. Screening for lymphedema and accurate quantitative assessment of dermal backflow patterns on ICG represents a major shift in current clinical practice paradigms, putting an emphasis on early detection of lymphedema rather than palliative treatments and symptomatic relief. These findings set the stage for the development of a practical, universal, ICG-based quantification system for the staging of lymphedema, a significant advancement in the field of plastic surgery.

The material extrusion fused deposition modeling (FDM) technique has become a widely used technique that enables the production of complex parts for various applications. To overcome limitations of PLA material such as low impact toughness, commercially available materials such as UltiMaker Tough PLA were produced to improve the parent PLA material that can be widely applied in many engineering applications. In this study, 3D-printed parts (test specimens) considering six different printing parameters (i.e., layer height, wall thickness, infill density, build plate temperature, printing speed, and printing temperature) are experimentally investigated to understand their impact on the mechanical properties of Tough PLA material. Three different standardized tests of tensile, flexural, and compressive properties were conducted to determine the maximum force and Young’s modulus. These six properties were used as responses in a design of experiment, definitive screening design (DSD), to build six regression models. Analysis of variance (ANOVA) is performed to evaluate the effects of each of the six printing parameters on Tough PLA mechanical properties. It is shown that all regression models are statistically significant (p<0.05) with high values of adjusted and predicted R2. Conducted confirmation tests resulted in low relative errors between experimental and predicted data, indicating that the developed models are adequately accurate and reliable for the prediction of tensile, flexural, and compressive properties of Tough PLA material.

We provide observational evidence that suggests the presence of a molten silicate layer above the core of Mars, which is overlain by a partially molten layer, indicating that the core of Mars is smaller than previously thought. The detection of deep reflected S waves on Mars inferred a core size of 1,830 ± 40 km (ref. ^ 1 ), requiring light-element contents that are incompatible with experimental petrological constraints. This estimate assumes a compositionally homogeneous Martian mantle, at odds with recent measurements of anomalously slow propagating P waves diffracted along the core–mantle boundary^ 2 . An alternative hypothesis is that Mars’s mantle is heterogeneous as a consequence of an early magma ocean that solidified to form a basal layer enriched in iron and heat-producing elements. Such enrichment results in the formation of a molten silicate layer above the core, overlain by a partially molten layer^ 3 . Here we show that this structure is compatible with all geophysical data, notably (1) deep reflected and diffracted mantle seismic phases, (2) weak shear attenuation at seismic frequency and (3) Mars’s dissipative nature at Phobos tides. The core size in this scenario is 1,650 ± 20 km, implying a density of 6.5 g cm^−3, 5–8% larger than previous seismic estimates, and can be explained by fewer, and less abundant, alloying light elements than previously required, in amounts compatible with experimental and cosmochemical constraints. Finally, the layered mantle structure requires external sources to generate the magnetic signatures recorded in Mars’s crust.

The study determined antimicrobial activity using the Disc-diffusion method and extracts of three plants: garlic (Allium sativumL.), turmeric (Curcuma longaL.), and parsley (Petroselinum crispumL.) on Escherichia coli, Salmonella ssp. and Listeria monocytogenes. These pathogens have attracted the attention of numerous agencies and researchers,because of the negative impact on food and human health -and because of the impact of disease development on the economy. These studies are necessary and represent potential natural antimicrobial drugs, and due to the resistance of bacteria to known antibiotics, there is a constant increase in global consumer demand for natural ingredients. The tested plant extracts showed excellent antibacterial activity in all three replicates on Escherichia coliwith an average inhibition zone of 21,86 mm and were characterized as ***S -sensitive for the mentioned bacterium. With an average zone of inhibition of 19,052 mm, Curcuma longashowed the strongest effect on the tested bacterium Salmonella enteritidis, which tells us that this bacteria is very sensitive to the extract of the mentioned plant. The study identified zones of inhibition of very low values (˃ 8 mm) in all three tested extracts: Allium sativumL., Curcuma longaL. Petroselinum crispumL., and Listeria monocytogenes.KEYWORDS:Antimicrobial activity, extract, Disc-diffusion method

Abstract Congenital malformations are defined as structural or functional anomalies that occur in utero or at birth and can be detected at an early age. They are also known as birth defects, disabilities or congenital malformations. Congenital malformations are accompanied by hereditary or developmental disabilities or disease. From the establishment of the registry in early 2019 until the end of 2021, the total number of reported congenital malformations is 449. According to available data from EUROCAT (European network of population-based registries for the epidemiological surveillance of congenital anomalies), the average rate of congenital malformations in the countries of the European Union (EU) is 262/per 10,000 live births, while the registered rate of congenital malformations in the Federation of Bosnia and Herzegovina is 261/per 10,000 live births. In the Federation of Bosnia and Herzegovina, the highest incidence rate was registered in Sarajevo Canton (175 cases with a rate of 416/10,000 live births) and Tuzla Canton (122 cases with a rate of 356/10,000 live births). The most common congenital malformations are heart defects, cleft lip and palate, musculoskeletal deformities and Down syndrome. In the Federation of Bosnia and Herzegovina (FBiH) in 2020, 135 children under the age of 5 died, among which 18 children (13.3%) died from congenital malformations, deformations and chromosomal abnormalities (Q00-Q99). Congenital malformations can lead to chronic diseases and disabilities, death of infants and children up to five years of age. Congenital malformations represent a significant public health problem, given that they lead to disability, incapacity and pressure on the health system, as well as the problem of social integration of patients. Key messages • The registered rate of congenital malformations in the Federation of Bosnia and Herzegovina is 261/per 10,000 live births. • Congenital malformations can lead to chronic diseases and disabilities, death of infants and children up to five years of age.

We propose a novel strategy to construct optimal controllers for continuous-time nonlinear systems by means of linear-like techniques, provided that the optimal value function is differentiable and quadratic-like. This assumption covers a wide range of cases and holds locally around an equilibrium under mild assumptions. The proposed strategy does not require solving the Hamilton–Jacobi–Bellman equation, i.e., a nonlinear partial differential equation, which is known to be hard or impossible to solve. Instead, the Hamilton–Jacobi–Bellman equation is replaced with an easy-solvable state-dependent Lyapunov matrix equation. We exploit a linear-like factorization of the underlying nonlinear system and a policy-iteration algorithm to yield a linear-like policy-iteration for nonlinear systems. The proposed control strategy solves optimal nonlinear control problems in an asymptotically exact, yet still linear-like manner. We prove optimality of the resulting solution and illustrate the results via four examples.

OBJECTIVES Infection is still the leading cause of morbidity and mortality among burn patients worldwide. Isolation and identification of pediatric burn wound bacterial colonizers can prevent infection and improve burn trauma treatment. In this study, we explored early microbial colonizers within the burn wounds and the susceptibility of those isolates to antibiotics among hospitalized pediatric patients with minor and moderate burns, clinically significant infections and outcomes. METHODS A retrospective analysis of pediatric patients admitted to the inpatient pediatric surgical ward and treated for minor and moderate burns from 2009 to 2018 was performed. RESULTS One hundred six patients met the inclusion criteria. The mean age was 3.6 ± three years (0.2-14.1 years). The most common type of burn was scald burns (82.1%). The mean TBSA of the hospitalized pediatric burn cases was 8.5% (IQR, 6-12%). Seventy-nine (74.5%) patients had positive wound cultures at admission, regardless of the hospital admission day. Fifty-eight (73.4%) had one bacterial growth (mono isolate), while 21 (26.6%) had mixed growth or poly isolates. Among patients with mixed growth or poly isolate, 16 had two bacteria, three had three bacteria, and one had four bacteria isolated, totaling 105 isolated microorganisms (14 different species, 70.5% Gram-positive bacteria and 29.5% Gram-negative bacteria). Twelve patients (11%) developed clinically significant infections (eleven got burn wound infection, and one had septicemia). All patients received prophylactic systemic antibiotics. Only 35.2% of the isolated bacteria from the wounds were sensitive to the prophylactic antibiotics, and only ∼17% in case of clinically significant infections. We found a statistically significant difference in the length of hospital stay between patients with initially colonized samples of burn wounds compared with patients with initial negative samples (p = 0.008). All patients in the cohort survived hospital discharge. CONCLUSION Despite common bacterial colonization of acute burn wounds, only ∼10% of the patients developed clinically significant infections, a minority of which were sensitive to prophylactic antibiotics. Our findings indicate the need to refine the antibiotic approach in pediatric patients with minor/moderate burns in our local setting.

INTRODUCTION Early diagnosis and treatment of primary vesicoureteral reflux (VUR) are essential for preserving renal function. OBJECTIVES The study explored whether preoperative cystoscopic grading of refluxing ureteric orifices (UO) correlated with their shape in an institution with non-performance of hydrodistention of the UO in the diagnosis and grading of VUR. We also assessed the relationship between the UO shape and VUR grade with the effectiveness of endoscopic correction of primary VUR in children. METHODS This retrospective study included consecutive patients ≤15 years treated for primary VUR. The reflux grade was based on the results of preoperative voiding cystourethrography as mild, moderate, or severe. RESULTS Fifty-one patients with 77 renal refluxing units (RRU) underwent endoscopic treatment with Deflux®. VUR was bilateral in 51 % of patients. VUR was mild in 13 %, moderate in 53 %, and severe in 34 % of cases. The patients with mild and moderate VUR had stadium-shaped UOs in 60 % and 54 % RRUs, respectively. Horseshoe-shaped UOs constituted 42 % of UOs in patients with severe VUR, followed by 31 % of golf-hole UOs. The reflux resolution rate after the first endoscopic injection was 84 %. The preoperative VUR grade correlated with UOs shape (p < 0.001). No significant correlation between UOs configuration and the outcome of endoscopic treatment was seen (p = 0.452). The preoperative VUR grade negatively correlated with a favorable endoscopic treatment (p = 0.043). DISCUSSION AND CONCLUSION Our data indicate ureteral orifice shapes are closely related to preoperative VUR grade. There was no correlation between the UO configuration and the success rate of endoscopic treatment of VUR, in contrast to the significant negative correlation between the VUR grade and the success rate of endoscopic treatment.

The communications between vehicle-to-vehicle (V2V) with high frequency, group sending, group receiving and periodic lead to serious collision of wireless resources and limited system capacity, and the rapid channel changes in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. For the Internet of Vehicles (IoV), it is a fundamental challenge to achieve low latency and high reliability communication for real-time data interaction over short distances in a complex wireless propagation environment, as well as to attenuate and avoid inter-vehicle interference in the region through a reasonable spectrum allocation. To solve the above problems, this paper proposes a resource allocation (RA) method using dueling double deep Q-network reinforcement learning (RL) with low-dimensional fingerprints and soft-update architecture (D3QN-LS) while constructing a multi-agent model based on a Manhattan grid layout urban virtual environment, with communication links between V2V links acting as agents to reuse vehicle-to-infrastructure (V2I) spectrum resources. In addition, we extend the amount of transmitted data in our work, while adding scenarios where spectrum resources are relatively scarce, i.e. the number of V2V links is significantly larger than the amount of spectrum, to compensate for some of the shortcomings in existing literature studies. We demonstrate that the proposed D3QN-LS algorithm leads to a further improvement in the total capacity of V2I links and the success rate of periodic secure message transmission in V2V links.

Radio-frequency fingerprint (RFF), which comes from the imperfect hardware, is a potential feature to ensure the security of communication. With the development of deep learning (DL), DL-based RFF identification methods have made excellent and promising achievements. However, on one hand, existing DL-based methods require a large amount of samples for model training. On the other hand, the RFF identification method is generally less effective with limited amount of samples, while the auxiliary data set and the target data set often needs to have similar data distribution. To address the data-hungry problems in the absence of auxiliary data sets, in this article, we propose a supervised contrastive learning (SCL)-based RFF identification method using data augmentation and virtual adversarial training (VAT), which is called “SCACNN.” First, we analyze the causes of RFF, and model the RFF identification problem with augmented data set. A nonauxiliary data augmentation method is proposed to acquire an extended data set, which consists of rotation, flipping, adding Gaussian noise, and shifting. Second, a novel similarity radio-frequency fingerprinting encoder (SimRFE) is used to map the RFF signal to the feature coding space, which is based on the convolution, long short-term-memory, and a fully connected deep neural network (CLDNN). Finally, several secondary classifiers are employed to identify the RFF feature coding. The simulation results show that the proposed SCACNN has a greater identification ratio than the other classical RFF identification methods. Moreover, the identification ratio of the proposed SCACNN achieves an accuracy of 92.68% with only 5% samples.

Wi-Fi-based passive sensing is considered as one of the promising sensing techniques in advanced wireless communication systems due to its wide applications and low deployment cost. However, existing methods are faced with the challenges of low sensing accuracy, high computational complexity, and weak model robustness. To solve these problems, we first propose a robust channel state information (CSI)-based Wi-Fi passive sensing method using attention mechanism deep learning (DL). The proposed method is called as convolutional neural network (CNN)-ABLSTM, a combination of CNNs and attention-based bi-directional long short-term memory (LSTM). Specifically, CSI-based Wi-Fi passive sensing is devised to achieve the high precision of human activity recognition (HAR) due to the fine-grained characteristics of CSI. Second, CNN is adopted to solve the problems of computational redundancy and high algorithm complexity which are often occurred by machine learning (ML) algorithms. Third, we introduce an attention mechanism to deal with the weak robustness of CNN models. Finally, simulation results are provided to confirm the proposed method in three aspects, high recognition performance, computational complexity, and robustness. Compared with CNN, LSTM, and other networks, the proposed CNN-ABLSTM method improves the recognition accuracy by up to 4%, and significantly reduces the calculation rate. Moreover, it still retains 97% accuracy under the different scenes, reflecting a certain robustness.