CAMELLIA QUYNHII (THEACEAE, SECT. STEREOCARPUS), A NEW YELLOW SPECIES FROM THE CENTRAL HIGHLANDS, VIETNAM

Camellia quynhii is described and illustrated as a new species of section Stereocarpus (Pierre) Sealy from 12th village, Vu Bon Commune, Krong Pak District, Dak Lak Province. C. quynhii resembles C. dormoyana (Pierre) Sealy but differs in several morphological characteristics: sepals 6–7; petals about 12–15; filaments tomentose at the base; style 3(–4), basally united; capsule 3(–4) locular, 2–6 seeds in each locule. Information on its phenology, distribution, ecology, and conservation status is also provided.Camellia quynhii is described and illustrated as a new species of section Stereocarpus (Pierre) Sealy from 12th village, Vu Bon Commune, Krong Pak District, Dak Lak Province. C. quynhii resembles C. dormoyana (Pierre) Sealy but differs in several morphological characteristics: sepals 6–7; petals about 12–15; filaments tomentose at the base; style 3(–4), basally united; capsule 3(–4) locular, 2–6 seeds in each locule. Information on its phenology, distribution, ecology, and conservation status is also provided

Assessment of the Effectiveness of Ich Tam Khang as a Supportive Therapy for Chronic Heart Failure

Background: Heart failure is a chronic disease needing lifelong management. Despite the advances that have been made in the treatment of the disease, both the longevity and quality of life for those with chronic heart failure remain impaired. A more effective therapeutic approach with less negative side effects is still needed. In this study, we evaluate Ich Tam Khang (ITK), the poly-ingredient herbal and nutritional preparation with multiple physiological actions, as a supportive therapy for patients with chronic heart failure.Aims of Study: To evaluate the effectiveness and safety of Ich Tam Khang as an adjunctive treatment of chronic heart failure.Methods: A total of 60 patients with chronic congestive heart failure were enrolled in this open label, cross-sectional and prospective study. All patients were treated with a conventional regimen (digoxin, diuretics, angiotensin-converting-enzyme (ACE) inhibitors or angiotensin II receptor blockers (ARBs), beta blockers) for at least 4 weeks before being divided into two equal groups. In the treated patients with ITK, patients received conventional therapy plus 4 tablets ITK per day added in two divided doses. In the control patients, all patients kept the same conventional regimen without ITK. All patients were followed up for 3 months for clinical and para-clinical outcomes.Result: The symptoms of heart failure (dyspnea, palpitation, peripheral edema, neck vein distention, heptojugular reflex) decreased. Heart rate and blood pressure stabilized during treatment in the treated patients with ITK. Additionally, total cholesterol and HDL-cholesterol normalized in the patients treated with ITK. Most of echocardiography parameters in the ITK treated patients were superior to the control patients. ITK is safe and it has no side effects.Conclusion: ITK as a combination of herbal and nutritional preparation is effective in reducing heart failure symptoms, improving patient's quality of life for the patients with decompensated heart failure and reducing total cholesterol and LDL-C

Risk Factors for Non-communicable Diseases in Vietnam: A Focus on Pesticides

Agent Orange, which was sprayed in southern Vietnam by the American government, was the main source of dioxin exposure in Vietnam. From the early 1990s, agriculture of Vietnam has attained advances under intensive cultivation. Both production and yields per crop has raised significantly at the farm level, but, on the other hand, the quantity of pesticides used in agriculture has been increased in the absence of regulations and good practices. Illegal business of pesticides with false labels, as well as marketing of expired or poor quality products in stores without license are so popular in Vietnam. Misuse and improper use in agriculture in Vietnam has led to a variety of problems, such as environment pollution (including food producing animals) and adverse health impact on animals and humans. Open dumpsites worsen the general scenario. Similar to the environmental exposure, human exposure to DDT in Vietnam was ranked among the highest worldwide, with recognized effects. Exposed communities have to face birth defects, health disorders and non-communicable diseases (NCDs), from metabolic syndrome, asthma, infertility and other reproductive disorders through to diabetes, obesity, cardiovascular and neurodegenerative diseases, and cancer. A common feature of many chronic disorders and NCDs is metabolic disruption: environmental chemical factors disturb cellular homeostasis, thus affecting the ability of the body to restore a functional internal environment. Among these, endocrine disrupting pesticides can interfere with the action of hormones including metabolic hormones, and are likely to represent the main concern for developmentally-induced NCDs. Since pesticides are often persistent and bio-accumulate in the food chain through the living environment of food-producing organisms, this paper discusses relevant aspects of risk assessment, risk communication and risk management

TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval

3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies.Comment: arXiv admin note: text overlap with arXiv:2304.0573

Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017.

BACKGROUND: Global development goals increasingly rely on country-specific estimates for benchmarking a nation's progress. To meet this need, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 estimated global, regional, national, and, for selected locations, subnational cause-specific mortality beginning in the year 1980. Here we report an update to that study, making use of newly available data and improved methods. GBD 2017 provides a comprehensive assessment of cause-specific mortality for 282 causes in 195 countries and territories from 1980 to 2017. METHODS: The causes of death database is composed of vital registration (VR), verbal autopsy (VA), registry, survey, police, and surveillance data. GBD 2017 added ten VA studies, 127 country-years of VR data, 502 cancer-registry country-years, and an additional surveillance country-year. Expansions of the GBD cause of death hierarchy resulted in 18 additional causes estimated for GBD 2017. Newly available data led to subnational estimates for five additional countries-Ethiopia, Iran, New Zealand, Norway, and Russia. Deaths assigned International Classification of Diseases (ICD) codes for non-specific, implausible, or intermediate causes of death were reassigned to underlying causes by redistribution algorithms that were incorporated into uncertainty estimation. We used statistical modelling tools developed for GBD, including the Cause of Death Ensemble model (CODEm), to generate cause fractions and cause-specific death rates for each location, year, age, and sex. Instead of using UN estimates as in previous versions, GBD 2017 independently estimated population size and fertility rate for all locations. Years of life lost (YLLs) were then calculated as the sum of each death multiplied by the standard life expectancy at each age. All rates reported here are age-standardised

RIS-aided AANETs: Security maximization relying on unsupervised projection-based neural networks

The security aspects of aeronautical {\em ad-hoc} networks (AANET) relying on reflective intelligent surface (RIS) are considered. A projection-based deep neural network is designed for maximizing the secrecy rate of the proposed RIS-aided AANET. It is shown that our design outperforms the state-of-the-art projected gradient descent algorithms and that the RIS is capable of enhancing the security

Deep learning based successive interference cancellation for the non-orthogonal downlink

Non-orthogonal communications are expected to play a key role in future wireless systems. In downlink transmissions, the data symbols are broadcast from a base station to different users, which are superimposed with different power to facilitate high-integrity detection using successive interference cancellation (SIC). However, SIC requires accurate knowledge of both the channel model and channel state information (CSI), which may be difficult to acquire. We propose a deep learningaided SIC detector termed SICNet, which replaces the interference cancellation blocks of SIC by deep neural networks (DNNs). Explicitly, SICNet jointly trains its internal DNNaided blocks for inferring the soft information representing the interfering symbols in a data-driven fashion, rather than using hard-decision decoders as in classical SIC. As a result, SICNet reliably detects the superimposed symbols in the downlink of non-orthogonal systems without requiring any prior knowledge of the channel model, while being less sensitive to CSI uncertainty than its model-based counterpart. SICNet is also robust tochanges in the number of users and to their power allocation. Furthermore, SICNet learns to produce accurate soft outputs, which facilitates improved soft-input error correction decoding compared to model-based SIC. Finally, we propose an onlinetraining method for SICNet under block fading, which exploits the channel decoding for accurately recovering online data labels for retraining, hence, allowing it to smoothly track the fading envelope without requiring dedicated pilots. Our numericalresults show that SICNet approaches the performance of classical SIC under perfect CSI, while outperforming it under realistic CSI uncertainty

Deep learning-aided optical IM/DD OFDM approaches the throughput of RF-OFDM

Deep learning-aided optical orthogonal frequency division multiplexing (O-OFDM) is proposed for intensity modulated direct detection transmissions, which is termed as OOFDMNet. In particular, O-OFDMNet employs deep neural networks (DNNs) for converting a complex-valued signal into a non-negative signal in the time-domain at the transmitter and vice versa at the receiver. The associated frequency-domain signal processing remains the same as in conventional radio frequency (RF) OFDM. As a result, our scheme achieves the same spectral efficiency as the RF scheme, which has never been attained by the existing O-OFDM schemes, because they have relied on the Hermitian symmetry of the spectral-domain signal to guarantee that the time-domain signal becomes real-valued. We show that O-OFDMNet can be viewed as an autoencoder architecture, which can be trained in an end-to-end manner in order to simultaneously improve both the bit error ratio (BER) and the peak-to-average power ratio (PAPR) for transmission over both additive white Gaussian noise and frequency-selective channels. Furthermore, we intrinsically integrate a soft-decision aided channel decoder with our O-OFDMNet and investigate its coded performance relying on both convolutional and polar codes. The simulation results show that our scheme improves both the uncoded and coded BER as well as a reducing the PAPR compared to the benchmarks at the cost of a moderate additional DNN complexity. Furthermore, our scheme is capable of approaching the throughput of RF-OFDM, which is notably higher than that of conventional O-OFDM. Finally, our complexity analysis shows that O-OFDMNet is suitable for real-time operation

Transparent and flexible high-performance supercapacitors based on single-walled carbon nanotube films

Transparent and flexible energy storage devices have garnered great interest due to their suitability for display, sensor and photovoltaic applications. In this paper, we report the application of aerosol synthesized and dry deposited single-walled carbon nanotube (SWCNT) thin films as electrodes for an electrochemical double-layer capacitor (EDLC). SWCNT films exhibit extremely large specific capacitance (178 F g-1 or 552 μF cm-2), high optical transparency (92%) and stability for 10 000 charge/discharge cycles. A transparent and flexible EDLC prototype is constructed with a polyethylene casing and a gel electrolyte.Peer reviewe