Differentiation between depression and bipolar disorder in child and adolescents by voice features.

OBJECTIVE: Major depressive disorder (MDD) and bipolar disorder (BD) are serious chronic disabling mental and emotional disorders, with symptoms that often manifest atypically in children and adolescents, making diagnosis difficult without objective physiological indicators. Therefore, we aimed to objectively identify MDD and BD in children and adolescents by exploring their voiceprint features. METHODS: This study included a total of 150 participants, with 50 MDD patients, 50 BD patients, and 50 healthy controls aged between 6 and 16 years. After collecting voiceprint data, chi-square test was used to screen and extract voiceprint features specific to emotional disorders in children and adolescents. Then, selected characteristic voiceprint features were used to establish training and testing datasets with the ratio of 7:3. The performances of various machine learning and deep learning algorithms were compared using the training dataset, and the optimal algorithm was selected to classify the testing dataset and calculate the sensitivity, specificity, accuracy, and ROC curve. RESULTS: The three groups showed differences in clustering centers for various voice features such as root mean square energy, power spectral slope, low-frequency percentile energy level, high-frequency spectral slope, spectral harmonic gain, and audio signal energy level. The model of linear SVM showed the best performance in the training dataset, achieving a total accuracy of 95.6% in classifying the three groups in the testing dataset, with sensitivity of 93.3% for MDD, 100% for BD, specificity of 93.3%, AUC of 1 for BD, and AUC of 0.967 for MDD. CONCLUSION: By exploring the characteristics of voice features in children and adolescents, machine learning can effectively differentiate between MDD and BD in a population, and voice features hold promise as an objective physiological indicator for the auxiliary diagnosis of mood disorder in clinical practice.

Hydrothermal carbonisation of polyvinyl chloride in ethanol-water/water system for solid fuels: Dechlorination, characteristics analysis of hydrochar, and reaction path.

Polyvinyl chloride (PVC) waste plastic is a typical solid waste. In this paper, the dechlorination and carbonization behavior of PVC in ethanol-water/water system under different process parameters (temperature, residence time, solid-liquid ratio) was studied, and hydrothermal carbon was characterized by SEM, elemental analysis, TG-DTG, XPS, Py-GC/MS. The results show that temperature is the key to the hydrothermal dechlorination of PVC, and the dechlorination efficiency of PVC is the highest by parameter optimization (220°C-90 min-10% S/D-80% E/D), which can reach 96.33 %. With the removal of Cl, the surface of the PVC matrix changed from full and smooth flocculent to honeycomb with uniform pore size distribution. Thermogravimetric analysis shows that the combustion of hydrochar can be divided into three stages: HCl precipitation and volatile combustion, semi-coke and coke combustion, and fixed carbon combustion. The combustion parameters and kinetic parameters of hydrochar were measured, and it was found that the hydrothermal carbonization of PVC at higher temperatures and ethanol-water ratio could improve the combustion performance of hydrochar. The highest calorific value can reach 36.68 MJ/mol. Py-GC/MS analyzed the distribution of the pyrolysis products, and alkylbenzene and aliphatic were the main products of pyrolysis. The structural analysis of hydrochar showed that C-C and CC accounted for the largest proportion, accompanied by a small amount of C-O and CO and trace C-Cl. The possible reaction mechanism of the hydrothermal carbonization of PVC was analyzed based on the distribution of functional groups and compound composition. This work provides an effective and sustainable method for the recycling of refractory chlorinated plastics.

Research progress in improving sludge dewaterability: sludge characteristics, chemical conditioning and influencing factors.

Sludge from wastewater treatment processes with high water content and large volume has become an inevitable issue in environmental management. Due to the challenging dewatering properties of sludge, current mechanical dewatering methods are no longer sufficient to meet the escalating water content standards of sludge. This paper summarizes the characteristics of various sludge and raises reasons for the their dewaterability differences. Affected by extracellular polymeric substances, biological sludge is hydrophilic and negatively charged, which limits the dewatering degree. The rheological properties, flocs, ionic composition, and solid phase concentration of the sludge also influence the dewatering to some extent. For these factors, the chemical conditioning measures with simple operation and excellent effect improve its dewaterability, which mainly include flocculation/coagulation, acid/alkali treatment, advanced oxidation, surfactant treatment and combined treatment. There is a growing necessity to explore the development of new chemical conditioning agents, even though traditional agents continue to remain widely used. However, the development of these new agents should prioritize finding a balance between various factors such as efficiency, effectiveness, ease of operation, environmental safety, and cost-effectiveness. Electrochemical dewatering enhances solid-liquid separation, and its coupling with chemical conditioning is also an excellent means to further reduce water content. In addition, the improvement of press filter is an effective way, which is influenced by pressure, processing time, sludge cake thickness and pore structure, filter media etc. In general, it is essential to develop new conditioning agents and enhance mechanical filtration press technology based on a thorough understanding of various sludge properties. Concurrently, an in-depth study of the principles of mechanical pressure filtration will contribute to establishing a theoretical foundation for effective deep sludge dewatering and propel further advancements in this field.

High-sensitivity non-cooled near-infrared detector based on lithium niobate surface acoustic wave resonators combined with MXene Ti3C2Tx quantum dot thin films.

In this study, we propose the design of a surface acoustic wave (SAW) near-infrared sensor combined with an MXene quantum dot thin film to improve the infrared absorption efficiency at near-infrared wavelengths. A YZ-cut lithium niobate (LiNbO3) SAW resonator is fabricated as an infrared sensing unit with a resonant frequency shift reflecting the change in infrared radiation. It was observed that the responsivity of the near-infrared sensor (with a base frequency of 460 MHz) increased by approximately 88.89%. Thus, the proposed device exhibits high-performance infrared detection. Owing to the passive wireless capability of the device, it has wide applications.

An Anionic Porous Indium-Organic Framework with Nitrogen-Rich Linker for Efficient and Selective Removal of Trace Cationic Dyes.

Metal-organic frameworks (MOFs) with porosity and functional adjustability have great potential for the removal of organic dyes in the wastewater. Herein, an anionic porous metal-organic framework (MOFs) [Me2NH2]2In2[(TATAB)4(DMF)4]·(DMF)4(H2O)4 (HDU-1) was synthesized, which is constructed from a [In(OOC)4]- cluster and a nitrogen-rich linker H3TATAB (4,4',4″-s-triazine-1,3,5-triyltri-p-aminobenzoic acid). The negatively charged [In(OOC)4]- cluster and uncoordinated -COOH on the linker result in one unit cell of HDU-1 having 8 negative sites. The zeta potential of -20.8 mV dispersed in pure water also shows that HDU-1 possesses negatively charged surface potential. The high electronegativity, water stability, and porosity of HDU-1 can facilitate the ion-exchange and Coulombic interaction. As expected, the HDU-1 exhibits high selectivity and removal rates towards trace cationic dyes with suitable size, such as methylene blue (MB) (96%), Brilliant green (BG) (99.3%), and Victoria blue B (VB) (93.6%).

Multidimensional voiceprint feature assessment system for identifying the depression in children and adolescents: a diagnostic test.

Objective: We designed a diagnostic test to evaluate the effectiveness and accuracy of a multidimensional voiceprint feature diagnostic assessment (MVFDA) system vs. the 24-item Hamilton Rating Scale for Depression (HAMD-24) for adjunctive diagnosis of children and adolescents with major depressive disorder (MDD). Methods: This study included 55 children aged 6-16 years who were clinically diagnosed with MDD according to the DSM-5 and analyzed by professional physicians, and 55 healthy children (typically developing). Each subject completed a voice recording and was scored on the HAMD-24 scale by a trained rater. We calculated the validity indices, including sensitivity, specificity, Youden's index, likelihood ratio, and other indices including predictive value, diagnostic odds ratio, diagnostic accuracy, and area under the curve (AUC), to assess the effectiveness of the MVFDA system in addition to the HAMD-24. Results: The sensitivity (92.73 vs. 76.36%) and the specificity (90.91 vs. 85.45%) of the MVFDA system are significantly higher than those of the HAMD-24. The AUC of the MVFDA system is also higher than that of the HAMD-24. There is a statistically significant difference between the groups (p < 0.05), and both of them have high diagnostic accuracy. In addition, the diagnostic efficacy of the MVFDA system is higher than that of HAMD-24 in terms of the Youden index, diagnostic accuracy, likelihood ratio, diagnostic odds ratio, and predictive value. Conclusion: The MVFDA has performed well in clinical diagnostic trials for the identification of MDD in children and adolescents by capturing objective sound features. Compared with the scale assessment method, the MVFDA system could be further promoted in clinical practice due to its advantages of simple operation, objective rating, and high diagnostic efficiency.

Lifetime visualization of femtosecond laser-induced plasma on GaP crystal.

Gallium phosphide (GaP) is a widely used and promising semiconductor material for photonics devices and we suppose the ultrafast laser can be a competitive tool for GaP processing. We used an 800 nm centered femtosecond (fs) laser with a pulse duration of 50 fs to irradiate the GaP crystal. The ablation threshold was first determined, and then the ultrafast dynamics including plasma expansion, shockwave formation and propagation, and spectral evolution were acquired and analyzed. The evolution of ejected plasma in the initial stage changed from cylindrical to planar propagation with the augment of laser fluence. The study on the propagation properties of shockwaves showed that the energy of propelling shockwaves accounted for 12% to 18% of the laser pulse energy at all fluences above the ablation threshold. A prominent plasma splitting was observed at a fluence slightly higher than the threshold, and a phenomenon that the plasma protruded out of the shockwaves was also found. Finally, the transient temperature and density of electron at different fluences were calculated. The temperature difference between the plasma and the shockwave proved the heating effect of the plasma during ablation.

Characteristic analysis of bio-oil from penicillin fermentation residue by catalytic pyrolysis.

The hazardous waste penicillin fermentation residue (PR) is a huge hazard to the environment. The bio-oil produced by the pyrolysis of the penicillin fermentation residue has the potential to become a biofuel in the future. This paper studied the pyrolysis characteristics of PR at 400°C ∼700°C. According to the weight loss and weight loss rate of PR, the whole process of pyrolysis can be divided into three stages for analysis: dehydration and volatilization, initial pyrolysis, and pyrolytic char formation. The experimental results showed that the yield of the liquid phase is the highest (33.11%) at 600°C. GC-MS analysis results showed that high temperature is beneficial to reduce the generation of oxygenated hydrocarbons (73%∼31%) and the yield of nitrogenous compounds gradually increased (19%∼43%); the yield of hydrocarbons was low in 400°C∼600°C pyrolysis (2%∼5%) but significantly increased around 700°C (22%). In the temperature range of 400°C to 700°C, the proportion of C5-C13 in bio-oil gradually increased (26%-64%), and the proportion of C14-C22 gradually decreased (47%-16%). The catalyst can increase the proportion of hydrocarbons in the bio-oil component. And the Fe2O3/HZSM-5 mixed catalyst has a significant reduction effect on oxygen-containing hydrocarbons and nitrogen-containing compounds.

An Extended Spatial Transformer Convolutional Neural Network for Gesture Recognition and Self-Calibration Based on Sparse sEMG Electrodes.

sEMG-based gesture recognition is widely applied in human-machine interaction system by its unique advantages. However, the accuracy of recognition drops significantly as electrodes shift. Besides, in applications such as VR, virtual hands should be shown in reasonable posture by self-calibration. We propose an armband fusing sEMG and IMU with autonomously adjustable gain, and an extended spatial transformer convolutional neural network (EST-CNN) with feature enhanced pretreatment (FEP) to accomplish both gesture recognition and self-calibration via a one-shot processing. Different from anthropogenic calibration methods, spatial transformer layers (STL) in EST-CNN automatically learn the transformation relation, and explicitly express the rotational angle for coarse correction. Due to the shape change of feature pattern as rotational shift, we design the fine tuning layer (FTL) which is able to regulate rotational angle within 45°. By combining STL, FTL and IMU-based posture, EST-CNN is able to calculate non-discretized angle, and achieves high resolution of posture estimation based on sparse sEMG electrodes. Experiments collect frequently-used 3 gestures of 4 subjects in equidistant angles to evaluate EST-CNN. The results under electrodes shift show that the accuracy of gesture recognition is 97.06%, which is 5.81% higher than CNN, the fitness between estimated and true rotational angle is 99.44%.

Study of MXene Ti3C2Tx quantum dot thin film on quartz bulk acoustic wave uncooled infrared sensors.

The infrared absorption efficiency is essential for an infrared sensor. We propose a quartz bulk acoustic wave (BAW) uncooled infrared sensor coated with MXene quantum dot film. The infrared detection is realized by measuring the resonant frequency of a Y-cut quartz BAW sensitive unit. An infrared sensor is fabricated by MEMS process, then the MXene quantum dot film is coated through the spin coating technology. The mechanism of infrared absorption enhancement is analyzed. Test results show that after coating the film, the responsivity (R) of the sensor increased by nearly 41% at a wavelength of 830nm, from 10.88MHz/W to 15.28 MHz/W. The quartz BAW infrared sensor combined with MXene quantum dots film has the potential of high-performance infrared detection.

Methods, mechanisms, models and tail gas emissions of convective drying in sludge: A review.

In tandem with the population and economic growth worldwide, the scale of wastewater treatment has been increasing each year. Thus, a large amount of sludge is being produced. If the problem of sludge treatment and disposal cannot be effectively solved, it will cause serious environmental pollution. The premise of sludge drying is that sludge is "harmless" and can be "recycled." Currently, the studies on convective drying focus on the direction of thin-layer drying, fluidized bed drying, spray drying and pneumatic drying. This paper systematically reviews the convective drying technology of sludge. First, the effects of air velocity temperature, relative humidity and particle size on the drying effect are precisely described, as well as the four different drying stages in the drying process, including preheating, constant rate drying, first falling rate drying, and second falling rate drying stages. Second, the research progress of different convective drying treatment technologies and the application of eight mathematical models of thin-layer drying in this field are elaborated. The effects of sludge shrinkage formation mechanisms and sludge viscous resistance generation during the drying process are also discussed in detail. The formation mechanism of sludge shrinkage and the effect of sludge viscosity resistance during drying are also elaborated. Finally, the main dry tail gases and restraining methods are elaborated during the drying process. This paper will provide a structured reference for the related research of sludge convective drying in the future.

High-Sensitivity and Environmentally Friendly Humidity Sensors Deposited with Recyclable Green Microspheres for Wireless Monitoring.

The reliable, high-sensitive, wireless, and affordable requirements for humidity sensors are needed in high-precision measurement fields. Quartz crystal microbalance (QCM) based on the piezoelectric effect can accurately detect the mass changes at the nanogram level. However, water-capture materials deposited on the surface of QCM generally show disadvantages in either cost, sensitivity, or recyclability. Herein, novel QCM-based humidity sensors (NQHSs) are developed by uniformly depositing green microspheres (GMs) of natural polymers prepared by the chemical synthesis of the emulsification/inner gel method on QCM as humidity-sensitive materials. The NQHSs demonstrate high accuracy and sensitivity (27.1 Hz/% RH) owing to the various hydrophilic groups and porous nano-3D deposition structure. Compared with the devices deposited with a smooth film, the frequency of the NQHSs shows almost no changes during the cyclic test and exhibits long-term stability. The NQHSs have been successfully applied to non-contact sensing human activities and remote real-time humidity monitoring via Bluetooth transmission. In addition, the deposited humidity-sensitive GMs and QCM substrate are fully recycled and reused (72% of the original value). This work has provided an innovative idea to construct environmental-friendly, high-sensitivity, and wireless humidity sensors.

A Bias Drift Suppression Method Based on ICELMD and ARMA-KF for MEMS Gyros.

The applications of Micro-Electro-Mechanical-System (MEMS) gyros in inertial navigation system is gradually increasing. However, the random drift of gyro deteriorates the system performance which restricting the applications of high precision. We propose a bias drift compensation model based on two-fold Interpolated Complementary Ensemble Local Mean Decomposition (ICELMD) and autoregressive moving average-Kalman filtering (ARMA-KF). We modify CELMD into ICELMD, which is less complicated and overcomes the endpoint effect. Further, the ICELMD is combined with ARMA-KF to separate and simplify the preprocessed signal, resulting improved denoising performance. In the model, the abnormal noise is removed in preprocess by 2σ criterion with ICELMD. Then, continuous mean square error (CMSE) and Permutation Entropy (PE) are both applied to categorize the preprocessed signal into noise, mixed and useful components. After abandon the noise components and denoise the mixed components by ARMA-KF, we rebuild the noise suppression signal of MEMS gyro. Experiments are carried out to validate the proposed algorithm. The angle random walk of gyro decreases from 2.4156∘/h to 0.0487∘/h, the zero bias instability lowered from 0.3753∘/h to 0.0509∘/h. Further, the standard deviation and the variance are greatly reduced, indicating that the proposed method has better suppression effect, stability and adaptability.

Sensitive Formaldehyde Detection with QCM Sensor Based on PAAm/MWCNTs and PVAm/MWCNTs.

Two formaldehyde detection methods are proposed by applying composite film quartz crystal microbalance (QCM) sensors. QCM sensor coated with PAAm/MWCNTs and PVAm/MWCNTs shows excellent characteristics of lower limit and high sensitivity. The lower limit of PVAm/MWCNTs is 0.5 ppm, and its detection sensitivity is 0.74 ppm/Hz. Upon working at different concentrations of formaldehyde and fabricating in different proportions, the reuse performance, gas selectivity, and response at room temperature show contrasting results. The main advantages of the two sensors presented are fast reaction, low cost, and easy manufacture. Compared to other formaldehyde sensors based on QCM, the PAAm/MWCNT- and PVAm/MWCNT-coated QCM sensors are able to concurrently show excellent selectivity, reuse performance, and high sensitivity, which is of great significance to detect the environmental quality.

Theoretical and Experimental Study on Nonlinear Failure of an MEMS Accelerometer under Dual Frequency Acoustic Interference.

In order to quantitatively study the interfered output of the accelerometer under an acoustic injection attack, a mathematical model for fitting and predicting the accelerometer output was proposed. With ADXL103 as an example, an acoustic injection attack experiment with amplitude sweeping and frequency sweeping was performed. In the mathematical model, the R-squared coefficient was R2 = 0.9990 in the acoustic injection attack experiment with amplitude sweeping, and R2 = 0.9888 with frequency sweeping. Based on the mathematical model, the dual frequency acoustic injection attack mode was proposed. The difference frequency signal caused by the nonlinear effect was not filtered by the low-pass filter. At a 115 dB sound pressure level, the maximum acceleration bias of the output was 4.4 m/s2 and the maximum amplitude of fluctuation was 4.97 m/s2. Two kinds of methods of prevention against acoustic injection attack were proposed, including changing the damping ratio of the accelerometer and adding a preposition low-pass filter.

A Filtering Algorithm of MEMS Gyroscope to Resist Acoustic Interference.

To reduce the impact of acoustic interference in a microelectromechanical system (MEMS) gyroscope and to improve the reliability of output data, a filtering algorithm based on orthogonal demodulation is proposed. According to the working principle and failure mechanism of a MEMS gyroscope, the sound and angular velocity frequencies are not identical, which lead to a different frequency signal output of the original single-channel demodulation scheme. Therefore, a Q channel demodulation filtering process was added to the origin single-channel demodulation scheme. For the Q channel demodulated signal, a Hilbert transform was used to compensate for the 90 degree phase shift. The IQ dual-channel difference can remove the acoustic interference signal. The simulation results indicate that the scheme can effectively suppress the acoustic interference signal and it can eliminate more than 95% of the impact of sound waves. We assembled the acoustic interference experimental platform, collected the driving and sensing data, and verified the denoising performance with our algorithm, which eliminated more than 70% of the noise signal. The simulation and experimental results demonstrate that the scheme can eliminate acoustic interference signal without destroying angular velocity signal.

Identifying determinants of bacterial fitness in a model of human gut microbial succession.

Human gut microbiota development has been associated with healthy growth but understanding the determinants of community assembly and composition is a formidable challenge. We cultured bacteria from serially collected fecal samples from a healthy infant; 34 sequenced strains containing 103,102 genes were divided into two consortia representing earlier and later stages in community assembly during the first six postnatal months. The two consortia were introduced alone (singly), or sequentially in different order, or simultaneously into young germ-free mice fed human infant formula. The pattern of fitness of bacterial strains observed across the different colonization conditions indicated that later-phase strains substantially outcompete earlier-phase strains, although four early-phase members persist. Persistence was not determined by order of introduction, suggesting that priority effects are not prominent in this model. To characterize succession in the context of the metabolic potential of consortium members, we performed in silico reconstructions of metabolic pathways involved in carbohydrate utilization and amino acid and B-vitamin biosynthesis, then quantified the fitness (abundance) of strains in serially collected fecal samples and their transcriptional responses to different histories of colonization. Applying feature-reduction methods disclosed a set of metabolic pathways whose presence and/or expression correlates with strain fitness and that enable early-stage colonizers to survive during introduction of later colonizers. The approach described can be used to test the magnitude of the contribution of identified metabolic pathways to fitness in different community contexts, study various ecological processes thought to govern community assembly, and facilitate development of microbiota-directed therapeutics.

Accurate and Robust Monocular SLAM with Omnidirectional Cameras.

Simultaneous localization and mapping (SLAM) are fundamental elements for many emerging technologies, such as autonomous driving and augmented reality. For this paper, to get more information, we developed an improved monocular visual SLAM system by using omnidirectional cameras. Our method extends the ORB-SLAM framework with the enhanced unified camera model as a projection function, which can be applied to catadioptric systems and wide-angle fisheye cameras with 195 degrees field-of-view. The proposed system can use the full area of the images even with strong distortion. For omnidirectional cameras, a map initialization method is proposed. We analytically derive the Jacobian matrices of the reprojection errors with respect to the camera pose and 3D position of points. The proposed SLAM has been extensively tested in real-world datasets. The results show positioning error is less than 0.1% in a small indoor environment and is less than 1.5% in a large environment. The results demonstrate that our method is real-time, and increases its accuracy and robustness over the normal systems based on the pinhole model. We open source in https://github.com/lsyads/fisheye-ORB-SLAM.

High-Precision Indoor Visible Light Positioning Using Modified Momentum Back Propagation Neural Network with Sparse Training Point.

In this letter, we propose an indoor visible light positioning technique using a Modified Momentum Back-Propagation (MMBP) algorithm based on received signal strength (RSS) with sparse training data set. Unlike other neural network algorithms that require a large number of training data points to locate accurately, we have realized high-precision positioning for 100 test points with only 20 training points in a 1.8 m × 1.8 m × 2.1 m localization area. In order to verify the adaptability of the MMBP algorithm, we experimentally demonstrate two different training data acquisition methods adopting either even or arbitrary training sets. In addition, we also demonstrate the positioning accuracy of the traditional RSS algorithm. Experimental results show that the average localization accuracy optimized by our proposed algorithm is only 1.88 cm for the arbitrary set and 1.99 cm for the even set, while the average positioning error of the traditional RSS algorithm reaches 14.34 cm. Comparison indicates that the positioning accuracy of our proposed algorithm is 7.6 times higher. Results also show that the performance of our system is higher than some previous reports based on RSS and RSS fingerprint databases using complex machine learning algorithms trained by a large amount of training points.

Hybrid indoor localization scheme with image sensor-based visible light positioning and pedestrian dead reckoning.

In this paper, we propose a hybrid indoor localization scheme with image sensor-based visible light positioning and pedestrian dead reckoning. The architecture and algorithm of the proposed positioning scheme are analyzed. We build and demonstrate two different positioning prototypes with single/multiple cells, which are based on a commercial light-emitting diode lamp and off-the-shelf mobile phone equipped with an image sensor and inertial measurement unit. Experimental results show that the proposed visible light positioning scheme can achieve both cell recognition and three-dimensional positioning with a single captured image of a single light source, and the positioning error is only several centimeters. Moreover, with the proposed hybrid positioning algorithm consisting of visible light positioning and pedestrian dead reckoning, we achieved real-time decimeter-level indoor location tracking continuously and robustly with sparse light-source beacons in an actual indoor scenario.