Acquisition Unit Research Articles

An Improved Nonnegative Matrix Factorization Algorithm Combined with K-Means for Audio Noise Reduction

Clustering algorithms have the characteristics of being simple and efficient and can complete calculations without a large number of datasets, making them suitable for application in noise reduction processing for audio module mass production testing. In order to solve the problems of the NMF algorithm easily getting stuck in local optimal solutions and difficult feature signal extraction, an improved NMF audio denoising algorithm combined with K-means initialization was designed. Firstly, the Euclidean distance formula of K-means has been improved to extract audio signal features from multiple dimensions. Combined with the initialization strategy of K-means decomposition, the initialization dictionary matrix of the NMF algorithm has been optimized to avoid getting stuck in local optimal solutions and effectively improve the robustness of the algorithm. Secondly, in the sparse coding part of the NMF algorithm, feature extraction expressions are added to solve the problem of noise residue and partial spectral signal loss in audio signals during the operation process. At the same time, the size of the coefficient matrix is limited to reduce operation time and improve the accuracy of feature extraction in high-precision audio signals. Then, comparative experiments were conducted using the NOIZEUS and NOISEX-92 datasets, as well as random noise audio signals. This algorithm improved the signal-to-noise ratio by 10–20 dB and reduced harmonic distortion by approximately −10 dB. Finally, a high-precision audio acquisition unit based on FPGA was designed, and practical applications have shown that it can effectively improve the signal-to-noise ratio of audio signals and reduce harmonic distortion.

Development of a silicon carbide radiation detection system and experimentation of the system performance

Silicon carbide (SiC) detectors have excellent radiation detection capabilities for various radiation particles, including high energy resolution, fast response times, and good radiation resistance. A SiC radiation detection system was developed to measure the neutron fluence rate and the γ-ray dose rate in high intensity radiation fields. The system was composed of two SiC detectors, a temperature monitor, two preamplifiers for each SiC detector, a data acquisition unit with two signal channels, three pairs of communication devices, and an application software to analyze and visualize the measurement data. The two SiC detectors were fabricated based on two kinds of 4H-SiC diodes and used to respectively respond to neutrons and γ-rays. Repeated experiments showed that the two SiC detectors of the system can respond to α-particles, neutrons, and γ-rays. To verify the performance of the SiC detection system, including the response linearity of the neutron fluence rate, the measurement range of the γ-ray dose rate, and the radiation resistance of the SiC radiation detectors, the system was tested in multiple neutron and γ-ray fields. The tests results show the system can measure the neutron fluence rate from 5.64 × 10 2 cm−2 s−1 to 1.03 × 10 5 cm−2 s−1 with excellent linearity response, and the γ-ray dose rate from 0.005 Gy/h to 20 Gy/h. Furthermore, the SiC detectors demonstrated good radiation resistance. The neutron and γ-ray radiation field can still be measured stably by the system after exposure to neutron fluence of 1.07 × 10 14 cm−2 and γ-ray dose of 3.52 × 10 4 Gy. This work is the preliminary research to continue the exploration how to measure the n/γ hybrid fields accurately using SiC detectors considering the different energy of neutrons.

Initial monitoring of a six-story lightweight timber frame building under different environmental conditions

AbstractThis study presents a comprehensive investigation of the dynamic behavior of a six-story lightweight timber frame building through periodic and continuous ambient vibration tests. Seasonal changes in temperature and relative humidity can influence the dynamic response of timber buildings, by affecting stiffness, strength, and connection properties due to changes in moisture content. Systematic tests were performed from October 2022 until May 2024 to identify natural frequencies, damping ratios, and mode shapes of the building using five battery-driven data acquisition units equipped with 15 uni-axial accelerometers. Two different only-output frequency and time domain Operational Modal Analysis (OMA) methods were used to evaluate the dynamic properties of the building. Additionally, the building has been continuously monitored with temperature and humidity sensors since its construction. Results obtained from the periodic measurements highlighted the need for a permanent system to capture the transient changes in the modal parameters. A complementary permanent system to record the accelerations at the roof level was installed in May 2024. The modal parameters from in situ measurements were compared with those obtained from the Finite Element (FE) model of the structure. The FE model, calibrated through a multi-stage approach, incorporating non-structural elements, and varying imposed loads, showed good agreement with experimental data. Comparative analysis of the results obtained under different temperature and humidity conditions showed the effect of the environmental conditions on the building dynamics properties, for both periodic and permanent measurements. This study highlights the importance of continuous monitoring and detailed FE modeling in understanding the dynamic properties and long-term structural behavior under varying conditions. This information can be used to ensure that the building meets safety and performance requirements and to identify potential issues that may need to be addressed during the design and maintenance phases.

Decode, Move and Speak! Self-supervised Learning of Speech Units, Gestures, and Sound Relationships Using Vocal Imitation

Abstract Speech learning encompasses mastering a complex motor system to produce speech sounds from articulatory gestures while simultaneously uncovering discrete units that provide entry to the linguistic system. Remarkably, children acquire these associations between speech sounds, articulatory gestures, and linguistic units in a weakly supervised manner, without the need for explicit labeling of auditory inputs or access to target articulatory gestures. This study uses self-supervised deep learning to investigate the respective roles of sounds, gestures, and linguistic units in speech acquisition and control. In a first experiment, we analyzed the quantized representations learned by vector-quantized variational autoencoders (VQ-VAE) from ground truth acoustic and articulatory data using ABX tests. We show an interesting complementarity between acoustic and articulatory modalities that may help in the discovery of phonemes. In a second experiment, we introduce a computational agent that repeats auditory speech inputs by controlling a virtual vocal apparatus. This agent integrates an articulatory synthesizer capable of reproducing diverse speech stimuli from interpretable parameters, along with two internal models implementing the articulatory-to-acoustic (forward) and acoustic-to-articulatory (inverse) mapping, respectively. Additionally, two inductive biases are used to regularize the ill-posed acoustic-to-articulatory inverse mapping. In line with the first experiment, we explore the complementarity between the auditory input and the articulatory parameters inferred by the agent. We also evaluate the impact of discretizing auditory inputs using VQ-VAE. While the majority of the agent’s productions are intelligible (according to perceptual evaluations), our analysis highlights inconsistencies in the underlying articulatory trajectories. In particular, we show that the agent’s productions only partially reproduce the complementarity between the auditory and articulatory modalities observed in humans.

Data-Driven Multi-Fault Detection in Pipelines Utilizing Frequency Response Function and Artificial Neural Networks

This research presents a data-driven structural health monitoring (SHM) approach for pipeline systems that leverages frequency response function (FRF) signals and artificial neural network (ANN) algorithms to accurately identify and classify diverse pipeline fault conditions. The study focuses on three specific faults: bolt looseness, scale deposits, and crack occurrence at pipeline supports, which were replicated on a pipeline segment located at the Sound and Vibration Research Group (SVRG) at University Putra Malaysia (UPM). The FRF signals were captured using accelerometers to monitor the structural health of the pipeline. The data acquisition stage involved collecting FRF signals from the accelerometers to capture vibrations and responses related to the identified faults using a Siemens LMS SCADAS data acquisition unit. The data underwent preprocessing, including the application of principal component analysis (PCA) for feature selection. The subsequent data processing stage involved the application of an artificial neural network (ANN) algorithm for pattern recognition to analyze and classify the acquired data, identifying patterns associated with the replicated fault conditions. The proposed methodology demonstrated exceptional performance, with the ANN model achieving consistently high overall accuracy (above 99.7%) and remarkably low mean squared error (in the range of 0.0088×10−3to0.3062×10−3) across multiple iterations and sensor datasets. The detailed class-specific metrics, including accuracy, precision, sensitivity, and F1-score, further substantiated the model's effectiveness in identifying the individual fault types with near-perfect or perfect results for the majority of the fault scenarios. The location-invariant performance of the ANN model across different sensor placements demonstrates the robustness of the proposed data-driven SHM methodology. This research highlights the transformative potential of integrating state-of-the-art data-driven techniques to revolutionize the monitoring and assessment of critical pipeline infrastructure, ultimately enhancing the safety, reliability, and longevity of these vital systems.

A novel sensor-based digital instrument for assessment of quality of fibre extracted from banana pseudostem

Banana (Musa paradisiaca) farming generates huge quantities of biomass, all of which goes to waste due to the non-availability of suitable technology for its commercial application. The potential solution to this issue could be the conversion of pseudo-stems into valuable assets by converting them into fibres for various textile and non-textile applications. The specific characteristics of banana pseudo-stem fibre i.e. high absorptivity, breathability and biodegradability made it sustainable as well as suitable for the development of diversified products and blending with other natural fibres. However, non-uniformity in availability, obscurity of its intended uses and lack of knowledge for assessment of fibre quality posed a biggest hurdle to reach the fibre into the textile markets. Hence, a novel sensor-based digital instrument for assessing the quality parameters i.e. bundle strength and fineness along with overall grade of banana pseudo-stem fibre is presented in this research article. The developed instrument mainly consists of a fibre bundle strength measurement unit, fineness measuring unit and visual interface cum data acquisition unit. Test results indicated that bundle strength and fineness measured by developed instrument varied from 20.92 g/tex to 28.31 g/tex and 5.63 tex to 6.41 tex respectively. Furthermore, a good correlation between the measured and actual outputs of bundle strength (One-Way ANOVA, F28,2 = 3.914, P = 0.224), fineness (One-Way ANOVA, F51,2 = 4.730, P = 0.190) and overall quality of fibre (Independent sample T-Test, F34,1 = 0.95, P = 0.190). Was observed at 5 % level of significance. The present study also introduced a grading system for quality assessment of banana fibre based on the well-established and well-recognized grading system of jute fibre developed by Indian Standard (IS: 271 2020). The developed instrument is easy to build as well as easy to use and have an approximate cost of $1800.00. The combination of developed instrument and grading system is an accurate, feasible and time-ordered technique for the assessment of the overall quality of the banana fibre and well suited for the actual conditions.

Prototype Implementation of a Digitizer for Earthquake Monitoring System.

A digitizer is considered one of the fundamental components of an earthquake monitoring system. In this paper, we design and implement a high accuracy seismic digitizer. The implemented digitizer consists of several blocks, i.e., the analog-to-digital converter (ADC), GPS receiver, and microprocessor. Three finite impulse response (FIR) filters are used to decimate the sampling rate of the input seismic data according to user needs. A graphical user interface (GUI) has been designed for enabling the user to monitor the seismic waveform in real time, and process and adjust the parameters of the acquisition unit. The system casing is designed to resist harsh conditions of the environment. The prototype can represent the three component sensors data in the standard MiniSEED format. The digitizer stream seismic data from the remote station to the main center is based on TCP/IP connection. This protocol ensures data transmission without any losses as long as the data still exist in the ring buffer. The prototype was calibrated by real field testing. The prototype digitizer is integrated with the Egyptian National Seismic Network (ENSN), where a commercial instrument is already installed. Case studies shows that, for the same event, the prototype station improves the solution of the ENSN by giving accurate timing and seismic event parameters. Field test results shows that the event arrival time and the amplitude are approximately the same between the prototype digitizer and the calibrated digitizer. Furthermore, the frequency contents are similar between the two digitizers. Therefore, the prototype digitizer captures the main seismic parameters accurately, irrespective of noise existence.

Hybrid Integrated Wearable Patch for Brain EEG-fNIRS Monitoring.

Synchronous monitoring electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention in brain science research for their provision of more information on neuro-loop interactions. There is a need for an integrated hybrid EEG-fNIRS patch to synchronously monitor surface EEG and deep brain fNIRS signals. Here, we developed a hybrid EEG-fNIRS patch capable of acquiring high-quality, co-located EEG and fNIRS signals. This patch is wearable and provides easy cognition and emotion detection, while reducing the spatial interference and signal crosstalk by integration, which leads to high spatial-temporal correspondence and signal quality. The modular design of the EEG-fNIRS acquisition unit and optimized mechanical design enables the patch to obtain EEG and fNIRS signals at the same location and eliminates spatial interference. The EEG pre-amplifier on the electrode side effectively improves the acquisition of weak EEG signals and significantly reduces input noise to 0.9 μVrms, amplitude distortion to less than 2%, and frequency distortion to less than 1%. Detrending, motion correction algorithms, and band-pass filtering were used to remove physiological noise, baseline drift, and motion artifacts from the fNIRS signal. A high fNIRS source switching frequency configuration above 100 Hz improves crosstalk suppression between fNIRS and EEG signals. The Stroop task was carried out to verify its performance; the patch can acquire event-related potentials and hemodynamic information associated with cognition in the prefrontal area.

Three-dimensional reconstruction method for layered structures based on a frequency modulated continuous wave terahertz radar

The feasibility of employing a continuous-wave terahertz detection system for non-contact and non-destructive testing (NDT) in multi-layered bonding structures is assessed in this study. The paper introduces the detection principle of terahertz frequency modulated continuous wave (FMCW) radar and outlines the two-dimensional (2D) scanning platform, which integrates optical lenses, three linear actuators, a control platform, and data acquisition units. Experimental results on two types of insulation with prefabricated defects demonstrate the capability of terahertz waves for transparent inspection imaging. These results confirm the viability of terahertz FMCW detection technology as an advanced NDT tool for multi-layered bonding structures. However, the inherent limitations of terahertz wavelength and hardware systems pose challenges in discriminating reflection peaks on upper and lower surfaces. To address this issue, a local adaptive empirical wavelet coefficient modal decomposition (LAEWCMD) method is proposed to enhance the longitudinal discrimination ability of terahertz detection. The proposed method involves segmenting the 2D terahertz detection image into regions to differentiate between defective and non-defective areas. Continuous wavelet transforms (CWT) are then applied to the range signals of each region to derive continuous wavelet coefficients (CWCs). Subsequently, empirical mode decomposition (EMD) is performed on the CWCs to decompose them into intrinsic mode functions (IMFs) and residual signals. The 1st IMF is utilized for three-dimensional (3D) reconstruction, and the regions are fused to generate the final output. The effectiveness of the proposed method is validated on aircraft thermal protection structures (TPS), achieving high-precision 3D reconstruction. This offers a novel approach for the application of terahertz computed tomography imaging and NDT.

Designing an artificial intelligence-based model for electric motor fault diagnosis to support maintenance decision-making

Motors play a crucial role in production systems. However, not everything always goes smoothly, and motor failures are one of the common challenges in the production process. Misalignment of the drive shaft is a frequent motor fault caused by improper installation or damage to machine components. This study proposes the design of a monitoring and fault diagnosis model for DC motors, which includes: (i) a PID controller for motor speed control; (ii) a vibration signal acquisition unit; and (iii) a motor monitoring unit via Blynk and signal processing for fault diagnosis. In the model, motor faults are classified using a convolutional neural network (CNN) based on analog signals that have been transformed to the frequency domain and denoised. Experimental results demonstrate that classification using the convolutional neural network is highly accurate and stable.

Entertainment robot based on IoT and VR interaction for motion training posture monitoring

With the development of artificial intelligence technology, the role of social robots in sports activities has gradually become prominent. Robots can help with intelligent control of sports venues, assist in motion guidance, and engage in emotional communication with users, helping athletes adjust their emotions. This article studies the motion training posture monitoring of entertainment robots based on the interaction between the Internet of Things and VR. A new high-intensity motion attitude monitoring system based on quantum dot photodetectors has been developed to improve monitoring accuracy and stability. Entertainment robots can simulate and analyze motion training postures, and provide relevant technical guidance. The quantum dot photodetectors were integrated into the sensor components, and the collected data was transmitted to the data acquisition and processing unit through signal transmission and processing modules. The collected data was processed and analyzed to achieve real-time monitoring and tracking of the target object’s attitude. Multiple experiments were conducted on the entire system, simulating various high-intensity exercise environments, and evaluating the performance parameters of the system. Through continuous optimization and improvement, the stability and accuracy of the system were ultimately ensured.

Transmission Line Power Theft

Abstract: Generation, transmission and distribution of electrical energy involve many operational losses. We can defined the losses in generation technically but distribution and transmission losses cannot be precisely quantified with the sending end information. This illustrates the involvement of nontechnical parameter in transmission and distribution of electricity. Moreover technical losses occur naturally and are caused because of power dissipation in transmission lines, transformers, and other power system components. Technical losses in Transmission & Distribution are computed with the information about total load and the total energy bill. While technology in the raising slopes, we should also note the increasing immoral activities. The system prevents the illegal usage of electricity. At this point of technological development the problem of illegal usage of electricity can be solved without any human control using IoT. With the implementation of this system will save large amount of electricity, and there by electricity will be available for more number of consumer then earlier, in highly populated country such as India, China. Power theft can be defined as the usage of the electrical power without any legal contract with the supplier. To see it, a proposed system (current measurement and comparison) is proposed in which the distribution of family power is done indirectly from the distribution box to each house. Power theft is a persistent issue in many countries, leading to significant financial losses for utility companies and affecting the reliability of power supply. In this study, we propose a novel approach for detecting power theft in transmission lines using NodeMCU ESP8266, a low-cost Wi-Fi-enabled microcontroller, and Blynk IoT platform for remote monitoring and control.The proposed system involves installing current sensors at various locations along the transmission lines to measure the current flow. The NodeMCU ESP8266 is used as a data acquisition unit that collects the current data from the sensors and sends it to the Blynk IoT platform via Wi-Fi for real-time monitoring. The Blynk platform provides a user-friendly interface for visualizing the data and setting up alarms and notifications for abnormal current patterns, which could indicate power theft. The system can also track power consumption trends, identify abnormal usage patterns, and provide insights for optimizing power distribution. The proposed system has several advantages, including low-cost implementation, real-time monitoring, remote access, and easy customization through the Blynk platform. It has the potential to significantly reduce power theft, improve revenue collection for utility companies, and enhance the overall reliability of power supply in transmission lines. Further research and development can be done to optimize the system's performance and expand its applications in different power distribution scenarios.

Effects of Alpha Lipoic Acid Supplementation on Slow Skeletal Muscle Mass and Contractile Functions in Type 2 Diabetic Male Sprague Dawley Rats

Objective: To see the Effects of Alpha-Lipoic Acid (ALA) supplementation on slow skeletal muscle mass andcontractile functions in type 2 diabetic male Sprague Dawley rats.Study Design: Quasi-experimental study.Place and Duration of Study: The study was carried out at the Physiology Research Lab, Army MedicalCollege, Rawalpindi, Pakistan from June 2019 to April 2021 in collaboration with the National Institute of Health(NIH) Islamabad, Pakistan.Methods: Sixty adult SD rats were divided into three equal groups. Rats were fed on a standard diet as per NIHprotocols. After 2 weeks type 2 Diabetes mellitus (T2DM) was induced in groups 2 and 3 by injecting low dose35mg of streptozotocin (STZ) in the abdomen intraperitoneally. T2DM was successfully developed andconfirmed by measuring glucose levels through a glucometer. Group 3 was injected with Alpha Lipoic acid30mg/kg/day at the lower abdomen for the next two weeks. After 04 weeks, soleus muscles were dissected.The animal data acquisition unit (iWorx) was used for assessing the contractile functions of soleus.Results: Alpha Lipoic acid group showed improvement in muscle mass, muscle tension strength, and recoveryfrom fatigue after applying fatigue protocol as compared to group 2.Conclusion: Alpha Lipoic acid supplementation improves contractile force and delays fatigue in the soleusmuscles of diabetic rats. How to cite this: Khan BU, Yousef I, Arshad S, Ikram F, Ali A, Ullah I. Effects of Alpha Lipoic Acid Supplementation on Slow Skeletal Muscle Mass and Contractile Functions in Type 2 Diabetic Male Sprague Dawley Rats. Life and Science. 2024; 5(2): 216-221. doi: http://doi.org/10.37185/LnS.1.1.408

Multiwavelength laser diode based portable photoacoustic and ultrasound imaging system for point of care applications.

Vascular diseases are a leading cause of death and disability worldwide. Despite having precursor conditions like peripheral arterial disease (PAD), they are often only diagnosed after the onset of stroke or heart attack. Low-cost, portable, noninvasive, point-of-care (POC), label-free assessment of deep vascular function benefits PAD diagnosis, especially in resource poor settings of the world. Doppler ultrasound-based blood flow measurements can diagnose PAD, albeit with limited sensitivity and specificity. To overcome this, here, we propose the first-of-its-kind dual-modality photoacoustic-and-ultrasound (PAUS) imaging system that integrates a multiwavelength pulsed laser diode (PLD) with a compact ultrasound data acquisition unit. The mesoscopic imaging depth of the portable PLD-PAUS system was validated using tissue phantoms, and its multispectral photoacoustic imaging capabilities were validated using an atherosclerosis-mimicking phantom. Furthermore, we demonstrated high-contrast volumetric in vivo photoacoustic imaging of rodent abdominal vasculature and quantified vessel reactivity due to hypercapnia stimulation. The multiparametric functional and molecular imaging capabilities of the PLD-PAUS system holds promise for POC applications.

IoT Enabled Real Time Low Cost Cardiac Monitor Using LabVIEW

The rapid advancement of the Internet of Things (IoT) and medical technology has paved the way for innovative healthcare solutions. The system incorporates IoT connectivity to enable remote monitoring, data collection, and analysis, enhancing patient care and medical decision making. Cardiac monitoring system used in hospitals to measure and record the cardiac parameters. Vital parameters are transferred within the hospitals either using Wi-Fi or Bluetooth. Range of transmission for such wireless communication is only effective within the hospitals and further transfer to a long distance is limited. The proposed cardiac monitoring system is developed using LabVIEW environment - a versatile Graphical User Interface (GUI). Data acquired through the electrodes are connected to my DAQ Unit (Data Acquisition Unit) which helps to integrate electrodes with computer or PC. Data acquired from the Software is connected to the firebase console and it will help the practitioners to share the data in live and updated. It offers benefits such as early detection of cardiac irregularities such as arrythmia, reduced hospitalization periods, and improved patient outcomes. Additionally, healthcare practitioners and caretakers can remotely access patient data, enabling timely interventions and personalized treatment plans. Since the cardiac monitors are often used by bedside patients, it may introduce bed sores in them. Hence, in addition to these parameters, body moisture level and temperature of the patient are measured using DHT11(temperature and humidity sensor) to ensure the prevention of bedsores. These additional parameters measured are also enabled to view through IoT.

Novel apparatus for visualizing hydraulic fracturing in soils

This paper presents a new apparatus designed specifically for visualizing hydraulic fracturing in soils. The apparatus comprises four main units: an injection cell, a visualization unit, a pressure/volume control unit, and a data acquisition unit. The injection cell is characterized by a transparent, high-transmittance polymethyl methacrylate top cover that enables direct visual observation. The visualization unit includes a visual studio and a high-resolution camera that captures macro-fracturing dynamics. The pressure/volume control unit enables specific test requirements to be satisfied during injection tests. The data acquisition unit records injection pressure and water volume data over time. To validate the performance of the novel apparatus, we conducted a systematic injection test on Gaomiaozi bentonite with a dry density of 1.3 g/cm3 and a water content of 9.39%. The results indicate that the apparatus accurately captures macro-fracturing dynamics and records injection pressure and water volume data in real-time.

Design of multi-channel electronic prototype for 101Sn energy level lifetime measurement

In this study, a 32-channel front-end data acquisition unit (DAQU) with high temporal resolution and count rate was developed at the Heavy Ion Research Facility in Lanzhou (HIRFL), China, to measure the lifetime of 101Sn on the Spectrometer for Heavy Atoms and Nuclear Structure. The DAQU data analysis module uses a field programmable gate array (FPGA) to implement and time stamp extraction through an FPGA-based tapped delay line (TDL) time-to-digital converter. The energy measurements were implemented using a trapezoidal algorithm and a sliding baseline averaging method. The DAQU was tested and calibrated using a pulse generator and cosmic rays. The results show that the system had a dead time of less than 500 ns, a dynamic range of “0” to “500” mV, and an energy resolution of better than 4.8‰ (full width at the half-maximum, FWHM). In addition, the single-channel temporal resolution under multi-channel settings surpasses 111.2 ps (FWHM), satisfying the experiment's requirements for the electronics system.

Multi-frequency signal acquisition and phase measurement in space gravitational wave detection.

To enhance the accuracy of phase measurement and to prevent tracking errors, it is crucial to effectively read the multi-frequency signal in space gravitational wave detection. In this paper, a novel signal acquisition method called the multi-frequency acquisition algorithm is proposed and implemented. Different from the traditional single-frequency acquisition, the signal characteristics of amplitude and frequency are both considered to better distinguish different frequency components. A phasemeter integrated with the acquisition method and narrow-bandwidth digital phase-locked loop is constructed for the method test and verification. The results show that the multi-frequency acquisition unit can capture all the frequencies of an input signal in several milliseconds. The precision is better than ±200Hz under a low SNR (signal-to-noise ratio) of 0 dB. The phase noise can reach 2 µrad/Hz1/2 in the frequency range of 0.1-1Hz and satisfy the requirement of the space gravitational wave detection in all frequency ranges.

A hydrate reservoir renovation device and its application in nitrogen bubble fracturing

Abstract. Natural gas hydrate (GH) is a significant potential energy source due to its large reserves, wide distribution, high energy density, and low pollution. However, the gas production rate of past gas hydrate production tests is much lower than the requirement of commercial gas production. Reservoir stimulation technologies like hydraulic fractures provide one potential approach to enhance gas production from GH. The reservoir reformation behavior of the hydrate-bearing sediments (HBSs), particularly sediments with a high clay content, is a complex process during a hydraulic fracturing operation which has been poorly understood and thus hardly predictable. This paper presents an experimental facility that was developed to analyze the hydraulic fracture mechanism in synthesized HBSs. This facility can be used to form GH in sediments, conduct visual observation of hydraulic fracturing experiments, and measure the permeability of HBSs under high-pressure (up to 30 MPa) and low-temperature conditions (from 253.15 to 323.15 K). It is mainly composed of a pressure control and injection unit, a low temperature and cooling unit, a cavitation unit, a visual sapphire reactor, and a data acquisition and measurement unit. The hydraulic fracture module consists of a gas cylinder, fracturing pump, hopper, proppant warehouse, and valves. The sapphire reservoir chamber is applied to observe and measure the fracture of HBSs during hydraulic fracturing. The permeability test module is composed of a constant-flux pump and pressure sensors, which can evaluate the permeability performance before and after the hydraulic fracture in HBSs. The fundamental principles of this apparatus are discussed. Some tests were performed to verify hydraulic fracture tests, and permeability tests could be practically applied in the HBS exploitation.

Cost-effectiveness analysis of HLA-B*15:02 screening before treatment of epilepsy in Indonesia

IntroductionAdverse skin reactions due to drugs such as Stevens Johnson Syndrome (SJS) and Toxic Epidermal Necrolysis (TEN) occur in 3% of people receiving anti epileptic drugs (AED). Although SJS/TEN has a low incidence, the mortality and morbidity rates are high. Indonesia has not adopted HLA-B*1502 screening prior to administration of carbamazepine (CBZ), although previous studies found a relationship between HLA-B*1502 and SJS/TEN. MethodsA hybrid decision tree and Markov model was developed to evaluate three strategies for treating newly diagnosed focal epilepsy: CBZ direct therapy, levetiracetam (LEV) direct therapy, and therapy based on HLA-B*15:02 test results. From a societal perspective, base case and sensitivity analyses were carried out over a lifetime. ResultsDirect administration of CBZ appears to have a slightly lower average cost than the HLA-B*15:02 allele screening strategy. The increase in quality-adjusted life year (QALY) in HLA-B*15:02 screening before treatment related to the cost difference reached 0.519 with an incremental cost-effectiveness ratio (ICER) of around USD 984 per unit of QALY acquisition. Direct treatment of LEV increased treatment costs by almost USD 2000 on average compared to the standard CBZ strategy. The increase in QALY is 0.834 in direct levetiracetam treatment, with an ICER of around USD 2230 for each QALY processing. ConclusionCalculation of the cost-effectiveness of lifetime epilepsy therapy in this study found that the initial screening strategy with the HLA-B*15:02 test was the most cost-effective.