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One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers’ abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations.

Objectives. To evaluate the usefulness of a novel acoustic uroflowmetry- (UFM-) based mobile application (app) voiding diary (VD) focusing on the (1) compliance and (2) correlation with a conventional paper-based VD. Materials and Methods. A total of 78 patients were included between December 2019 and June 2020, and a subsequent review of all data was performed. The analyzed data were as follows: (1) survey of convenience/satisfaction/preference comparing the two methods, (2) compliance regarding the completeness of both methods, and (3) correlation of each metric (24-hour urine volume, nocturnal urine volume, nocturnal polyuria index, total number of voids, number of daytime voids, number of nocturnal voids, and maximal bladder capacity) between the two methods. Results. The survey results of convenience, satisfaction, and preference were as follows. With regard to convenience and satisfaction area, higher scores are reported in the mobile app VD ( mean ± standard deviation (SD); convenience: 7.47 ± 2.19 [app] vs. 4.20 ± 2.49 [paper]; satisfaction: 7.36 ± 2.17 [app] vs. 5.07 ± 2.65 [paper]). The median score of the overall preference for using the mobile app instead of the paper-based VD was 9 out of 10 ( mean ± SD 7.82 ± 2.68 ). We also found a good correlation between the two methods for nocturnal urine volume ( r = 0.55 , p = 0.04 ), nocturnal polyuria index ( r = 0.66 , p = 0.23 ), total number of voids ( r = 0.9 , p = 0.02 ), number of nocturnal voids ( r = 0.83 , p = 0.02 ), and maximal bladder capacity ( r = 0.89 , p = 0.04 ). Conclusion. The acoustic UFM-based mobile app VD demonstrated favorable findings compared to the conventional paper-based VD.

The plate-like graphene shells (GS) produced by an original methane pyrolysis method and their derivatives graphene oxide (GO) and graphene oxide paper (GO-P) were evaluated with luminescentEscherichia colibiotests and additional bacterial-based assays which together revealed the graphene-family nanomaterials’ toxicity and bioactivity mechanisms. Bioluminescence inhibition assay, fluorescent two-component staining to evaluate cell membrane permeability, and atomic force microscopy data showed GO expressed bioactivity in aqueous suspension, whereas GS suspensions and the GO-P surface were assessed as nontoxic materials. The mechanism of toxicity of GO was shown not to be associated with oxidative stress in the targetedsoxS::luxandkatG::luxreporter cells; also, GO did not lead to significant mechanical disruption of treated bacteria with the release of intracellular DNA contents into the environment. The well-coordinated time- and dose-dependent surface charge neutralization and transport and energetic disorders in theEscherichia colicells suggest direct membrane interaction, internalization, and perturbation (i.e., “membrane stress”) as a clue to graphene oxide’s mechanism of toxicity.

High-yield pulps (HYPs), such as CTMP (chemi-thermo-mechanical pulp), are attractive due to their low cost and high wood utilization. However, their drawback of rapid brightness reversion (yellowing) limits wide use of the HYPs. In this study, a fungus, Fusarium concolor X4, was applied to treat poplar CTMP for exploring the effects of biotreatment on brightness and light-induced yellowing of the pulp. The results indicated that the biotreatment with Fusarium concolor X4 could improve the brightness of poplar CTMP and inhibit light-induced yellowing of the pulp. The yellowing inhibition mechanism was explored by the analysis of enzyme production regularity during biotreatment, changes in chemical components, and the UV-Vis absorption spectra and FTIR-ATR spectra of pulps before and after biotreatment.

Objective. To monitor the number of bacterial colonies in the air of computed tomography (CT) room for COVID-19 using different disinfection methods and to identify the most effective method for disinfection and protection of equipment. Methods. Three methods for disinfection using ultraviolet germicidal irradiation (group A), plasma circulation air sterilizer (group B), and ultraviolet germicidal irradiation plus plasma circulation air sterilizer (group C) were utilized to sanitize the air in the CT room dedicated to COVID-19 cases. Single-factor ANOVA was used to evaluate and compare the disinfection effect of the three air disinfection methods; an air microbial sampler was used to sample and measure the number of bacteria in the air of the machine room. Results. The number of bacteria in the air immediately after disinfection was significantly lower than before disinfection ( p < 0.01 ). All three disinfection methods met the disinfection requirement. No significant differences in the number of air bacteria in the machine room immediately after disinfection were observed among the three methods ( p > 0.05 ). In addition, the effect of disinfection after 2 h was compared, and the number of bacteria in group C after 2 h was significantly lower than that in group A and group B. Conclusions. All three disinfection methods have significant disinfection effects. In addition, using ultraviolet disinfection lamps combined with a plasma air disinfection machine to sterilize the air in CT machine room has the best disinfection effect for the longest duration. Therefore, we recommend the combined disinfection method (ultraviolet disinfection lamps plus plasma air disinfection), as well as formulating relevant disinfection management norms, which should thus be the method to use during pandemics.

On March 11th, 2020, the World Health Organization declared COVID-19 infection as a pandemic. Since it is a novel virus, there are basically no proven drugs or therapies; although many laboratories in different countries are working to develop a vaccine, it will take time to make it available. Passive immunization is the therapy born from the intuition of Behring and Kisato in the late 19th century. It was widely used for the treatment of bacterial infections until the discovery of antibiotics, as well as during the viral pandemics of the 20th century and of the beginning of the 21st; it still has clinical applications (e.g., tetanus prevention). This paper summarizes the basic principles of passive immunization, with particular reference to convalescent plasma. The literature concerning its use during past epidemics and the results of the first clinical studies concerning its use during the current pandemic are discussed too. A large section is dedicated to the analysis of the possible, although rare, side effects. Recently, in 2017, the WHO Blood Regulators Network (BRN) published a position paper, recommending convalescent plasma as the first-choice treatment to be tested in the absence of authorized drugs; however, this strategy has not been followed. In the current epidemic, the principle of passive immunization through convalescent plasma has been applied in several circumstances and particularly in patients with serious complications. The first reported results are encouraging and confirm the effectiveness of plasma therapy and its safety. Also, the FDA has proposed plasma treatment in order to face the increasingly complex situation and manage patients with serious or immediately life-threatening COVID-19 disease. Several studies and clinical programs are still ongoing.

Constructed wetland (CW) with monoculture of Arundo donax L. was investigated for the posttreatment of anaerobic bioreactor (ABR) treating combined industrial wastewater. Different dilutions of combined industrial wastewater (20, 40, 60, and 80) and original wastewater were fed into the ABR and then posttreated by the laboratory scale CW. The respective removal efficiencies of COD, BOD, TSS, nitrates, and ammonia were 80%, 78-82%, 91.7%, 88-92%, and 100% for original industrial wastewater treated in ABR. ABR was efficient in the removal of Ni, Pb, and Cd with removal efficiencies in the order of Cd (2.7%)Ni (79%)Pb (85%). Posttreatment of the ABR treated effluent was carried out in lab scale CW containing A. donax L. CW was effective in the removal of COD and various heavy metals present in ABR effluents. The posttreatment in CW resulted in reducing the metal concentrations to 1.95 mg/L, 0 mg/L, and 0.004 mg/L for Ni, Pb, and Cd which were within the permissible water quality standards for industrial effluents. The treatment strategy was effective and sustainable for the treatment of combined industrial wastewater.

The interest in utilizing algae for wastewater treatment has been increased due to many advantages. Algae-wastewater treatment system offers a cost-efficient and environmentally friendly alternative to conventional treatment processes such as electrocoagulation and flocculation. In this biosystem, algae can assimilate nutrients in the wastewater for their growth and simultaneously capture the carbon dioxide from the atmosphere during photosynthesis resulting in a decrease in the greenhouse gaseousness. Furthermore, the algal biomass obtained from the treatment process could be further converted to produce high value-added products. However, the recovery of free suspended algae from the treated effluent is one of the most important challenges during the treatment process as the current methods such as centrifugation and filtration are faced with the high cost. Immobilization of algae is a suitable approach to overcome the harvesting issue. However, there are some drawbacks with the common immobilization carriers such as alginate and polyacrylamide related to low stability and toxicity, respectively. Hence, it is necessary to apply a new carrier without the mentioned problems. One of the carriers that can be a suitable candidate for the immobilization is zeolite. To date, various types of zeolite have been used for the immobilization of cells of bacteria and yeast. If there is any possibility to apply them for the immobilization of algae, it needs to be considered in further studies. This article reviews cell immobilization technique, biomass immobilization onto zeolites, and algal immobilization with their applications. Furthermore, the potential application of zeolite as an ideal carrier for algal immobilization has been discussed.

Subcritical water extraction (SCW) was used to extract oil from Chlorella pyrenoidosa. The operational factors such as reaction temperature, reaction time, and biomass loading influence the oil yield during the extraction process. In this study, response surface methodology was employed to identify the desired extraction conditions for maximum oil yield. Experiments were carried out in batch reactors as per central composite design with three independent factors including reaction temperature (170, 220, 270, 320, and 370°C), reaction time (1, 5, 10, 15, and 20 min), and biomass loading (1, 3, 5, 10, and 15%). A maximum oil yield of 12.89 wt.% was obtained at 320°C and 15 min, with 3% biomass loading. Sequential model tests showed the good fit of experimental data to the second-order quadratic model. This study opens the great potential of SCW to extract algal oil for use in algal biofuel production.

An alternative low and high ammonium influent regime was proposed and adopted to keep a completely autotrophic nitrogen removal over nitrite (CANON) effective when treating low ammonium wastewater. Results show that, by cyclic operating at an alternative low and high ammonium concentration for 10 days and 28 days, the CANON system could effectively treat low ammonium wastewater. Excessive proliferation of nitrite oxidizing bacteria (NOB) under low ammonium environment was still the challenge for the stable CANON operation; but with 28 days of a high ammonium treatment combined with a sludge retention time control, the NOB overproliferated in the low ammonium operational period could be under control. Specifically, when the nitrite oxidation rate reached 8 g N/m3/h, the CANON system should enter the high ammonium influent operating mode. 16S rDNA high-throughput sequencing results show that the appropriate sludge discharging provided an environment favoring Candidatus Jettenia.

Concerns in the last few decades regarding the environmental and socioeconomic impacts of the dependence on fossil fuels have resulted in calls for more renewable and alternative energy sources. This has led to recent interest in copyrolysis of biomass and coal. Numerous reviews have been found related to individual pyrolysis of coal and biomass. This review deals mainly with the copyrolysis of coal and biomass and then compares their results with those obtained using coal and biomass pyrolysis in detail. It is controversial whether there are synergistic or additive behaviours when coal and biomass are blended during copyrolysis. In this review, the effects of reaction parameters such as feedstock types, blending ratio, heating rate, temperature, and reactor types on the occurrence of synergy are discussed. Also, the main properties of the copyrolytic products are pointed out. Some possible synergistic mechanisms are also suggested. Additionally, several outlooks based on studies in the literature are also presented in this paper.

Three semicontinuous continuous stirred-tank reactors (CSTR) operating at mesophilic conditions (35°C) were used to investigate the effect of hydraulic retention time (HRT) on anaerobic digestion of wheat straw. The results showed that the average biogas production with HRT of 20, 40, and 60 days was 46.8, 79.9, and 89.1 mL/g total solid as well as 55.2, 94.3, and 105.2 mL/g volatile solids, respectively. The methane content with HRT of 20 days, from 14.2% to 28.5%, was the lowest among the three reactors. The pH values with HRT of 40 and 60 days were in the acceptable range compared to that with HRT of 20 days. The propionate was dominant in the reactor with HRT of 20 days, inhibiting the activities of methanogens and causing the lower methane content in biogas. The degradation of cellulose, hemicellulose, and crystalline cellulose based on XRD was also strongly influenced by HRTs.

A modified single sequencing batch reactor (SBR) was developed to remove the nitrogen of the real landfill leachate in this study. To take the full advantage of the SBR, stir phase was added before and after aeration, respectively. The new mechanism in this experiment could improve the removal of nitrogen efficiently by the utilization of carbon source in the raw leachate. This experiment adopts the SBR process to dispose of the real leachate, in which the COD and ammonia nitrogen concentrations were about 3800 mg/L and 1000 mg/L, respectively. Results showed that the removal rates of COD and total nitrogen were above 85% and 95%, respectively, and the effluent COD and total nitrogen were less than 500 mg/L and 40 mg/L under the condition of not adding any carbon source. Also, the specific nitrogen removal rate was 1.48 mgN/(h·gvss). In this process, polyhydroxyalkanoate (PHA) as a critical factor for the highly efficient nitrogen removal (>95%) was approved to be the primary carbon source in the sludge. Because most of the organic matter in raw water was used for denitrification, in the duration of this 160-day experiment, zero discharge of sludge was realized when the effluent suspended solids were 30–50 mg/L.

Vinasse, a residue from bioethanol production containing high organic matter concentration, was used as substrate in submerged fermentation ofPseudomonas aeruginosaPA1 for biosurfactant production. About 2.7 g/L of rhamnolipids was obtained, with surface tension of 29.2 mN/m and critical micelle concentration of 80.3 mg/L. After separation of rhamnolipid and biomass, residual fermentation media were submitted to anaerobic biodegradation in mesophilic conditions. The residual medium derived from fermentation with vinasse diluted to 1 : 1, without addition of nitrogen, C : N 21, and for 168 h, led to 63.2% chemical oxygen demand (COD) removal and 97.6 mL CH4/gCODremoved. Compared to results obtained with fresh vinasse (73.7% COD removal and 112.4 mL CH4/gCODremoved), it could be concluded that both processes can be integrated in order to add value to the residue and obtain energy, reducing production costs and at the same time environmental impacts related to vinasse disposal.

To enhance the codigestion of degradation and improve biomethane production potential, sugarcane bagasse and filter mud were pretreated by sodium hydroxide NaOH 1 N at 100°C for 15, 30, and 45 minutes, respectively. Biomethane generation from 1-liter batch reactor was studied at mesophilic temperature (37±1)°C, solid concentrations of 6%, and five levels of mixing proportion with and without pretreatment. The results demonstrate that codigestion of filter mud with bagasse produces more biomethane than fermentation of filter mud as single substrate; even codigested substrate composition presented a better balance of nutrients (C/N ratio of 24.70) when codigestion ratio between filter mud and bagasse was 25 : 75 in comparison to filter mud as single substrate (C/N ratio 9.68). All the pretreatments tested led to solubilization of the organic matter, with a maximum lignin reduction of 86.27% and cumulative yield of biomethane (195.8 mL·gVS-1, digestion of pretreated bagasse as single substrate) obtained after 45 minutes of cooking by NaOH 1 N at 100°C. Under this pretreatment condition, significant increase in cumulative methane yield was observed (126.2 mL·gVS-1) at codigestion ratio of 25 : 75 between filter mud and bagasse by increase of 81.20% from untreated composition.

The performance of a laboratory scale upflow anaerobic sludge blanket (UASB) reactor and its posttreatment unit of sand-chemically carbonized rubber wood sawdust (CCRWSD) column system for the treatment of a metal contaminated municipal wastewater was investigated. Copper ion contaminated municipal wastewater was introduced to a laboratory scale UASB reactor and the effluent from UASB reactor was then followed by treatment with sand-CCRWSD column system. The laboratory scale UASB reactor and column system were observed for a period of 121 days. After the posttreatment column the average removal of monitoring parameters such as copper ion concentration (91.37%), biochemical oxygen demand (BODT) (93.98%), chemical oxygen demand (COD) (95.59%), total suspended solid (TSS) (95.98%), ammonia (80.68%), nitrite (79.71%), nitrate (71.16%), phosphorous (44.77%), total coliform (TC) (99.9%), and fecal coliform (FC) (99.9%) was measured. The characterization of the chemically carbonized rubber wood sawdust was done by scanning electron microscope (SEM), X-ray fluorescence spectrum (XRF), and Fourier transforms infrared spectroscopy (FTIR). Overall the system was found to be an efficient and economical process for the treatment of copper contaminated municipal wastewater.

The start-up and performance of the completely autotrophic nitrogen removal via nitrite (CANON) process were examined in a sequencing batch reactor (SBR) with intermittent aeration. Initially, partial nitrification was established, and then the DO concentration was lowered further, surplus water in the SBR with high nitrite was replaced with tap water, and continuous aeration mode was turned into intermittent aeration mode, while the removal of total nitrogen was still weak. However, the total nitrogen (TN) removal efficiency and nitrogen removal loading reached 83.07% and 0.422 kgN/(m3·d), respectively, 14 days after inoculating 0.15 g of CANON biofilm biomass into the SBR. The aggregates formed in SBR were the mixture of activated sludge and granular sludge; the volume ratio of floc and granular sludge was 7 : 3. DNA analysis showed that Planctomycetes-like anammox bacteria and Nitrosomonas-like aerobic ammonium oxidization bacteria were dominant bacteria in the reactor. The influence of aeration strategies on CANON process was investigated using batch tests. The result showed that the strategy of alternating aeration (1 h) and nonaeration (1 h) was optimum, which can obtain almost the same TN removal efficiency as continuous aeration while reducing the energy consumption, inhibiting the activity of NOB, and enhancing the activity of AAOB.

Discharge of high NH4-N containing wastewater into water bodies has become a critical and serious issue due to its negative impact on water and environmental quality. In this research, the performance of three different reactors was assessed and compared with regard to the removal of NH4-N from wastewater. The highest nitrogen removal efficiency of 98.3% was found when the entrapped sludge reactor (ESR), in which the sludge was entrapped in polyethylene glycol polymer, was used. Under intermittent aeration, nitrification and denitrification occurred simultaneously in the aerobic and anaerobic periods. Moreover, internal carbon was consumed efficiently for denitrification. On the other hand, internal carbon consumption was not found to occur in the suspended sludge reactor (SSR) and the mixed sludge reactor (MSR) and this resulted in nitrogen removal efficiencies of SSR and MSR being 64.7 and 45.1%, respectively. Nitrification and denitrification were the main nitrogen removal processes in the aerobic and anaerobic periods, respectively. However, due to the absence of sufficient organic carbon, denitrification was uncompleted resulting in high NO3-N contents in the effluent.

Keratinases are proteolytic enzymes predominantly active when keratin substrates are available that attack disulfide bridges in the keratin to convert them from complex to simplified forms. Keratinases are essential in preparation of animal nutrients, protein supplements, leather manufacture, textile processing, detergent formulation, feather meal processing for feed and fertilizer, the pharmaceutical and biomedical industries, and waste management. Accordingly, it is necessary to develop a method for continuous production of keratinase from reliable sources that can be easily managed. Microbial keratinase is less expensive than conventionally produced keratinase and can be obtained from fungi, bacteria, and actinomycetes. In this overview, the expansion of information about microbial keratinases and important considerations in keratinase production are discussed.

To study the optimum process conditions for pretreatments and anaerobic codigestion of oil refinery wastewater (ORWW) with chicken manure, L9 (34) Taguchi’s orthogonal array was applied. The biogas production (BGP), biomethane content (BMP), and chemical oxygen demand solubilization (CODS) in stabilization rate were evaluated as the process outputs. The optimum conditions were obtained by using Design Expert software (Version 7.0.0). The results indicated that the optimum conditions could be achieved with 44% ORWW, 36°C temperature, 30 min sonication, and 6% TS in the digester. The optimum BGP, BMP, and CODS removal rates by using the optimum conditions were 294.76 mL/gVS, 151.95 mL/gVS, and 70.22%, respectively, as concluded by the experimental results. In addition, the artificial neural network (ANN) technique was implemented to develop an ANN model for predicting BGP yield and BMP content. The Levenberg-Marquardt algorithm was utilized to train ANN, and the architecture of 9-19-2 for the ANN model was obtained.

Bioenergy is of increasing interest as a renewable, environmentally friendly alternative to energy derived from fossil fuels. Through a variety of processes, biomass can be converted to solid, liquid, or gaseous biofuels. However, various challenges persist to the maintenance and development of biomass-to-energy utilization. This special issue focuses on the most recent advances in the frontier research of bioenergy and biomass utilization and features six selected papers with high quality. The paper titled “Nanofibrillated Cellulose and Copper Nanoparticles Embedded in Polyvinyl Alcohol Films for Antimicrobial Applications” by T. Zhong et al. develops a method to produce hybrids of TEMPO nanofibrillated cellulose (TNFC) and copper nanoparticles. The hybrid material is embedded in polyvinyl alcohol thermoplastic resin and the films are produced using a solvent casting method. The authors evaluate the films in terms of its morphology and thermal and antimicrobial properties and prove that TNFC-copper nanoparticles as antimicrobial nanofillers are valuable for PVA applications. The paper titled “Response of Arbuscular Mycorrhizal Fungi to Hydrologic Gradients in the Rhizosphere of Phragmites australis (Cav.) Trin ex. Steudel Growing in the Sun Island Wetland” by L. Wang et al. assesses the variations of hydrologic gradients in the relationships among AM fungi, reed, and rhizospheric microorganisms. The authors discover water content in soil and reed growth parameters are both positively associated with AM fungi colonization, but only the positive correlations between reed biomass parameters and the colonization could be expected, or both the host plant biomass and the AM fungi could be beneficial. This study could shed light on the mechanisms inside AM fungi-reed symbioses and would be referred to for optimizing the combined phytorhizoremediation. The paper titled “Production by Tobacco Transplastomic Plants of Recombinant Fungal and Bacterial Cell-Wall Degrading Enzymes to Be Used for Cellulosic Biomass Saccharification” by L. Paolo et al. presents work on saccharification of plant biomass. The authors express in tobacco chloroplasts microbial genes encoding five cellulases and a polygalacturonase. Leaf extracts containing the recombinant enzymes show the ability to degrade various cell-wall components under different conditions. In addition, a thermostable xylanase is also tested in combination with a cellulase and a polygalacturonase to study the cumulative effect on the depolymerization of a complex plant substrate. Results demonstrate the feasibility of using transplastomic tobacco leaf extracts to convert cell-wall polysaccharides into reducing sugars, fulfilling a major prerequisite of large scale availability of a variety of cell-wall degrading enzymes for biofuel industry. The paper titled “Enzymatic Saccharification of Lignocellulosic Residues by Cellulases Obtained from Solid State Fermentation Using Trichoderma viride” by T. Sartori et al. presents an approach that uses cellulolytic complex produced by Trichoderma viride in solid state fermentation to hydrolyze lignocellulosic residues. This produced enzyme shows viability in comparison with the commercial cellulase enzyme. The synthesis of cellulases by microorganisms from lignocellulosic residues is a process of great interest, representing the search for renewable sources to replace the fossil energetic matrix. The paper titled “Improving Biomethane Production and Mass Bioconversion of Corn Stover Anaerobic Digestion by Adding NaOH Pretreatment and Trace Elements” by C. Liu et al. describes an approach to improve biomethane production from corn stover. The authors use NaOH pretreatment and trace elements supplementation as a strategy to enhance the biodegradability of corn stover for biomethane production. The approach is compared with only NaOH-pretreated and untreated corn stover, and the paper highlights the supplementation of trace elements. The paper titled “Biochemical Modulation of Lipid Pathway in Microalgae Dunaliella sp. for Biodiesel Production,” by A. F. Talebi et al., explores the use of myoinositol to modulate LP and biodiesel quality. Inclusion of myoinositol in the media indeed improves the total lipid accumulation and biodiesel quality parameters, and this work highlights the fact that the biochemical modulation strategies should be progressively considered in the hope of finding more efficient and economically feasible strategies leading to more viable production systems.

The aim of the present special issue was to explore some recent advances in environmental biotechnology and chemistry with respect to the possibility of using bioresidues as renewable feedstock in the frame of 2nd generation biorefineries. As a matter of fact, pauperization of fossil resources is favouring the development of biorefinery platforms, where goods derive from biomass processing. Because of the ethical issues related to the use of food crops for fuel production, exploitation of organic residues present in agroindustrial, forestry, zootechnical, fishery, and municipal leftovers would enhance biorefinery competitiveness and social acceptance. This approach is referred to as “2nd generation biorefinery” and is still in its infancy, with the large majority of studies being conducted at laboratory scale and very few at pilot scale. Among the major bioresidues/by-products generated by (agro)industries, lignocellulose is one of the cheapest and more abundant nonfood materials. It derives from plant biomass and it is made by 75% of polysaccharides, making carbohydrates an ideal basic platform for the generation of multiple products of societal and industrial interest. Lignin, another important compound of lignocellulosic-based biomass, may be also processed and/or extracted to increase the portfolio of such biorefineries. The present special issue majorly focuses on actual site lignocellulosic residues to produce either chemicals or fuels. Bioconversion of brewer spent grains (BSG) was described by R. Liguori, C. R. Soccol et al. using six Lactobacillus strains to generate lactic acid. Fermentation yields were compared with synthetic media also with respect to different pretreatments. Optimized cultivation reached as much as 22 g/L lactic acid. In P.-L. Tang et al. production of low molecular weight phenolic compounds was carried out using oil palm empty fruit bunch fiber (OPEFBF). While palm oil industry is rapidly expanding, an environmentally friendly solution to treat the lignocellulosic biowaste resulting from its processing is missing. P.-L. Tang et al. focused on the exploitation of lignin through enzymatic and chemical reactions following an alkaline pretreatment, which yielded a mixture of aromatic compounds such as hydroxybenzoic acid, vanillin, syringaldehyde, p-coumaric acid, and ferulic acid. V. Batori et al. reported on the generation of multiple products using whole stillage as renewable feedstock, a by-product from ethanol production from dry-mill grains. Edible fungi such as Neurospora intermedia and Aspergillus oryzae were used for the integrated bioconversion of this organic residue into ethanol and protein-enriched fungal biomass. Economic implications following the upgrade of the present industrial process with the proposed scheme are discussed. This study has close connections with the one by R. Liguori, E. Ionata et al. who exploited the biotechnological potential of fungal activity to produce sugars. Here, cellulolytic and hemicellulolytic enzymes (a mixture of cellulases and xylanases) from Pleurotus ostreatus were used to optimize the saccharification yields of Arundo donax biomass. The influences of temperature, pH, and time as operational parameters were investigated in a 33 factorial experimental design to find out the most critical ones. Significance of these experimental findings was described with respect to commercial enzymes. Finally, C. Nitsos et al. evaluated a number of methods to improve methane yields production by anaerobic digestion of three Mediterranean bioresidues. Pretreatment protocols applied hydrothermal, dilute acid, and steam explosion treatment to gain more energy from lignocellulosic residues such as olive tree pruning, grapevine pruning, and almond shells. Implementation of enzymatic hydrolysis was also tested for the lowest efficient treatments. We believe that this collection of papers will be useful to people working in the area of green chemistry and biotechnology and will help to turn an environmental issue as waste disposal into a new opportunity for a sustainable biobased economy. Alberto Scoma Lorenzo Bertin Maria A. M. Reis Michael Kornaros Marta Coma

The quick recovery process of contaminants removal of aerobic granular sludge (AGS) is complex, and the influencing factors are still not clear. The effects of dissolved oxygen (DO, air intensive aeration rate), organic loading rate (OLR), and C/N on contaminants removal characteristics of AGS and subsequently long-term operation of AGS bioreactor were investigated in this study. DO had a major impact on the recovery of AGS. The granules reactivated at air intensive aeration rate of 100 L/h achieved better settling property and contaminants removal efficiency. Moreover, protein content in extracellular polymeric substance (EPS) was almost unchanged, which demonstrated that an aeration rate of 100 L/h was more suitable for maintaining the biomass and the structure of AGS. Higher OLR caused polysaccharides content increase in EPS, and unstable C/N resulted in the overgrowth of filamentous bacteria, which presented worse NH4+-N and PO43−-P removal. Correspondingly, quick recovery of contaminants removal was accomplished in 12 days at the optimized operation conditions of aeration rate 100 L/h, OLR 4 g/L·d, and C/N 100 : 10, with COD, NH4+-N, and PO43−-P removal efficiencies of 87.2%, 86.9%, and 86.5%, respectively. The renovation of AGS could be successfully utilized as the seed sludge for the rapid start-up of AGS bioreactor.

Constructed wetland (CW) with monoculture ofArundo donaxL. was investigated for the posttreatment of anaerobic bioreactor (ABR) treating combined industrial wastewater. Different dilutions of combined industrial wastewater (20, 40, 60, and 80) and original wastewater were fed into the ABR and then posttreated by the laboratory scale CW. The respective removal efficiencies of COD, BOD, TSS, nitrates, and ammonia were 80%, 78–82%, 91.7%, 88–92%, and 100% for original industrial wastewater treated in ABR. ABR was efficient in the removal of Ni, Pb, and Cd with removal efficiencies in the order of Cd (2.7%) > Ni (79%) > Pb (85%). Posttreatment of the ABR treated effluent was carried out in lab scale CW containingA. donaxL. CW was effective in the removal of COD and various heavy metals present in ABR effluents. The posttreatment in CW resulted in reducing the metal concentrations to 1.95 mg/L, 0 mg/L, and 0.004 mg/L for Ni, Pb, and Cd which were within the permissible water quality standards for industrial effluents. The treatment strategy was effective and sustainable for the treatment of combined industrial wastewater.

From discovery in the early 1990s to completion of full-scale anammox reactor, it took almost two decades to uncover the secret veil of anammox bacteria. There were three milestones during the commercialization of anammox: the development of the first enrichment culture medium, the completion of the first commercial anammox reactor, and the fast start-up of full-scale anammox plant. Till now, the culture of anammox bacteria experienced a big progress through two general strategies: (a) to start up a reactor from scratch and (b) to seed the reactor with enriched anammox sludge. The first full-scale anammox reactor took 3.5 years to realize full operation using the first approach due to several reasons besides the lack of anammox sludge. On the other hand, the first Asian anammox reactor started up in two months, thanks to the availability of anammox seed. Along with the implementation of anammox plants, anammox eventually becomes the priority choice for ammonium wastewater treatment.

Studies on the occurrence of pharmaceuticals show that the widely used pharmaceuticals ibuprofen and diclofenac are present in relevant concentrations in the environment. A pilot plant treating hospital wastewater with relevant concentrations of these pharmaceuticals was evaluated for its performance to reduce the concentration of the pharmaceuticals. Ibuprofen was completely removed, whereas diclofenac yielded a residual concentration, showing the necessity of posttreatment to remove diclofenac, for example, activated carbon. Successively, detailed laboratory experiments with activated sludge from the same wastewater treatment plant showed bioremediation potential in the treatment plant. The biological degradation pathway was studied and showed a mineralisation of ibuprofen and degradation of diclofenac. The present microbes were further studied in laboratory experiments, and DGGE analyses showed the enrichment and isolation of highly purified cultures that degraded either ibuprofen or diclofenac. This research illuminates the importance of the involved bacteria for the effectiveness of the removal of pharmaceuticals in a wastewater treatment plant. A complete removal of pharmaceuticals from wastewater will stimulate water reuse, addressing the worldwide increasing demand for clean and safe fresh water.

This paper investigates the opportunity of using almond oil as a renewable and alternative fuel source. Different fuel blends containing 10, 30, and 50% almond biodiesel (B10, B30, and B50) with diesel fuel (B0) were prepared and the influence of these blends on emissions and some performance parameters under various load conditions were inspected using a diesel engine. Measured engine performance parameters have generally shown a slight increase in exhaust gas temperature and in brake specific fuel consumption and a slight decrease in brake thermal efficiency. Gases investigated were carbon monoxide (CO) and oxides of nitrogen (NOx). Furthermore, the concentration of the total particulate and the unburned fuel emissions in the exhaust gas were tested. A blend of almond biodiesel with diesel fuel gradually reduced the engine CO and total particulate emissions compared to diesel fuel alone. This reduction increased with more almond biodiesel blended into the fuel. Finally, a slight increase in engineNOxusing blends of almond biodiesel was measured.

Sugar beet pulp, a byproduct of sugar beet processing, can be used as a feedstock in second-generation ethanol production. The objective of this study was to investigate the effects of pretreatment, of the dosage of cellulase and hemicellulase enzyme preparations used, and of aeration on the release of fermentable sugars and ethanol yield during simultaneous saccharification and fermentation (SSF) of sugar beet pulp-based worts. Pressure-thermal pretreatment was applied to sugar beet pulp suspended in 2% w/w sulphuric acid solution at a ratio providing 12% dry matter. Enzymatic hydrolysis was conducted using Viscozyme and Ultraflo Max (Novozymes) enzyme preparations (0.015–0.02 mL/g dry matter). Two yeast strains were used for fermentation: Ethanol Red (S. cerevisiae) (1 g/L) andPichia stipitis(0.5 g/L), applied sequentially. The results show that efficient simultaneous saccharification and fermentation of sugar beet pulp was achieved. A 6 h interval for enzymatic activation between the application of enzyme preparations and inoculation with Ethanol Red further improved the fermentation performance, with the highest ethanol concentration reaching26.9±1.2 g/L and86.5±2.1%fermentation efficiency relative to the theoretical yield.

This article proposes a methane production approach through sequenced anaerobic digestion of kitchen waste, determines the hydrolysis constants and reaction orders at both low total solid (TS) concentrations and high TS concentrations using the initial rate method, and examines the population growth model and first-order hydrolysis model. The findings indicate that the first-order hydrolysis model better reflects the kinetic process of gas production. During the experiment, all the influential factors of anaerobic fermentation retained their optimal values. The hydrolysis constants and reaction orders at low TS concentrations are then employed to demonstrate that the first-order gas production model can describe the kinetics of the gas production process. At low TS concentrations, the hydrolysis constants and reaction orders demonstrated opposite trends, with both stabilizing after 24 days at 0.99 and 1.1252, respectively. At high TS concentrations, the hydrolysis constants and the reaction orders stabilized at 0.98 (after 18 days) and 0.3507 (after 14 days), respectively. Given sufficient reaction time, the hydrolysis involved in anaerobic fermentation of kitchen waste can be regarded as a first-order reaction in terms of reaction kinetics. This study serves as a good reference for future studies regarding the kinetics of anaerobic digestion of kitchen waste.

In response to the energy crisis, global warming, and climate changes, microalgae have received a great deal of attention as a biofuel feedstock. Due to a high lipid content in microalgal cells, microalgae present as a promising alternative source for the production of biodiesel. Environmental and culturing condition variations can alter lipid production as well as chemical compositions of microalgae. Therefore, application of the strategies to activate lipid accumulation opens the door for lipid overproduction in microalgae. Until now, many original studies regarding the approaches for enhanced microalgal lipid production have been reported in an effort to push forward the production of microalgal biodiesel. However, the current literature demonstrates fragmented information available regarding the strategies for lipid production improvement. From the systematic point of view, the review highlights the main approaches for microalgal lipid accumulation induction to expedite the application of microalgal biodiesel as an alternative to fossil diesel for sustainable environment. Of the several strategies discussed, the one that is most commonly applied is the design of nutrient (e.g., nitrogen, phosphorus, and sulfur) starvation or limitation. Other viable approaches such as light intensity, temperature, carbon dioxide, salinity stress, and metal influence can also achieve enhanced microalgal lipid production.

Large volumes of untreated palm oil mill effluent (POME) pose threat to aquatic environment due to the presence of very high organic content. The present investigation involved two pilot-scale anaerobic expanded granular sludge bed (EGSB) reactors, continuously operated for 1 year to treat POME. Setting HRT at 9.8 d, the anaerobic EGSB reactors reduced COD from 71179 mg/L to 12341 mg/L and recycled half of sludge by a dissolved air flotation (DAF). The average effluent COD was 3587 mg/L with the consistent COD removal efficiency of 94.89%. Adding cationic polymer (PAM) dose of 30 mg/L to DAF unit and recycling its half of sludge caused granulation of anaerobic sludge. Bacilli and small coccid bacteria were the dominant microbial species of the reactor. The reactor produced 27.65 m3of biogas per m3of POME which was utilized for electricity generation.

This study evaluated the use of an anaerobic packed-bed reactor for hydrogen production at different hydraulic retention times (HRT) (1–8 h). Two reactors filled with expanded clay and fed with glucose (3136–3875 mg L−1) were operated at different total upflow velocities: 0.30 cm s−1(R030) and 0.60 cm s−1(R060). The effluent pH of the reactors was maintained between 4 and 5 by adding NaHCO3and HCl solutions. It was observed a maximum hydrogen production rate of 0.92 L H2 h−1 L−1in R030 at HRT of 1 h. Furthermore, the highest hydrogen yield of 2.39 mol H2 mol−1glucose was obtained in R060. No clear trend was observed by doubling the upflow velocities at this experiment. High ethanol production was also observed, indicating that the ethanol-pathway prevailed throughout the experiment.

Aerobic granulation of activated sludge was successfully achieved in a full-scale sequencing batch reactor (SBR) with 50,000 m3 d−1for treating a town’s wastewater. After operation for 337 days, in this full-scale SBR, aerobic granules with an average SVI30of 47.1 mL g−1, diameter of 0.5 mm, and settling velocity of 42 m h−1were obtained. Compared to an anaerobic/oxic plug flow (A/O) reactor and an oxidation ditch (OD) being operated in this wastewater treatment plant, the sludge from full-scale SBR has more compact structure and excellent settling ability. Denaturing gradient gel electrophoresis (DGGE) analysis indicated thatFlavobacteriumsp., uncultured beta proteobacterium, unculturedAquabacteriumsp., and unculturedLeptothrixsp. were just dominant in SBR, whereas uncultured bacteroidetes were only found in A/O and OD. Three kinds of sludge had a high content of protein in extracellular polymeric substances (EPS). X-ray fluorescence (XRF) analysis revealed that metal ions and some inorganics from raw wastewater precipitated in sludge acted as core to enhance granulation. Raw wastewater characteristics had a positive effect on the granule formation, but the SBR mode operating with periodic feast-famine, shorter settling time, and no return sludge pump played a crucial role in aerobic sludge granulation.

Artificial intelligence (AI) scholars and mediciners have reported AI systems that accurately detect medical imaging and COVID-19 in chest images. However, the robustness of these models remains unclear for the segmentation of images with nonuniform density distribution or the multiphase target. The most representative one is the Chan-Vese (CV) image segmentation model. In this paper, we demonstrate that the recent level set (LV) model has excellent performance on the detection of target characteristics from medical imaging relying on the filtering variational method based on the global medical pathology facture. We observe that the capability of the filtering variational method to obtain image feature quality is better than other LV models. This research reveals a far-reaching problem in medical-imaging AI knowledge detection. In addition, from the analysis of experimental results, the algorithm proposed in this paper has a good effect on detecting the lung region feature information of COVID-19 images and also proves that the algorithm has good adaptability in processing different images. These findings demonstrate that the proposed LV method should be seen as an effective clinically adjunctive method using machine-learning healthcare models.

Purpose. As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics “Molecular Modeling,” “Gene Expression,” “Biomarker,” “Coronavirus,” “Immunoinformatics,” “Cancer Bioinformatics,” and “Systems Biology.” Moreover, “Systems Biology” and “Coronavirus” had the largest and smallest clusters, respectively. Conclusion. The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other.

This paper has reviewed the global research on the pandemic critical care research during the COVID-19 from 2020 to 2022. To this end, a bibliometric and cluster analysis by full counting has been carried out using VOSviewer software and bibliographic data extracted from the Scopus database. The research found and studied 2778 documents. The types of research documents were limited to an article (81.46%), a letter (9.43%), an editorial (3.92%), a note (3.92%), a conference paper (0.90), and a short survey (0.04%). The results show an incessant increase in the number of research documents published and citations received during the COVID-19 pandemic. The U.S., U.K., Italy, and France have been shown to be the most productive countries, and there is a predominance of European institutions supporting and fostering research on pandemic critical care. Cecconi, M. (Italy) and Shankar-Hari, M. (U.K.) produced the highest number of research documents. Mapping of citation, co-citation, co-authorship, and keyword cooccurrence highlighted the hotspot, knowledge structure, and important themes. Citation dynamics for the top-cited research documents revealed static discourse. By reviewing the evolutionary trends of pandemic critical care research investigated factors, such as the influential works, main research topics, and the research frontiers, this paper reveals the scientific literature production’s main research objectives and directions that could be addressed and explored in future studies. This paper reveals the scientific literature production’s main research objectives and directions that could be addressed and explored in future studies after reviewing the evolutionary trends of pandemic critical care research during the COVID-19 and the investigated factors, such as influential works, main research topics, and research frontiers.

The incidence of liver cancer is increasing year by year, and how to effectively diagnose early-stage liver cancer and improve the survival rate of liver cancer patients has become one of the current research topics of concern. Aiming at this problem, it is of great significance for the diagnosis of early liver cancer. With the in-depth research on the diagnosis of early-stage liver cancer, the research on growth differentiation factor 15 is gradually carried out, and its performance advantages are of great significance to solve the problem of detection and diagnosis of early-stage liver cancer. This study can improve the accuracy of early diagnosis of liver cancer. The purpose of this paper is to study the application of data mining in the study of clinical value of growth and differentiation factor 15 detection and diagnosis of early liver cancer. In this paper, the data mining algorithm is analyzed, the performance of the algorithm is experimentally analyzed, and the relevant theoretical formulas are used to explain. The results showed that the expression level of GDF-15 was significant in early primary liver cancer (tumor diameter P < 0.01 ), there was a significant difference ( P < 0.01 ) compared with adjacent tissue ( P < 0.01 ). Serum GDF-15 can be used as a tumor marker for predicting early stage liver cancer. The high expression of GDF-15 in early stage liver cancer is an independent risk factor affecting the prognosis of liver cancer patients.

Objective. To review the progress of research on photoplethysmography- (PPG-) based cuffless continuous blood pressure monitoring technologies and prospect the challenges that need to be addressed in the future. Methods. Using Web of Science and PubMed as search engines, the literature on cuffless continuous blood pressure studies using PPG signals in the recent five years were searched. Results. Based on the retrieved literature, this paper describes the available open datasets, commonly used signal preprocessing methods, and model evaluation criteria. Early researches employed multisite PPG signals to calculate pulse wave velocity or time and predicted blood pressure by a simple linear equation. Later, extensive researches were dedicated to mine the features of PPG signals related to blood pressure and regressed blood pressure by machine learning models. Most recently, many researches have emerged to experiment with complex deep learning models for blood pressure prediction with the raw PPG signal as input. Conclusion. This paper summarized the methods in the retrieved literature, provided insight into the artificial intelligence algorithms employed in the literature, and concluded with a discussion of the challenges and opportunities for the development of cuffless continuous blood pressure monitoring technologies.

The incidence of oral cancer is still increasing. It has become very common in patients with malignant tumors, which has forced medical personnel to continuously explore its treatment methods. What kind of method can effectively and correctly diagnose the disease in the early stage and improve the survival rate has become one of the research topics that have attracted much attention. Aiming at this problem, it has great research significance for the field of oral precancerous lesions diagnosis. With the in-depth research on oral precancerous diagnosis, the research on artificial neural network (ANN) in medical diagnosis is gradually carried out. Its performance advantage is of great significance to solve the problem of early and correct disease diagnosis. This paper aimed to investigate the application of ANN-assisted cancer risk prediction method in risk prediction of oral precancerous lesions. Through the analysis and research of ANN and oral cancer, the construction of oral cancer risk prediction model was applied to solve the problem of improving the survival rate of oral cancer patients. In this paper, ANN and oral precancerous lesions were analyzed, the performance of the algorithm was experimentally analyzed, and the relevant theoretical formulas were used to explain. The results showed that the method had higher accuracy than traditional forecasting methods. When N = 2 , the output accuracy was above 90%. It can be seen that the algorithm can meet the needs of the diagnosis of high-risk groups of oral cancer lesions, and the diagnosis efficiency and patient survival rate has been greatly improved.

In recent years, due to the increase in the incidence of traffic accidents, the number of people with limb injuries has also increased. At the same time, among the aging population, neurological diseases or cardiovascular and cerebrovascular diseases have caused many people to have limb hemiplegia. It has been clinically proven that the use of rehabilitation equipment can help patients with limb injuries to restore limb motor function. This paper takes wearable lower limb rehabilitation exoskeleton as the research object, and its main contents are mechanical structure design, kinematics analysis, gait planning, virtual prototype simulation, and experimental verification and analysis. Based on the physiological characteristics of human body and the principle of comfortable and reliable wearing, this paper designs wearable exoskeleton for lower limb rehabilitation. Firstly, the physiological structure characteristics and movement mechanism of human lower limbs were studied and analyzed. By referring to the rotation range and height and size of each joint of human lower limbs, the overall scheme of wearable lower limb rehabilitation exoskeleton was designed and the degree of freedom was allocated. At the same time, Solidworks was used to establish a three-dimensional model. On the basis of a 3D model, a kinematics model was established, and the forward kinematics solution was obtained by using homogeneous coordinate transformation. Since the inverse kinematics solution was relatively complicated, the inverse kinematics solution was conducted in this paper according to the geometric relations of the joints of the lower limbs. Kinematics analysis of the exoskeleton structure of wearable lower limb rehabilitation was carried out to lay a theoretical foundation for gait planning. The off-line gait planning was carried out by using the method based on ZMP stability criterion, and the gait planning was divided into five stages: squat, start, middle step, stop and rise, and the motion trajectory of the center of mass and ankle joint was planned. Based on the inverse kinematics formula, the function of the change of joint angle with time in walking process is derived. The virtual prototype is established in ADAMS, and the simulation of virtual prototype is carried out by using the function of gait planning’s joint angle and time. The correctness of structural design and gait planning was verified by measuring the trajectories of the centroid and ankle joints in each gait stage and the functional relationship between the rotation angle of each joint and time. Then, using a 3D dynamic capture system to capture the human lower limb motion trajectory of each joint, each joint trajectory data output, using the MATLAB software to output data, gets the joint trajectory change over time function curve and is used to verify feasibility and applicability of human gait planning. Through the research and analysis of the joints of the lower limbs of the human body, it can be concluded that the hip joint and the knee joint have 3 degrees of freedom, respectively, and the knee joint has 1 degree of freedom.

Surgery is one of the larger wounds in conventional surgery, and patients often experience different pain and postural discomfort after surgery. With the ever-changing standards of medical care and patient care requirements, providing high-quality care to postoperative patients is an important measure to reduce complications and promote rapid recovery. However, in the traditional postsurgical nursing methods, there are often the phenomenon that wrong patients are connected, patient data is messy, and medicines are counted incorrectly, which directly leads to a rapid decline in nursing efficiency. In the context of the rapid development of artificial intelligence and big data, intelligent medical data analysis technology has gradually been integrated into the medical field. This paper analyzes and studies the application effect of intelligent medical data analysis technology in postoperative nursing. It is aimed at changing the traditional postoperative nursing methods and improving nursing efficiency, and it provides important suggestions for the development of postoperative nursing in the new era. Combining big data and Internet of Things technology, this paper builds a smart medical Internet of Things framework and an intelligent postoperative care system and uses machine learning algorithms to preprocess relevant medical data. The final experimental results show that the intelligent medical data analysis technology has a good effect in improving the nursing efficiency after surgery, and the nursing efficiency has increased by 6.9%.

Introduction. Ethiopia has made significant efforts to enhance family planning services despite variations in the community’s use of modern contraception in different parts of the country. Various studies have reported the proportion and determinant factors of adolescents’ contraceptive uptake in Ethiopia. These studies are not consistent in terms of size, scope, and geographic coverage, and the results need to be systematically collated to inform policies. Therefore, this review was aimed at analyzing the findings of those primary studies to obtain more representative evidence of adolescents’ contraceptive uptake in Ethiopia. Methods. Five databases (MEDLINE via PubMed, Google Scholar, Scopus, ScienceDirect, and CINAHL) were searched for papers published from January 2000 up to June 2022 in English. Of thirty eligible studies, eight papers were included in this meta-analysis. Between-study heterogeneity was evaluated by the forest plot and inconsistency index ( I 2 ). A random-effects model was used to calculate the pooled estimates of adolescents’ contraceptive uptake. Results. The overall pooled proportion of adolescents’ contraceptive uptake was 40% ( I 2 = 99.70 , p ≤ 0.001 ; 95 % CI = 19 , 61 ). Adolescents’ usage of contraception was influenced by a number of factors: individual-, sociocultural-, knowledge- (about contraceptive methods), and healthcare service-related factors. Individual-related factors include the educational status of adolescents, being of young age, and the income status of adolescents’ families. Sociocultural-related factors comprise discussion with the family/relatives, parent disapproval and pressure from partners, and being married or having a partner. Healthcare service-related factors include the availability of youth clubs and inconvenient service hours for SRH services. Knowing contraceptive methods and SRH services was also positively associated with adolescents’ contraceptive utilization. Conclusions. The proportion of adolescents who used contraception in Ethiopia was 40%. Adolescents’ use of contraceptives was influenced by a variety of factors: individual-, sociocultural-, healthcare-, and knowledge-related factors. Hence, integrated interventions targeted at tackling barriers to contraceptive uptake may be helpful to improve adolescents’ contraceptive utilization in Ethiopia.

This paper presents a systematic study in reviewing the application of finite element method for the analysis of correction mechanism of spine deformity due to scoliosis. The study is aimed at systematically (1) reviewing the use of finite element analysis in spine deformity case, (2) reviewing the modelling of pedicle screw and rod system in scoliosis surgery, and (3) analysing and discussing gap between the studies. Using the restricted key phrases, the review gathered studies from 2001 to 2021 from various electronic databases (Scopus, ScienceDirect, PubMed, Medline, and WorldCAT). Studies were included if they reported a finite element study on spine deformity. Studies that did not fully disclose their methodology and results had significant discrepancies, not published in English or not yet published were all disqualified. Regardless of inconsistencies in the methodological design of the studies, the quality of all papers was above the acceptable level. A total of fifteen manuscripts were considered for inclusion and were given a comprehensive review. This study indicates that analysing the forces acting on the spine, as well as the interrelationship between the force, stress, and degree of correction (which measured as the Cobb angle), could help to improve the corrective mechanism procedure of spine deformity. Pedicle screws and its placement strategies are also important as it influence the corrective forces for scoliosis treatment. Hence, the findings of this study could potentially be used as a guidance to develop a reliable finite element analysis that can predict the biomechanics responses during the corrective spine deformity treatment.

Objective. Artificial intelligence-powered screening systems of coronavirus disease 2019 (COVID-19) are urgently demanding since the ongoing outbreak of SARS-CoV-2 worldwide. Chest CT or X-ray is not sufficient to support the large-scale screening of COVID-19 because mildly-infected patients do not have imaging features on these images. Therefore, it is imperative to exploit supplementary medical imaging strategies. Traditional Chinese medicine has played an essential role in the fight against COVID-19. Methods. In this paper, we conduct two kinds of verification experiments based on a newly-collected multi-modality dataset, which consists of three types of modalities: tongue images, chest CT scans, and X-ray images. First, we study a binary classification experiment on tongue images to verify the discriminative ability between COVID-19 and non-COVID-19. Second, we design extensive multimodality experiments to validate whether introducing tongue image can improve the screening accuracy of COVID-19 based on chest CT or X-ray images. Results. Tongue image screening of COVID-19 showed that the accuracy (ACC), sensitivity (SEN), specificity (SPEC), and Matthew correlation coefficient (MCC) of the improved AlexNet and Googlenet both reached 98.39%, 98.97%, 96.67%, and 99.11%. The fusion of chest CT and tongue images used a tandem multimodal classifier fusion strategy to achieve optimal classification, and the results and screening accuracy of COVID-19 reached 98.98%, resulting in a significant improvement of 4.75% the highest accuracy in 375 years compared with the single-modality model. The fusion of chest x-rays and tongue images also had good classification accuracy. Conclusions. Both experimental results demonstrate that tongue image not only has an excellent discriminative ability for screening COVID-19 but also can improve the screening accuracy based on chest CT or X-rays. To the best of our knowledge, it is the first work that verifies the effectiveness of tongue image on screening COVID-19. This paper provides a new perspective and a novel solution that contributes to large-scale screening toward fast stopping the pandemic of COVID-19.

In the process of competition and training, tennis players often carry out explosive force and extreme centripetal and eccentric contraction, while the ligament and joint capsule on the shoulder joint are relatively weak, which also makes the joint often appear injury caused by overuse. It has been the direction of scientific research to help athletes recover their functions and return to the arena through effective rehabilitation training and prerehabilitation training. In this paper, the reliability and short-term effect of wearing dynamic and static shoulder joint brace after arthroscopic repair of rotator cuff injury were studied through a controlled trial of tennis exercise for the treatment of shoulder injury. The purpose of this study was to apply the dynamic and static shoulder joint brace to patients with rotator cuff injury and shorten the recovery time of shoulder joint function the operation. This paper also studies the therapeutic effect verification of patients with rotator cuff injury during rehabilitation period by wearing shoulder joint brace with dynamic and static combination. Through the comparison between the experimental group and the control group, it is verified that the effect of rehabilitation intervention is better than that of the control group, which shows that the use of dynamic and static shoulder joint brace can improve the range of motion of shoulder joint and patient satisfaction, and the effect increases with time. This study improves the shoulder strength of tennis players through functional training, demonstrates the value and significance of functional training for the development of tennis through experiments, and provides a reference for athletes to improve their physical fitness training. At the same time, the research content of this paper can also provide references and promotion for sports events.

Deep learning has become a research hotspot in multimedia, especially in the field of image processing. Pooling operation is an important operation in deep learning. Pooling operation can reduce the feature dimension, the number of parameters, the complexity of computation, and the complexity of time. With the development of deep learning models, pooling operation has made great progress. The main contributions of this paper on pooling operation are as follows: firstly, the steps of the pooling operation are summarized as the pooling domain, pooling kernel, step size, activation value, and response value. Secondly, the expression form of pooling operation is standardized. From the perspective of “invariable” to “variable,” this paper analyzes the pooling domain and pooling kernel in the pooling operation. Pooling operation can be classified into four categories: invariable of pooling domain, variable of pooling domain, variable of pooling kernel, and the pooling of invariable “+” variable. Finally, the four types of pooling operation are summarized and discussed with their advantages and disadvantages. There is great significance to the research of pooling operations and the iterative updating of deep learning models.

The rapid development of antibiotic resistance in K. pneumonia has led to a major concern. In order to analyze the hotspots and develop trends in this field through visual the analysis, this study used CiteSpace software to summarize the available data in the literature to provide insights. A total of 9366 research articles were retrieved from the Web of Science Core Collection, and the number of published papers is increasing year by year. The country with the most articles was the USA, followed by China and India. The institution with the highest number of publications was LERU. The author with the highest number of articles was Li. The journal with the highest citation rate was Antimicrobial Agents and Chemotherapy. In addition, based on keyword coword analysis and cited literature prominence analysis by CiteSpace, the current research focus in the field was therapy, CRKP, and resistance genes. This paper provides a new quantitative visualization way for the development of the field in the recent ten years. The results show global trends that researchers can use to determine future directions.

Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. In this paper, DenseNet is summarized from the following aspects. First, the basic principle of DenseNet is introduced; second, the development of DenseNet is summarized and analyzed from five aspects: broaden DenseNet structure, lightweight DenseNet structure, dense unit, dense connection mode, and attention mechanism; finally, the application research of DenseNet in the field of medical image analysis is summarized from three aspects: pattern recognition, image segmentation, and object detection. The network structures of DenseNet are systematically summarized in this paper, which has certain positive significance for the research and development of DenseNet.

This paper incorporates the adaptive neurofuzzy inference system (ANFIS) technique to model the yield of bio-oil. The estimation of this parameter was performed according to pyrolysis conditions and biomass compositions of feedstock. For this purpose, this paper innovates two optimization methods including a genetic algorithm (GA) and particle swarm optimization (PSO). Primary data were gathered from previous studies and included 244 data of biodiesel oils. The findings showed a coefficient determination ( R 2 ) of 0.937 and RMSE of 2.1053 for the GA-ANFIS model, and a coefficient determination ( R 2 ) of 0.968 and RMSE of 1.4443 for PSO-ANFIS. This study indicates the capability of the PSO-ANFIS algorithm in the estimation of the bio-oil yield. According to the performed analysis, this model shows a higher ability than the previously presented models in predicting the target values and can be a suitable alternative to time-consuming and difficult experimental tests.