Strategies for improving expression of recombinant human chorionic gonadotropin in Chinese Hamster Ovary (CHO) cells.

Optimizations of the gene expression cassette combined with the selection of an appropriate signal peptide are important factors that must be considered to enhance heterologous protein expression in Chinese Hamster Ovary (CHO) cells. In this study, we investigated the effectiveness of different signal peptides on the production of recombinant human chorionic gonadotropin (r-hCG) in CHO-K1 cells. Four optimized expression constructs containing four promising signal peptides were stably transfected into CHO-K1 cells. The generated CHO-K1 stable pool was then evaluated for r-hCG protein production. Interestingly, human serum albumin and human interleukin-2 signal peptides exhibited relatively greater extracellular secretion of the r-hCG with an average yield of (16.59 ± 0.02 µg/ml) and (14.80 ± 0.13 µg/ml) respectively compared to the native and murine IgGκ light chain signal peptides. The stably transfected CHO pool was further used as the cell substrate to develop an optimized upstream process followed by a downstream phase of the r-hCG. Finally, the biological activity of the purified r-hCG was assessed using in vitro bioassays. The combined data highlight that the choice of signal peptide can be imperative to ensure an optimal secretion of a recombinant protein in CHO cells. In addition, the stable pool technology was a viable approach for the production of biologically active r-hCG at a research scale with acceptable bioprocess performances and consistent product quality.

Synthesis of nitrogen-doped amorphous carbon nanotubes from novel cobalt-based MOF precursors for improving potassium-ion storage capability.

Amorphous carbon materials with sophisticated morphologies, variable carbon layer structures, abundant defects, and tunable porosities are favorable as anodes for potassium-ion batteries (PIBs). Synthesizing amorphous carbon materials typically involves the pyrolysis of carbonaceous precursors. Nonetheless, there is still a lack of studies focused on achieving multifaceted structural optimizations of amorphous carbon through precursor formulation. Herein, nitrogen-doped amorphous carbon nanotubes (NACNTs) are derived from a novel composite precursor of cobalt-based metal-organic framework (CMOF) and graphitic carbon nitride (g-CN). The addition of g-CN in the precursor optimizes the structure of amorphous carbon such as morphology, interlayer spacing, nitrogen doping, and porosity. As a result, NACNTs demonstrate significantly improved electrochemical performance. The specific capacities of NACNTs after cycling at current densities of 100 mA/g and 1000 mA/g increased by 194 % and 230 %, reaching 346.6 mAh/g and 211.8 mAh/g, respectively. Furthermore, the NACNTs anode is matched with an organic cathode for full-cell evaluation. The full-cell attains a high specific capacity of 106 mAh/gcathode at a current density of 100 mA/g, retaining 90.5 % of the specific capacity of the cathode half-cell. This study provides a valuable reference for multifaceted structural optimization of amorphous carbon to improve potassium-ion storage capability.

Vacuum ammonia stripping from liquid digestate: Effects of pH, alkalinity, temperature, negative pressure and process optimization.

High ammonia-nitrogen digestate has become a key bottleneck limiting the anaerobic digestion of organic solid waste. Vacuum ammonia stripping can simultaneously remove and recover ammonia nitrogen, which has attracted a lot of attention in recent years. To investigate the parameter effects on the efficiency and mass transfer, five combination conditions (53 °C 15 kPa, 60 °C 20 kPa, 65 °C 25 kPa, 72 °C 35 kPa, and 81 °C 50 kPa) were conducted for ammonia stripping of sludge digestate. The results showed that 80% of ammonia nitrogen was stripped in 45 min for all experimental groups, but the ammonia transfer coefficient varied under different conditions, which increased with the rising of boiling point temperature, and reached the maximum value (39.0 mm/hr) at 81 °C 50 kPa. The ammonia nitrogen removal efficiency was more than 80% for 30 min vacuum stripping after adjusting the initial pH to above 9.5, and adjustment of the initial alkalinity also affects the pH value of liquid digestate. It was found that pH and alkalinity are the key factors influencing the ammonia nitrogen dissociation and removal efficiency, while temperature and vacuum mainly affect the ammonia nitrogen mass transfer and removal velocity. In terms of the mechanism of vacuum ammonia stripping, it underwent alkalinity destruction, pH enhancement, ammonia nitrogen dissociation, and free ammonia removal. In this study, two-stage experiments of alkalinity destruction and ammonia removal were also carried out, which showed that the two-stage configuration was beneficial for ammonia removal. It provides a theoretical basis and practical technology for the vacuum ammonia stripping from liquid digestate of organic solid waste.

Elliptical Halbach magnet and gradient modules for low-field portable magnetic resonance imaging.

This study aims to develop methods to design the complete magnetic system for a truly portable MRI scanner for neurological and musculoskeletal (MSK) applications, optimized for field homogeneity, field of view (FoV), and gradient performance compared to existing low-weight configurations. We explore optimal elliptic-bore Halbach configurations based on discrete arrays of permanent magnets. In this way, we seek to improve the field homogeneity and remove constraints to the extent of the gradient coils typical of Halbach magnets. Specifically, we have optimized a tightly packed distribution of magnetic Nd2Fe14B cubes with differential evolution algorithms and a second array of shimming magnets with interior point and differential evolution methods. We have also designed and constructed an elliptical set of gradient coils that extend over the whole magnet length, maximizing the distance between the lobe centers. These are optimized with a target field method minimizing a cost function that considers also heat dissipation. We have employed the new toolbox to build the main magnet and gradient modules for a portable MRI scanner designed for point-of-care and residential use. The elliptical Halbach bore has semi-axes of 10 and 14& cm, and the magnet generates a field of 87& mT homogeneous down to 5700& ppm (parts per million) in a 20-cm diameter FoV; it weighs 216& kg and has a width of 65& cm and a height of 72& cm. Gradient efficiencies go up to around 0.8& mT/m/A, for a maximum of 12& mT/m within 0.5& ms with 15& A and 15& V amplifier. The distance between lobes is 28& cm, significantly increased with respect to other Halbach-based scanners. Heat dissipation is around 25& W at maximum power, and gradient deviations from linearity are below 20% in a 20-cm sphere. Elliptic-bore Halbach magnets enhance the ergonomicity and field distribution of low-cost portable MRI scanners, while allowing for full-length gradient support to increase the FoV. This geometry can be potentially adapted for a prospective low-cost whole-body technology.

Research status and prospect of flexible optimization design methodology of propeller CNC polishing machines.

Among the components of high-tech ships, the structural complexity of the propeller profile requires a high degree of flexibility in the CNC polishing machine. In addressing this requirement, the study formulates the flexible optimization problem pertaining to research on the propeller CNC polishing machine. A comprehensive analysis is undertaken to scrutinize the geometric features of the propeller and the phenomenon of polished contact. The propeller profile-polishing head dynamic contact mechanism is revealed, and the contact force characteristics of propeller polishing are obtained. It is suggested that the propeller configuration-process-polishing machine structure coupling mechanism be explored under the influence of polishing contact force. Subsequently, a dynamic model of the propeller CNC polishing process is formulated. Based on the above model, a simulation of the motion personification and structural flexibility of the propeller CNC polishing machine is proposed to obtain dynamic personification and flexibility rules. Integrating polishing contact force characteristics with dynamic personification and flexibility rules, the dynamic flexible collaborative optimization principle of the propeller CNC polishing machine is revealed. On this basis, multi-objective optimization modeling and solving are carried out, forming a new method for the flexible optimization design of propeller CNC polishing machines.

Preoperative very low-energy diets for obese patients undergoing intra-abdominal colorectal surgery: a retrospective cohort study (RetroPREPARE).

BACKGROUND: Very low-energy diets (VLEDs) prescribed prior to bariatric surgery have been associated with decreased operative time, technical difficulty, and postoperative morbidity. To date, limited data are available regarding the impact of VLEDs prior to colorectal surgery. We designed this study to determine whether preoperative VLEDs benefit patients with obesity undergoing colorectal surgery. METHODS: This is a single-center retrospective cohort study. Individuals undergoing elective colorectal surgery with a body mass index (BMI) of greater than 30 kg/m2 from 2015 to 2022 were included. The exposure of interest was VLEDs for 2-4 weeks immediately prior to surgery. The control group consisted of patients prior to January 2018 who did not receive preoperative VLED. The primary outcome was 30 day postoperative morbidity. Multivariable logistic regression modeling was used to determine associations with 30 day postoperative morbidity. RESULTS: Overall, 190 patients were included, 89 patients received VLEDs (median age: 66 years; median BMI: 35.9 kg/m2; 48.3% female) and 101 patients did not receive VLEDs (median age: 68 years; median BMI: 32.1 kg/m2; 44.6% female). One-hundred four (54.7%) patients experienced 30 day postoperative morbidity. Multivariable regression analysis identified three variables associated with postoperative morbidity: VLEDs [odds ratio (OR) 0.22, 95% confidence intervals (CI) 0.08-0.61, P < 0.01], Charlson comorbidity index (OR 1.25, 95% CI 1.03-1.52, P = 0.02), and rectal dissections (OR 2.71, 95% CI 1.30-5.65, P < 0.01). CONCLUSIONS: The use of a preoperative VLED was associated with a significant reduction in postoperative morbidity in patients with obesity prior to colorectal surgery. A high-quality randomized controlled trial is required to confirm these findings.

Machine learning as a tool predicting short-term postoperative complications in Crohn's disease patients undergoing intestinal resection: What frontiers?

The recent study, "Predicting short-term major postoperative complications in intestinal resection for Crohn's disease: A machine learning-based study" investigated the predictive efficacy of a machine learning model for major postoperative complications within 30 days of surgery in Crohn's disease (CD) patients. Employing a random forest analysis and Shapley Additive Explanations, the study prioritizes factors such as preoperative nutritional status, operative time, and CD activity index. Despite the retrospective design's limitations, the model's robustness, with area under the curve values surpassing 0.8, highlights its clinical potential. The findings align with literature supporting preoperative nutritional therapy in inflammatory bowel diseases, emphasizing the importance of comprehensive assessment and optimization. While a significant advancement, further research is crucial for refining preoperative strategies in CD patients.

Tail Bounds on the Runtime of Categorical Compact Genetic Algorithm.

The majority of theoretical analyses of evolutionary algorithms in the discrete domain focus on binary optimization algorithms, even though black-box optimization on the categorical domain has a lot of practical applications. In this paper, we consider a probabilistic model-based algorithm using the family of categorical distributions as its underlying distribution and set the sample size as two. We term this specific algorithm the categorical compact genetic algorithm (ccGA). The ccGA can be considered as an extension of the compact genetic algorithm (cGA), which is an efficient binary optimization algorithm. We theoretically analyze the dependency of the number of possible categories K, the number of dimensions D, and the learning rate η on the runtime. We investigate the tail bound of the runtime on two typical linear functions on the categorical domain: categorical OneMax (COM) and KVAL. We derive that the runtimes on COM and KVAL are O(Dln(DK)/η) and Θ(DlnK/η) with high probability, respectively. Our analysis is a generalization for that of the cGA on the binary domain.

An efficient water quality index forecasting and categorization using optimized Deep Capsule Crystal Edge Graph neural network.

The world's freshwater supply, predominantly sourced from rivers, faces significant contamination from various economic activities, confirming that the quality of river water is critical for public health, environmental sustainability, and effective pollution control. This research addresses the urgent need for accurate and reliable water quality monitoring by introducing a novel method for estimating the water quality index (WQI). The proposed approach combines cutting-edge optimization techniques with Deep Capsule Crystal Edge Graph neural networks, marking a significant advancement in the field. The innovation lies in the integration of a Hybrid Crested Porcupine Genghis Khan Shark Optimization Algorithm for precise feature selection, ensuring that the most relevant indicators of water quality (WQ) are utilized. Furthermore, the use of the Greylag Goose Optimization Algorithm to fine-tune the neural network's weight parameters enhances the model's predictive accuracy. This dual optimization framework significantly improves WQI prediction, achieving a remarkable mean squared error (MSE) of 6.7 and an accuracy of 99%. By providing a robust and highly accurate method for WQ assessment, this research offers a powerful tool for environmental authorities to proactively manage river WQ, prevent pollution, and evaluate the success of restoration efforts. PRACTITIONER POINTS: Novel method combines optimization and Deep Capsule Crystal Edge Graph for WQI estimation. Preprocessing includes data cleanup and feature selection using advanced algorithms. Deep Capsule Crystal Edge Graph neural network predicts WQI with high accuracy. Greylag Goose Optimization fine-tunes network parameters for precise forecasts. Proposed method achieves low MSE of 6.7 and high accuracy of 99%.

A simple and intuitive model for long-range 3D potential distributions of in-operando TEM-samples: Comparison with electron holographic tomography.

Electron holography is a powerful tool to investigate the properties of micro- and nanostructured electronic devices. A meaningful interpretation of the holographic data, however, requires an understanding of the 3D potential distribution inside and outside the sample. Standard approaches to resolve these potential distributions involve projective tilt series and their tomographic reconstruction, in addition to extensive simulations. Here, a simple and intuitive model for the approximation of such long-range potential distributions surrounding a nanostructured coplanar capacitor is presented. The model uses only independent convolutions of an initial potential distribution with a Gaussian kernel, allowing the reconstruction of the entire potential distribution from only one measured projection. By this, a significant reduction of the required computational power as well as a drastically simplified measurement process is achieved, paving the way towards quantitative electron holographic investigation of electrically biased nanostructures.

Integrated water resource management in the Segura Hydrographic Basin: An artificial intelligence approach.

Managing resources effectively in uncertain demand, variable availability, and complex governance policies is a significant challenge. This paper presents a paradigmatic framework for addressing these issues in water management scenarios by integrating advanced physical modelling, remote sensing techniques, and Artificial Intelligence algorithms. The proposed approach accurately predicts water availability, estimates demand, and optimizes resource allocation on both short- and long-term basis, combining a comprehensive hydrological model, agronomic crop models for precise demand estimation, and Mixed-Integer Linear Programming for efficient resource distribution. In the study case of the Segura Hydrographic Basin, the approach successfully allocated approximately 642 million cubic meters (hm3) of water over six months, minimizing the deficit to 9.7% of the total estimated demand. The methodology demonstrated significant environmental benefits, reducing CO2 emissions while optimizing resource distribution. This robust solution supports informed decision-making processes, ensuring sustainable water management across diverse contexts. The generalizability of this approach allows its adaptation to other basins, contributing to improved governance and policy implementation on a broader scale. Ultimately, the methodology has been validated and integrated into the operational water management practices in the Segura Hydrographic Basin in Spain.

Mapping between measurement scales in meta-analysis, with application to measures of body mass index in children.

Quantitative evidence synthesis methods aim to combine data from multiple medical trials to infer relative effects of different interventions. A challenge arises when trials report continuous outcomes on different measurement scales. To include all evidence in one coherent analysis, we require methods to "map" the outcomes onto a single scale. This is particularly challenging when trials report aggregate rather than individual data. We are motivated by a meta-analysis of interventions to prevent obesity in children. Trials report aggregate measurements of body mass index (BMI) either expressed as raw values or standardized for age and sex. We develop three methods for mapping between aggregate BMI data using known or estimated relationships between measurements on different scales at the individual level. The first is an analytical method based on the mathematical definitions of z-scores and percentiles. The other two approaches involve sampling individual participant data on which to perform the conversions. One method is a straightforward sampling routine, while the other involves optimization with respect to the reported outcomes. In contrast to the analytical approach, these methods also have wider applicability for mapping between any pair of measurement scales with known or estimable individual-level relationships. We verify and contrast our methods using simulation studies and trials from our data set which report outcomes on multiple scales. We find that all methods recreate mean values with reasonable accuracy, but for standard deviations, optimization outperforms the other methods. However, the optimization method is more likely to underestimate standard deviations and is vulnerable to non-convergence.

Comprehensive Insights on MXene-Based TENGs: from Structures, Functions to Applications.

The rapid advancement of triboelectric nanogenerators (TENGs) has introduced a transformative approach to energy harvesting and self-powered sensing in recent years. Nonetheless, the untapped potential of TENGs in practical scenarios necessitates multiple strategies like material selections and structure designs to enhance their output performance. Given the various superior properties, MXenes, a kind of novel 2D materials, have demonstrated great promise in enhancing TENG functionality. Here, this review comprehensively delineates the advantages of incorporating MXenes into TENGs, majoring in six pivotal aspects. First, an overview of TENGs is provided, stating their theoretical foundations, working modes, material considerations, and prevailing challenges. Additionally, the structural characteristics, fabrication methodologies, and family of MXenes, charting their developmental trajectory are highlighted. The selection of MXenes as various functional layers (negative and positive triboelectric layer, electrode layer) while designing TENGs is briefed. Furthermore, the distinctive advantages of MXene-based TENGs and their applications are emphasized. Last, the existing challenges are highlighted, and the future developing directions of MXene-based TENGs are forecasted.

Integrated municipal solid waste management using ArcGIS tools: A case of akaki-kality sub-city, addis ababa, ethiopia.

This study aimed to optimize the solid waste collection and transportation system using ArcGIS Network Analyst and location-allocation tools. The generated solid waste was characterized by proximate analysis. The generation rate and composition were determined according to standard methods. The average solid waste generation rates for households, commercial sites, institutions, and recreational places were 0.48 kg/c/day, 15.03 kg/fac/day, 9.32 kg/fac/day, and 22.8 kg/fac/day, respectively. The estimated total generation rate of the sub-city is 207,004.03 kg/day and 712.13 m3/day as discarded base. Composition analysis revealed that food waste is the major component of municipal solid waste, with estimated weight and volume of 134,696.08 kg and 299.46 m3, respectively. Proximate analysis indicated that food and textile wastes have relatively high moisture content and fixed carbon. Candidate pre-collection bin allocations were optimized based on factors such as road network, distribution of solid waste generators, and existing temporary dumping sites, resulting in 1052 potential bin locations. Transfer station allocation was optimized by considering land use-land cover, slope, and geology. Twelve transfer routes and four transport routes were established to efficiently serve the bins and final waste destinations. In conclusion, the study demonstrates that ArcGIS Network Analyst and location-allocation tools can effectively optimize the municipal solid waste collection and transportation system, providing a robust framework for improving waste management efficiency. However, further research is recommended to validate these findings through field application.

Enhancement of carotenogenesis by Blakeslea trispora in a mixed culture with bacteria.

Among carotenoids, ꞵ-carotene has the highest biological activity and is found as an all-trans isomer in many biological systems. Blakeslea trispora is a microorganism that is of interest to industries for the commercial production of ꞵ-carotene. This study investigated the effect of different bacteria on carotenogenesis in B. trispora. The B. trispora bisexual mold was cultured in a production medium, and different bacterial cells were added to it after 24 h. Then, the culture conditions and the culture medium were optimized in the presence of the selected bacteria using the experimental design. The percentage of carotenoids obtained from the mixed culture was determined using high-performance liquid chromatography (HPLC). Results showed that Kocuria rhizophila had the greatest effect on increasing the production of carotenoids in B. trispora. The highest content of carotenoids obtained during optimization was 770 ± 7.5 mg/L, a 6.8-fold increase compared to the control. HPLC analysis of carotenoids indicated the presence of two main peaks, ꞵ-carotene and γ-carotene, in which the primary carotenoid was ꞵ-carotene followed by γ-carotene with a lower content. Therefore, due to the importance of ꞵ-carotene in industry, the use of biostimulants is one of the appropriate strategies to increase the production of this pigment in industry.

Maiden application of mountaineering team-based optimization algorithm optimized 1PD-PI controller for load frequency control in islanded microgrid with renewable energy sources.

Load Frequency Control (LFC) is essential for maintaining the stability of Islanded Microgrids (IMGs) that rely extensively on Renewable Energy Sources (RES). This paper introduces a groundbreaking 1PD-PI (one + Proportional + Derivative-Proportional + Integral) controller, marking its inaugural use in improving LFC performance within IMGs. The creation of this advanced controller stems from the amalgamation of 1PD and PI control strategies. Furthermore, the paper presents the Mountaineering Team Based Optimization (MTBO) algorithm, a novel meta-heuristic technique introduced for the first time to effectively tackle LFC challenges. This algorithm, inspired by principles of intellectual and environmental evolution and coordinated human behavior, is utilized to optimize the controller gains. The effectiveness of the proposed methodology is rigorously evaluated within a simulated IMG environment using MATLAB/SIMULINK. This simulated IMG incorporates diverse power generation sources, including Diesel Engine Generators (DEGs), Microturbines (MTs), Fuel Cells (FCs), Energy Storage Systems (ESSs), and RES units like Wind Turbine Generators (WTGs) and Photovoltaics (PVs). This paper employs the Integral Time Multiplied by the Squared Error (ITSE) and Integral of Time Multiplied By Absolute Error (ITAE) indicators as the primary performance metrics, conventionally used to mitigate frequency deviations. To achieve optimal controller parameter tuning, a weighted composite objective function is formulated. This function incorporates multiple components: modified objective functions related to both ITSE and ITAE, along with a term addressing overshoot and settling time. Each component is assigned an appropriate weighting factor to prioritize specific performance aspects. By employing distinct objective functions for different aspects of control performance, the derivation of optimized controller gains is facilitated. The efficacy and contribution of the proposed methodology are rigorously demonstrated within the context of RES-based IMGs, featuring a comparative analysis with well-known optimization algorithms, including Particle Swarm Optimization (PSO) and the Whale Optimization Algorithm (WOA). These algorithms are used to optimize the 1PD-PI controller, resulting in three control schemes: 1PD-PI/MTBO, 1PD-PI/WOA, and 1PD-PI/PSO. The effectiveness of these control schemes is evaluated under various loading conditions, incorporating parametric uncertainties and nonlinear factors of physical constraints. Three case studies, presented in eight scenarios (I-VIII), are utilized to comprehensively assess the efficiency, robustness, and sensitivity of the proposed approach. This analysis extends beyond the time domain, considering the stability evaluation of the proposed control scheme. Simulation results unequivocally establish the superior performance of the MTBO algorithm-optimized 1PD-PI controller compared to its counterparts. This superiority is evident in terms of minimized settling time, reduced peak undershoot and overshoot, and enhanced error-integrating performance characteristics within the system responses. Improvements are observed in both the high range and within the 80-90% range for criteria such as overshoot, undershoot, and the numerical values of the objective functions. This paper underscores the practicality and effectiveness of the 1PD-PI/MTBO control scheme, offering valuable insights into the management of frequency disturbances in RES-based IMGs.

Development of an advanced analytical technique for detecting multiple pesticide residues in vegetables through liquid chromatography tandem mass spectroscopy (LC-MS/MS).

A comprehensive LC-MS/MS method, which employs Positive Electrospray Ionization (PEI) and Multiple Reaction Monitoring (MRM) was developed for the simultaneous determination of 35 pesticides belonging to various chemical classes in tomato, brinjal, chili, and okra samples. Extraction was facilitated using a modified QuEChERS method, which allows efficient sample analysis in a single run. Calibration curves for each pesticide exhibited linearity within the concentration range of 0.0025 to 0.1 µg mL-1, with correlation coefficients ranging from 0.993 to 0.999. Mean recoveries at five fortification levels (0.01 to 0.5 µg kg-1) ranged from 80 to 90%, demonstrating satisfactory precision (RSD < 20%). The matrix effects, mitigated through an optimized cleanup process, were observed within the range of 6.42% to 19.52%. The developed method having the limit of quantification of 0.01 mg kg-1 for all 35 pesticides, proved to be highly sensitive and rapid for multi-residue estimation in diverse vegetable samples. Subsequently, the method was used to analyze the market samples from Varanasi, India, which revealed the presence of pesticides like Chlorpyrifos, Chlorantraniliproleand Indoxacarb in tomato, brinjal, chili and okra. Therefore, the method could be considered as a robust tool for monitoring pesticide residues in vegetables, aiding in quality assessment and regulatory compliance in the agriculture sector.

Homogeneous continuous flow nitration of O-methylisouronium sulfate and its optimization by kinetic modeling.

Nitration of O-methylisouronium sulfate under mixed acid conditions gives O-methyl-N-nitroisourea, a key intermediate of neonicotinoid insecticides with high application value. The reaction is a fast and highly exothermic process with a high mass transfer resistance, making its control difficult and risky. In this paper, a homogeneous continuous flow microreactor system was developed for the nitration of O-methylisouronium sulfate under high concentrations of mixed acids, with a homemade static mixer eliminating the mass transfer resistance. In addition, the kinetic modeling of this reaction was performed based on the theory of NO2 + attack, with the activation energy and pre-exponential factor determined. Finally, based on the response surface generated by the kinetic model, the reaction was optimized with a conversion of 87.4% under a sulfuric acid mass fraction of 94%, initial reactant concentration of 0.5 mol/L, reaction temperature of 40 °C, molar ratio of reactants at 4.4:1, and a residence time of 12.36 minutes.

Optimization of 3D Printing Parameters of Polylactic-Co-Glycolic Acid-Based Biodegradable Antibacterial Materials Using Fused Deposition Modeling.

A high incidence of ureteral diseases was needed to find better treatments such as implanting ureteral stents. The existing ureteral stents produced a series of complications such as bacterial infection and biofilm after implantation. The fused deposition modeling (FDM) of 3D printing biodegradable antibacterial ureteral stents had gradually become the trend of clinical treatment. But it was necessary to optimize the FDM 3D printing parameters of biodegradable bacteriostatic materials to improve the precision and performance of manufacturing. In this study, polylactic-co-glycolic acid (PLGA), polycaprolactone (PCL), and nanosilver (AgNP) were mixed by the physical blending method, and the 3D printing parameters and properties were studied. The relationship between printing parameters and printing errors was obtained by single-factor variable method and linear fitting. The performance of 3D printing samples was obtained through infrared spectrum detection, molecular weight detection, and mechanical testing. The printing temperature and the printing pressure were proportional to the printing error, and the printing speed was inversely proportional to the printing error. The 3D printing has little effect on the functional groups and molecular weights of biodegradable antibacterial materials. The addition of AgNP increases the compressive strength and breaking strength by 8.332% and 37.726%, which provided ideas for regulating the mechanical properties. The parameter range of biodegradable bacteriostatic materials for thermal melting 3D printing was precisely established by optimizing the parameters of printing temperature, printing pressure, and printing speed, which would be further applied to the advanced manufacturing of biodegradable implant interventional medical devices.

Development of a bionic hexapod robot with adaptive gait and clearance for enhanced agricultural field scouting.

High agility, maneuverability, and payload capacity, combined with small footprints, make legged robots well-suited for precision agriculture applications. In this study, we introduce a novel bionic hexapod robot designed for agricultural applications to address the limitations of traditional wheeled and aerial robots. The robot features a terrain-adaptive gait and adjustable clearance to ensure stability and robustness over various terrains and obstacles. Equipped with a high-precision Inertial Measurement Unit (IMU), the robot is able to monitor its attitude in real time to maintain balance. To enhance obstacle detection and self-navigation capabilities, we have designed an advanced version of the robot equipped with an optional advanced sensing system. This advanced version includes LiDAR, stereo cameras, and distance sensors to enable obstacle detection and self-navigation capabilities. We have tested the standard version of the robot under different ground conditions, including hard concrete floors, rugged grass, slopes, and uneven field with obstacles. The robot maintains good stability with pitch angle fluctuations ranging from -11.5° to 8.6° in all conditions and can walk on slopes with gradients up to 17°. These trials demonstrated the robot's adaptability to complex field environments and validated its ability to maintain stability and efficiency. In addition, the terrain-adaptive algorithm is more energy efficient than traditional obstacle avoidance algorithms, reducing energy consumption by 14.4% for each obstacle crossed. Combined with its flexible and lightweight design, our robot shows significant potential in improving agricultural practices by increasing efficiency, lowering labor costs, and enhancing sustainability. In our future work, we will further develop the robot's energy efficiency, durability in various environmental conditions, and compatibility with different crops and farming methods.