Tetragonal ZrO₂, synthesized by the sol–gel method and dip-coating technique, was found to be photocatalytically active for the degradation of methylene blue. The ZrO₂ thin films were characterized by X-ray diffraction (XRD), Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and UV-vis spectroscopy. The photocatalytic degradation of methylene blue was carried out with this material. We identified the tetragonal phase in ZrO₂ thin film at different annealing temperatures from 400 °C to 550 °C. The XRD study indicated that the films were monocrystalline in nature with preferred grain orientation along (011) plane and exhibited a tetragonal crystal structure. The crystallite size of the films increased with increasing annealing temperature. FTIR explained the bonding nature and confirmed the formation of the composite. UV-Vis showed the optical absorbance was high in the visible region and the optical band gap value increased with annealing temperature. The photocatalytic experimental results revealed that ZrO₂ thin films degraded MB by 20%, 24%, 29%, and 36%, with annealing temperatures of 400 °C at 550 °C for 10 h, respectively. Our results provide useful insights into the development of photocatalytic materials and degradation of methylene blue. Full article

In indoor visible light communication (VLC), the received signals are subject to severe interference due to factors such as high-brightness backgrounds, long-distance transmissions, and indoor obstructions. This results in an increase in misclassification for modulation format recognition. We propose a novel model called VLCMnet. Within this model, a temporal convolutional network and a long short-term memory (TCN-LSTM) module are utilized for direct channel equalization, effectively enhancing the quality of the constellation diagrams for modulated signals. A multi-mixed attention network (MMAnet) module integrates single- and mixed-attention mechanisms within a convolutional neural network (CNN) framework specifically for constellation image classification. This allows the model to capture fine-grained spatial structure features and channel features within constellation diagrams, particularly those associated with high-order modulation signals. Experimental results obtained demonstrate that, compared to a CNN model without attention mechanisms, the proposed model increases the recognition accuracy by 19.2%. Under severe channel distortion conditions, our proposed model exhibits robustness and maintains a high level of accuracy. Full article

Background/Objectives: Lower extremity amputations (LEAs) are a burdensome complication of peripheral artery disease (PAD) and/or arterial embolism and thrombosis (AET). We assessed the trends in PAD- and/or AET-related LEAs in Romania. Methods: This retrospective study (2015–2019) analyzed data on minor and major LEAs in hospitalized patients recorded in the National School for Public Health, Management, and Health Education database. The absolute numbers and incidences of LEAs were analyzed by diagnosis type, year, age, sex, and amputation level. Results: Of 38,590 vascular disease-related amputations recorded nationwide, 36,162 were in PAD and 2428 in AET patients. The average LEA incidence in the general population was 34.73 (minimum: 31.96 in 2015; maximum: 36.57 in 2019). The average incidence of major amputations, amputations above the knee, hip amputations, amputations below the knee, and minor amputations was 16.21 (15.62 in 2015; 16.84 in 2018), 13.76 (13.33 in 2015; 14.28 in 2018), 0.29 (0.22 in 2017; 0.35 in 2019), 2.15 (2.00 in 2015; 2.28 in 2019), and 18.52 (16.34 in 2015; 20.12 in 2019), respectively. Yearly PAD- and/or AET-related amputations were significantly higher in men versus women. The overall number of LEAs increased with age, particularly in patients ≥ 70 years. The increase in the total number of amputations was mainly due to a constant rise in minor amputations for both groups, regardless of gender. Conclusions: PAD- and/or AET-related LEAs in Romania increased from 2015 to 2019, with men having a greater incidence than women. Raising awareness and effective management strategies are needed to prevent LEAs. Full article

Moiré patterns caused by aliasing between the camera’s sensor and the monitor can severely degrade image quality. Image demoiréing is a multi-task image restoration method that includes texture and color restoration. This paper proposes a new multibranch wavelet-based image demoiréing network (MBWDN) for moiré pattern removal. Moiré images are separated into sub-band images using wavelet decomposition, and demoiréing can be achieved using the different learning strategies of two networks: moiré removal network (MRN) and detail-enhanced moiré removal network (DMRN). MRN removes moiré patterns from low-frequency images while preserving the structure of smooth areas. DMRN simultaneously removes high-frequency moiré patterns and enhances fine details in images. Wavelet decomposition is used to replace traditional upsampling, and max pooling effectively increases the receptive field of the network without losing the spatial information. Through decomposing the moiré image into different levels using wavelet transform, the feature learning results of each branch can be fully preserved and fed into the next branch; therefore, possible distortions in the recovered image are avoided. Thanks to the separation of high- and low-frequency images during feature training, the proposed two networks achieve impressive moiré removal effects. Based on extensive experiments conducted using public datasets, the proposed method shows good demoiréing validity both quantitatively and qualitatively when compared with the state-of-the-art approaches. Full article

Myelodysplastic syndrome/neoplasm (MDS) comprises a group of heterogeneous hematopoietic disorders that present with genetic mutations and/or cytogenetic changes and, in the advanced stage, exhibit wide-ranging gene hypermethylation. Patients with higher-risk MDS are typically treated with repeated cycles of hypomethylating agents, such as azacitidine. However, some patients fail to respond to this therapy, and fewer than 50% show hematologic improvement. In this context, we focused on the potential use of epigenetic data in clinical management to aid in diagnostic and therapeutic decision-making. First, we used the F-36P MDS cell line to establish an azacitidine-resistant F-36P cell line. We performed expression profiling of azacitidine-resistant and parental F-36P cells and used biological and bioinformatics approaches to analyze candidate azacitidine-resistance-related genes and pathways. Eighty candidate genes were identified and found to encode proteins previously linked to cancer, chronic myeloid leukemia, and transcriptional misregulation in cancer. Interestingly, 24 of the candidate genes had promoter methylation patterns that were inversely correlated with azacitidine resistance, suggesting that DNA methylation status may contribute to azacitidine resistance. In particular, the DNA methylation status and/or mRNA expression levels of the four genes (AMER1, HSPA2, NCX1, and TNFRSF10C) may contribute to the clinical effects of azacitidine in MDS. Our study provides information on azacitidine resistance diagnostic genes in MDS patients, which can be of great help in monitoring the effectiveness of treatment in progressing azacitidine treatment for newly diagnosed MDS patients. Full article

Due to the favorable features obtained through the incorporation of fluorine atom(s), fluorinated drugs are a group with emerging pharmaceutical importance. As their commercial availability is still very limited, to expand the range of possible candidates, new fluorinated tryptophan analogs were synthesized. Control of enantiopurity during the synthesis procedure requires that highly efficient enantioseparation methods be available. In this work, the enantioseparation of seven fluorinated tryptophans and tryptophan was studied and compared systematically to (i) develop analytical methods for enantioselective separations and (ii) explore the chromatographic features of the fluorotrytophans. For enantioresolution, macrocyclic glycopeptide-based selectors linked to core-shell particles were utilized, applying liquid chromatography-based methods. Application of the polar-ionic mode resulted in asymmetric and broadened peaks, while reversed-phase conditions, together with mobile-phase additives, resulted in baseline separation for all studied fluorinated tryptophans. The marked differences observed between the methanol and acetonitrile-containing eluent systems can be explained by the different solvation abilities of the bulk solvents of the applied mobile phases. Among the studied chiral selectors, teicoplanin and teicoplanin aglycone were found to work effectively. Under optimized conditions, baseline separations were achieved within 6 min. Ionic interactions were semi-quantitatively characterized and found to not influence enantiorecognition. Interestingly, fluorination of the analytes does not lead to marked changes in the chromatographic characteristics of the methanol-containing eluents, while larger differences were noticed when the polar but aprotic acetonitrile was applied. Experiments conducted on the influence of the separation temperature indicated that the separations are enthalpically driven, with only one exception. Enantiomeric elution order was found to be constant on both teicoplanin and teicoplanin aglycone-based chiral stationary phases (L < D) under all applied chromatographic conditions. Full article

Ginkgo biloba L. leaves are rich in secondary metabolites with important medicinal values; to increase their contents, foliar spraying of micronutrients is a potential strategy. Zinc, a multifunctional element, has a significant impact on the content of secondary metabolites in other plants, but relevant research into ginkgo is still lacking. In our study, different spraying time and concentration strategies were used to investigate the effects of zinc sulfate (ZnSO₄) on physiological indicators and secondary metabolites of 2-year-old ginkgo. The results demonstrated that ZnSO₄ could increase the contents of hydrogen peroxide, abscisic acid, and free amino acids in ginkgo leaves. It also enhances the antioxidant enzyme activity of ginkgo leaves, decreases the content of plant auxin, and ultimately facilitates the accumulation of ginkgo terpene lactones (TTL). Spraying ZnSO₄ in June resulted in a more significant increase in the contents of TTL and flavonoids compared to spraying in August. After spraying 12 mmol/L ZnSO₄ in June, the contents of TTL and flavonoids in ginkgo leaves were significantly elevated by 35.95% and 24.30%, respectively, compared to those in the CK (p < 0.05). The contents of ginkgolide A, B, and C were notably increased by 45.93%, 46.56%, and 74.29%, respectively, compared to those in the CK (p < 0.05). Therefore, our study suggests that the optimal timing for spraying ZnSO₄ on ginkgo is in June, with a recommended concentration of 12 mmol/L. Our study provides a theoretical basis for the accumulation of secondary metabolites in ginkgo and guides the production of its leaf-utilization plantations. Full article

Global warming, climate change and the energy crisis are trending topics around the world, especially within the energy sector. The rising cost of energy, greenhouse gas (GHG) emissions and global temperatures stem from the over-reliance on fossil fuel as the major energy resource. These challenges have highlighted the need for alternative energy resources and urgent intervention strategies like energy consumption reduction and improving energy efficiency. The heating, ventilation, and air-conditioning (HVAC) system in a building accounts for about 70% of energy consumption, and a decision to reduce energy consumption may impact the indoor environmental quality (IEQ) of the building. It is important to adequately balance the tradeoff between IEQ and energy management. Artificial intelligence (AI)-based solutions are being explored for improving building energy performance without compromising IEQ. This paper systematically reviews recent studies on AI and machine learning (ML) for building energy management and IEQ by exploring common use areas, the methods or algorithms applied and the results obtained. The overall purpose of this research is to add to the existing body of work and to highlight energy-related AI applications in buildings and the related gaps. The result shows five common application areas: thermal comfort and indoor air quality (IAQ) control; energy management and energy consumption prediction; indoor temperature prediction; anomaly detection; and HVAC controls. Gaps involving policy, real-life scenario applications, and insufficient study of the visual and acoustic comfort areas are also identified. Very few studies take into consideration the need to follow IEQ standards in the selection process and positioning of sensors in AI applications for IEQ in buildings. This study reveals a need for more systematically summarized research. Full article

This study focuses on recycling Shammi corn stalks in the cement industries, further avoiding air and soil pollution caused by their improper disposal. This crop residue was thermally treated at 700 °C for 2 h under an oxygen-rich environment to produce Shammi corn stalk ash (SCSA). This SCSA was used as a cement replacement material (2–10%, w/w), whereas the control sample included only cement. The compressive strength values for the 4% (w/w) replacement ratio at 2-, 7-, and 28-day ages were greater than those for the control by 26.5%, 15.8%, and 11.4%, respectively. This 4% (w/w) also maintained a better flexural strength than other mixtures, with proper initial and final setting times (135 and 190 min), workability (18.5 cm), and water consistency (27.5%). These mechanical/physical properties were integrated with socio-enviro-economic data collected from experts through a pairwise comparison questionnaire, forming the inputs of a multi-criteria decision-making (MCDM) model. Recycling SCSA in the cement-manufacturing process attained positive scores in the achievement of the three pillars of sustainable development, revealing an overall score greater than the control. Hence, the study outcomes could be essential in developing green concrete, cement blocks, and mortar, based on the sustainable development goals (SDGs) agenda. Full article

The development of floating photovoltaic systems (FPV) for coastal and offshore locations requires a solid understanding of a design’s hydrodynamic performance through reliable methods. This study aims to extend insights into the hydrodynamic behavior of a superficial multi-body FPV system in mild and harsh wave conditions through basin tests at scale 1:10, with specific interest in the performance of hinges that interconnect the PV panels. Particular effort is put into correctly scaling the elasticity of the flexible hinges that interconnect the PV modules. Tests of a 5 × 3 FPV matrix are performed, with and without shelter, by external floating breakwater (FBW). The results show that the PV modules move horizontally in the same phase when the wave length exceeds the length of the FPV system, but shorter waves result in relative motions between modules and, for harsh seas, in hinge buckling. Relative motions suggest that axial loads are highest for the hinges that connect the center modules in the system and for normal wave incidence, while shear loads are highest on the outward hinges and for oblique incidence. The FBW reduces hinge loads as it attenuates the high-frequency wave energy that largely drives relative motions between PV modules. Full article

Performance adaptation is an effective way to improve the accuracy of gas turbine performance models. Although current performance adaptation methods, such as those using genetic algorithms or evolutionary computation to modify component characteristic maps, are useful for finding good solutions, they are essentially searching methods and suffer from long computation time. This paper presents a novel approach that can achieve good performance adaptation with low time complexity and without using any searching method. In this method, the actual component performance parameters are first estimated using engine measurements at different operating conditions. For each operating condition, some scaling factors are introduced and calculated to indicate the difference between the actual and predicted component performance parameters. Afterward, an interpolating algorithm is adopted to synthesize the scaling factors for modifying all major component maps. The adapted component maps are then able to make the engine model match all the gas path measurements and achieve the required accuracy of the engine performance model. The proposed approach has been tested with a model high-bypass turbofan engine using simulated data. The results show that the proposed performance adaptation approach can effectively improve the model’s accuracy. Specifically, the prediction errors can be reduced from about 9% to about 0.6%. In addition, this approach has much less computational complexity compared to other optimization-based counterparts. Full article

Our previous retrospective observational study demonstrated the safety of laparoscopically assisted subtotal colectomy with ileorectal anastomosis and preservation of the superior rectal artery (SRA), without instances of leakage, in patients with slow-transit constipation (STC). Thus, we extended the enrollment period and enlarged the sample size to detect the differences in the postoperative complications and surgical and functional outcomes between patients who underwent laparoscopically assisted subtotal colectomy with and without SRA preservation. We conducted a retrospective single-center analysis of patients with STC who underwent laparoscopically assisted subtotal colectomy between 2016 and 2020. The diagnosis of STC was based on the colonic transit and anal functional tests and barium enema to exclude secondary causes. Patients were divided into group A, which underwent surgery with SRA preservation, and group B, which underwent ligation of the SRA during surgery. Outcome assessments for both groups included the incidence of anastomotic breakdown, intraoperative complications, length of hospital stay, estimated blood loss, time to first flatus, and complications. Propensity score matching allocated 34 patients to groups A and B each. Postoperative bowel function, including time to first flatus, stool, and oral intake, recovered better in group A than in group B. Anastomotic leakage, a significant postoperative complication, was less frequent in patients with SRA preservation. In conclusion, preservation of the SRA in patients undergoing laparoscopically assisted subtotal colectomy with ileorectal anastomosis for STC is associated with favorable postoperative bowel function recovery and lower anastomotic leakage rates. Full article

The organic fraction of municipal solid waste (OFMSW) is a significant environmental threat, and an economic and social challenge to manage. As such, the efficient treatment of OFMSW is a significant key factor in achieving sustainable waste management. Decentralized composting (DC) offers a new framework of waste management. The DC analysis model (DCAM) proposed in our previous study provides a powerful tool for decision makers, based on the quantification of the DC project characteristics. In this paper, we focus on qualitative analysis as a complementary tool to support decision making in cases where the quantitative analysis is equivocal. The qualitative analysis identifies the main players in the field, the critical stakeholders, and the potential conflicts between them. It also reveals the root problems and the core competencies for the project’s implementation. The DCAM qualitative analysis in the Shefa-Amr case study indicates that unresolved root problems, such as “lack of national regulation”, “clear ownership of the project”, and “lack of ongoing budget” can result in an unsustainable composting system. Countering that, “commitment of the municipality” together with “economic viability” and securing “suitable areas for placing composters” are among the most important core competencies for the effective implementation of DC projects. Full article

The enzyme-based degradation of lignocellulose for bioenergy production is an eco-friendly and sustainable approach. This study aimed to elucidate the enzymatic characteristics of endoglucanase (EGL), β-glucosidase (BGL), and xylanase (XYN) from Trichoderma guizhouence NJAU4742, and to explore the potential mechanisms underlying their synergistic degradation of different natural substrates. The results demonstrated that the three enzymes possessed remarkable high-temperature catalytic activity, broad pH adaptability, and responsiveness to different metal ions. The functional group absorption peaks of different substrates were shifted and altered after the synergistic action, particularly for C=O and O-H. Simultaneously, the crystallinity index of wheat straw, soybean straw, rice straw, and corn straw decreased by 7.40%, 2.37%, 20.60%, and 7.67%, respectively, compared to CK (natural straw). Additionally, the dense structure of different substrates was destroyed, and the inner parenchyma began to be exposed after the synergistic action, as observed by SEM. These findings offer valuable theoretical guidance for the development of lignocellulase applications. Full article

Purpose: Minimal clinically important difference (MCID), substantial clinical benefit (SCB), and patient acceptable symptomatic state (PASS) serve as metrics to gauge orthopedic treatment efficacy based on anchoring questions that do not account for a patient’s satisfaction with their surgical outcome. This study evaluates if reaching MCID, SCB, or PASS values for American Shoulder and Elbow Surgeons score (ASES), Single Alpha Numeric Evaluation (SANE), Simple Shoulder Test (SST), and Visual Analog Score (VAS) for pain following arthroscopic rotator cuff repair (RCR) correlates with overall patient satisfaction. Methods: This was a single-institution, retrospective study of patients who underwent RCR from 2015 to 2019. Pre-operative and 2 year postoperative ASES, SANE, SST, and VAS scores were recorded. Patients underwent a survey to assess: (1) what is your overall satisfaction with your surgical outcome? (scale 1 to 10); (2) if you could go back in time, would you undergo this operation again? (yes/no); (3) for the same condition, would you recommend this operation to a friend or family member? (yes/no). Spearman correlation coefficients were run to assess relationship between reaching MCID, SCB, or PASS and satisfaction. Results: Ninety-two patients were included. Mean preoperative ASES was 51.1 ± 16.9, SANE was 43.3 ± 20.9, SST was 5.4 ± 2.9, and VAS was 4.6 ± 2.1. Mean 2 year ASES was 83.9 ± 18.5, SANE was 81.7 ± 27.0, SST was 9.8 ± 3.2, and VAS was 1.4 ± 1.9. Mean patient satisfaction was 9.0 ± 1.9; 89 (96.7%) patients would undergo surgery again and recommend surgery. Correlation for reaching PASS for SANE and satisfaction was moderate. Correlation coefficients were very weak for all other outcome metrics. Conclusions: Reaching MCID, SCB, and PASS in ASES, SANE, SST, or VAS following RCR did not correlate with a patient’s overall satisfaction or willingness to undergo surgery again or recommend surgery. Further investigation into the statistical credibility and overall clinical value of MCID, SCB, and PASS is necessary. Full article

Water quality monitoring plays a crucial role in urban water supply systems for the production of safe drinking water. However, the traditional approach to water monitoring in Norway relies on a periodic (weekly/biweekly/monthly) sampling and analysis of biological indicators, which fails to provide a timely response to changes in water quality. This research addresses this issue by proposing a data-driven solution that enhances the timeliness of water quality monitoring. Our research team applied a case study in Ålesund Kommune. A sensor platform has been deployed at Lake Brusdalsvatnet, the water source reservoir in Ålesund. This sensor module is capable of collecting data for 10 different physico-chemical indicators of water quality. Leveraging this sensor platform, we developed a CNN-AutoEncoder-SOM solution to automatically monitor, process, and evaluate water quality evolution in the lake. There are three components in this solution. The first one focuses on anomaly detection. We employed a recurrence map to encode the temporal dynamics and sensor correlations, which were then fed into a convolutional neural network (CNN) for classification. It is noted that this network achieved an impressive accuracy of up to $99.6\%$. Once an anomaly is detected, the data are calibrated in the second component using an AutoEncoder-based network. Since true values for calibration are unavailable, the results are evaluated through data analysis. With high-quality calibrated data in hand, we proceeded to cluster the data into different categories to establish water quality standards in the third component, where a self-organizing map (SOM) is applied. The results revealed that this solution demonstrated significant performance, with a silhouette score of 0.73, which illustrates a small in-cluster distance and large intra-cluster distance when the water was clustered into three levels. This system not only achieved the objective of developing a comprehensive solution for continuous water quality monitoring but also offers the potential for integration with other cyber–physical systems (CPSs) in urban water management. Full article

Nuts are nutrient-dense foods and can be incorporated into a healthy diet. Artificial intelligence-powered diet-tracking apps may promote nut consumption by providing real-time, accurate nutrition information but depend on data and model availability. Our team developed a dataset comprising 1380 photographs, each in RGB color format and with a resolution of 4032 × 3024 pixels. These images feature 11 types of nuts that are commonly consumed. Each photo includes three nut types; each type consists of 2–4 nuts, so 6–9 nuts are in each image. Rectangular bounding boxes were drawn using a visual geometry group (VGG) image annotator to facilitate the identification of each nut, delineating their locations within the images. This approach renders the dataset an excellent resource for training models capable of multi-label classification and object detection, as it was meticulously divided into training, validation, and test subsets. Utilizing transfer learning in Python with the IceVision framework, deep neural network models were adeptly trained to recognize and pinpoint the nuts depicted in the photographs. The ultimate model exhibited a mean average precision of 0.7596 in identifying various nut types within the validation subset and demonstrated a 97.9% accuracy rate in determining the number and kinds of nuts present in the test subset. By integrating specific nutritional data for each type of nut, the model can precisely (with error margins ranging from 0.8 to 2.6%) calculate the combined nutritional content—encompassing total energy, proteins, carbohydrates, fats (total and saturated), fiber, vitamin E, and essential minerals like magnesium, phosphorus, copper, manganese, and selenium—of the nuts shown in a photograph. Both the dataset and the model have been made publicly available to foster data exchange and the spread of knowledge. Our research underscores the potential of leveraging photographs for automated nut calorie and nutritional content estimation, paving the way for the creation of dietary tracking applications that offer real-time, precise nutritional insights to encourage nut consumption. Full article

Coke formation poses a significant obstacle in the direct conversion of methane into valuable chemicals such as ethylene, benzene, and hydrogen via methane dehydro-aromatization (MDA). At the elevated temperatures necessary for this reaction, coke is the thermodynamically favored product, causing rapid catalyst deactivation and hence necessitating frequent catalyst regeneration. Successful industrial implementation of MDA requires the advancement of catalyst regeneration processes and a comprehensive understanding of coke formation to enhance catalyst performance. Here, we examined the types of coke generated during MDA over a Fe-ZSM-5 catalyst and their impact on deactivation. By combining reactivity studies using catalysts with carefully controlled coke populations with the characterization of the catalyst via XRD, H₂-TPR, and pyridine FTIR, we find that soft coke is formed at the Brønsted acid sites, resulting in loss of selectivity, while hard coke is formed at the metal sites causing a loss of activity. While soft coke can be removed at low regeneration temperatures, the removal of hard coke requires harsh conditions which compromise catalyst stability. An investigation into the use of CO₂ as an alternative, mild oxidant for catalyst regeneration, however, shows that the mild oxidation strength of CO₂ requires even higher regeneration temperatures and hence irreversible loss of Brønsted acid sites. Full article

As seafarers are involved in Maritime Autonomous Surface Ships (MASS), except for those in the fourth level of autonomy, the decision making of autonomous berthing should be carried out and be understood by human beings. This paper proposes a fuzzy logic-based human-like decision-making method for ultra-large ship berthing, which considers locations, ship particulars and the natural environment, and these factors are treated as the input variables. The IF–THEN rules are then established after the fuzzification of the input variables and are used for fuzzy inference to derive the decision of ship handling. It can be implemented in the decision-making system for safe navigation or be included in the process of autonomous berthing. The pilotage data are collected with nautical instruments and a distance measurement system during the berthing process, which are used to validate the proposed model and calculate the speed and turn errors. The overall and individual error of the decision-making model is in a reasonable and small range, which indicates that the model has good accuracy. The results of this research offer theoretical and practical insights into the development of a human-like decision-making method for autonomous navigation in port waters and maritime safety management in the shipping industry. The model can be further applied to develop a more widely applicable decision-making system for autonomous navigation in confined waters. Full article

Projects related to green aviation designed to achieve fuel savings and emission reductions are increasingly being established in response to growing concerns over climate change. Within the aviation industry, there is a growing trend towards the electrification of aircraft, with more-electric aircraft (MEA) and all-electric aircraft (AEA) being proposed. However, increasing electrification causes challenges with conventional thermal management system (TMS) and power management system (PMS) designs in aircraft. As a result, the integrated power and thermal management system (IPTMS) has been developed for energy-optimised aircraft projects. This review paper aims to review recent IPTMS progress and explore potential design solutions for civil aircraft. Firstly, the paper reviews the IPTMS in electrified propulsion aircraft (EPA), presenting the architectures and challenges of the propulsion systems, the TMS cooling strategies, and the power management optimisation. Then, several research topics in IPTMS are reviewed in detail: architecture design, power management optimisation, modelling, and analysis method development. Through the review of state-of-the-art IPTMS research, the challenges and future opportunities and requirements of IPTMS design are discussed. Based on the discussions, two potential solutions for IPTMS to address the challenges of civil EPA are proposed, including the combination of architecture design and power management optimisation and the combination of modelling and analysis methods. Full article

Rhamnolipids (RLs) are widely used biosurfactants produced mainly by Pseudomonas aeruginosa and Burkholderia spp. in the form of mixtures of diverse congeners. The global transcriptional regulator gene irrE from radiation-tolerant extremophiles has been widely used as a stress-resistant element to construct robust producer strains and improve their production performance. A P_rhlA-irrE cassette was constructed to express irrE genes in the Pseudomonas aeruginosa YM4 of the rhamnolipids producer strain. We found that the expression of irrE of Deinococcus radiodurans in the YM4 strain not only enhanced rhamnolipid production and the strain’s tolerance to environmental stresses, but also changed the composition of the rhamnolipid products. The synthesized rhamnolipids reached a maximum titer of 26 g/L, about 17.9% higher than the original, at 48 h. The rhamnolipid production of the recombinant strain was determined to be mono-rhamnolipids congener Rha–C₁₀–C₁₂, accounting for 94.1% of total products. The critical micelle concentration (CMC) value of the Rha–C₁₀–C₁₂ products was 62.5 mg/L and the air-water surface tension decreased to 25.5 mN/m. The Rha–C₁₀–C₁₂ products showed better emulsifying activity on diesel oil than the original products. This is the first report on the efficient production of the rare mono-rhamnolipids congener Rha–C₁₀–C₁₂ and the first report that the global regulator irrE can change the components of rhamnolipid products in Pseudomonas aeruginosa. Full article

To improve the nitrogen removal and reduce the chemical oxygen demand (COD) of a full-scale wastewater treatment plant, two sequential batch reactor devices were used to treat chemical wastewater with biocarriers in low carbon-to-nitrogen (C/N) ratio conditions. The results showed that the addition of biocarriers to the anoxic tank reduced the average concentration of COD in the effluent from 98.1 mg/L to 80.7 mg/L and increased total nitrogen (TN) removal by 9.4%. Metagenomic sequencing was performed to study the composition and function of microbial community samples taken from anoxic sludge and anoxic-carrier biofilms in this wastewater treatment plant. The results showed that Proteobacteria and Actinobacteria were the dominant phyla in the two samples, ensuring their capability for organic matter removal. The anoxic-carrier biofilms were mainly enriched with denitrifying bacteria such as Thauera (10.7%) and Comammonas (2.2%) and the anammox bacteria Candidatus Kuenenia (0.03%). Meanwhile, the nitrogen metabolism pathway was elaborated and the abundance of the functional genes involved in the nitrogen metabolism pathway was quantified. In addition, results from qPCR showed increased copy numbers of denitrification and anammox genes in the anoxic-carrier biofilms compared to those in the anoxic sludge, further confirming the enrichment of functional bacteria. Full article