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Intensity modulation/direct detection (IM/DD) remains to be the preferred optical transmission scheme for short-range applications for its simplicity of application, inexpensiveness, and small footprint. However, the impairments of low-cost device and fiber chromatic dispersion lead to the limitation of system performance when the data rate rises to 100 Gbps or higher. In this paper, we demonstrated that an equalizer using neural networks can effectively improve the transmission performance of high-speed IM/DD systems. An optimization of a long short-term memory (LSTM) structure in terms of network depth and distribution of neurons in hidden layers leads to an enhancement of the overall performance of the 50 Gbaud PAM4 communications. Furthermore, the results for a system using a LSTM-based equalizer give the better outcome than the traditional feed-forward equalizer (FFE) or artificial neural network (ANN)-based equalizer.

This study presents novel findings on the Psocoptera (Insecta) fauna of Vietnam, contributing to the understanding of biodiversity in the region. Psocoptera specimens were collected from Tam Dao National Park during October 2023, adding 19 new species to the existing list. These discoveries include species previously unknown to Vietnam and some new to the Indochina Peninsula and continental Asia. Material examination and species identification were conducted following established methodologies, with specimens deposited at the National Museum of Natural History, Sofia, Bulgaria. The findings underscore the importance of continued research efforts in documenting and understanding the psocid fauna of Vietnam, particularly in tropical forest ecosystems.

The natural ecosystem incorporates thousands of plant species and distinguishing them is normally manual, complicated, and time-consuming. Since the task requires a large amount of expertise, identifying forest plant species relies on the work of a team of botanical experts. The emergence of Machine Learning, especially Deep Learning, has opened up a new approach to plant classification. However, the application of plant classification based on deep learning models remains limited. This paper proposed a model, named PlantKViT, combining Vision Transformer architecture and the KNN algorithm to identify forest plants. The proposed model provides high efficiency and convenience for adding new plant species. The study was experimented with using Resnet-152, ConvNeXt networks, and the PlantKViT model to classify forest plants. The training and evaluation were implemented on the dataset of DanangForestPlant, containing 10,527 images and 489 species of forest plants. The accuracy of the proposed PlantKViT model reached 93%, significantly improved compared to the ConvNeXt model at 89% and the Resnet-152 model at only 76%. The authors also successfully developed a website and 2 applications called ‘plant id’ and ‘Danangplant’ on the iOS and Android platforms respectively. The PlantKViT model shows the potential in forest plant identification not only in the conducted dataset but also worldwide. Future work should gear toward extending the dataset and enhance the accuracy and performance of forest plant identification.

Knee osteoarthritis (KOA) is a leading cause of disability, particularly affecting older adults due to the deterioration of articular cartilage within the knee joint. This condition is characterized by pain, stiffness, and impaired movement, posing a significant challenge in medical diagnostics and treatment planning, especially due to the current inability for early and accurate detection or monitoring of disease progression. This research introduces a multifaceted approach employing feature extraction and machine learning (ML) to improve the accuracy of diagnosing and classifying KOA stages from radiographic images. Utilizing a dataset of 3154 knee X-ray images, this study implemented feature extraction methods such as Histogram of Oriented Gradients (HOG) with Linear Discriminant Analysis (LDA) and Min–Max scaling to prepare the data for classification. The study evaluates six ML classifiers—K Nearest Neighbors classifier, Support Vector Machine (SVM), Gaussian Naive Bayes, Decision Tree, Random Forest, and XGBoost—optimized via GridSearchCV for hyperparameter tuning within a 10-fold Stratified K-Fold cross-validation framework. An ensemble model has also been made for the already high-accuracy models to explore the possibility of enhancing the accuracy and reducing the risk of overfitting. The XGBoost classifier and the ensemble model emerged as the most efficient for multiclass classification, with an accuracy of 98.90%, distinguishing between healthy and unhealthy knees. These results underscore the potential of integrating advanced ML methodologies for the nuanced and accurate diagnosis and classification of KOA, offering new avenues for clinical application and future research in medical imaging diagnostics.

This study proposes a flexible and stretchable 3D polymer film with photonic crystals for the visible detection of spoiled milk. Thermoplastic polyurethane (TPU) was used as the substrate for the photonic crystal structure. The back barrier layer with a regular array of nanohemispheres of anodic aluminum oxide film was used as a template for electroforming a nickel mold. The nanohemisphere array was then nanoimprinted onto the TPU substrate to form a strain-controllable photonic crystal (SCPC) structure on it. Food spoilage can be easily detected by the structural color change caused by the gas it produces. Experimental results confirmed that the structural color change of the fabricated SCPC TPU film occurred when the elongation (ΔL) of the film reached 0.2 mm (1.2 %). Furthermore, spoiled milk detection experiments showed that the proposed SCPC TPU film is a highly intelligent and cost-effective biosensor for detecting spoiled food in a container.

Artificial intelligence (AI) radar technology offers several advantages over other technologies, including low cost, privacy assurance, high accuracy, and environmental resilience. One challenge faced by AI radar technology is the high cost of equipment and the lack of radar datasets for deep-learning model training. Moreover, conventional radar signal processing methods have the obstacles of poor resolution or complex computation. Therefore, this paper discusses an innovative approach in the integration of radar technology and machine learning for effective surveillance systems that can surpass the aforementioned limitations. This approach is detailed into three steps: signal acquisition, signal processing, and feature-based classification. A hardware prototype of the signal acquisition circuitry was designed for a Continuous Wave (CW) K-24 GHz frequency band radar sensor. The collected radar motion data was categorized into non-human motion, human walking, and human walking without arm swing. Three signal processing techniques, namely short-time Fourier transform (STFT), mel spectrogram, and mel frequency cepstral coefficients (MFCCs), were employed. The latter two are typically used for audio processing, but in this study, they were proposed to obtain micro-Doppler spectrograms for all motion data. The obtained micro-Doppler spectrograms were then fed to a simplified 2D convolutional neural networks (CNNs) architecture for feature extraction and classification. Additionally, artificial neural networks (ANNs) and 1D CNN models were implemented for comparative analysis on various aspects. The experimental results demonstrated that the 2D CNN model trained on the MFCC feature outperformed the other two methods. The accuracy rate of the object classification models trained on micro-Doppler features was 97.93%, indicating the effectiveness of the proposed approach.

The real-time monitoring of food freshness in refrigerators is of significant importance in detecting potential food spoiling and preventing serious health issues. One method that is commonly reported and has received substantial attention is the discrimination of food freshness via the tracking of volatile molecules. Nevertheless, the ambient environment of low temperature (normally below 4 °C) and high humidity (90% R.H.), as well as poor selectivity in sensing gas species remain the challenge. In this research, an integrated smart gas-tracking device is designed and fabricated. By applying pump voltage on the yttria-stabilized zirconia (YSZ) membrane, the oxygen concentration in the testing chamber can be manually tailored. Due to the working principle of the sensor following the mixed potential behavior, distinct differences in sensitivity and selectivity are observed for the sensor that operated at different oxygen concentrations. Typically, the sensor gives satisfactory selectivity to H2S, NH3, and C2H5OH at the oxygen concentrations of 10%, 30%, and 40%, respectively. In addition, an acceptable response/recovery rate (within 24 s) is also confirmed. Finally, a refrigerator prototype that includes the smart gas sensor is built, and satisfactory performance in discriminating food freshness status of fresh or semi-fresh is verified for the proposed refrigerator prototype. In conclusion, these aforementioned promising results suggest that the proposed integrated smart gas sensor could be a potential candidate for alarming food spoilage.

Micro-structured molecular semiconductor film-based surface-enhanced Raman scattering (SERS) probes are an important analytical tool for both fundamental and technological research. This study proposed a novel zinc oxide (ZnO)-based, three-dimensional (3D) semiconductor nanoflower (NF) superstructure probe with unique physicochemical properties, including engineered hotspots allowing an arrangement of metallic nanoparticles (NPs), as a means to analyse target molecules. By changing the size, high-density hotspots distributed throughout the nanopetal-like ‘nanorods’ of ZnO supports. When used to analyse crystal violet (CV), there were synergistic effects of silver (Ag), ZnO, and CV molecules in the synthesised ZnONFs@Ag-CV SERS system. The SERS results revealed that the plasmonic surfaces of the self-assembled hotspots on the 3D ZnO superstructures provided effective molecular interactions between the ZnONFs@Ag platform and the Raman probe molecule. These interactions influenced the configuration and detection performance of SERS. Moreover, the performance was closely associated with enhancement of the electromagnetic mechanism and the charge transfer contribution in the platform between the semiconductor, metallic NPs, and the analyte molecules. As a result, the characteristic CV peaks were obvious even at a low concentration of 10−10 M. In a mixture of two probes, the ZnONFs@Ag chip provided an outstanding selectivity in the quantitative and qualitative evaluation of each target molecule at low concentrations. The synthesised 3D ZnONFs@Ag heterostructure chip possesses excellent practical reproducibility and represents a promising candidate for chemical and biomedical inspection.

Dielectrophoresis (DEP) is known as an attractive and frugal technique to manipulate biological particles in microfluidics. This study presents the advanced solution strategy of a DEP-based microfluidic channel for focusing and separating cancerous cells in continuous flow. Theoretical calculations were carried out to define the favorable parameters in the electric field operation of the microchip. A simulation model was also used to explore the performance of the design in the isolation of circulating tumor cells (CTCs). It revealed that the optimal conditions of the device are suitable to effectively separate CTCs from red blood cells (RBCs) within the channel structure, with a high flow rate of 1.5 μL/min, and an electric amplitude as low as 10 Vpp, at the frequency of 1 kHz. The proposed method has shown potential as a simple, easy-to-operate, and low-cost approach enable to enhance the diagnosis systems for cancer detection at early stages.

Research on coping strategies and social support among Vietnamese cancer caregivers remains limited. In this study, we aim to examine the relationships between types of coping strategies utilized and social support among cancer caregivers. This was a cross-sectional study conducted in three main cancer hospitals in the Northern, Central and Southern regions of Vietnam. The 28-item Brief COPE Inventory (BCI) Scale and the Multidimensional Scale of Perceived Social Support (MSPSS) were utilized. Descriptive statistics and multivariate linear regression were performed. Active coping, acceptance and positive reframing were the most used coping strategies among participants, while substance use was the least commonly used. Level of social support was positively correlated with the utilization of coping mechanisms. Receiving high social support and utilizing positive coping strategies enables caregivers to mitigate their caregiving burden, control the situation and enhance their own quality of life.

The monitoring and classification of different gases using a single resistive semiconductor sensor are challenging because of the similar response characteristics. An array of separated sensors can be used as an electronic nose, but such arrays have a bulky structure and complex fabrication processes. Herein, we easily fabricated a gas-sensor array based on edge-grown SnO2 nanowires for the real-time monitoring and classification of multiple gases. The array comprised four sensors and was designed on a glass substrate. SnO2 nanowires were grown on-chip from the edge of electrodes, made contact together, and acted as sensing elements. This method was advantageous over the post-synthesis technique because the SnO2 nanowires were directly grown from the edge of the electrodes rather than on the surface. Accordingly, damage to the electrode was avoided by alloying Sn with Pt at a high growth temperature. The sensing characteristics of the sensor array were further examined for different gases, including methanol, isopropanol, ethanol, ammonia, hydrogen sulphide and hydrogen. Radar plots were used to improve the selective detection of different gases and enable effective classification.

Vật liệu hai chiều có cấu trúc lớp đã được quan tâm đặc biệt trong suốt gần hai thập kỷ qua do chúng có nhiều tính chất vật lý và hóa học nổi trội. Trong bài báo này, sử dụng lý thuyết phiếm hàm mật độ, chúng tôi nghiên cứu một cách có hệ thống sự ảnh hưởng của biến dạng lên các tính chất điện tử và quang học của đơn lớp GaSe. Các tính toán của chúng tôi chỉ ra rằng đơn lớp GaSe ở trạng thái cân bằng là một chất bán dẫn có vùng cấm xiên với năng lượng là 1,903 eV. Các tính chất điện tử của đơn lớp GaSe, đặc biệt là năng lượng của vùng cấm, phụ thuộc rất lớn vào biến dạng. Đơn lớp GaSe có phổ hấp thụ rộng, trải dài từ miền ánh sáng nhìn thấy đến vùng tử ngoại gần. Bên cạnh đó, biến dạng làm thay đổi đáng kể cường độ cũng như vị trí của các đỉnh trong phổ quang học của đơn lớp GaSe.

Monitoring of toxic gas in air is important because air pollution, especially in developing countries, has rapidly become severe. The high cost of installation and maintenance of a stationary analysis system by using methods such as gas chromatography limits its applications. Low-power, portable devices with relatively low-cost gas sensors are effective for mapping pollution levels in real-time in urban areas and in other living environmentts. Herein, the realization of a portable H2S sensing instrument based on SnO2 nanowires is reported. The sensor chip was prepared by the on-chip growth of SnO2 nanowires directly from the edges of Pt electrodes. The electronic system and software for signal acquisition, data processing, data storage, and output of the instrument were developed. A prototype for zero series of the instrument was also realized. The instrument is capable of monitoring H2S gas in air at ppm level and in biogas production with satisfation. Keywords: Portable gas sensors, SnO2 nanowires, Real-time monitoring, H2S gas

Day-old male chick culling is one of the world’s most inhumane problems in the poultry industry. Every year, seven billion male chicks are slaughtered in laying-hen hatcheries due to their higher feed exchange rate, lower management than female chicks, and higher production costs. This study describes a novel non-invasive method for determining the gender of chicken eggs. During the incubation period of fourteen days, four electrodes were attached to each egg for data collection. On the last day of incubation, a standard polymerase chain reaction (PCR)-based chicken gender determination protocol was applied to the eggs to obtain the gender information. A relationship was built between the collected data and the egg’s gender, and it was discovered to have a reliable connection, indicating that the chicken egg gender can be determined by measuring the impedance data of the eggs on day 9 of incubation with the four electrodes set and using the self-normalization technique. This is a groundbreaking discovery, demonstrating that impedance spectroscopy can be used to sex chicken eggs before they hatch, relieving the poultry industry of such an ethical burden.

The research has identified the factors affecting the level of response to the job requirement of social workers when conducting counseling for families of children with autism in autism care and education centers. To achieve this goal, in this study we surveyed 89 social workers in Hanoi city. The results of the analysis of the multivariate linear regression model have shown that 6 factors have a significant impact on the level of response to the requirement of counseling skills when assisting autistic children's families, including (i) passion for work, (ii) professional knowledge and practical experience, (iii) proactive activeness, (iv) training opportunities to improve qualifications, (v) employment incentives, (vi) job requirements. The results showed that the passion for the job was most appreciated. On this basis, the study offers several meaningful solutions to improve the job response level of social workers when conducting counseling for autistic children's families.

The label-free biosensor has emerged as an effective tool for the purpose of early detection of causative pathogens such as Escherichia coli as a preventive measure. In this study, a biorecognition-element-free interdigitated microelectrode (IDμE) sensor is designed and developed with this in mind, with good reliability and affordability. Results show that the designed sensor can identify E. coli with good selectivity using an impedance and capacitance of 7.69 MHz. At its optimum impedance of 1.3 kHz, the IDμE sensor can reliably quantify E. coli in a range of measurement (103.2~106 cfu/mL), linearity (R2 = 0.97), sensitivity (18.15 kΩ/log (cfu/mL)), and limit of detection (103.2 cfu/mL). In summary, the IDμE sensor developed possesses high potential for industrial and clinical applications.

In Vietnam, little is elucidated in scientific literatures about the mental health of caregivers of people with cancer. We conducted a cross-sectional study to report the situation and correlates of self-reported psychological distress among caregivers of cancer patients in Vietnam in 2019. Multiple logistic and linear regression analyses were performed. A total of 16.5% of the study participants had psychological distress. Respondent’s mean score of negative emotion was 7.6 ± 2. Educational level and type of support were significantly associated with having psychological distress among caregivers. Gender, occupation, financial difficulty, treatment belief and social support significantly correlated to psychological distress level of caregivers.

The optical resonance of a three-level system of the strongly correlated electrons in the twolevel semiconductor quantum dot interacting with the linearly polarized monochromatic electromagnetic radiation is studied. With the application of the Green function method the expressions of the state vectors and the energies of the stationary states of the system in the regime of the optical resonance are derived. The Rabi oscillations of the electron populations at different levels as well as the Rabi splitting of the peaks in the photon emission spectra are investigated. PACS numbers: 71.35.-y, 78.55.-m, 78.67.Hc

The theory of the photocurrent through the photodetector based on a two-level semiconductor quantum dot (QD) is presented. The analytical expressions of the matrix elements of the electronic transitions generated by the absorption of the circularly polarized photons are derived in the lowest order of the perturbation theory with respect to the electron tunneling interaction as well as the electron-photon interaction. From these expressions the mechanism of the generation of the spin-polarized of electrons in the photocurrent is evident. It follows that the photodetector based on the two-level semiconductor QD can be used as the model of a source of highly spinpolarized electrons.

Semiconductor metal oxide nanowires (SMO-NWs) show great potential for novel gas sensor applications because of their distinct properties, such as a high surface area to volume aspect ratio, high crystallinity and perfect pathway for electron transfer (length of NW). SMO-NW sensors can be configured as resistors or field-effect transistors for gas detection and different configurations, such as a single NW, multiple NWs, and networked NW films, have been established. Surface-functionalizing NWs with catalyst elements and self-heating NWs provide additional advantages for highly selective and low-power consumption gas sensors. However, an appropriate design of SMO-NWs is of practical importance in enhancing the gas-sensing performance of SMO-NW sensors. The on-chip growth of SMO-NWs possesses many advantages which can thus be effectively used for the large-scale fabrication of SMO-NW sensors with improved gas response and stability. This review aims to provide up-to-date information on the on-chip fabrication of SnO2, ZnO, WO3, CuO, and other SMO-NW sensors. It also discusses a variety of promising approaches that help advance the on-chip fabrication of SMO-NW-based gas sensors and other NW-based devices.

The synthesis of porous metal-oxide semiconductors for gas-sensing application is attracting increased interest. In this study, α-Fe2O3 hollow balls were synthesized using an inexpensive, scalable, and template-free hydrothermal method. The gas-sensing characteristics of the semiconductors were systematically investigated. Material characterization by XRD, SEM, HRTEM, and EDS reveals that single-phase α-Fe2O3 hollow balls with an average diameter of 1.5 μm were obtained. The hollow balls were formed by self assembly of α-Fe2O3 nanoparticles with an average diameter of 100 nm. The hollow structure and nanopores between the nanoparticles resulted in the significantly high response of the α-Fe2O3 hollow balls to ethanol at working temperatures ranging from 250 °C to 450 °C. The sensor also showed good selectivity over other gases, such as CO and NH3 promising significant application.

Pancreatic cancer (PC) is a global health problem that features a very high mortality rate. The UL16 binding protein 2 (ULBP2) is a new biomarker for PC detection. This study develops a simple, reliable, and inexpensive immunosensor for the detection of the ULBP2 antigen while also investigating the effects of an array configuration of connected sensors and zinc oxide (ZnO) nanoparticles on the immunosensor’s sensitivity. The ULBP2 antibody was immobilized onto the screen-printed carbon electrode (SPCE) surfaces of three different sensors: a simple SPCE (ULBP2-SPCE); an SPCE array, which is a series of identical SPCE connected to each other at different arrangements of rows and columns (ULBP2-SPCE-1x2 and ULBP2-SPCE-1x3); and an SPCE combined with ZnO nanoparticles (ULBP2-ZnO/SPCE). Impedance spectrum measurements for the immunosensors to ULBP2 antigen were conducted and compared. According to the result, the array configurations (ULBP2-SPCE-1x2 and ULBP2-SPCE-1x3) show an improvement of sensitivity compared to the ULBP2-SPCE alone, but the improvement is not as significant as that of the ULBP2-ZnO/SPCE configuration (ULBP2-ZnO/SPCE > ULBP2-SPCE: 18 times larger). The ULBP2-ZnO/SPCE immunosensor has a low limit of detection (1 pg/mL) and a high sensitivity (332.2 Ω/Log(pg/mL)), excellent linearity (R2 = 0.98), good repeatability (coefficients of variation = 5.03%), and is stable in long-term storage (retaining 95% activity after 28 days storage). In an array configuration, the immunosensor has an increased signal-to-noise ratio (ULBP2-SPCE-1x3 > ULBP2-SPCE: 1.5-fold) and sensitivity (ULBP2-SPCE-1x3 > ULBP2-SPCE: 2.6-fold). In conclusion, either the modification with ZnO nanoparticles onto the sensor or the use of an array configuration of sensors can enhance the immunosensor’s sensitivity. In this study, the best immunosensor for detecting ULBP2 antigens is the ULBP2-ZnO/SPCE immunosensor.

Regular monitoring of blood α-fetoprotein (AFP) and/or carcino-embryonic antigen (CEA) levels is important for the routine screening of liver cancer. However, AFP and CEA have a much lower specificity than des-γ-carboxyprothrombin (DCP) to detect liver cancer. Therefore, the study reported here was designed, to develop a screen-printed DCP immunosensor incorporating zinc oxide nanoparticles, for accurate determination of DCP. The designed immunosensor shows low detection limits for the detection of DCP: 0.440 ng/mL (based on impedance measurement), 0.081 ng/mL (based on real part of impedance measurement) and 0.078 ng/mL (based on imaginary part of impedance measurement), within the range of 3.125 ng/mL to 2000 ng/mL. In addition, there was little interference to DCP determination by molecules such as Na+, K+, Ca2+, Cl−, glucose, urea, and uric acid. It is therefore concluded that the DCP immunosensor developed and reported here is simple, inexpensive and effective, and shows promise in the rapid screening of early-stage liver cancer at home with a point-of-care approach.

In this paper the smoothed strain based four-node flat element MISQ24 with driiling degrees of freedom is extended for geometrically nonlinear analysis of plate and shell structures. The von-Karman's large deflection theory and the Total Lagrangian (TL) approach are employed in the formulation of the elements to describe small strain geometric nonlinearity with large deformations using the first-order shear deformation theory (FSDT). The predictive capability of the present models is demonstrated by comparing the present results with analytical/experimental and other numerical solutions available in the literature. Numerical examples show that the presented formulations can prevent loss of accuracy in severely distorted meshes, and therefore, are superior to those of other quadrilateral elements with inplanes rotations.

Thyroid nodule is a common disease in clinical practice. The diagnosis of malignant thyroid tumors determines the treatment strategy. Among a number of methods have claimed to help evaluating thyroid nodules, ultrasound is a usable one in spite of several disadvantages (dependent on the physician/technician, incomparable, etc.) and magnetic resonance imaging (MRI) accompanied by quantitative apparent diffusion coefficient (ADC) is a promising diagnostic tool. This study was designed to investigate the usefulness of ADC cut-off values and the protocol of thyroid MRI derived from quantitative diffusion weighted imaging (DWI) in differentiating benign and malignant thyroid nodules. The study was conducted on 93 patients with 128 thyroid nodules, diagnosed and underwent surgery at Hanoi Medical University Hospital. All the patients took thyroid MRI with different b levels (from 200 to 800). ADC value was calculated to each b level, and the statistical tests were conducted with the Statistical Package for Social Sciences (SPSS—Windows and Mac version 20) and STATA 12. The mean ADC with all the b ranging from 200 to 800 of malignant groups was significantly higher than the group of benign lesions (p from b = 500 as a standard b-value in the protocol of thyroid MRI. The ADC cut-off point for distinguishing malignant from benign thyroid lesions: 1.7 × 10−3 mm2/s with high accuracy (87.1%, 95% CI: 79.59−92.07%). The study revealed that quantitative diffusion weighted MRI with ADC measurement could potentially quantitatively differentiate between benign and malignant thyroid nodules.

Ham Yen orange (Citrus nobilis Lour) is the highly valuable commercial fruit of Vietnam. With the blooming of fruit production and farming area, this specialty crop is facing threats from several serious diseases; therefore the search for new effective biocontrollers is required to prevent the existing excessive use of fertilizers and plant protection chemicals. Endophytic actinomycetes are of great scientific interest due to their high potential of application in agriculture and pharmaceutical research. In this work, endophytic actinomycetes were isolated from a native orange species of Northeast mountainous province Tuyen Quang. Among 49 isolates obtained, the isolate TQR12-4 strongly inhibited test pathogens Colletotrichum truncatum, Geotrichum candidum, Fusarium oxysporum, and F. udum. This isolate gave comparatively high biomass yields on different substrates, for example, carboxy methyl cellulose, starch, protein, and chitin, within a wide range of temperature from 15 to 45°C and pH from 4 to 10. Sequence analysis of 16S rDNA gene showed that TQR12-4 shared 99% similarity to Streptomyces prasinopilosus; however, it slightly differed from the latter in spore morphology and hence was named as Streptomyces sp. TQR12-4. A thermostable antifungal substance of nonpeptide nature produced by Streptomyces sp. TQR12-4 had MIC against Fusarium udum of 100 μg/mL and 400 μg/mL respective to extract fractions X4 and X5.

Conventional metamaterial absorbers, which consist of a dielectric spacer sandwiched between metamaterial resonators and a metallic ground plane, have been inherently anisotropic. In this paper, we present an alternative approach for isotropic perfect absorbers using symmetric metamaterial structures. We show that by systematically manipulating the electrically and magnetically induced losses, one can achieve a desired absorption without breaking the structural homogeneity. Finite integration simulations and standard retrieval method are performed to elaborate on our idea.

Caryodaphnopsis is a small genus of the Lauraceae family with disjunct distribution in tropical Asia and tropical America. Taxa from each region form a well-supported monophyletic clade. However, the species circumscription in Asian Caryodaphnopsis remains ambiguous due to few and generally unstable morphological characters used for classification. To address this, we conducted an integrative taxonomy of the Asian clade. We investigated 13 populations, sampled 57 accessions covering the entire taxonomic diversity, geographic range, and morphological variation in Asia, and reconstructed a new phylogeny using single‐nucleotide polymorphisms. We obtained a well‐resolved phylogeny of the Asian Caryodaphnopsis. The first diverged clade consists of three species endemic to lowland forests. The second (largest) clade (eight species in total) mainly occurs in limestone mountain forests and has a double‐layered lower leaf epidermis morphology of type 2 (less complete roomy with closed "roof") and type 3 (complete roomy with closed "roof"). Based on the diagnostic morphological characters, distinct distributional ranges, and monophyly of each clade, we recognize 12 species in the Asian Caryodaphnopsis, including three new species: C. cangyuanensis sp. nov., C. hekouensis sp. nov., and C. rubrinervis sp. nov. We also reinstate C. latifolia, and re‐circumscribe the widespread C. tonkinensis and other species based on fruit and inflorescence morphology and molecular evidence. [ABSTRACT FROM AUTHOR]