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In this study, the drying of bitter gourd slices with a halogen dryer was done at different thicknesses of bitter gourd (3, 5, and 7 mm) and temperatures (60, 65, and 70 °C). The effect of varying drying characteristics in the experiment was explored. Experimental results were evaluated based on the drying time and moisture content. The results indicate that the material drying thickness and drying temperature significantly impact the drying time and the equilibrium moisture content. Furthermore, the Multivariate Adaptive Regression Splines (MARS) model is also used to train and predict the moisture content of bitter gourd in this research. The temperature, thickness of the bitter gourd, and drying time were used as input parameters for the model. Two measures R2 and Root Mean Ssquare Error (RMSE) were used to determine the accuracy of the trained MARS model. During training, the values of R2 and RMSE obtained were 0.9846 and 3.7324, respectively. The test of trained MARS was successful, with a satisfactory correlation between experimental data points and predicted points. The results show that MARS can accurately predict the moisture content of bitter gourd in a halogen dryer.

Summary: This paper presents an ultrahigh input impedance, low noise, and wide bandwidth four‐channels analog front‐end (AFE) for low‐power neural recording systems. To achieve high input impedance, the buffer channels are placed between the electrodes and the main amplifier stage of the AFE. The buffer is designed with low noise and low power consumption to obtain high input impedance of overall AFE while maintaining low input‐referred noise and low power consumption. A chopper capacitively coupled chopper instrumentation amplifier (CCIA) is placed after the buffer as the main amplifier stage of the AFE to improve the common mode rejection ratio (CMRR) and the input‐referred noise of the overall AFE design. A new chopper stabilization control technique is proposed and used in the CCIA stage to reduce the charge injection and clock feedthrough and consequently the high‐frequency ripple of the AFE output signal. A programmable gain amplifier (PGA) is designed as the third stage to adjust the overall gain of the AFE. Benefiting from PGA, the AFE can adapt its gain with both action potential and local field potential signals. To reduce the number of the electrode to be implanted and reduce the impedance mismatch that causes the degradation on overall CMRR performance, two AFE channels shared a reference electrode followed by a reference buffer. The proposed AFE is designed and simulated using a standard 180 nm CMOS process and operates in a wide frequency band of 2.1 to 2500 Hz with low input‐referred noise of 1.6 μVrms and a CMRR over 80 dB at 2.1 Hz. The total power consumption is lower than 4.3 μW per channel. With the proposed structure of AFE, the input impedance is 35 GΩ @ 21 Hz and the minimum impedance over operational bandwidth is 75 MΩ @ 2.5 kHz. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: While wavelength division multiplexing (WDM) technology combines several wavelengths onto a single waveguide, the technology of mode division multiplexing (MDM) allows many orthogonal modes of the same wavelength to operate simultaneously without interchannel crosstalk. Thus, the hybrid WDM and MDM network in which the two above‐mentioned techniques cooperate could give a several‐fold increase in the overall network capacity. Constructing this network requires hybrid wavelength‐and‐mode multiplexers, especially ones with high integration and complementary metal‐oxide‐semiconductor (CMOS) compatibility. In this paper, we propose a design of a four‐mode triple‐band multiplexer that is capable of multiplexing up to 12 separate optical signal flows by utilizing four eigenmodes (TE0, TE1, TE2, and TE3) and three‐wavelength windows, which center at 1310, 1490, and 1550 nm. The device is on silicon‐on‐insulator (SOI) platform, consisting of four butterfly‐shaped multimode interference (MMI) couplers, four directional couplers, and a 4×1 cascaded asymmetric Y‐junction coupler. Via numerical simulations, the proposed design is verified to be able to operate effectively on the three aforementioned bandwidth slots with an optical conversion efficiency of over 93% in all functions. Moreover, it exhibits insertion loss less than 1.5 dB and crosstalk smaller than −16 dB within 25 nm bandwidth at each wavelength window. These results can affirm the success of wavelength–mode combination, which leads to a massive improve in the channel capacity on the same optical multiplexing system for optical telecommunications and photonics on‐chip interconnections. [ABSTRACT FROM AUTHOR]

Incursions of Mimosa pigra L., a super-invasive plant, were detected in Hoa Vang district, Da Nang city, Vietnam. This invasive species posed threats to the local agricultural and natural areas, especially to Ba Na - Nui Chua Nature Reserve located in the district. In this study, a habitat model was developed to predict potential areas for the upcoming occurrences of the plant. Detected locations of the species were analyzed in association with seven environmental layers (15 m spatial resolution), which characterized the habitat conditions facilitating the plant incursion, to calculate a multivariate statistic, Mahalanobis distance (D2). Mimosa occurrences were divided into subsets of modelling (for model construction) and validating data (for selecting the best model from replicate runs). The model performance was tested using a null model of 1,000 random points and indicated a significant relationship between D2 values and mimosa occurrence. The D2 model performed markedly better than the random model. The null model in combination with the entire dataset of mimosa locations was also used to identify the threshold D2 value. Using that threshold value, 99.5% of existing mimosa locations were detected and 20.3% of the study area was determined as high-risk areas for mimosa occurrence. These identified high risk areas would make an important contribution to the local alien invasive species management. Given the potential threats to these species from illegal harvesting, that information may serve as an important benchmark for future habitat and population assessments. The spatial modelling techniques in this study can easily be applied to other species and areas.

Aim: To describe stress and analyze its associated factors among nursing students. Introduction: Stress is a common mental health problem among medical students, especially nursing students. Therefore, understanding stress and its associated factors is necessary to improve stress in students. Methods: A cross‐sectional study was conducted among 300 nursing students using the stratified sampling method. This study used questionnaires such as demographic form, the Depression Anxiety Stress Scale 21, and the Stressors in Nursing Students scale. The STROBE checklist was used for reporting this study. Results: Results showed that nearly half of the nursing students were stressed at various levels. Students experienced stress the most due to financial stressors, followed by education, clinical, and confidence stressors. Most of the factors in the four groups—clinical, education, confidence, and finance—increased the risk of stress in nursing students. There was a close relationship between personal problems other than health and the students' risk of stress (p < 0.001). Conclusions: This study indicated that stress in nursing students was at a high level. Determining the prevalence and associated factors of stress in nursing students will help schools, families, and society take measures to reduce stress among students. Implications for nursing and health policy: Educational administrators need to develop policies to support nursing students, such as reviewing the academic load of nursing students, improving the design of curricula, and supporting finance for students with difficult situations. Support services should be organized and make it easier for students to access mental health counseling services. [ABSTRACT FROM AUTHOR]

In this paper, a study on multi-objective optimization of the cylindrical grinding process is presented. The experimental material used in this study is X12M steel. The two output parameters of the grinding process considered in this study are surface roughness and material removal rate (MRR). The cutting mode parameters including cutting speed, feed rate, and cutting depth have been selected as input parameters of the experimental process. Experimental matrix by Taguchi method has been used to design a matrix with 27 experiments. Analysis of experimental results by Pareto chart has determined the effect of input parameters on output parameters. The Data Envelopment Analysis-based Ranking (DEAR) method has been applied to determine the values of input parameters to simultaneously ensure the two criteria of minimum surface roughness and maximum MRR. Finally, the development direction for further studies has also been recommended in this study.

In this study, the effect of oxygen in the shielding gas on the material flow behavior of the weld pool surface was discussed to clarify the dominant driving weld pool force in keyhole plasma arc welding (KPAW). To address this issue, the convection flow on the top surface of weld pool was observed using a high-speed video camera. The temperature distribution on the surface along keyhole wall was measured using the two-color pyrometry method to confirm the Marangoni force activity on the weld pool. The results show that the inclination angle of the keyhole wall (keyhole shape) increased especially near the top surface due to the decrease in the surface tension of weld pool through surface oxidation when a shielding gas of Ar + 0.5% O2 was used. Due to the change in the keyhole shape, the upward and backward shear force compositions created a large inclination angle at the top surface of the keyhole. From the temperature measurement results, the Marangoni force was found to alter the direction when 0.5% O2 was mixed with the shielding gas. The shear force was found to be the strongest force among the four driving forces. The buoyant force and Lorentz force were very weak. The Marangoni force was stronger than the Lorentz force but was weaker than shear force. The interaction of shear force and Marangoni force controlled the behavior and speed of material flow on the weld pool surface. A strong upward and backward flow was observed in the case of mixture shielding gas, whereas a weak upward flow was observed for pure Ar. The heat transportation due to the weld pool convection significantly changed when only a small amount of oxygen was admixed in the shielding gas. The results can be applied to control the penetration ratio in KPAW.

Industrial wastewater often consists of toxic chemicals and pollutants, which are extremely harmful to the environment. Heavy metals are toxic chemicals and considered one of the major hazards to the aquatic ecosystem. Analytical techniques, such as potentiometric methods, are some of the methods to detect heavy metals in wastewaters. In this work, the quantitative structure–property relationship (QSPR) was applied using a range of machine learning techniques to predict the stability constant (logβML) and potentiometric sensitivity (PSML) of 200 ligands in complexes with the heavy metal ions Cu2+, Cd2+, and Pb2+. In result, the logβML models developed for four ions showed good performance with square correlation coefficients (R2) ranging from 0.80 to 1.00 for the training and 0.72 to 0.85 for the test sets. Likewise, the PSML displayed acceptable performance with an R2 of 0.87 to 1.00 for the training and 0.73 to 0.95 for the test sets. By screening a virtual database of coumarin-like structures, several new ligands bearing the coumarin moiety were identified. Three of them, namely NEW02, NEW03, and NEW07, showed very good sensitivity and stability in the metal complexes. Subsequent quantum-chemical calculations, as well as physicochemical/toxicological profiling were performed to investigate their metal-binding ability and developability of the designed sensors. Finally, synthesis schemes are proposed to obtain these three ligands with major efficiency from simple resources. The three coumarins designed clearly demonstrated capability to be suitable as good florescent chemosensors towards heavy metals. Overall, the computational methods applied in this study showed a very good performance as useful tools for designing novel fluorescent probes and assessing their sensing abilities. [ABSTRACT FROM AUTHOR]