consider features that are organized in a flat structure, where features are independent and in a same level. However, auxiliary features are often organized in rich knowledge structures (e.g. hierarchy) to describe their relationships. In this paper, we propose a novel matrix factorization framework with recursive regularization -- ReMF, which jointly models and learns the influence of hierarchically-organized features on user-item interactions, thus to improve recommendation accuracy. It also provides characterization of how different features in the hierarchy co-influence the modeling of user-item interactions. Empirical results on real-world data sets demonstrate that ReMF consistently outperforms state-of-the-art feature-based recommendation methods. @en

We propose TrustSVD, a trust-based matrix factorization technique for recommendations. TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance. An analysis of social trust data from four real-world data sets suggests that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. TrustSVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further incorporating both the explicit and implicit influence of trusted and trusting users on the prediction of items for an active user. The proposed technique is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that TrustSVD achieves better accuracy than other ten counterparts recommendation techniques.@en