Metres Research Articles

Development of a Competitive Chemiluminescent Assay for Quantitative Determination of TP53 Fusion Protein Using Reagent Strips.

Recent studies have shown that almost half of all cancers occur due to DNA damage. For the early diagnosis of cancer, a highly sensitized and swift identification for TP53 is needed since the corresponding TP53 protein is effectively recognized as "the guardian of the genome." To improve the detection sensitivity, numerous analytical methods were previously used for the determination of the TP53 protein, including denaturing high-performance liquid chromatography and enzyme-linked immunosorbent assay (ELISA). Currently, immunochromatographic tests (ICTs) that are simple to use, stable over time, and show low interference are regarded as valuable tools for the quick screening of food and environmental monitoring along with clinical diagnosis. ICTs often have limited sensitivity even if a variety of novel reporters possessing optimum photostability and improved brightness are used as signal-intensity reporters. Compared with N-(4-aminobutyl)-N-(ethylisoluminol) or luminol, a novel luminescent probe, 2',6'-diMethyl-4'-(N-succiniMidyloxycarbonyl) phenyl-10-sulfopropylacridiniuM-9-carboxylate (NSP-DMAE-NHS) has achieved a much higher efficiency, improvement in the biosensor's performance, and amplification of the signal without causing any damage to the biomolecule in terms of its biochemical activity. In this study, the reagent strip method was initially used to detect TP53 fusion protein by combining the advantages of NSP-DMAE-NHS and immunochromatography. In our experiment, the control and study lines on the strips were immobilized through HRP-conjugated goat anti-rabbit IgG and TP53 antigen, respectively. The optimized concentration of the anti-TP53 antibody-NSP-DMAE-NHS immunoconjugates was then added to the TP53 antigen samples. After, the test strips were inserted and left in the aforementioned buffer solution for an additional 20 min. Finally, a lab-made luminous measurement device was used to analyze the corresponding control and study lines on the strips. Under optimized conditions, this method was found to be ultrasensitive, with a wide range of linear responses from 0.0008 ng mL-1 to 1 µg mL-1 and a limit of detection of 0.0008 ng mL-1 (0.013 pM). Thus, a novel competitive chemiluminescent assay based on reagent strips was established for the determination of the TP53 fusion proteins. The strategy has potential applications for ultrasensitive detection in the early diagnosis of cancer.

Stretchable Device for Simultaneous Measurements of Contractility and Electrophysiology of Neuromuscular Tissue in the Gastrointestinal Tract.

Devices interfacing with biological tissues can provide valuable insights into function, disease, and metabolism through electrical and mechanical signals. However, certain neuromuscular tissues, like those in the gastrointestinal tract, undergo significant strains of up to 40%. Conventional inextensible devices cannot capture the dynamic responses in these tissues. This study introduces electrodes made from poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and polydimethylsiloxane (PDMS) that enable simultaneous monitoring of electrical and mechanical responses of gut tissue. The soft PDMS layers conform to tissue surfaces during gastrointestinal movement. Dopants, including Capstone FS-30 and polyethylene glycol, are explored to enhance the conductivity, electrical sensitivity to strain, and stability of the PEDOT:PSS. The devices are fabricated using shadow masks and solution-processing techniques, providing a faster and simpler process than traditional clean-room-based lithography. Tested on ex vivo mouse colon and human stomach, the device recorded voltage changes of up to 300 µV during contraction and distension consistent with muscle activity, while simultaneously recording resistance changes of up to 150% due to mechanical strain. These devices detect and respond to chemical stimulants and blockers, and can induce contractions through electrical stimulation. They hold great potential for studying and treating complex disorders like irritable bowel syndrome and gastroparesis.

Anxiety immediately before endodontic treatment: cross-sectional quantitative analysis

IntroductionEndodontic treatments are considered by the patients to be one of the most painful and anxiety-generating procedures and this feeling may be linked to both environmental and individual aspects. This study aimed to evaluate the degree of anxiety of patients immediately before the endodontic treatment. MethodsThe study design was a cross-sectional quantitative analysis where patient sampling was performed on those who met the inclusion criteria and needed endodontic treatment. The interview instruments for the investigation were a questionnaire with semi-structured questions, the Corah Dental Anxiety Scale, and a digital device for measurement of blood pressure and heart rate. The variables investigated were gender, age, whether the patient liked to visit the dentist, depression, whether the patient used medication for depression, systolic and diastolic pressure, and heart rate. Data statistical were analysed using descriptive statistics and to evaluate the association between variables Pearson’s chi-square tests were used with a significance level of 5%.Results175 patients were included in the study and categorized as little anxious (33.7%), mildly anxious (45.1%), moderately anxious (15.4%), and extremely anxious (5.7%). There was a strong association between higher degrees of anxiety and whether the patient declared not liking to visit the dentist; no other significant correlation was found.ConclusionEndodontic patients who claim not to like to visit the dentist are more anxious. Healthcare professionals can pursue strategies to treat these patients with anxiety reduction techniques avoiding the postponement or evasion of endodontic treatments.

The development of the soderberg electrolyzer electromagnetic field’s state monitoring system

This study is devoted to improving the economic efficiency of the cell, due to the field of the generated electromagnetic field’s accurate diagnostics. To solve this problem, the authors had developed a hardware-software complex for electromagnetic field diagnostics. This complex includes a measurement device and a software package for data collection and analysis. On the laboratory prototype of the aluminum electrolysis complex, a study was carried out on the formation and structure of the electromagnetic field. A number of experiments have been carried out showing the degree of formation of the electromagnetic field by the anode, the relationship of electromagnetic fields in the inter-anode space has been shown. Based on the results of the studies, conclusions were drawn about the possibility of diagnosing the current state of the anode, determining the direction of rotation of aluminum in the electrolytic cell and estimating the life of the anode and its burnout time.

Analysis of unsupervised learning approach for classification of vehicle fuel type using psychoacoustic features

Much research has been done in the field of classifying vehicles based on their fuel type. One of the many potential applications is to improve the quality of life in urban areas by separating vehicles based on their pollution level. Real-time classification and implementation of appropriate on-site IoT measurement devices is critical to developing a system that accurately identifies vehicle's fuel type without violating driver privacy. In this paper, a classification system based on psychoacoustic features extracted from sound recordings is investigated. Unsupervised learning was implemented as it is able to detect hidden connections within the input space without relying on labelling data. Our goal was to develop and explore a relatively fast classification system, focusing on a short acquisition time. A self-organizing map with a 10-dimensional input space was used and effective classification with five single-valued features was demonstrated. The analysis of the input space shows the difficulty and complexity of such an approach and leaves room for further improvement.

Impact of Fatigue on Ergonomic Risk Scores and Foot Kinetics: A Field Study Employing Inertial and In-Shoe Plantar Pressure Measurement Devices.

(1) Background: Occupational fatigue is a primary factor leading to work-related musculoskeletal disorders (WRMSDs). Kinematic and kinetic experimental studies have been able to identify indicators of WRMSD, but research addressing real-world workplace scenarios is lacking. Hence, the authors of this study aimed to assess the influence of physical strain on the Borg CR-10 body map, ergonomic risk scores, and foot pressure in a real-world setting. (2) Methods: Twenty-four participants (seventeen men and seven women) were included in this field study. Inertial measurement units (IMUs) (n = 24) and in-shoe plantar pressure measurements (n = 18) captured the workload of production and office workers at the beginning of their work shift and three hours later, working without any break. In addition to the two 12 min motion capture processes, a Borg CR-10 body map and fatigue visual analog scale (VAS) were applied twice. Kinematic and kinetic data were processed using MATLAB and SPSS software, resulting in scores representing the relative distribution of the Rapid Upper Limb Assessment (RULA) and Computer-Assisted Recording and Long-Term Analysis of Musculoskeletal Load (CUELA), and in-shoe plantar pressure. (3) Results: Significant differences were observed between the two measurement times of physical exertion and fatigue, but not for ergonomic risk scores. Contrary to the hypothesis of the authors, there were no significant differences between the in-shoe plantar pressures. Significant differences were observed between the dominant and non-dominant sides for all kinetic variables. (4) Conclusions: The posture scores of RULA and CUELA and in-shoe plantar pressure side differences were a valuable basis for adapting one-sided requirements in the work process of the workers. Traditional observational methods must be adapted more sensitively to detect kinematic deviations at work. The results of this field study enhance our knowledge about the use and benefits of sensors for ergonomic risk assessments and interventions.

Fault Location Method for Distribution Network Using an Additional Inductance Strategy

In distribution networks, time asynchrony exists between the phasor measuring unit (PMU) at both ends of a line, and the effective measurement time of the devices is short, leading to insufficient accuracy in phasor measurements. This paper proposes a fault location method for distribution networks that employ an additional inductance strategy to address the limited location accuracy caused by time asynchrony and the inadequate accuracy of phasor measurement devices. The method enhances the stability and accuracy of phase measurement by connecting an additional inductance after the online circuit breaker, thus extending the effective measurement time. It uses the symmetrical component method to obtain the positive-sequence and negative-sequence networks following a fault. Time asynchrony is treated as an equivalent asynchronous phase angle, which is then applied to the positive and negative-sequence voltage components. The impact of time asynchrony is mitigated by compensating for the phase angle difference using the ratio of the positive-sequence voltage component to the negative-sequence voltage component. This approach provides the fault location function and has improved the accuracy of fault location, which is advantageous for rapid fault repair.

The MLDAR Model: Machine Learning-Based Denoising of Structural Response Signals Generated by Ambient Vibration

Engineers have consistently prioritized the maintenance of structural serviceability and safety. Recent strides in design codes, computational tools, and Structural Health Monitoring (SHM) have sought to address these concerns. On the other hand, the burgeoning application of machine learning (ML) techniques across diverse domains has been noteworthy. This research proposes the combination of ML techniques with SHM to bridge the gap between high-cost and affordable measurement devices. A significant challenge associated with low-cost instruments lies in the heightened noise introduced into recorded data, particularly obscuring structural responses in ambient vibration (AV) measurements. Consequently, the obscured signal within the noise poses challenges for engineers in identifying the eigenfrequencies of structures. This article concentrates on eliminating additive noise, particularly electronic noise stemming from sensor circuitry and components, in AV measurements. The proposed MLDAR (Machine Learning-based Denoising of Ambient Response) model employs a neural network architecture, featuring a denoising autoencoder with convolutional and upsampling layers. The MLDAR model undergoes training using AV response signals from various Single-Degree-of-Freedom (SDOF) oscillators. These SDOFs span the 1–10 Hz frequency band, encompassing low, medium, and high eigenfrequencies, with their accuracy forming an integral part of the model’s evaluation. The results are promising, as AV measurements in an image format after being submitted to the trained model become free of additive noise. This with the aid of upscaling enables the possibility of deriving target eigenfrequencies without altering or deforming of them. Comparisons in various terms, both qualitative and quantitative, such as the mean magnitude-squared coherence, mean phase difference, and Signal-to-Noise Ratio (SNR), showed great performance.

The results of experimental research of a rotor seed-metering unit for sowing non-free-flowing seeds

The production and cultivation of new high-quality seed varieties are linked to the sowing of various crops with diverse physical and mechanical seed properties. Efficient seed-metering unit operation is critical during the technological process of fodder crop cultivation, predominantly when sowing non-free-flowing seeds. The quality of seed sowing and crop yield significantly rely on the design precision of seed-metering devices, technical maintenance and appropriate calibration. A rotary seed metering device was incorporated to ensure that non-friable seeds are uniformly sown, thus maintaining consistent seed supply and consumption at all stages of circulation. The study of the proposed device's productivity dependence on its operating parameters is justified because these variables affect crucial indicators such as the capacity to achieve and sustain the desired seeding rate over the entire operational duration. The study presents findings from an experimental investigation on sowing non-free-flowing (non-flowing) and finely dispersed seeds using a rotor seed-metering unit. The tests aimed to ascertain the precision and evenness of sowing such crops. It was observed that the speed of rotation of the seed-metering unit's vane disk is a key factor in the uniformity and supply of sown seeds. The limits of variation in rotor rotation speed and rotor seed-metering unit productivity per second were established to guarantee the desired seeding rate for various crops, including alfalfa, Agropyron, and Bromus inermis.

Design, construction and evaluation of a device for non-destructive measurement of firmness in fruits using vibration analysis (case study: Kiwifruit)

Firmness has been identified as a reliable indicator for evaluating fruit quality due to its significant correlation with attributes such as taste, ripeness, shelf life and mechanical impact sensitivity. In order to prevent product loss during evaluation of fruit quality, it is essential to employ non-destructive measurement techniques. This study aimed to determine the firmness of Kiwifruit using a non-destructive vibrating method. After extracting the vibrational (amount of displacement, time delay) and physical (length, width, weight and area of a fruit) features, the most effective features were selected to predict firmness using the random forest (RF) regression method. To optimize the performance of the RF model, the Bayesian optimization algorithm was employed to identify the most appropriate hyper-parameters (number of learning cycles, learning rate, minimum number of leaves and maximum number of divisions). The results demonstrated that the developed RF model could accurately predict the kiwifruit firmness with an R2 of 0.9561 and an RMSE of 0.0125.

Design and Development of an IoT-based Pulmonary Function and Oxygen Saturation Measurement Device

Health information technology plays a crucial role in managing the healthcare of patients and their families during illness. One of the frequently encountered diseases is Asthma, a chronic inflammatory disorder of the respiratory tract that is reversible and fluctuating, capable of causing exacerbations with mild to severe symptoms and even death. The objective of this research is to develop a device to facilitate the monitoring and input of data regarding pulmonary volume measurements (spirometry) and biosignals (SpO2). The sensors used for measuring pulmonary volume are the flow turbine sensor, while the SpO2 sensor used is the MAX30102. The data obtained from the sensor measurements will be processed on the ESP32. A health monitoring application is created using Kodular software, which incorporates a MySQL database for data storage. Furthermore, the examination results can be accessed through an Android application on a tablet or smartphone. The results obtained from this research indicate an error value of 8.78% for FVC, 14% for FEV1, and a FEV1/FVC ratio of 4.6%, with zero data loss. It is expected that the spirometer with Internet of Things (IoT) capabilities will be implemented, as monitoring can be easily conducted anywhere. The portable design will facilitate future examinations.

PV output estimation method of power distribution station area based on federated learning framework and improved transformer neural network

In contemporary distribution networks (DNs), characterized by extensive integration of distributed energy, the photovoltaic (PV) power output data from the distribution station areas become crucial for system planning and operational optimization. Since many PVs are installed behind-the-meter (BTM), it is difficult to directly obtain PV power data through measurement devices. Therefore, it is important to estimate the BTM PV power from the aggregating data that can be directly obtained. However, the existing estimation methods usually require centralized large-scale data training, which brings certain privacy leakage risks. In order to solve these problems, we propose a federated learning-based improved Transformer Neural Network strategy to estimate BTM PV generation at the community level with data privacy protection. Initially, enhanced Transformer neural networks, employing a fused-attention mechanism, are deployed to precisely delineate the solar power generation pattern. Subsequently, federated learning principles facilitate the sharing of specific parameters among multiple edge endpoints and a central server. This model bifurcates into two layers: an individual layer, where parameters are retained locally, and an exchange layer, where parameters are collectively shared and conveyed through momentum aggregation. This dual-layer structure effectively synchronizes the capture of both unique and common characteristics. The test on the Australian residential load dataset verifies the effectiveness of the proposed method.

Prognostic nutritional index instead of serum Vitamin D levels as a determinant of the presence of osteoporosis in adult male patients with neurological impairment

Background: Complications of non-traumatic fractures and osteoporosis, which reduce mobility and quality of life, should not be ignored in patients with neurological impairment (NI). Aim: To diagnose osteoporosis in adult patients with NI, a readily available and easily obtained index, instead of serum Vitamin D level or bone mineral density (BMD), was explored. Methods: This was a single-center retrospective study. The participants were inpatients with NI admitted between August 2020 and June 2022. Patient data regarding (1) patient information, (2) blood data, including the prognostic nutrition index (PNI), which predicts outcomes of various diseases, (3) body composition, (4) T-score by BMD, (5) nutritional measures, and (6) outcome measures were collected. Enrolled patients were divided into two groups, with or without osteoporosis, according to their T-score. The data were analyzed by three methods: (1) comparison of all collected data between the two groups to analyze the factors influencing osteoporosis; (2) multiple logistic regression analysis; and (3) receiving operating characteristic curve analysis. Results: Patients with osteoporosis had a significantly lower PNI (45 vs. 49, P = 0.045), and higher Vitamin D insufficiency (71% vs. 31%, P = 0.031). PNI was the strongest influencing factor, and its cutoff value for osteoporosis was 50. Conclusion: The PNI is the strongest determinant of osteoporosis in patients with NI. Therefore, PNI can potentially be used as a surrogate for BMD instead of serum Vitamin D levels in institutionalized and homebound patients who do not have BMD measurement devices. Relevance for Patients: Prognostic nutrition index, which is a simple blood test, outperforms serum vitamin D concentration as a good indicator for early detection of osteoporosis.

Investigating the Reliability and Validity of the Portable Osteometric Device

ABSTRACT Metric analysis of skeletal material is integral to the analysis and identification of human remains, though one commonly used measuring device, the osteometric board, has lagged in recent advancement. Traditional boards are bulky and require manual measurement recording, potentially generating intra- and interobserver error. To address these limitations, we tested the reliability, validity, and error rates of a novel device, the Portable Osteometric Device Version 1 (PODv1), which measures distance using laser sensors with time-of-flight technology. Forty-five volunteers measured four skeletal elements with the PODv1 and a PaleoTech osteometric board in three rounds. Comparison of tibia, humerus, and femur measurements with both devices showed no significant differences, although the maximum length of the ulna did differ, potentially because of observer confusion regarding the PODv1's user instructions for this element. Our results suggest that the PODv1 is a reliable, valid measurement device compared to traditional osteometric boards. Although both device types can produce calibration, transcription, and observer errors, the time-of-flight technology and the absence of manual recording built into the PODv1 may limit those errors. These advancements and their potential positive impacts on the accuracy of osteometric data collection may have far-reaching benefits for osteological, bioarchaeological, paleopathological, and forensic anthropological data collection.

(125) Measurement of Clitoral Pulse Amplitude with a Wearable Device

Abstract Introduction Penile erection is well understood and has been extensively studied. The Erectile Hardness Score is a commonly used tool to quantify the strength of an erection. Few such measurement tools exists for women. This study builds on the work done by Mechelmans et al exploring Clitoral Pulse Amplitude (CPA) as a marker for physical arousal in women. CPA has been shown to be related to clitoral blood volume, erection, and engorgement of the clitoris. The erection in the clitoris is homologous to penile erection. Objective To test a new, wearable, clitoral pulse sensor designed for at-home use for correlation with subjective arousal for proof of concept. To test the comfort and usability of such a device configured with an internal (vaginal) portion and a clitoral portion. Methods 20 healthy female participants were shipped the device and completed the study from their home with an online module which guided them through the study material. Participants wore the device while watching a control (non-arousing) video and during progressively more sexually arousing activities. They were allowed to choose their own materials to watch, read, or listen to for arousal. A masturbation phase was included for additional information. Participants also reported their subjective arousal after each phase. After the test, participants were also surveyed about the comfort and usability of the device. Results The pulse sensor successfully detected CPA. Results show that most participants had increasing levels of subjective arousal correlating with increasing CPA. Statistical correlation between CPA and arousal was positive but low, which could be attributed to differences of sensor calibration and/or device placement by participants. Overall, this positive relationship indicates that a commercially available pulse sensor could be integrated into a future device to measure physical arousal in women, a metric which was previously not widely available. Participants reported high satisfaction with the comfort and usability of the device. These insights validate that this device shape will be comfortable and usable in further iterations, with the ultimate goal of producing a device which would give clitoris-owners greater insight into their sexual health and provide valuable information to their healthcare providers. Conclusions This study showed a positive relationship between Clitoral Pulse Amplitude and subjective sexual arousal using an at-home wearable device, opening possibilities for future measurement devices. Participants felt the device was very comfortable and easy to use, suggesting this form factor with vaginal (internal) and clitoral (external) portions is a viable configuration for further development. We anticipate improving both the pulse sensing system and device usability as the device is further commercialized. Disclosure Yes, this is sponsored by industry/sponsor: FirmTech. Clarification: Industry initiated, executed and funded study. Any of the authors act as a consultant, employee or shareholder of an industry for: FirmTech.

A remote self-calibration design for three-phase power monitoring terminal

In view of the situation that the existing three-phase power metering device is prone to excessive error and inaccurate measurement during long-term operation, and the device must be removed for re-calibration, a self-calibration power monitoring terminal based on NB-IOT communication is proposed. The power monitoring terminal adopts an external voltage transformer and current transformer, and the input signal is mV and mA level AC small signal. The integrated design of single-chip microcomputer and metering chip is used in the power monitoring terminal. The single-chip microcomputer reads and writes the data collected by the metering chip and displays and transmits the power data. After the remote measurement instruction is issued, the three-phase sinusoidal signal generator chip inside the system is used to simulate the voltage signal and current model, and the external voltage and current input channel are switched with the analog signal channel through the analog switch to complete the internal self-verification work of the device. This scheme can significantly reduce the workload caused by dismantling, installing and verifying the instrument, ensure the measurement accuracy of the instrument for a long time, and has great practical value.

A scanning device for spatial quantum efficiency measurements of photomultipliers tubes

This paper presents a quantum efficiency measurement setup based on a 2D motorized stage, a wide spectrum xenon lamp, a beam splitter system, and two calibrated photo-diodes for measuring the quantum efficiency (QE) of photomultiplier tubes (1 to 10 inches). We will demonstrate the effectiveness of technical refinements on the measurements procedures over some existing setups already shown in literature. The large area covered by the 2D stages permit to study the quantum efficiency of PMTs with diameter up to ten inches. The results obtained will show the high precision and accuracy in characterizing the quantum efficiency versus wavelength over the range of 250 nm to 1100 nm and along the photo-catode surface. The setup monitors the light intensity synchronously with the output current yield from photosensors under test. This ensures the accuracy and repeatability of the measurements. The motorized stage allows precise positioning of the light source with respect to the active area. The emission spectrum of the xenon lamp provides a broad range of illumination in terms of dynamics and wavelength span.

Comparison of Thames Medical CAT+ Doppler and SunTech Vet 20 oscillometric devices for non-invasive blood pressure measurement in conscious cats.

A comparative assessment of systolic blood pressure (BP) measurement agreement and precision in two commonly used non-invasive BP devices was carried out in conscious cats. Systolic BP measurements were obtained from 50 conscious cats as part of their clinical investigations. All measurements were taken by the same operator and were performed according to the American College of Veterinary Internal Medicine (ACVIM) consensus guidelines. The same cuff location and cuff size were used for paired measurements. The order of device use was randomised, and an arousal score was assigned during each procedure. Precision was assessed using standard deviation and coefficient of variance comparisons. Agreement was assessed using graphical and statistical comparisons of derived 'delta mean' and 'mean of means' data. A total of 50 cats aged between 3 months and 15 years were enrolled (29 domestic shorthair, seven domestic longhair, three British Shorthair, two Ragdoll, two Sphynx, two Persian, one Siamese, one Burmese, one Russian Blue, one Maine Coon and one Oriental) with a body weight in the range of 1.2-6.2 kg. BP measurements were in the range of 95-179 mmHg. Oscillometric measurements were associated with lower arousal scores. The Doppler device demonstrated superior repeatability precision. Body weight, sex, cuff size, cuff location, arousal score and the order in which the devices were used had no significant effect on precision or agreement. Correlation between the two devices was 0.0837 and agreement was considered clinically acceptable (<10 mmHg) in 32/50 (64%) cats. Of the 18 cats with suboptimal agreement, the oscillometric mean BP was higher in 14 (78%) cats. Based on this study sample, suboptimal agreement between paired CAT+ Doppler and SunTech Vet20 oscillometric BP measurements in 36% of conscious cats suggests that these devices should not be used interchangeably. Compared with Doppler, oscillometric measurements were consistently higher, particularly at higher blood pressures. Further studies are required to assess which device has superior accuracy in conscious cats.

Data-Driven Distributed Grid Topology Identification Using Backtracking Jacobian Matrix Approach

The topology identification of distribution system is the foundation of state estimation and system analyses. However, the lack of sufficient measurement devices in practice makes full-scale identification of distributed grid hardly achievable. On the other hand, the high-scale distributed renewable energy grid-connected aggravates the complex associations between nodal injection power and voltage. Taking full advantage of it, this paper proposes a novel data-driven approach to identify the topology by backtracking Jacobian matrix from the voltage and power data from gird terminal units. Specifically, a data-driven power-to-voltage mapping model is proposed. It uses the adaptive online deep learning algorithm based on hedge backpropagation to model the mapping relationship of terminal active power, reactive power, and voltage. Then, Backtracking the mapping model, a Jacobian matrix consisting of the partial derivative of voltage to the active and reactive power is obtained, which reflects the connectivity between nodes. Finally, through the Jacobian matrix, a maximum connectivity screening algorithm is developed to generate the topology of medium distribution systems. The effectiveness of the proposed method is verified on IEEE 33-node, IEEE 123-node, and actual distribution systems.

Implementation of multi-sensors for the measurement of pneumatically conveyed particles

Due to the complex flow state of pneumatically conveyed particles and the influence of the conveying conditions, existing measurement techniques have limitations in detecting the dynamic parameters of full-sections particles in large-scale pipelines. In order to further study and realize the measurement of local parameters of particles in pneumatic conveying pipelines, as well as to provide basic data for full-sections measurement, optimize material distribution and reduce energy consumption. Combined with electrostatic induction and piezoelectric effect, an invasive multi-sensors fusion measurement device was designed to measure local parameters such as particle velocity and mass flow rate in different areas of circular pipelines.