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Steel slag and waste clay bricks are two prevalent solid waste materials generated during industrial production. The complex chemical compositions of these materials present challenges to their utilization in conventional alumina silicate ceramics manufacturing. A new type of ceramic tile, which utilizes steel slag and waste clay brick as raw materials, has been successfully developed in order to effectively utilize these solid wastes. The optimal composition of the ceramic material was determined through orthogonal experimentation, during which the effects of the sample molding pressure, the soaking time, and the sintering temperature on the ceramic properties were studied. The results show that the optimal ceramic tile formula was 45% steel slag, 35% waste clay bricks, and 25% talc. The optimal process parameters for this composition included a molding pressure of 25 MPa, a sintering temperature of 1190 °C, and a soaking time of 60 min. The prepared ceramic tile samples had compositions in which solid waste accounted for more than 76% of the total material. Additionally, they possessed a modulus of rupture of more than 73.2 MPa and a corresponding water absorption rate of less than 0.05%.

Monkeypox (Mpox) is a virus that first emerged in Africa in 1970 [...]

The application of digital technologies has a significant impact on organizational performance by way of different talent management methods, thereby enabling the maintenance of the organization’s continuous competitive advantage. Therefore, this paper studied the four key factors that influence organizational performance: digital technology application (DTA), inclusive talent management (ITM), exclusive talent management (ETM), and non-equilibrium investment (NET), aiming to investigate how digital technology application and talent management methods positively affect organizational performance, explore how this relationship is regulated by NET, and provides suggestions for selecting appropriate talent management methods. To conduct quantitative analysis, questionnaires were used with a sample size of 534 middle and senior managers as well as human resources practitioners from different enterprises. The structural equation model (SEM) was employed along with 5000 iterations to test the research hypotheses. The results indicate a positive correlation between digital technology application and organizational performance. Furthermore, ITM and ETM act as intermediaries between digital technology application and organizational performance, whereas NET plays a regulatory role in relation to ITM and organizational performance. This paper offers comprehensive insights into the factors influencing organizational performance and sheds light on how organizations make decisions regarding data-driven talent management methods at different stages of development.

In recent years, artificial intelligence (AI) technology has promoted the development of electroencephalogram (EEG) emotion recognition. However, existing methods often overlook the computational cost of EEG emotion recognition, and there is still room for improvement in the accuracy of EEG emotion recognition. In this study, we propose a novel EEG emotion recognition algorithm called FCAN–XGBoost, which is a fusion of two algorithms, FCAN and XGBoost. The FCAN module is a feature attention network (FANet) that we have proposed for the first time, which processes the differential entropy (DE) and power spectral density (PSD) features extracted from the four frequency bands of the EEG signal and performs feature fusion and deep feature extraction. Finally, the deep features are fed into the eXtreme Gradient Boosting (XGBoost) algorithm to classify the four emotions. We evaluated the proposed method on the DEAP and DREAMER datasets and achieved a four-category emotion recognition accuracy of 95.26% and 94.05%, respectively. Additionally, our proposed method reduces the computational cost of EEG emotion recognition by at least 75.45% for computation time and 67.51% for memory occupation. The performance of FCAN–XGBoost outperforms the state-of-the-art four-category model and reduces computational costs without losing classification performance compared with other models.

Obtaining reliable and detailed energy consumption information about building service (BS) systems is an essential prerequisite for identifying energy-saving potential and improving energy efficiency of a building. Therefore, in recent years, energy sub-metering systems have been widely implemented in public buildings in China. A majority of electrical systems and equipment can be directly metered. However, in actual sub-metering systems, the terminal units of heating, ventilation and air conditioning (HVAC) systems, such as fan coils, air handling units and so on, are often mixed with the lighting-plug circuit. This mismatch between theoretical sub-metering systems and actual electricity supply circuits constitutes a lot of challenges in BS system management and control optimization. This study proposed an indirect method to disaggregate the energy consumption of HVAC terminal units from mixed sub-metering data based on the CART algorithm. This method was demonstrated in two buildings in Shanghai. The case study results show that the weighted mean absolute percentage errors (WMAPE) are within 5% and 15% during working hours in the cooling and heating seasons, respectively.

Solar radiation has important impacts on buildings such as for cooling/heating load forecasting, energy consumption forecasting, and multi-energy complementary optimization. Two types of solar radiation data are commonly used in buildings: radiation data in typical meteorological years and measured radiation data from meteorological stations, both of which are types of historical data. However, it is difficult to predict the hourly global solar radiation, which affects the application of relevant prediction models in practical engineering. Most existing methods for predicting hourly global solar radiation have issues such as difficulty in obtaining input parameters or complex data processing, which limits their practical engineering applications. This study proposed a simplified method to accurately predict the hourly horizontal solar radiation using extraterrestrial solar radiation, weather types, cloud cover, air temperature, relative humidity, and time as the input parameters. The back-propagation network, support vector machine, and light gradient boosting machine (LightGBM) models were used to establish the prediction model, and Shapley additive explanations were used to analyze the relationship between the input variables and the prediction results to simplify the structure of the prediction model. Taking Lanzhou New District in Gansu Province as an example, the results showed that the LightGBM model performed the best, with the root mean square error of 126.1 W/m2. Shapley additive explanations analysis showed that weather type was not a significant factor in the LightGBM model. Therefore, the weather type was removed from the LightGBM model and the root mean square error was 135.2 W/m2. The results showed that extra-terrestrial radiation and limited weather forecast parameters can be used to predict hourly global solar radiation with satisfactory prediction results.

Online customer reviews (OCRs) are the real feelings of customers in the process of using products, which have great reference value for potential customers’ purchase decisions. However, it is difficult for consumers to extract helpful information from very large numbers of OCRs. To support consumers’ purchase decisions, this paper proposes a hybrid method to rank alternative products through OCRs. In this method, we use the fine-grained Bidirectional Encoder Representation from Transformers (BERT) model for aspect-level sentiment analysis (SA) and convert SA results of sub-criteria into a corresponding interval intuitionistic fuzzy number, accurately extracting customer satisfaction in OCRs and reducing the errors caused by different amounts of OCRs. Furthermore, in order to obtain the ranking results of products, the subjective and objective weights are combined to determine weight of feature. Subsequently, an improved interval intuitionistic fuzzy VIKOR method is proposed to rank mobile games. Finally, we conduct a case study and make some comparisons, which show that our method can reduce the complexity of accurately obtaining consumers’ personal preferences and help consumers make more accurate decisions.

Due to the complexity and uncertainty of decision-making circumstances, it is difficult to provide an accurate compensation cost in strategic weight manipulation, making the compensation cost uncertain. Simultaneously, the change in the attribute weight is also accompanied by risk, which brings a greater challenge to manipulators’ decision making. However, few studies have investigated the risk aversion behavior of manipulators in uncertain circumstances. To address this research gap, a robust risk strategic weight manipulation approach is proposed in this paper. Firstly, mean-variance theory (MVT) was used to characterize manipulators’ risk preference behavior, and a risk strategic weight manipulation model was constructed. Secondly, the novel robust risk strategic weight manipulation model was developed based on the uncertainty caused by the estimation error of the mean and covariance matrix of the unit compensation cost. Finally, a case of emergency facility location was studied to verify the feasibility and effectiveness of the proposed method. The results of the sensitivity analysis and comparative analysis show that the proposed method can more accurately reflect manipulators’ risk preference behavior than the deterministic model. Meanwhile, some interesting conclusions are revealed.

Bovine lactoferrin (bLf) is a multifunctional glycoprotein and a good candidate for producing diverse bioactive peptides, which are easily lost during over-digestion. Accordingly, the effects of laccase-mediated pectin–ferulic acid conjugate (PF) and transglutaminase (TG) on improving the production of bLf active peptides by in vitro gastrointestinal digestion were investigated. Using ultra-high-performance liquid chromatography tandem mass spectroscopy (UPLC-MS-MS), the digests of bLf alone, PF-encapsulated bLf complex (LfPF), and TG-treated LfPF complex (LfPFTG) produced by conditioned in vitro gastric digestion (2000 U/mL pepsin, pH 3.0, 37 °C, 2 h) were identified with seven groups of active peptide-related fragments, including three common peptides (VFEAGRDPYKLRPVAAE, FENLPEKADRDQYEL, and VLRPTEGYL) and four differential peptides (GILRPYLSWTE, ARSVDGKEDLIWKL, YLGSRYLT, and FKSETKNLL). The gastric digest of LfPF contained more diverse and abundant detectable peptides of longer lengths than those of bLf and LfPFTG. After further in vitro intestinal digestion, two active peptide-related fragments (FEAGRDPYK and FENLPEKADRDQYE) remained in the final digest of LfPFTG; one (EAGRDPYKLRPVA) remained in that of bLf alone, but none remained in that of LfPF. Conclusively, PF encapsulation enhanced the production of bLf active peptide fragments under the in vitro gastric digestion applied. TG treatment facilitated active peptide FENLPEKADRDQYE being kept in the final gastrointestinal digest.

Monkeypox virus is a double-stranded DNA virus species that causes disease in humans and mammals. It is a zoonotic virus belongs the genus Orthopoxviral, the family of Poxviridae, associated with the smallpox virus in many aspects. The first human case of monkeypox was reported throughout the Democratic Republic of Congo in 1970. In April 2022, several cases were recorded in widespread regions of Africa, the Northern and western hemispheres. The current review spotlights taxonomic classification, clinical presentations during infection, and the pathogenicity of the monkeypox virus in humans. Furthermore, the current review also highlights different diagnostics used for virus detection.

Monkeypox is a serious public health issue in tropical and subtropical areas. Antivirals that target monkeypox proteins might lead to more effective and efficient therapy. The F13 protein is essential for the growth and maturation of the monkeypox virus. F13 inhibition might be a viable therapeutic target for monkeypox. The in silico fragment-based drug discovery method for developing antivirals may provide novel therapeutic options. In this study, we generated 800 compounds based on tecovirimat, an FDA-approved drug that is efficacious at nanomolar quantities against monkeypox. These compounds were evaluated to identify the most promising fragments based on binding affinity and pharmacological characteristics. The top hits from the chemical screening were docked into the active site of the F13 protein. Molecular dynamics simulations were performed on the top two probable new candidates from molecular docking. The ligand–enzyme interaction analysis revealed that the C2 ligand had lower binding free energy than the standard ligand tecovirimat. Water bridges, among other interactions, were shown to stabilize the C2 molecule. Conformational transitions and secondary structure changes in F13 protein upon C2 binding show more native three-dimensional folding of the protein. Prediction of pharmacological properties revealed that compound C2 may be promising as a drug candidate for monkeypox fever. However, additional in vitro and in vivo testing is required for validation.

Occupancy, which refers to the occupant count in this paper, is one of the main factors affecting the energy consumption of commercial buildings. It is important for both building managers and energy simulation engineers to understand how an entire building’s energy consumption varies with different occupancy levels in the process of building automation systems or in assessments of building performance with benchmarking lines. Because commercial buildings usually have large scales, complex layouts and a large number of people, it is a challenge to simulate the relationships between an entire building’s energy consumption and occupancy. This study proposes a fast method for calculating the influence of occupancy on the energy consumption of commercial buildings with different building layouts and existing occupancies. Other occupant behaviors, such as the opening of windows and adjustment of shading devices, are comprehensively reflected in two basic building parameters: the balance point temperature and the total heat transmission coefficient of the building. This new method can be easily used to analyze how building energy varies with occupancy without a physical building’s energy model. An office building in Shanghai is taken as a case study to validate the proposed method. The results show that the coefficient of determination R2 between the calculated value and actual value is 0.86, 0.8 and 0.71 for lighting, cooling and heating energy, respectively, which is suitable in engineering applications.

Central nervous system disorders, especially neurodegenerative diseases, are a public health priority and demand a strong scientific response. Various therapy procedures have been used in the past, but their therapeutic value has been insufficient. The blood–brain barrier (BBB) and the blood–cerebrospinal fluid barrier is two of the barriers that protect the central nervous system (CNS), but are the main barriers to medicine delivery into the CNS for treating CNS disorders, such as brain tumors, Parkinson’s disease, Alzheimer’s disease, and Huntington’s disease. Nanotechnology-based medicinal approaches deliver valuable cargos targeting molecular and cellular processes with greater safety, efficacy, and specificity than traditional approaches. CNS diseases include a wide range of brain ailments connected to short- and long-term disability. They affect millions of people worldwide and are anticipated to become more common in the coming years. Nanotechnology-based brain therapy could solve the BBB problem. This review analyzes nanomedicine’s role in medication delivery; immunotherapy, chemotherapy, and gene therapy are combined with nanomedicines to treat CNS disorders. We also evaluated nanotechnology-based approaches for CNS disease amelioration, with the intention of stimulating the immune system by delivering medications across the BBB.

The newly identified porcine Kobuvirus (PKV) has raised concerns owing to its association with diarrheal symptom in pigs worldwide. The process involving the emergence and global spread of PKV remains largely unknown. Here, the origin, genetic diversity, and geographic distribution of PKV were determined based on the available PKV sequence information. PKV might be derived from the rabbit Kobuvirus and sheep were an important intermediate host. The most recent ancestor of PKV could be traced back to 1975. Two major clades are identified, PKVa and PKVb, and recombination events increase PKV genetic diversity. Cross-species transmission of PKV might be linked to interspecies conserved amino acids at 13–17 and 25–40 residue motifs of Kobuvirus VP1 proteins. Phylogeographic analysis showed that Spain was the most likely location of PKV origin, which then spread to pig-rearing countries in Asia, Africa, and Europe. Within China, the Hubei province was identified as a primary hub of PKV, transmitting to the east, southwest, and northeast regions of the country. Taken together, our findings have important implications for understanding the evolutionary origin, genetic recombination, and geographic distribution of PKV thereby facilitating the design of preventive and containment measures to combat PKV infection.

Lung cancer is one of the 10 most common cancers in the world, which seriously affects the normal life and health of patients. According to the investigation report, the 3-year survival rate of patients with lung cancer is less than 20%. Heredity, the environment, and long-term smoking or secondhand smoke greatly promote the development and progress of the disease. The mechanisms of action of the occurrence and development of lung cancer have not been fully clarified. As a new type of gas signal molecule, hydrogen sulfide (H2S) has received great attention for its physiological and pathological roles in mammalian cells. It has been found that H2S is widely involved in the regulation of the respiratory system and digestive system, and plays an important role in the occurrence and development of lung cancer. H2S has the characteristics of dissolving in water and passing through the cell membrane, and is widely expressed in body tissues, which determines the possibility of its participation in the occurrence of lung cancer. Both endogenous and exogenous H2S may be involved in the inhibition of lung cancer cells by regulating mitochondrial energy metabolism, mitochondrial DNA integrity, and phosphoinositide 3-kinase/protein kinase B co-pathway hypoxia-inducible factor-1α (HIF-1α). This article reviews and discusses the molecular mechanism of H2S in the development of lung cancer, and provides novel insights for the prevention and targeted therapy of lung cancer.

We consider an optimization problem on the maximal magnitude of angular acceleration of the output-links of a commercially available center-driven linkage system (CDLS) for vehicle wipers on windshield. The purpose of this optimization is to improve the steadiness of a linkage system without weakening its normal function. Thus this optimization problem is considered under the assumptions that the frame of the fixed links of linkage system is unchanged and that the input-link rotates at the same constant angular speed with its length unchanged. To meet the usual requirements for vehicle wipers on windshield, this optimization problem must be solved subject to 10 specific constraints. We expect that optimizing the maximal magnitude of angular acceleration of the output-links of a linkage system would also be helpful for reducing the amplitudes of sound waves of wiper noise. We establish the motion model of CDLS and then justify this model with ADAMS. We use a “Differential Evolution” type method to search for the minimum of an objective function subject to 10 constraints for this optimization problem. Our optimization computation shows that the maximal magnitude of angular acceleration of both output-links of this linkage system can be reduced by more than 10%.

Background: Circular RNAs (circRNAs) play a key role in regulating the drug resistance of numerous human tumors. However, whether circKRT17 involves in the osimertinib resistance of lung adenocarcinoma (LUAD) remains undetermined. Methods: Relative mRNA/circRNA and protein levels were detected by qRT-PCR and western blotting. Localization of circKRT17 and YAP1 was determined by FISH and immunofluorescence staining. Cell growth and apoptosis were evaluated using colony formation, EdU assays, and flow cytometry. The N6-methyladenosine (m6A) modification was analyzed by MeRIP. The interplay between EIF4A3 and circKRT17 or YAP1 was verified by RNA pull-down or/and RIP assays. Subcutaneous tumor growth was monitored in nude mice, and Ki-67 and TUNEL staining were carried out to evaluate cell proliferation and apoptosis, respectively. Results: CircKRT17 and METTL3 were elevated in osimertinib-insensitive LUAD tissues and cells. Knockdown of circKRT 17 and METTL3 increased the sensitivity of LUAD cells to osimertinib. Knockdown of METTL3 decreased the expression of circKRT17 by inhibiting m6A modification. CircKRT17 promoted the stability and nuclear transportation of YAP1 by recruiting EIF4A3 in LUAD cells. Overexpression of YAP1 abolished the impacts of circKRT17 knockdown on the osimertinib sensitivity of LUAD cells. CircKRT17 knockdown increased the repressive effects of osimertinib on tumor growth in vivo by inhibiting YAP1 signaling. Conclusion: METTL3 initiated the m6A modification of circKRT17, thus promoting osimertinib resistance of LUAD by enhancing YAP1 stability through EIF4A4 recruitment.

Gymnodimines (GYMs) are a group of fast-acting phycotoxins and their toxicological effects on human beings are still unclear due to the lack of sufficiently well-characterized large quantities of purified toxins for toxicology studies. In this study, a certified reference material (CRM) of GYM-A was prepared from the dinoflagellate Karenia selliformis, followed by multi-step chromatography separation and purification. Subsequently, the stability of GYM-A in methanolic media was evaluated at different temperature (−20, 4, and 20 °C) and pH (3, 5, and 7) conditions for 8 months, and the conversion products of GYM-A were explored by liquid chromatography–high resolution mass spectrometry (LC-HRMS). The results show that the stability of GYM-A decreased with increasing temperature and pH values. The GYM-A was stable during storage at −20 °C regardless of pH, but it decreased rapidly (81.8% ± 9.3%) at 20 °C in pH 7 solution after 8 months. Moreover, the concentrations of GYM-A did not significantly change at all temperatures in solutions with pH 3 (p > 0.05). It is recommended that GYM-A should be stored at low temperature (≤−20 °C) and pH (≤3) conditions for long-term storage in aqueous methanolic media. In addition, two conversion products of GYM-A, tentatively named as GYM-K (m/z 540) and GYM-L (m/z 524), were identified in the samples stored at high levels of pH and temperature. Based on the LC-HRMS data, the hypothetical chemical structures of both converting derivatives were proposed. A useful strategy for long-term storage of GYM-A CRM in aqueous methanolic media was suggested and two hypothesized conversion products of GYM-A were discovered in this study.

Hydrogen sulfide (H2S), a gaseous signaling molecule, is associated with the development of various malignancies via modulating various cellular signaling cascades. Published research has established the fact that inhibition of endogenous H2S production or exposure of H2S donors is an effective approach against cancer progression. However, the effect of pharmacological inhibition of endogenous H2S-producing enzymes (cystathionine-γ-lyase (CSE), cystathionine-β-synthase (CBS), and 3-mercaptopyruvate sulfurtransferase (3-MPST)) on the growth of breast cancer (BC) remains unknown. In the present study, DL-propargylglycine (PAG, inhibitor of CSE), aminooxyacetic acid (AOAA, inhibitor of CBS), and L-aspartic acid (L-Asp, inhibitor of 3-MPST) were used to determine the role of endogenous H2S in the growth of BC by in vitro and in vivo experiments. An in silico study was also performed to confirm the results. Corresponding to each enzyme in separate groups, we treated BC cells (MCF-7 and MDA-MB-231) with 10 mM of PAG, AOAA, and L-Asp for 24 h. Findings reveal that the combined dose (PAG + AOAA + L-Asp) group showed exclusive inhibitory effects on BC cells’ viability, proliferation, migration, and invasion compared to the control group. Further, treated cells exhibited increased apoptosis and a reduced level of phospho (p)-extracellular signal-regulated protein kinases such as p-AKT, p-PI3K, and p-mTOR. Moreover, the combined group exhibited potent inhibitory effects on the growth of BC xenograft tumors in nude mice, without obvious toxicity. The molecular docking results were consistent with the wet lab experiments and enhanced the reliability of the drugs. In conclusion, our results demonstrate that the inhibition of endogenous H2S production can significantly inhibit the growth of human breast cancer cells via the AKT/PI3K/mTOR pathway and suggest that endogenous H2S may act as a promising therapeutic target in human BC cells. Our study also empowers the rationale to design novel H2S-based anti-tumor drugs to cure BC.

Neurotoxin β-N-methylamino-L-alanine (BMAA) is hypothesized as an important pathogenic factor for neurodegenerative diseases such as amyotrophic lateral sclerosis/parkinsonism-dementia complex (ALS-PDC). Comparative study on the accuracy of BMAA analyzed by the regular LC-MS/MS methods is still limited for different biological matrices. In this study, a free-BMAA sample of cyanobacterium and BMAA-containing positive samples of diatom, mussel, scallop, and oyster were extracted with varied extraction ratios (ER) ranging from 1:20 to 1:2000. These extracts were then purified by MCX cartridges. After SPE purification, these different biological samples were analyzed by two common LC-MS/MS analysis methods, a direct analysis without derivatization by a hydrophilic interaction liquid chromatography (HILIC)-MS/MS and pre-column 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) derivatization combined with a C18 column. The results suggested that the recoveries of BMAA spiked in the cyanobacterial sample were close to 100% in the total soluble form extracts with the ER of 1:100 (g/mL) and the precipitated bound form extracts with the ER of 1:500. The recommended ER for the precipitated bound form of BMAA in diatoms and the total soluble form of BMAA in mollusks are 1:500 and 1:50, respectively. The quantitative results determined by the AQC derivatization method were lower than those determined by the direct analysis of the HILIC method in diatom and mollusk samples. The results of the HILIC method without the derivatization process were closer to the true value of BMAA in cyanobacteria. This work contributes to the performance of the solid-phase extraction (SPE) purification protocol and the accuracy of BMAA analysis by LC-MS/MS in diverse biological samples.

For developing an effective interventional approach and treatment modality for PM2.5, the effects of omega-3 fatty acids on alleviating inflammation and attenuating lung injury induced by inhalation exposure of PM2.5 were assessed in murine models. We found that daily oral administration of the active components of omega-3 fatty acids, docosahexaenoic acid (DHA), and eicosapentaenoic acid (EPA) effectively alleviated lung parenchymal lesions, restored normal inflammatory cytokine levels and oxidative stress levels in treating mice exposed to PM2.5 (20 mg/kg) every 3 days for 5 times over a 14-day period. Especially, CT images and the pathological analysis suggested protective effects of DHA and EPA on lung injury. The key molecular mechanism is that DHA and EPA can inhibit the entry and deposition of PM2.5, and block the PM2.5-mediated cytotoxicity, oxidative stress, and inflammation.

Heavy smoke from building fires is the main cause of casualties. As smoke typically diffuses through building openings, smoke control performance of building openings is critical to survival and requires considerable attention. In the past, the detection method could only be used in the laboratory, and the detection equipment could not be moved. Therefore, the main purpose of this research was to develop a methodology for field testing of smoke control properties of doors in order to ensure that the smoke control performance of doors tested in the laboratory and doors installed in the field can be realized without any discrepancy. Furthermore, this test method underwent a comparison test with the CNS 15038 “Method of Test for Evaluating Smoke Control Performance of Doors” for the same subject. The test results showed no significant difference based on independent sample testing, demonstrating the feasibility of this test method and test apparatus. The instrument developed by this research is light and easy to carry, and the operation method is simple. Such a test method can be applied to different doors and is non-destructive, non-hazardous, and reusable. In the future, by extending the design principle of the system, this test method can be applied to other fire protection equipment for the inspection of smoke control capabilities and can be used as a reference for relevant organizations to establish test specifications and standards.

Gymnodimine-A (GYM-A) is a fast-acting microalgal toxin and its production of certified materials requires an efficient harvesting technology from the large-scale cultures of toxigenic microalgae. In this study the recoveries of GYM-A were compared between several liquid-liquid extraction (LLE) treatments including solvents, ratios and stirring times to optimize the LLE technique for harvesting GYM-A from Karenia selliformis cultures, of which the dichloromethane was selected as the extractant and added to microalgal cultures at the ratio 55 mL L−1 (5.5%, v/v). The recovery of GYM-A obtained by the LLE technique was also compared with filtration and centrifugation methods. The stability of GYM-A in culture media were also tested under different pH conditions. Results showed that both the conventional filter filtration and centrifugation methods led to fragmentation of microalgal cells and loss of GYM-A in the harvesting processes. A total of 5.1 µg of GYM-A were obtained from 2 L of K. selliformis cultures with a satisfactory recovery of 88%. Interestingly, GYM-A obviously degraded in the culture media with the initial pH 8.2 and the adjusted pH of 7.0 after 7 days, but there was no obvious degradation in the acidic medium at pH 5.0. Therefore, the LLE method developed here permits the collection of large-volume cultures of K. selliformis and the high-efficiency extraction of GYM-A. This work provides a simple and valuable technique for harvesting toxins from large-scale cultures of GYM-producing microalgae.

The proliferation of distributed renewable energy resources (RESs) poses major challenges to the operation of microgrids due to uncertainty. Traditional online scheduling approaches relying on accurate forecasts become difficult to implement due to the increase of uncertain RESs. Although several data-driven methods have been proposed recently to overcome the challenge, they generally suffer from a scalability issue due to the limited ability to optimize high-dimensional continuous control variables. To address these issues, we propose a data-driven online scheduling method for microgrid energy optimization based on continuous-control deep reinforcement learning (DRL). We formulate the online scheduling problem as a Markov decision process (MDP). The objective is to minimize the operating cost of the microgrid considering the uncertainty of RESs generation, load demand, and electricity prices. To learn the optimal scheduling strategy, a Gated Recurrent Unit (GRU)-based network is designed to extract temporal features of uncertainty and generate the optimal scheduling decisions in an end-to-end manner. To optimize the policy with high-dimensional and continuous actions, proximal policy optimization (PPO) is employed to train the neural network-based policy in a data-driven fashion. The proposed method does not require any forecasting information on the uncertainty or a prior knowledge of the physical model of the microgrid. Simulation results using realistic power system data of California Independent System Operator (CAISO) demonstrate the effectiveness of the proposed method.

Alginate (Alg) and bacterial nanocellulose (BNC) have exhibited great potential in biomedical applications, especially wound dressing. Non-toxicity and a moisture-maintaining nature are common features making them favorable for functional dressing fabrication. BNC is a natural biopolymer that promotes major advances to the current and future biomedical materials, especially in a flat or tubular membrane form with excellent mechanical strength at hydrated state. The main drawback limiting wide applications of both BNC and Alg is the lack of antibacterial activity, furthermore, the inherent poor mechanical property of Alg leads to the requirement of a secondary dressing in clinical treatment. To fabricate composite dressings with antibacterial activity and better mechanical properties, sodium alginate was efficiently incorporated into the BNC matrix using a time-saving vacuum suction method followed by cross-linking through immersion in separate solutions of six cations (manganese, cobalt, copper, zinc, silver, and cerium). The results showed the fabricated composites had not only pH-responsive antibacterial activities but also improved mechanical properties, which are capable of acting as smart dressings. All composites showed non-toxicity toward fibroblast cells. Rat model evaluation showed the skin wounds covered by the dressings healed faster than by BNC.

As most residents spend more than 90% of their time in buildings, acceptable and reasonable control of both indoor thermal comfort and air quality is imperative to ensure occupants’ health status and work productivity. However, current control strategies generally take either thermal comfort or indoor air quality as a single loop, rather than the concurrent control of two. To analyze their mutual influence, this study investigated the performance of three multi-control approaches, i.e., proportional integral derivative (PID) control of thermal comfort and a fixed outdoor air ratio, PID control of thermal comfort and design outdoor air rate, and PID control of thermal comfort and occupancy-based demand-controlled ventilation. As a pilot study, three typical control methods were implemented to a multi-zone building via OpenModelica modeling. The results indicate that indoor air temperature can be well-maintained under three control methods, however, the CO2 concentration under the fixed outdoor air ratio was over 1000 ppm, leading to poor indoor air quality. The control strategy with the design outdoor air rate could not properly ensure the CO2 concentration, due to the over-ventilated or under-ventilated phenomena, subsequently resulting in unnecessary energy waste. The occupancy-based demand controlled ventilation could maintain the CO2 concentration under the set-point with an intermediate power energy utilization.

Intuitionistic fuzzy distance measurement is an effective method to study multi-attribute emergency decision-making (MAEDM) problems. Unfortunately, the traditional intuitionistic fuzzy distance measurement method cannot accurately reflect the difference between membership and non-membership data, where it is easy to cause information confusion. Therefore, from the intuitionistic fuzzy number (IFN), this paper constructs a decision-making model based on intuitionistic fuzzy cross-entropy and a comprehensive grey correlation analysis algorithm. For the MAEDM problems of completely unknown and partially known attribute weights, this method establishes a grey correlation analysis algorithm based on the objective evaluation value and subjective preference value of decision makers (DMs), which makes up for the shortcomings of traditional model information loss and greatly improves the accuracy of MAEDM. Finally, taking the Wenchuan Earthquake on May 12th 2008 as a case study, this paper constructs and solves the ranking problem of shelters. Through the sensitivity comparison analysis, when the grey resolution coefficient increases from 0.4 to 1.0, the ranking result of building shelters remains stable. Compared to the traditional intuitionistic fuzzy distance, this method is shown to be more reliable.

Driven by the recent advances and applications of smart-grid technologies, our electric power grid is undergoing radical modernization. Microgrid (MG) plays an important role in the course of modernization by providing a flexible way to integrate distributed renewable energy resources (RES) into the power grid. However, distributed RES, such as solar and wind, can be highly intermittent and stochastic. These uncertain resources combined with load demand result in random variations in both the supply and the demand sides, which make it difficult to effectively operate a MG. Focusing on this problem, this paper proposed a novel energy management approach for real-time scheduling of an MG considering the uncertainty of the load demand, renewable energy, and electricity price. Unlike the conventional model-based approaches requiring a predictor to estimate the uncertainty, the proposed solution is learning-based and does not require an explicit model of the uncertainty. Specifically, the MG energy management is modeled as a Markov Decision Process (MDP) with an objective of minimizing the daily operating cost. A deep reinforcement learning (DRL) approach is developed to solve the MDP. In the DRL approach, a deep feedforward neural network is designed to approximate the optimal action-value function, and the deep Q-network (DQN) algorithm is used to train the neural network. The proposed approach takes the state of the MG as inputs, and outputs directly the real-time generation schedules. Finally, using real power-grid data from the California Independent System Operator (CAISO), case studies are carried out to demonstrate the effectiveness of the proposed approach.

In recent years, detection of trace amounts of dissolved lipophilic phycotoxins in coastal waters has been possible using solid phase adsorption toxin tracking (SPATT) samplers. To explore the contribution of dissolved diarrhetic shellfish toxins (DST) to the accumulation of toxins by cultivated bivalves, mussels (Mytilus galloprovincialis) were exposed to different concentrations of purified okadaic acid (OA) and dinophysistoxin-1 (DTX1) in filtered (0.45 µm) seawater for 96 h. Accumulation and esterification of DST by mussels under different experimental conditions, including with and without the addition of the food microalga Isochrysis galbana, and with the addition of different size-fractions of suspended particulate matter (SPM) ( gills > mantle > residual tissues. However, the percentage of total DST in the digestive gland of mussels in filtered seawater (67%) was higher than with the addition of SPM particles (75–150 µm) (51%), whereas the gills showed the opposite trend in filtered seawater with (27%) and without (14.4%) SPM particles. Results presented here will improve our understanding of the mechanisms of DST accumulation by bivalves in marine aquaculture environments.

The present study investigated the effects of diesel exhaust (DE) on an experimental model of bleomycin (BLM)-induced lung injury and fibrosis in mice. BLM was intravenously administered to both Nrf2+/+ and Nrf2−/− C57BL/6J mice on day 0. The mice were exposed to DE for 56 days from 28 days before the BLM injection to 28 days after the BLM injection. Inhalation of DE induced significant inhibition of airway clearance function and the proinflammatory cytokine secretion in macrophages, an increase in neutrophils, and severe lung inflammatory injury, which were greater in Nrf2−/− mice than in Nrf2+/+ mice. In contrast, inhalation of DE was observed to induce a greater increase of hydroxyproline content in the lung tissues and significantly higher pulmonary antioxidant enzyme mRNA expression in the Nrf2+/+ mice than in Nrf2−/− mice. DE is an important risk factor, and Nrf2 regulates the risk of a DE inhalation induced immune response during BLM lung injury and fibrosis in mice.