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Glioblastoma is a highly aggressive malignancy affecting the brain and central nervous system. It is the most common malignant primary brain tumor, yet its prognosis remains poor. Median survival typically ranges from around 13 months with standard treatment to up to 19.9 months in some recent clinical trials. Despite advances in treatment, the aggressive nature of glioblastoma continues to present significant challenges for improving patient outcomes. This study aimed to analyze various biological, radiological, and molecular factors associated with glioblastoma recurrence and to estimate survival outcomes. A total of 104 glioblastoma patients diagnosed between January 2017 and September 2022 were included. Patient demographics, treatment received, and molecular characteristics were obtained from the Electronic Patient Record (EPR). Tumor molecular characteristics were analyzed using the OnkoSight Advanced CNS NGS panel. Statistical analyses were performed to develop a prognostic model for glioblastoma recurrence and estimate survival rates. Among the patients, 65.4% had no recurrence, with a mean age of 63 years. No gender or BMI differences were observed, but ages 60 years), or bifocal tumors were associated with decreased glioblastoma recurrence. However, factors like tumor site, size, necrosis, MGMT promoter methylation, and EGFR alteration showed no significant association with recurrence. Median survival was 12 months, with older age significantly associated with shorter survival. Tumor sizes >4 cm showed shorter survival trends but not statistically significantly. Patients who lived longer experienced more tumor recurrence incidents. Standard or non-standard treatments were associated with longer median survival compared to no treatment. Our findings provide insights into factors influencing glioblastoma recurrence and survival. Age, gender, and tumor characteristics play pivotal roles in recurrence. Understanding these factors could aid in optimizing treatment strategies and improving patient outcomes. However, further multicentric investigations are needed to validate these findings. This study emphasizes the importance of considering biological and radiological factors in clinical decision-making for glioblastoma cases.

The advancement of wireless networking has significantly enhanced beamforming capabilities in Autonomous Unmanned Aerial Systems (AUAS). This paper presents a simple and efficient classical algorithm to route a collection of AUAS or drone swarms extending our previous work on AUAS. The algorithm is based on the sparse factorization of frequency Vandermonde matrices that correspond to each drone, and its entries are determined through spatiotemporal data of drones in the AUAS. The algorithm relies on multibeam beamforming, making it suitable for large-scale AUAS networking in wireless communications. We show a reduction in the arithmetic and time complexities of the algorithm through theoretical and numerical results. Finally, we also present an ML-based AUAS routing algorithm using the classical AUAS algorithm and feed-forward neural networks. We compare the beamformed signals of the ML-based AUAS routing algorithm with the ground truth signals to minimize the error between them. The numerical error results show that the ML-based AUAS routing algorithm enhances the accuracy of the routing. This error, along with the numerical and theoretical results for over 100 drones, provides the basis for the scalability of the proposed ML-based AUAS algorithms for large-scale deployments.

Antimicrobial peptides (AMPs) have recently gained attention for their potential to treat diseases related to bacterial and viral infections, as many traditional antimicrobial drugs have reduced efficacy in treating these infections due to the increased prevalence of drug-resistant pathogens. PLG0206, an engineered cationic antibiotic peptide that is 24 residues long, has been designed to address some limitations of other natural AMPs, such as toxicity and limited activity due to pH and ion concentrations. Nonclinical studies have shown that PLG0206 is highly selective for targeting bacterial cells and is not toxic to human blood cells. Antibiofilm experiments demonstrated that PLG0206 is effective at reducing both biotic and abiotic biofilm burdens following direct biofilm contact. PLG0206 has rapid and broad-spectrum activity against both Gram-positive and Gram-negative bacteria that are implicated as etiologic agents in periprosthetic joint infections, including multidrug-resistant ESKAPE pathogens and colistin-resistant isolates. A recent first-in-human study demonstrated that PLG0206 is well tolerated and safe as an intravenous infusion in healthy volunteers. Studies are planned to determine the efficacy of PLG0206 in patients for the treatment of periprosthetic joint infections. This review summarizes the chemistry, pharmacology, and microbiology of PLG0206 and explores its current preclinical, clinical, and regulatory status.

Owing to their pleiotropic metabolic benefits, glucagon-like peptide-1 receptor (GLP-1R) agonists have been successfully utilized for treating metabolic diseases, such as type 2 diabetes and obesity. As part of our efforts in developing long-acting peptide therapeutics, we have previously reported a peptide engineering strategy that combines peptide side chain stapling with covalent integration of a serum protein-binding motif in a single step. Herein, we have used this strategy to develop a second generation extendin-4 analog rigidified with a symmetrical staple, which exhibits an excellent in vivo efficacy in an animal model of diabetes and obesity. To simplify the scale-up manufacturing of the lead GLP-1R agonist, a semisynthesis protocol was successfully developed, which involves recombinant expression of the linear peptide followed by attachment of a polyethylene glycol (PEG)-fatty acid staple in a subsequent chemical reaction step.

In recent years, hybrid electric vehicles (HEVs) have increased significantly due to climate change and the demand for high-efficiency power sources. HEVs that combine an internal combustion engine (ICE) and an electric motor (EM) can improve the power output of the ICE and overcome the challenges of the insufficient battery life of electric vehicles. In this study, a parallel HEV with a power distribution mechanism is developed for energy saving and carbon reduction. The driver′s power demands are used as input sources, and a rule-based control strategy is used to determine the power distribution of the generator, EM, and ICE. The NEDC2000 driving cycle is used as the test benchmark to demonstrate the performance of the HEV. In comparison to ICE vehicles, the fuel efficiency of HEVs significantly improved. In addition, other parameters, including the average brake-specific fuel consumption (BSFC), brake-specific carbon monoxide emission (BSCO), and brake-specific hydrocarbons (BSHCs), were lower, which can effectively save fossil fuel and reduce air pollution.

A novel approach, using low Earth orbit (LEO) satellite microwave communication links for cloud liquid water measurements, is proposed in this paper. The feasibility of this approach is studied through simulations of the retrieval system including a LEO satellite communicating with a group of ground receivers equipped with signal-to-noise ratio (SNR) estimators, a synthetic cloud attenuation field and a tomographic retrieval algorithm. Rectangular and Gaussian basis functions are considered to define the targeted field. Simulation results suggest that the proposed least-squares based retrieval algorithm produces satisfactory outcomes for both types of basis functions. The root-mean-square error of the retrieved field is around 0.2 dB/km, with the range of the reference field as 0 to 2.35 dB/km. It is also confirmed that the partial retrieval of the cloud field is achievable when a limited number of receivers with restricted locations are available. The retrieval outcomes exhibit properties of high resolution and low error, indicating that the proposed approach has great potential for cloud observations.

Currently, in the field of vehicle engineering, researchers are focused on finding a new type of high-efficiency vehicle. Based on the conventional powertrain system, the hybrid pneumatic power system (HPPS) is a type of hybrid system that focuses on compressed air power instead of the electrochemical energy conversion of a battery. This study concentrates on the integral efficiency of the HPPS. The HPPS was operated under different conditions of internal combustion engine speed, fuel consumption, compressor speed, pressure in air tank, cross-sectional area, and air motor efficiency. Based on this, the best operating condition of the HPPS was defined, and the integral efficiency of the HPPS was measured under this condition. The experimental results show that the system integral efficiency can reach up to 45.3%. It is higher than 28% when using individual internal combustion engines. In addition, the HPPS could reduce fuel consumption by 38% during its best performance.

This study investigated the matching designs between a power integration mechanism (PIM) and transmission system for single-motor parallel hybrid electric vehicles. The optimal matching design may lead to optimal efficiency and performance in parallel hybrid vehicles. The Simulink/Simscape environment is used to model the powertrain system of parallel hybrid electric vehicles, which the characteristics of the PIM, location of the gearbox at the driveline, and design of the gear ratio of a gearbox influenced. The matching design principles for torque-coupled–type PIM (TC-PIM) parameters and the location of the gearbox are based on the speed range of the electric motor and the internal combustion engine. The parameters of the TC-PIM (i.e., k 1 and k 2 ) are based on the k ratio theory. Numerical simulations of an extra-urban driving cycle and acceleration tests reveal that a higher k r a t i o has greater improved power-assist ability under a pre-transmission architecture. For example, a k r a t i o of 1.6 can improve the power-assist ability by 8.5% when compared with a k r a t i o of 1. By using an appropriate gear ratio and k r a t i o , the top speed of a hybrid electric vehicle is enhanced by 9.3%.

In this study, inverse differential gear and power mode switching control were used to develop a hybrid electric motorcycle (HEM). An inverse differential gear power splitter was installed to integrate or distribute the power of an internal combustion engine (ICE), thus achieving single/dual power output. In addition, the transmission system was configured with continuously variable transmission to adjust the transmission speed reduction ratio and stabilize the power output. As a result, three power modes (i.e., the motor drive mode, ICE drive/generator mode, and dual power drive mode) could be switched between each other smoothly. Finally, our HEM was tested by a chassis power gauge. The test results showed that the HEM consumed 41.1% less fuel and produced 58.6% less exhaust emissions compared with conventional ICE motorcycles. In terms of controllability, the acceleration time for 0−100 m was 2.4 s less than the Taiwan E-scooter Standard (TES). The fastest highest vehicle speed was 2.1 times greater than the test of the TES.

Zinc (Zn)-air fuel cells (ZAFC) are a widely-acknowledged type of metal air fuel cells, but optimization of several operational parameters and components will facilitate enhanced power performance. This research study has been focused on the investigation of ZAFC Zn particle fuel with flowing potassium hydroxide (KOH) electrolyte. Parameters like optimum electrolyte concentration, temperature, and flow velocity were optimized. Moreover, ZAFC components like anode current collector and cathode conductor material were varied and the appropriate materials were designated. Power performance was analyzed in terms of open circuit voltage (OCV), power, and current density production and were used to justify the results of the study. The flow rate of the electrolyte was determined as 150 mL/min in the self-designed configuration. KOH electrolyte of 40 wt% concentration, at a temperature of 55 to 65 ℃, and with a flow velocity of 0.12 m/s was considered to be beneficial for the ZAFCs operated in this study. Nickel mesh with a surface area of 400 cm2 was chosen as anode current collector and copper plate was considered as cathode conductor material in the fuel cells designed and operated in this study. The power production of this study was better compared to some previously published works. Thus, effective enhancement and upgrading process of the ZAFCs will definitely provide great opportunities for their applications in the future.

In this study, we investigated the development of a computational fluid dynamics (CFD) model for simulating the physical and chemical processes in a zinc (Zn)–air fuel cell. Theoretically, the model was based on time-dependent, three-dimensional conservation equations of mass, momentum, and species concentration. The complex electrochemical reactions occurring within the porous electrodes were described by the Butler–Volmer equation with velocity, pressure, current density, and electronic and ionic phase potentials computed in electrodes. The Zn–air fuel cell for the present study comprised of four major components, such as a porous Zn anode electrode, air cathode electrode, liquid potassium hydroxide (KOH) electrolyte, and air flow channels. The numerical results were first compared with the experiments, showing close agreement with the predicted and experimental values of the measured voltage–current data of a single Zn–air fuel cell. Numerical results also exhibited mass fraction contours of oxygen (O2) and zinc oxide (ZnO) in the mid-cross-sectional plane. A parametric study was extended to assess the performance of a Zn–air fuel cell at various cathode and electrolyte parameters.