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Abstract 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.

Polymer-based wastewater disinfection, which is typically performed using chemical oxidation or irradiation, can result in various toxic byproducts and corrosion under harsh environments. This study introduces a robust bio-adsorbent prepared from naturally abundant polydopamine-modified medical stone (MS@PDA) for the high-efficiency removal of bacteria from water. The PDA nanocoating can be easily applied through an in situ self-polymerization process, resulting in a considerably high bacterial adsorption capacity of 6.6 k pcs mm−2 for Staphylococcus aureus. A cyclic flow-through dynamic filtration and a disinfection system was implemented using an MS@PDA porous filter with an average pore size of 21.8 ± 1.4 µm and porosity of ~83%, achieving a 5.2–6.0-fold enhancement in the cumulative removal efficiency for MS@PDA2. The underlying mechanisms were elucidated through the synergistic effects of interfacial bio-adsorption and size-dependent interception. Notably, the bacteria captured on the surface could be killed using the enhanced photothermal effects of the PDA nanocoating and the inherent antimicrobial properties of the mineral stone. Thus, this study not only provides a new type of advanced bio-adsorbent but also provides new perspectives on an efficient and cost-effective approach for sustainable wastewater treatment.

ObjectiveWe 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).MethodsThis 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.ResultsThe 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.ConclusionThe 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.

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%).

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.

In this letter, we propose an indoor visible light positioning technique that combines deep neural network based on the Bayesian Regularization (BR-DNN) with sparse diagonal training data set. Unlike other neural networks, which require a large number of training data points to locate accurately, we realize the high precision positioning with only 20 training points in a 1.8 m × 1.8 m × 2.1 m location area. Furthermore, we test a new optimization method of training data set, which is the diagonal set. To verify our ideas, we experimentally demonstrate three different training data acquisition methods that contain the common choice of training points (even set), arbitrary selection (arbitrary set), and diagonal selection (diagonal set). Experimental results show that the average localization accuracy optimized by the BR-DNN is 3.40 cm with the diagonal set, while the average localization accuracy is 4.35 cm for the arbitrary set and 4.58 cm for the even set. In addition, the training time and positioning time are only 11.25 and 8.66 ms due to a significant reduction of the sparse training data. All of the aforementioned experimental results show that the algorithm and training data optimization we proposed provide a new solution for real-time and high-accuracy positioning with the neural network.

Owing to different positioning methods in indoor locations that require base stations, the positioning accuracy cannot be guaranteed, and the cost of reconstructing the base station becomes high. In this paper, a positioning method based on a two-dimensional code with feature graphs using the advantage of scale-invariant feature transform (SIFT) is proposed, where the reconstruction cost of the scene is minimized, and the SIFT matching accuracy is improved with iteration of the transformation matrix. The experimental results demonstrated the positioning error is within 1.2 cm for a room with the dimension of 7 m × 6 m × 2.8 m. Compared with the previously published results under the similar conditions, our proposed scheme has the advantage of low-cost and high-precision system.

A light-emitting diode (LED) can be modulated in visible light positioning systems so that it acts as a transmitter with a unique ID. However, we must be able to support unique IDs for many different LEDs, and LEDs sometimes also need to transmit other information. Therefore, it is necessary to increase the length of an LED ID. However, long codes are prone to contain excessive continuous strings of 0s or 1s, which is not conductive to correct ID identification. The Manchester code is usually used to eliminate this phenomenon, but its encoding efficiency is low. Furthermore, the Manchester code has no error-correction function. In this paper, we present a new method for encoding a long LED ID. We apply an exclusive OR (XOR) operation to the code using an appropriate mask so that no long continuous strings of 0s or 1s appear in the XOR result. Then the LED transmits the XOR result and the mask serial number. The ID of the LED is obtained by decoding. Compared with the Manchester code, the mask XOR code approach is more efficient. When a 34-bit ID is divided into two blocks, the encoding efficiency of the mask XOR code is 33% higher than that of the Manchester code. Compared with the Manchester code, when the information transmitted by an LED is encoded with the same ID length, the mask XOR code can additionally incorporate an error correction function to reduce the bit error rate. Adding an artificial baffle to the above methods results in a 5% error rate in the Manchester code, but the mask XOR code has no code errors.

We demonstrate a Gb/s real-time visible light communication (VLC) system based on NRZ-OOK by a phosphorescent white light-emitting diode (LED). We propose an equalizer employed by an RC series-parallel connection cascade circuit based on the T-bridge structure. With the proposed circuit, the 3-dB bandwidth of the VLC system can be extended from 1 to 520 MHz. The Bit Error Ratio (BER) of the system was 7.36 × 10-4, and the data rate was 1 Gb/s at a distance of 1.5 m. To the best of our knowledge, this is the highest real-time data rate and 3-dB bandwidth achieved based on a white LED.

In this study, penicillin residue (PR) was used to prepare bio-oil by hydrothermal liquefaction. The effects of homogeneous (organic acid and alkaline catalysts) and heterogeneous catalysts (zeolite molecular sieve) on the yield and properties of bio-oil were investigated. The results show that there are significant differences in the catalytic performance of the catalysts. The effect of homogeneous catalysts on the bio-oil yield was not significant, which only increased from 26.09 (no catalysts) to 31.44 wt.% (Na2CO3, 8 wt.%). In contrast, heterogeneous catalysts had a more obvious effect, and the oil yield reached 36.44 wt.% after adding 5 wt.% MCM-48. Increasing the amount of catalyst enhanced the yield of bio-oil, but excessive amounts of catalyst led to a secondary cracking reaction, resulting in a reduction in bio-oil. Catalytic hydrothermal liquefaction reduced the contents of heteroatoms (oxygen, mainly), slightly increased the contents of C and H in the bio-oil and increased the higher heating value (HHV) and energy recovery (ER) of bio-oil. FTIR and GC-MS analyses showed that the addition of catalysts was beneficial in increasing hydrocarbons and oxygen-containing hydrocarbons in bio-oil and reducing the proportion of nitrogen-containing substances. Comprehensive analyses of the distribution of aromatic, nitrogen-containing and oxygen-containing components in bio-oil were also performed. This work is beneficial for further research on the preparation of bio-oil by hydrothermal liquefaction of antibiotic fermentation residue.

Recently, several positioning systems based on visible light communications (VLC) have been proposed, and most of them are based on received signal strength (RSS) because of its simplicity and high accuracy. In order to improve the accuracy further, a differential detection-based positioning algorithm is proposed. It can reduce positioning instability caused by light intensity fluctuation and the area outside the light-emitting diode (LED) cell can also be positioned. Experimental results show that the proposed method can improve the positioning accuracy from 10.0 to 4.0 cm and reduce the standard deviation from 9.0 to 2.5 cm. Meanwhile, the positioning accuracy outside the cell can reach 10.0 cm or less.

As a new technique of indoor localization, visible light positioning has the advantages of high accuracy and antielectromagnetic interference. Received signal strength (RSS) based visible light positioning method is easy to implement because it requires simple hardware structure. However, in practical scenarios, the actual channel gain does not follow the ideal channel model. Instead of Lambertian channel model, some approximations have to be adopted to describe the relationship between received optical power and distance, and these approximations will cause deviation of positioning calculation. In this paper, an optical antenna based on freeform lens design is proposed to improve the accuracy of RSS-based positioning. With the optical antenna, the luminous intensity distribution is optimized, and the relationship between received optical power and distance becomes a simple linear relationship. Results show that RSS-based positioning system using freeform lens optical antenna can improve the positioning accuracy by 44%. Furthermore, the advantage of the optical antenna we designed for a visible light communication positioning system demonstrated uniformity under different conditions.

With the increasing demand for indoor positioning-based services, indoor positioning methods based on Bluetooth, Wi-Fi, ultra wide band (UWB), inertial navigation, and visual light communications (VLC) have been proposed. Considering the limitations of accuracy, cost and complexity, we propose a fusion positioning scheme integrating VLC positioning and inertial navigation base on particle filter. The experimental results demonstrated that the performance degradation caused by the multipath effect and light obstruction in VLC-based positioning and the cumulative error associated with inertial navigation are solved in the proposed fusion system. The accuracy of the fusion positioning system is in centimeters, which is two to four times better as compared to the VLC-based positioning or inertial navigation alone. Furthermore, the fusion positioning system has the advantages of high accuracy, energy saving, low cost, and easy to install, making it a promising candidate for future indoor positioning applications.

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.

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.

An indoor positioning method based on metameric white light sources and subpixels on a color image sensor is proposed in this paper. Metameric white light sources are obtained by merging multiple-color light-emitting diode (LED) chips with different intensity ratios. The output intensity ratios of subpixels on color image sensor at the receiver are employed to identify the respective metameric white light sources. Proof of concept experiments demonstrates that the average error of 2-D indoor positioning is 1.50 cm and 3-D indoor positioning is 3.58 cm. The indoor positioning system with the proposed method is cost effective and convenient because image sensors in nowadays available mobile devices can be utilized.

Sludge dewatering can effectively reduce the volume and mass of sludge for subsequent treatment and disposal. The work validated the potential of Fenton's reagent combined with dodecyl dimethyl benzyl ammonium chloride (DDBAC) in improving sludge dewaterability and proposed the mechanism of joint conditioning. The composite conditioner dosage was optimized using response surface methodology. Results indicated the good conditioning capability of the composite conditioners. The optimum dosages for H2O2, Fe2+, and DDBAC were 44.6, 39.6, and 71.0 mg/g, respectively, at which a sludge cake water content of 59.67% could be achieved. Moreover, a second-order polynomial equation was developed to describe the behavior of joint conditioning. Analysis of the reaction mechanism showed that Fenton oxidation effectively decomposed extracellular polymeric substance (EPS), including loosely bound EPS (LB-EPS) and tightly bound EPS (TB-EPS), into dissolved organics, such as proteins and polysaccharides. The process facilitated the conversion of the bound water into free water. Furthermore, DDBAC further released the bound water through solubilization of TB-EPS and LB-EPS after the Fenton reaction. The bound water content of the sludge conditioned with Fenton's reagent decreased from 3.15 to 1.36 g/g and further decreased to 1.08 g/g with the addition of DDBAC. High-performance liquid chromatography analysis verified that the composite conditioning could oxidize and hydrolyze EPS into low-molecular-mass organics (e.g., formic and acetic acid), thereby facilitating the release of bound water.

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.

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.

A novel multi-fork z-axis gyroscope is presented in this paper. Different from traditional quartz gyroscopes, the lateral electrodes of the sense beam can be arranged in simple patterns; as a result, the fabrication is simplified. High sensitivity is achieved by the multi-fork design. The working principles are introduced, while the finite element method (FEM) is used to simulate the modal and sensitivity. A quartz fork is fabricated, and a prototype is assembled. Impedance testing shows that the drive frequency and sense frequency are similar to the simulations, and the quality factor is approximately 10,000 in air. The scale factor is measured to be 18.134 mV/(°/s) and the nonlinearity is 0.40% in a full-scale input range of ±250 °/s.

Regulated intramembrane proteolysis (RIP) by the Site-2 protease (S2P) results in the release of a transmembrane signaling protein. Curiously, however, S2P cleavage must be preceded by the action of the Site-1 protease (S1P). To decipher the underlying mechanism, we reconstituted sequential, in vitro cleavages of the Escherichia coli transmembrane protein RseA by DegS (S1P) and RseP (S2P). After DegS cleavage, the newly exposed carboxyl-terminal residue Val-148 of RseA plays an essential role for RseP cleavage, and its mutation to charged or dissimilar amino acids crippled the Site-2 cleavage. By contrast, the identity of residues 146 and 147 of RseA has no impact on Site-2 cleavage. These results explain why Site-1 cleavage must precede Site-2 cleavage. Structural analysis reveals that the putative peptide-binding groove in the second, but not the first, PDZ domain of RseP is poised for binding to a single hydrophobic amino acid. These observations suggest that after DegS cleavage, the newly exposed carboxyl terminus of RseA may facilitate Site-2 cleavage through direct interaction with the PDZ domain. membrane protein | PDZ domain | regulated intramembrane proteolysis | S2P