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Chlorinated paraffins (CP) are complex molecular mixtures occurring in a wide range of isomers and homologs of environmental hazards, whose analytical complexity demand advanced mass spectrometry (MS) methods for their characterization. The reported formation of chlorinated olefins (COs) and other transformation products during CP biotransformation and degradation can alter the MS analysis, increasing the high resolution required to distinguish CPs from their degradation products. An advanced setup hyphenating a plasma ionization source and an external high-performance data acquisition and processing system to the legacy hybrid LTQ Orbitrap XL mass spectrometer is reported. First, the study demonstrated the versatility of a liquid sampling atmospheric pressure glow discharge, as a soft ionization technique, for CP analysis. Second, enhanced resolution and sensitivity provided by the absorption mode Fourier transform spectral representation on this legacy mass spectrometer are shown. The developed Orbitrap-based platform allowed the detection of new isotopic clusters and CPs and COs to be distinguished at medium resolution (setting 30,000 at m/z 400, ~ 400 ms transients), and even chlorinated di-olefins (CdiOs) at higher resolution (setting 100,000 at m/z 400, ~ 1500 ms transients). Overall, such proof-of-principle instrumental improvements are promising for environmental and analytical research in the field of CP analysis. [ABSTRACT FROM AUTHOR]

StreamSAXS is a Python‐based small‐ and wide‐angle X‐ray scattering (SAXS/WAXS) data analysis workflow platform with graphical user interface (GUI). It aims to provide an interactive and user‐friendly tool for analysis of both batch data files and real‐time data streams. Users can easily create customizable workflows through the GUI to meet their specific needs. One characteristic of StreamSAXS is its plug‐in framework, which enables developers to extend the built‐in workflow tasks. Another feature is the support for both already acquired and real‐time data sources, allowing StreamSAXS to function as an offline analysis platform or be integrated into large‐scale acquisition systems for end‐to‐end data management. This paper presents the core design of StreamSAXS and provides user cases demonstrating its utilization for SAXS/WAXS data analysis in offline and online scenarios. [ABSTRACT FROM AUTHOR]

Microwave sensing is envisioned to be a reliable imaging technique for detecting and monitoring brain tumours. It uses low-power, nonionized microwave signals to visualize the interior of the living tissues. This study automatically exploits categorization of brain tumours using a deep neural network called the YOLOv5 l algorithm in non-invasive microwave images. A modified antipodal structure (including parasitic patch between flares) that operates over the wideband is proposed for the collection of scattering parameters. The staircase parasitic patch located at the centre of the radiator provides the improved radiation performance at low frequencies. Subsequently, a simulation environment was modelled using CST Microwave Studio 2020, which contained a data acquisition system using a designed microwave antenna and head phantom. Next, the image reconstruction is implemented MATLAB software. The detection and classification of tumours were performed using the YOLOv5l model. A dataset with 500 image samples was formed by pre-processing, which was divided into training (70%), testing (15%) and validation (15%). Then, augmentation is used to create final dataset with 1000 training images. The model was successful in locating an early-stage brain tumour with a size of 5 mm. The YOLOv5 model automatically detected tumour (s) and categorized them as benign tumour or malignant tumour based on a predicted bounding box with an accuracy of 91.5%. [ABSTRACT FROM AUTHOR]

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Visualizing the electrochemical and temperature distribution characteristics inside proton exchange membrane fuel cell (PEMFC) should greatly contribute to the product development and performance optimization. In the study, a customized 27-segment 108 cm2 fuel cell was fabricated based on the printed circuit board methodology to achieve the local current density and local voltage measurement. In addition, 27 thermocouples were embedded in the segmented cathode graphite gas flow plate for local temperature measurement. Detailed information of fuel cell core components, multi-channel data acquisition system, external temperature control system, and uncertainty analysis was presented. According to the test results of 4000 s current rising conditions and 2400 s current decreasing conditions, the measurement deviation of total current between data acquisition system and TOYO test station is 2.4%–3.2%. Meanwhile, the deviation of output voltage ranges from 0.1% to 2.6%. To quantify the degree of distribution non-uniformity, the range and standard deviation of local parameter values as well as contour map are demonstrated. The non-uniformity of local current density and local temperature becomes more significant under higher operating current conditions. Furthermore, the degree of parameter non-uniformity is more noticeable under current rising conditions compared with current decreasing conditions. The transient increase of operating current leads to the sudden overshoot phenomena of local current range, and the change of local current density among all segments is instantaneously finished. However, the range of current demonstrates relatively smooth transition trends when reactant flow rate rises. Higher temperature area appears in the middle of membrane electrode assembly, especially in the position with higher local current density. The parameter distribution and transition characteristics could provide helpful guidance for fuel cell optimization and diagnosis. • A 27-segment 108 cm2 fuel cell was fabricated for visualizing distribution characteristics. • Measurement deviation of total current and voltage is 2.4%–3.2%, 0.1%–2.6%, respectively. • Degree of parameter non-uniformity is more noticeable under current rising conditions. • Change of current among all segments is instantaneously finished if the total current varies. • Range of local current demonstrates smooth transition trends when reactant flow rate rises. [ABSTRACT FROM AUTHOR]

Our prototype system designed for clinical data acquisition and recording of studies is a novel electronic data capture (EDC) software for simple and lightweight data capture in clinical research. Existing software tools are either costly or suffer from very limited features. To overcome these shortcomings, we designed an EDC software together with a mobile client. We aimed at making it easy to set-up, modifiable, scalable and thereby facilitating research. We wrote the software in R using a modular approach and implemented existing data standards along with a meta data driven interface and database structure. The prototype is an adaptable open-source software, which can be installed locally or in the cloud without advanced IT-knowledge. A mobile web interface and progressive web app for mobile use and desktop computers is added. We show the software's capability, by demonstrating four clinical studies with over 1600 participants and 679 variables per participant. We delineate a simple deployment approach for a server-installation and indicate further use-cases. The software is available under the MIT open-source license. Conclusively the software is versatile, easily deployable, highly modifiable, and extremely scalable for clinical studies. As an open-source R-software it is accessible, open to community-driven development and improvement in the future. [ABSTRACT FROM AUTHOR]

This paper describes a multistage framework for face image analysis in computer-aided speech diagnosis and therapy. Multimodal data processing frameworks have become a significant factor in supporting speech disorders' treatment. Synchronous and asynchronous remote speech therapy approaches can use audio and video analysis of articulation to deliver robust indicators of disordered speech. Accurate segmentation of articulators in video frames is a vital step in this agenda. We use a dedicated data acquisition system to capture the stereovision stream during speech therapy examination in children. Our goal is to detect and accurately segment four objects in the mouth area (lips, teeth, tongue, and whole mouth) during relaxed speech and speech therapy exercises. Our database contains 17,913 frames from 76 preschool children. We apply a sequence of procedures employing artificial intelligence. For detection, we train the YOLOv6 (you only look once) model to catch each of the three objects under consideration. Then, we prepare the DeepLab v3+ segmentation model in a semi-supervised training mode. As preparation of reliable expert annotations is exhausting in video labeling, we first train the network using weak labels produced by initial segmentation based on the distance-regularized level set evolution over fuzzified images. Next, we fine-tune the model using a portion of manual ground-truth delineations. Each stage is thoroughly assessed using the independent test subset. The lips are detected almost perfectly (average precision and F1 score of 0.999), whereas the segmentation Dice index exceeds 0.83 in each articulator, with a top result of 0.95 in the whole mouth. [ABSTRACT FROM AUTHOR]

Rubidium fountain clocks are operated by a control system. In this study, first, the control requirements of a rubidium fountain clock are analyzed; then, a control system is designed and divided into a timing sequence control system, a data acquisition system, and a servo control system. Multiple boards based on PCI extensions for the instrumentation (PXI) bus and Labwindows/CVI software 2019 are adopted. The timing sequence control system outputs 16 transistor–transistor logic (TTL) signals and three arbitrary waveforms. The results show that these signals are synchronized within 380 ns. Moreover, the digital locking frequency of the master laser is triggered by one of the timing signal, sweeping the laser frequency in a wide range during polarization gradient cooling. Two time-of-flight signals are acquired, and Ramsey fringes are scanned using the data acquisition system. An error signal is obtained every two cycles, and it is feed back to the frequency synthesizer and a high resolution offset generator using the servo control system. Then the frequency synthesizer is locked to the Ramsey center resonance. The rubidium atomic fountain clock exhibited a frequency stability of 7.4 × 10−16 at 86,400 s. Moreover, the phase of the Rb fountain clock is compared with that of UTC (NTSC) in real time, demonstrating a frequency drift of 0.00044 ns/day/day. The whole control system is full-featured, robust and flexible, thus satisfying the requirements of rubidium fountain clocks. [ABSTRACT FROM AUTHOR]

Cardioprotective Unani drug, Saad Kufi (Cyperus scariosus R.Br.), mentioned by the intellectual colossus Ibn Sina (Avicenna) a thousand years back in his book, "Kitab al-Adviya al-Qalbia" and still widely used by Unani physicians, was selected for experimental study. The main objective of the study was to explore the trial drug ex vivo for validation, hemodynamic elucidation, and molecular mechanism identification for the improvement of Unani therapeutics and to identify a safe and effective novel cardiovascular drug for mainstream medicine. Five doses of 50% ethanolic extract of the physiochemically standardized root were studied in a normal rat heart, on a semi-automated Langendorff apparatus supported with an advanced data acquisition system, perfused with carbogenated non-recirculating Kreb's-Hensleit solution, at constant flow mode, by continuous recording. The effect of 6 repetitions (n = 6) of each dose was studied with the following parameters on the Lab chart pro version: heart rate, left ventricular developed pressure, cardiac work, and coronary pressure. The significance of the difference was determined by the student's "t" test. Results of all doses of Saad Kufi showed a significant increase in the above parameters. The maximum increase was observed by Dose III. It could be concluded that Unani's clinical use of Saad Kufi (C. scariosus R.Br) as cardioprotective was validated and higher doses were indicated to be clinically optimal. Hemodynamically and molecular mechanism-wise, it is indicated to be a sympathomimetic or inotropic-like agent and possibly a direct vasodilator. Since existing long-term treatment of heart failure is not done by cardiac stimulants or positive inotropic and chronotropic agents, the possible future clinical demonstration of therapeutic improvement in heart failure will bring Saad Kufi as a novel drug in mainstream medicine. [ABSTRACT FROM AUTHOR]

Objective. Develop a prototype on-line positron emission tomography (PET) scanner and evaluate its capability of on-line imaging and intra-fractionated proton-induced radioactivity range measurement. Approach. Each detector consists of 32 × 32 array of 2 × 2 × 30 mm3 Lutetium–Yttrium Oxyorthosilicate scintillators with single-scintillator-end readout through a 20 × 20 array of 3 × 3 mm2 Silicon Photomultipliers. The PET can be configurated with a full-ring of 20 detectors for conventional PET imaging or a partial-ring of 18 detectors for on-line imaging and range measurement. All detector-level readout and processing electronics are attached to the backside of the system gantry and their output signals are transferred to a field-programable-gate-array based system electronics and data acquisition that can be placed 2 m away from the gantry. The PET imaging performance and radioactivity range measurement capability were evaluated by both the offline study that placed a radioactive source with known intensity and distribution within a phantom and the online study that irradiated a phantom with proton beams under different radiation and imaging conditions. Main results. The PET has 32 cm diameter and 6.5 cm axial length field-of-view (FOV), ∼2.3–5.0 mm spatial resolution within FOV, 3% sensitivity at the FOV center, 18%–30% energy resolution, and ∼9 ns coincidence time resolution. The offline study shows the PET can determine the shift of distal falloff edge position of a known radioactivity distribution with the accuracy of 0.3 ± 0.3 mm even without attenuation and scatter corrections, and online study shows the PET can measure the shift of proton-induced positron radioactive range with the accuracy of 0.6 ± 0.3 mm from the data acquired with a short-acquisition (60 s) and low-dose (5 MU) proton radiation to a human head phantom. Significance. This study demonstrated the capability of intra-fractionated PET imaging and radioactivity range measurement and will enable the investigation on the feasibility of intra-fractionated, range-shift compensated adaptive proton therapy. [ABSTRACT FROM AUTHOR]

Neutron diffraction beamlines have traditionally relied on deploying large detector arrays of 3He tubes or neutron‐sensitive scintillators coupled with photomultipliers to efficiently probe crystallographic and microstructure information of a given material. Given the large upfront cost of custom‐made data acquisition systems and the recent scarcity of 3He, new diffraction beamlines or upgrades to existing ones demand innovative approaches. This paper introduces a novel Timepix3‐based event‐mode imaging neutron diffraction detector system as well as first results of a silicon powder diffraction measurement made at the HIPPO neutron powder diffractometer at the Los Alamos Neutron Science Center. Notably, these initial measurements were conducted simultaneously with the 3He array on HIPPO, enabling direct comparison. Data reduction for this type of data was implemented in the MAUD code, enabling Rietveld analysis. Results from the Timepix3‐based setup and HIPPO were benchmarked against McStas simulations, showing good agreement for peak resolution. With further development, systems such as the one presented here may substantially reduce the cost of detector systems for new neutron instrumentation as well as for upgrades of existing beamlines. [ABSTRACT FROM AUTHOR]

Ultrahigh-frequency (UHF) sensing is one of the most promising techniques for assessing the quality of power transformer insulation systems due to its capability to identify failures like partial discharges (PDs) by detecting the emitted UHF signals. However, there are still uncertainties regarding the frequency range that should be evaluated in measurements. For example, most publications have stated that UHF emissions range up to 3 GHz. However, a Cigré brochure revealed that the optimal spectrum is between 100 MHz and 1 GHz, and more recently, a study indicated that the optimal frequency range is between 400 MHz and 900 MHz. Since different faults require different maintenance actions, both science and industry have been developing systems that allow for failure-type identification. Hence, it is important to note that bandwidth reduction may impair classification systems, especially those that are frequency-based. This article combines three operational conditions of a power transformer (healthy state, electric arc failure, and partial discharges on bushing) with three different self-organized maps to carry out failure classification: the chromatic technique (CT), principal component analysis (PCA), and the shape analysis clustering technique (SACT). For each case, the frequency content of UHF signals was selected at three frequency bands: the full spectrum, Cigré brochure range, and between 400 MHz and 900 MHz. Therefore, the contributions of this work are to assess how spectrum band limitation may alter failure classification and to evaluate the effectiveness of signal processing methodologies based on the frequency content of UHF signals. Additionally, an advantage of this work is that it does not rely on training as is the case for some machine learning-based methods. The results indicate that the reduced frequency range was not a limiting factor for classifying the state of the operation condition of the power transformer. Therefore, there is the possibility of using lower frequency ranges, such as from 400 MHz to 900 MHz, contributing to the development of less costly data acquisition systems. Additionally, PCA was found to be the most promising technique despite the reduction in frequency band information. [ABSTRACT FROM AUTHOR]

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 μs and ultra-low power consumption of 15 μW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ −4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant. [ABSTRACT FROM AUTHOR]

The interest in using new technologies to obtain recordings of on-water kinetic variables for assessing the performance of elite sprint kayakers has increased over the last decades but systematic approaches are warranted to ensure the validity and reliability of these measures. This study has an innovative approach, and the aim was to develop a new versatile jig including reference force sensors for both the calibration and validation of mutual static and dynamic stroke forces as measured with instrumented paddles at the high force levels used in elite sprint kayaking. Methods: A jig was constructed using a modified gym weight stack and a frame consisting of aluminum profiles permitting a fastening of custom-made kayak paddle shaft and blade support devices with certified force transducers combined with a data acquisition system to record blade and hand forces during static (constant load) and dynamic conditions (by paddle stroke simulation). A linear motion path incorporating a ball-bearing equipped carriage with sensors for the measurement of vertical distance and horizontal displacement was attached to the frame for recordings of various position measures on the paddle. The jig design with all components is extensively described to permit replication. The procedures for assessing the accuracy of the jig force instrumentation are reported, and with one brand of instrumented paddle used as an example, methods are described for force calibration and validation during static and dynamic conditions. Results: The results illustrate that the measured force with the jig instrumentation was similar to the applied force, calculated from the applied accurate mass (within a −1.4 to 1.8% difference) and similar to the force as calculated from the applied mass with the weight stack (within a −0.57 to 1.16% difference). The jig was suitable for the calibration and validation of forces in a range relevant for elite sprint kayaking under both static and dynamic conditions. During static conditions with a force direction equal to the calibration conditions and a force range from 98 to 590 N, all values for the instrumented paddle were within a −3.4 to 3.0% difference from the jig sensor values and 28 of 36 values were within ±2%. During dynamic conditions with paddle stroke simulations at 60 and 100 strokes/min and a target peak force of 400 N, the common force variables as measured by the instrumented paddle were not significantly different from the same measures by the jig (values at 100 strokes/min: peak force; 406.9 ± 18.4 vs. 401.9 ± 17.2 N, mean force; 212.8 ± 15.4 vs. 212.0 ± 14.4 N, time to peak force; 0.17 ± 0.02 vs. 0.18 ± 0.02 s, force impulse; 90.8 ± 11.2 vs. 90.5 ± 10.8 Ns, impulse duration; 0.43 ± 0.03 vs. 0.43 ± 0.03 s). Conclusion: A novel jig with several new functions is presented that enables the calibration and validation of force measurements with instrumented paddles by providing standardized conditions for calibration and force validation during both static and dynamic conditions in a force range relevant to elite sprint kayaking. [ABSTRACT FROM AUTHOR]

A set of magnetic diagnostics has been designed, manufactured, and calibrated for the first operational phase of the small aspect ratio tokamak. The sensor suite comprises of Rogowski coils; 2D magnetic probes; and poloidal, saddle, and diamagnetic flux loops. A set of continuous Rogowski coils has been manufactured for the measurement of plasma current and induced eddy currents in conductive elements. A set of flux loops and magnetic probes will be used as input for the reconstruction of the magnetohydrodynamic equilibrium. The quantity and position of these sensors have been verified to be sufficient with synthetic equilibrium reconstructions using the equilibrium fitting code and baseline scenarios computed with the Fiesta code. These sensors will also be used as input for the real-time control system, and magnetic probes will be used for the detection of plasma instabilities. The calibration procedure for the magnetic probes is described, and the results are shown. The signal conditioning and data acquisition systems are described. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low detection accuracy, slow detection speed and poor stability of traditional coal gangue detection based on image processing, a new YOLOv5s-EB (You Only Look Once Version-5s) network model is constructed for coal gangue target detection and location. Replace the PANet (Path Aggregation Network) layer of the original network with the BiFPN (Bidirectional Feature Pyramid Network) feature fusion network to enhance the global information extraction strength. And combined with the attention mechanism module of ECANet (Efficient Channel Attention Networks). 490 sets of multispectral data were obtained through the multispectral data acquisition system, and 980 original samples of coal and gangue were obtained, the original samples are preprocessed by histogram equalization to establish the coal gangue data set, they are trained in the original YOLOv5s, YOLOv5s-ECA, YOLOv5s-BiFPN, YOLOv5s-EB, YOLOv4 and YOLOv7 neural networks respectively. According to the results, the accuracy rate P of YOLOv5s-EB neural network model reaches 98.5%, and the recall rate R reaches 98.4%, the mAP reached 87.55%. Moreover, YOLOv5s-EB network model is relatively small, only 12.5MB. At the same time, the YOLOv5s-EB model obtains the coordinate information of the upper left and lower right corners of the target detection frame during the identification and detection process, and locates the coal gangue target for the convenience of subsequent sorting. [ABSTRACT FROM AUTHOR]

Traditional frame-based cameras, despite their effectiveness and usage in computer vision, exhibit limitations such as high latency, low dynamic range, high power consumption, and motion blur. For two decades, researchers have explored neuromorphic cameras, which operate differently from traditional frame-based types, mimicking biological vision systems for enhanced data acquisition and spatio-temporal resolution. Each pixel asynchronously captures intensity changes in the scene above certain user-defined thresholds, and streams of events are captured. However, the distinct characteristics of these sensors mean that traditional computer vision methods are not directly applicable, necessitating the investigation of new approaches before being applied in real applications. This work aims to fill existing gaps in the literature by providing a survey and a discussion centered on the different application domains, differentiating between computer vision problems and whether solutions are better suited for or have been applied to a specific field. Moreover, an extensive discussion highlights the major achievements and challenges, in addition to the unique characteristics, of each application field. [ABSTRACT FROM AUTHOR]

This paper focuses on designing a simple yet efficient H∞ controller for regulating the voltage/frequency, output current, and active/reactive power-sharing in a microgrid (MG). The purpose of designing the robust H∞ controller is to regulate the frequency and voltage in face of the uncertainties in the microgrid and load variation. The studied DC MG test bed includes an SMPS (switching-mode power supply) and DC-DC converter, necessary sensor and data acquisition systems. The proposed control scheme is simulated using MATLAB/Simulink and is then verified in real-time using LabVIEW. The control scheme is developed in the LabVIEW software's block diagram panel of the virtual instrumentation interface. From both simulation and LabVIEW implementation, load sharing among the DGs, and voltage and frequency regulation at the nominal values are achieved by employing the proposed H∞ controller. The robustness of the proposed controller is evaluated by comparing it with a robust observer-based controller (Zarei A, Mousavi Y, Mosalanezhad R, Atazadegan MH. Robust voltage control in inverter-interfaced microgrids under plug-and-play functionalities. IEEE Syst J 2020;14:2813–24) under balanced, unbalanced, and non-linear loads. The comparison results envisage that the proposed controller provides improved voltage/frequency regulation and power-sharing during disturbances including the addition of non-linear load to the MG system. [ABSTRACT FROM AUTHOR]

This study examines the development of an Arduino-based real-time data acquisition system and its effect on secondary students’ STEM career interest (STEM-CIS). A total of 74 students were sampled from a prestigious private school in Jakarta, Indonesia, and a learning device was developed using the A.D.D.I.E. method. A one-group pretest-posttest research design was used to evaluate the effect of using the device during blended classroom activities. Data were collected through surveys using the STEM-CIS instrument and interviews. The study was based on the general practice of using Arduino software and hardware for practical purposes in Chemistry Laboratories and Sick Bay. The setup was successfully used in these different environments as a temperature monitoring system to record thermochemistry data and monitor a patient’s body temperature. The findings are consistent with prior research indicating that hands-on robotics activities can increase STEM interest and inspire students to pursue STEM careers. The results suggest that strong engagement in this activity facilitated the development of digital literacy and STEM skills. The STEM-CIS score at the 5% significance level was significantly increased after the experimentation with the device, with a paired t-test result of p<0.001. The effect size (Cohen’s d) showed a moderate effect of 0.74. [ABSTRACT FROM AUTHOR]

Real-time gas sensing based on wavelength modulation spectroscopy (WMS) has been widely adopted for several gas sensing applications. It is attractive for its accurate, non-invasive, and fast determination of critical gas parameters such as concentration, temperature, and pressure. To implement real-time gas sensing, data acquisition and processing must be implemented to accurately extract harmonics of interest from transmitted laser signals. In this work, we present an FPGA-based data acquisition architecture with embedded processing capable of achieving both real-time and accurate gas detection. By leveraging real-time processing on-chip, we minimised the data transfer bandwidth requirement, hence enabling better resolution of data transferred for high-level processing. The proposed architecture has a significantly lower bandwidth requirement compared to both the conventional offline processing architecture and the standard I-Q architecture. Specifically, it is capable of reducing data transfer overhead by 25% compared to the standard I-Q method, and it only requires a fraction of the bandwidth needed by the offline processing architecture. The feasibility of the proposed architecture is demonstrated on a commercial off-the-shelf SoC board, where measurement results show that the proposed architecture has better accuracy compared to the standard I-Q demodulation architecture for the same signal bandwidth. The proposed DAQ system has potential for more accurate and fast real-time gas sensing. [ABSTRACT FROM AUTHOR]

The main objective of this work is to address problems related to the speed control of mobile robots with non-holonomic constraints and differential traction—specifically, robots for football games in the VSS (Very Small Size) category. To achieve this objective, an implementation and comparison is carried out between two control strategies: an adaptive control strategy by gain scheduling and a fuzzy multimodel intelligent control strategy. The mathematical models of the wheel motors for each operating range are approximated by a first-order system since data acquisition is performed using the step response. Tuning of the proportional and integral gains of the local controllers is carried out using the root locus technique in discrete time. For each mathematical model obtained for an operating range, a local controller is tuned. Finally, with the local controllers in hand, the implementation of and comparison between the gain scheduling adaptive control strategy and the fuzzy multimodel intelligent control strategy are carried out, in which the control strategies are programmed into the low-level code of a non-holonomic robot with a differential drive to verify the performance of the speed tracking dynamics imposed on the wheel motors to improve robot navigation during a robot football match. [ABSTRACT FROM AUTHOR]

Evaluating remote sensing techniques (RST)-based water quality monitoring systems in hydrological applications is a complex multi-attribute decision-making (MADM) problem due to several integrated issues. These issues include the need to consider 15 evaluation criteria, the importance of these criteria in relation to their application and network infrastructure, data variation, and trade-offs and conflicts between them. Therefore, this study proposes an MADM integrated modelling approach to weight these criteria and rank the selected RSTs using the "Fuzzy Weighted with Zero Inconsistency" (FWZIC) method coupled with the "Vlse-kriterijumska Optimizcija I Kaompromisno Resenje" (VIKOR) method. First, a decision matrix was developed for the evaluation criteria and their intersection with various RSTs in different network categories. Next, the assessment criteria were weighted using FWZIC, followed by ranking the RSTs for each category using VIKOR. The findings reveal that the criteria weighting varied across categories, as assigned by specialists-based FWZIC. For example, the "data acquisition system complexity" criterion received the maximum weight (w = 0.081) for mesh-based sensing and the lowest (w = 0.057) for cellular-based sensing. Using the obtained weights and based on the ascending order of the performance score value (Q), various RSTs were benchmarked using VIKOR. This includes (n = 22) RSTs in fixed star, (n = 8) RSTs in mesh sensing, (n = 6) RSTs in cellular sensing, (n = 3) RSTs in fixed cable sensing, and (n = 2) RSTs in movable sensing. Finally, the evaluation process for the benchmarked RSTs involved systematic ranking, sensitivity analysis, and comparison analysis, which confirmed the robustness of the proposed approach across all RST categories. [ABSTRACT FROM AUTHOR]

Sleep, or the lack thereof, has far-reaching consequences on many aspects of human physiology, cognitive performance, and emotional wellbeing. To ensure undisturbed sleep monitoring, unobtrusive measurements such as ballistocardiogram (BCG) are essential for sustained, real-world data acquisition. Current analysis of BCG data during sleep remains challenging, mainly due to low signal-to-noise ratio, physical movements, as well as high inter- and intra-individual variability. To overcome these challenges, this work proposes a novel approach to improve J-peak extraction from BCG measurements using a supervised deep learning setup. The proposed method consists of the modeling of the discrete reference heartbeat events with a symmetric and continuous kernel-function, referred to as surrogate signal. Deep learning models approximate this surrogate signal from which the target heartbeats are detected. The proposed method with various surrogate signals is compared and evaluated with state-of-the-art methods from both signal processing and machine learning approaches. The BCG dataset was collected over 17 nights using inertial measurement units (IMUs) embedded in a mattress, together with an ECG for reference heartbeats, for a total of 134 h. Moreover, we apply for the first time an evaluation metric specialized for the comparison of event-based time series to assess the quality of heartbeat detection. The results show that the proposed approach demonstrates superior accuracy in heartbeat estimation compared to existing approaches, with an MAE (mean absolute error) of 1.1 s in 64-s windows and 1.38 s in 8-s windows. Furthermore, it is shown that our novel approach outperforms currentmethods in detecting the location of heartbeats across various evaluation metrics. To the best of our knowledge, this is the first approach to encode temporal events using kernels and the first systematic comparison of various event encodings for event detection using a regression-based sequence-to-sequence model. [ABSTRACT FROM AUTHOR]

Data acquisition systems that receive analog signals, convert them to digital, and perform signal processing are used in a variety of systems that use acoustics, radar, sonar, indoor localization, and navigation. The previous systems, such as NI USRP-RIO, are expensive to build, and the number of signals a single device can receive is limited to between two and four. In order to receive more channels of signals, multi-channel data acquisition systems using ADCs operating at tens of MSPS have been proposed. However, these systems require additional processing time because data acquisition and signal processing are performed on different devices. In this paper, we propose a multi-channel data acquisition system using a 16-channel ADC that can support up to 100 MSPS. In particular, to reduce unnecessary signal transmission time, we propose a one-chip structure where all processes are performed on a single chip. Also, we propose a data acquisition system that applies pipelining techniques to enable real-time processing. To verify the proposed system, we used TI ADS52J90 and a Kintex UltraScale KCU105 evaluation board, and confirmed that it is possible to receive and process all channels simultaneously. Furthermore, it is possible to configure a real-time system by adjusting the speed of the signal-processing operation and the speed of the communication interface. Therefore, the proposed system is expected to reduce the cost of system construction by performing signal reception and processing with a single chip, and to reduce the time required for overall signal processing. [ABSTRACT FROM AUTHOR]

Rationale: Knudsen effusion mass spectrometry (KEMS) has been a powerful tool in physical chemistry since 1954. There are many excellent reviews of the basic principles of KEMS in the literature. In this review, we focus on the current status and potential growth areas for this instrumental technique. Methods: We discuss (1) instrumentation, (2) measurement techniques, and (3) selected novel applications of the technique. Improved heating methods and temperature measurement allow for better control of the Knudsen cell effusive source. Accurate computer models of the effusive beam and its introduction to the ionizer allow optimization of such parameters as sensitivity and removal of background signals. Computer models of the ionizer allow for optimized sensitivity and resolution. Additionally, data acquisition systems specifically tailored to a KEMS system permit improved quantity and quality of data. Results: KEMS is traditionally utilized for thermodynamic measurements of pure compounds and solutions. These measurements can now be strengthened using first principles and model‐based computational thermochemistry. First principles can be used to calculate accurate Gibbs energy functions (gefs) for improving third law calculations. Calculated enthalpies of formation and dissociation energies from ab initio methods can be compared to those measured using KEMS. For model‐based thermochemistry, solution parameters can be derived from measured thermochemical data on metallic and nonmetallic solutions. Beyond thermodynamic measurements, KEMS has been used for many specific applications. We select examples for discussion: measurements of phase changes, measurement/control of low‐oxygen potential systems, thermochemistry of ultrahigh‐temperature ceramics, geological applications, nuclear applications, applications to organic and organometallic compounds, and thermochemistry of functional room temperature materials, such as lithium ion batteries. Conclusions: We present an overview of the current status of KEMS and discuss ideas for improving KEMS instrumentation and measurements. We discuss selected KEMS studies to illustrate future directions of KEMS. [ABSTRACT FROM AUTHOR]

This paper analyzes the influence of three different shape memory alloy (SMA) wires (i.e., Ni-Ti, Cu-Al-Ni, and Cu-Zn wires) for mitigating model cable vibration. Shape memory alloy (SMA) wires because of their superelasticity or shape memory properties can promise to be one of the effective damping materials in suppressing stay cable oscillations. The investigation explores through experimental works, the capabilities of three Ni-Ti, Cu-Al-Ni, and Cu-Zn SMA wires by estimating the vibration amplitude suppression of the model stay cable. The effective location of the SMA wires on the model cable for vibration suppression was estimated in free as well as forced vibration cases. To impart free and forced vibration, respectively, in the cable, a transducer-based hammer and exciter were used. An accelerometer and data acquisition system was used to record and acquire vibration data. It is also very appropriate to ascertain which wire among one of the three SMA wires performs well. It is found that the Ni-Ti SMA wire has a better performance in vibration suppression as compared to Cu-Al-Ni and Cu-Zn wires. It is also established that the attachment of the SMA wire at the mid-length of the cable gives better vibration suppression for the first mode. [ABSTRACT FROM AUTHOR]

The application of machine learning techniques for monitoring and diagnosing faults in photovoltaic (PV) systems has been shown to enhance the reliability of PV power generation. This research introduced a novel machine learning classifier for fault diagnosis in PV systems, utilizing an ensemble algorithm known as extra trees (ETC). The study initially proposed a system with two PV modules and developed a low-cost Arduino-based data logger to gather data from the PV system in free-fault and faulty conditions. Subsequently, the study evaluated six other advanced classifiers for fault diagnosis in PV systems, namely logistic regression (LR), k-nearest neighbor (kNN), support vector machine (SVM), decision tree (DT), AdaBoost, and random forest (RF) models using the collected data from the proposed PV system. The assessment of the various models' performance indicated that the extra trees model exhibits superior classification capabilities for partial shading (PS), open circuit (OCF), partial shading with bypass diode disconnected (PSBD), and combined partial shading with bypass diode disconnected plus open circuit (PSBDOC) faults. The results demonstrated that the new ETC classifier achieves an accuracy of 92%, surpassing the 91%, 87%, 7%, and 59% accuracy of the RF, DT, kNN, and LR classifiers, respectively. This highlights the effectiveness of the extra trees model in enhancing fault detection and classification by distinguishing between open circuits and twin faults. Consequently, these results can be utilized to develop advanced diagnostic tools for photovoltaic systems, thereby improving the reliability of solar technology and accelerating the rate of installation. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) is recognized for its adeptness at compressing signals, making it a pivotal technology in the context of sensor data acquisition. With the proliferation of image data in Internet of Things (IoT) systems, CS is expected to reduce the transmission cost of signals captured by various sensor devices. However, the quality of CS-reconstructed signals inevitably degrades as the sampling rate decreases, which poses a challenge in terms of the inference accuracy in downstream computer vision (CV) tasks. This limitation imposes an obstacle to the real-world application of existing CS techniques, especially for reducing transmission costs in sensor-rich environments. In response to this challenge, this paper contributes a CV-oriented adaptive CS framework based on saliency detection to the field of sensing technology that enables sensor systems to intelligently prioritize and transmit the most relevant data. Unlike existing CS techniques, the proposal prioritizes the accuracy of reconstructed images for CV purposes, not only for visual quality. The primary objective of this proposal is to enhance the preservation of information critical for CV tasks while optimizing the utilization of sensor data. This work conducts experiments on various realistic scenario datasets collected by real sensor devices. Experimental results demonstrate superior performance compared to existing CS sampling techniques across the STL10, Intel, and Imagenette datasets for classification and KITTI for object detection. Compared with the baseline uniform sampling technique, the average classification accuracy shows a maximum improvement of 26.23%, 11.69%, and 18.25%, respectively, at specific sampling rates. In addition, even at very low sampling rates, the proposal is demonstrated to be robust in terms of classification and detection as compared to state-of-the-art CS techniques. This ensures essential information for CV tasks is retained, improving the efficacy of sensor-based data acquisition systems. [ABSTRACT FROM AUTHOR]

Enclosed public spaces are hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of particulate matter sensors was developed and named Dynamic Aerosol Transport for Indoor Ventilation, or DATIV, system. This system can use multiple particulate matter sensors (PMSs) simultaneously and can be remotely controlled using a Raspberry Pi-based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or smartphone with a corresponding IP address. The software architecture and validation measurements are presented together with possible future developments. [ABSTRACT FROM AUTHOR]

With the growing demand for infrastructure and transportation facilities, the need for advanced structural health monitoring (SHM) systems is critical. This study introduces two innovative, cost-effective, standalone, and open-source data acquisition devices designed to enhance SHM through the latest sensing technologies. The first device, termed CEDAS_acc, integrates the ADXL355 MEMS accelerometer with a RaspberryPi mini-computer, ideal for measuring strong ground motions and assessing structural modal properties during forced vibration tests and structural monitoring of mid-rise buildings. The second device, CEDAS_geo, incorporates the SM24 geophone sensor with a Raspberry Pi, designed for weak ground motion measurements, making it suitable for seismograph networks, seismological research, and early warning systems. Both devices function as acceleration/velocity Data Acquisition Systems (DAS) and standalone data loggers, featuring hardware components such as a single-board mini-computer, sensors, Analog-to-Digital Converters (ADCs), and micro-SD cards housed in protective casings. The CEDAS_acc includes a triaxial MEMS accelerometer with three ADCs, while the CEDAS_geo uses horizontal and vertical geophone elements with an ADC board. To validate these devices, rigorous tests were conducted. Offset Test, conducted by placing the sensor on a leveled flat surface in six orientations, demonstrating the accelerometer's ability to provide accurate measurements using gravity as a reference; Frequency Response Test, performed at the Gebze Technical University Earthquake and Structure Laboratory (GTU-ESL), comparing the devices' responses to the GURALP-5TDE reference sensor, with CEDAS_acc evaluated on a shaking table and CEDAS_geo's performance assessed using ambient vibration records; and Noise Test, executed in a low-noise rural area to determine the intrinsic noise of CEDAS_geo, showing its capability to capture vibrations lower than ambient noise levels. Further field tests were conducted on a 10-story reinforced concrete building in Gaziantep, Turkey, instrumented with 8 CEDAS_acc and 1 CEDAS_geo devices. The building's response to a magnitude 3.2 earthquake and ambient vibrations was analyzed, comparing results to the GURALP-5TDE reference sensors and demonstrating the devices' accuracy in capturing peak accelerations and modal frequencies with minimal deviations. The study also introduced the Record Analyzer (RECANA) web application for managing data analysis on CEDAS devices, supporting various data formats, and providing tools for filtering, calibrating, and exporting data. This comprehensive study presents valuable, practical solutions for SHM, enhancing accessibility, reliability, and efficiency in structural and seismic monitoring applications and offering robust alternatives to traditional, costlier systems. [ABSTRACT FROM AUTHOR]

Background: There currently exists no widespread high dose‐rate (HDR) brachytherapy afterloader quality assurance (QA) tool for simultaneously assessing the afterloader's positional, temporal, transit velocity and air kerma strength accuracy. Purpose: The purpose of this study was to develop a precise and rigorous technique for performing daily QA of HDR brachytherapy afterloaders, incorporating QA of: dwell position accuracy, dwell time accuracy, transit velocity consistency and relative air kerma strength (AKS) of an Ir‐192 source. Method: A Sharp ProGuide 240 mm catheter (Elekta Brachytherapy, Veenendaal, The Netherlands) was fixed 5 mm above a 256 channel epitaxial diode array 'dose magnifying glass' (DMG256) (Centre for Medical and Radiation Physics, University of Wollongong). Three dwell positions, each of 5.0 s dwell times, were spaced 13.0 mm apart along the array with the Flexitron HDR afterloader (Elekta Brachytherapy, Veenendaal, The Netherlands). The DMG256 was connected to a data acquisition system (DAQ) and a computer via USB2.0 link for live readout and post‐processing. The outputted data files were analyzed using a Python script to provide positional and temporal localization of the Ir‐192 source by tracking the centroid of the detected response. Measurements were repeated on a weekly basis, for a period of 5 weeks to determine the consistency of the measured parameters over an extended period. Results: Using the DMG256 for relative AKS measurements resulted in measured values within 0.6%–3.0% of the expected activity over a 7‐week period. The sub‐millisecond temporal accuracy of the device allowed for measurements of the transit velocity with an average of (10.88 ± 1.01) cm/s for 13 mm steps. The dwell position localization for 1, 2, 3, 5, and 10 mm steps had an accuracy between 0.1 and 0.3 mm (3σ), with a fixed temporal accuracy of 10 ms. Conclusion: The DMG256 silicon strip detector allows for clinics to perform rigorous daily QA of HDR afterloader dwell position and dwell time accuracy with greater precision than the current standard methodology using closed circuit television and a stopwatch. Additionally, DMG256 unlocks the ability to perform measurements of transit velocity/time and relative AKS, which are not possible using current standard techniques. [ABSTRACT FROM AUTHOR]

An adjustable multi degree of freedom exoskeleton robot for lower limb rehabilitation was designed to address the current situation of a large number of lower limb paralysis patients and a shortage of rehabilitators and intelligent rehabilitation equipment. The exoskeleton robot includes a waist freely retractable adjustment mechanism and a leg retractable mechanism driven by a motor. Based on the walking posture, the motion characteristics of the lower limb rehabilitation exoskeleton robot were analysed, and kinematics and dynamics simulations were carried out using ADAMS software; based on the optical human motion capture data acquisition system. Based on the optical human motion capture data acquisition system, the experiment site and plan were arranged. L-shaped and T-shaped calibration rods were used to adjust the lenses within the motion capture area. After calibration, marker balls were placed at specific anatomical positions on the subject. Walking gait experiments were then conducted according to the subjects' natural walking habits. During the experiments, a 3D motion camera captured the marker points on the subject, and the motion data was transmitted to post-processing software installed on a computer for gait analysis and data processing. The processing method involved defining and matching the marker points and establishing a rigid body model of the lower limb exoskeleton robot. Finally, the accuracy of the marker point data was verified, analyzed, and processed. The experimental results show that: in one gait cycle, the angle changes of the hip and knee joints of the test experimenter are smooth, the hip joint turning angle range is 5-25 degrees, and the knee joint is 7-47 degrees, and the fluctuation of the hip joint turning angle is flatter than that of the knee joint, and compared with the virtual simulation results, which proves the feasibility of the model, and provides a model reference for the structural design of the exoskeleton robot and the establishment of the kinematics model. [ABSTRACT FROM AUTHOR]

Decay energy spectroscopy (DES) is an increasingly popular technique for measuring isotopic composition of actinide samples for nuclear safeguards applications. Current approaches for actinide DES utilize milligram-scale external gold absorbers (> 0.1 nJ/K) that are integrated with actinide samples through mechanical kneading and are thermally connected to microcalorimeters using indium or gold wire bonds. This leads to relatively slow sensor rise time and, consequently, limits counting speed to a few counts per second. We are developing faster metallic magnetic calorimeter-based DES by integrating actinide samples with magnetic sensor materials. This reduces signal rise time and enables high counting speed while maintaining the ability to knead the radioactive source with the absorber. We have measured signal rise time of 0.7 μs with a 1.5 mg external gold absorber using this approach. We also demonstrated online DES operation using an Ortec DSPEC 50, a commercially available data acquisition system developed for semiconductor detectors. [ABSTRACT FROM AUTHOR]

Background: The dynamic mechanical properties and permeability evolution of deep rocks under coupled osmotic-mechanical conditions are vital for evaluating the stability of surrounding rock in deep rock engineering and further improving deep mining efficiency. However, there is currently no valid experimental system to measure both the dynamic mechanical response and the permeability evolution of deep rocks. Objective: In this study, a novel experimental system is developed for determining dynamic compressive properties and permeability evolution of deep rocks subjected to coupled differential pore pressure and confinement. Methods: The experimental system is composed of a dynamic loading system, an in-situ stress system, a differential pore pressure system, and a data acquisition system. The differential pore pressure system is introduced in the dynamic loading system, and the validation of the proposed system is verified by checking the stress wave propagation in the bars and the dynamic force balance on the two loading ends of specimens. It indicates that the differential pore pressure device added to the dynamic loading system barely influences the measurement of the dynamic behaviors of rocks. A homogenous green sandstone (GS) is employed to verify the feasibility and reliability of the proposed system. Dynamic compressive strength, permeability evolution, and failure mode of GS under cyclic dynamic impact loading in combination with coupled osmotic-confining pressure are explored using the proposed system. Results: The stress–strain curves change with the increase of impact number, and the cyclic impacts deteriorate the dynamic compressive strength of GS. The permeability of GS first increases and then decreases with the impact number. The differential pore pressure enhanced the permeability of GS under the same impact cycle. The main fracture mode of the GS specimen is mainly compressive-shear fracture in combination with a tensile fracture in the middle of the specimen due to the coupling effect of the reflected stress wave and the osmotic-confining pressure. Conclusions: The proposed experimental system is valid and effective to measure and observe the dynamic compressive behaviors and permeability evolution of rocks under coupled osmotic-mechanical conditions. [ABSTRACT FROM AUTHOR]

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This paper presents a new framework using MEMS flow sensors on turbine blades (SOTB) to investigate unsteady flow features of a rotating wind turbine. Self-heating flow sensors were implemented by the U18 complementary metal-oxide semiconductor (CMOS) MEMS foundry provided by Taiwan Semiconductor Research Institute (TSRI). Flow sensor chips with a size of 1.5 mm × 1.5 mm were parylene-coated, output via a wireless data acquisition system (WDAQ), and mounted at the root, middle and tip of a 1.2 m diameter semi-rigid turbine blade of a 400 W horizontal axis wind turbine (HAWT). The instantaneous angles of attack (AOAs) of the SOTB were found to be 46~62°, much higher than the general stall AOA of 15 ° , but were accurate considering the normal detection of the flow sensors. The computational fluid dynamics (CFD) simulation of the HAWT was also compared with the SOTB output. The onsite measurement herein revealed that the 3D secondary flow increment, mostly obvious near the middle part of the turbine blades, degraded both the sensor and the turbine performance and initially justified the onsite measurement application. [ABSTRACT FROM AUTHOR]

The classification of surrounding rock is crucial for formulating safe tunnel construction plans and support measures. However, the complex geological environment of tunnels presents a challenge in obtaining accurate drilling parameters for rock mass classification. This paper presents the development of a rock drilling testing system, which includes a propulsion speed acquisition system, oil pressure acquisition system, air pressure acquisition system, and an automatic data acquisition system. This system enables real-time, high-precision automatic collection and storage of parameters such as propulsion speed, with data collected twice per second for each parameter. Leveraging the Qingdao Metro Line 6 as a case study, we conducted rock mass drilling and constructed a rock mass classification database. By employing kernel density estimation and Pearson correlation analysis, we quantified the correlation between rock mass classification and the drilling parameters. The results indicated that relying on a single drilling parameter is insufficient for accurately determining rock mass classification. Both impact pressure and rotational pressure showed the strongest correlation with rock mass classification, each with a correlation coefficient below −0.8 (indicating a strong negative correlation). Outlier values of drilling parameters were excluded using the interval method. Based on the remaining data, we established an intelligent rock mass classification model using the random forest algorithm. This model demonstrated good accuracy and generalization performance, with an average accuracy exceeding 0.9. The proposed rock drilling testing system, combined with the intelligent rock mass classification model, forms an integrated system for the intelligent identification of rock mass grades. This system has significant implications for the intelligent and safe construction of drill-and-blast tunnels. [ABSTRACT FROM AUTHOR]

Background. Cadaveric identification represents a legal, moral and medico-legal obligation and also fulfills the ethical function to allow the living to offer their dead a merciful burial. In the Mediterranean basin we have been witnessing for years a struggle for life where hundreds of migrants die in an attempt to reach Europe. For these people right to be recognized is not easy because of ante-death biological data are not always available for comparison, and DNA analysis is not always feasible cause for the absence of data with which to compare. In Italy a multidisciplinary protocol has been adopted that involves the acquisition of data provided by family members present in the countries of origin. Materials and Methods. Between August 2015 and December 2021, bodies of 879 migrants of various nationalities washed up on the shores of Sicily and Calabria. These men, women, and children, in various states of preservation, were examined. Each body underwent a multidisciplinary approach. External examination and autopsy aimed to determine the cause of death for judicial purposes and to facilitate individual identification. Whenever possible bodies underwent total body post-mortem computed tomography examination to identify fractures and prosthetic implants. including inspection of the third molar to provide an estimate of the age. Conclusions. The activity carried out in cooperation with Prefecture, Scientific Police, and National and International Red Cross highlighted the importance of employing multiple scientific skills to compensate for the lack of biological information. The importance of appropriate behaviors to avoid contamination and ensure proper preservation underlines the importance of the establishment of a dedicated database. [ABSTRACT FROM AUTHOR]

This research explores how random events influence the respiration rate in Hass avocado beyond deterministic models in order to develop better strategies for extending its shelf life. Understanding these factors can enhance the accuracy of postharvest management strategies. The Autoregressive Integrated Moving Average (ARIMA) model with exogenous variables (ARIMAX) is an alternative stochastic probability model which is capable of modeling complex, externally influenced phenomena such as respiration. This study aimed to elucidate the effect of three exogenous variables, namely temperature, relative humidity, and ambient illumination, on the respiration rate of Hass avocado fruits. Data on the respiration rate and exogenous variables were obtained using sensors coupled to a data acquisition system in a prototype of continuous airflow. The Box–Jenkins methodology was employed to construct the ARIMA models. The temperature, relative humidity, ambient illumination, and respiration rate variables were adjusted to the ARIMA models (3,1,2), ARIMA (1,1,2), ARIMA (1,1,2), and ARIMA (1,1,3), respectively. The ARIMAX (1,1,3) models were obtained from the pre-whitened respiration rate series. The impact detected in the transfer functions indicates increases in the respiration rate of 0.34%, 1.52%, and 0.99% for each unit increase in the temperature, relative humidity, and ambient illumination variables, respectively. In this regard, ARIMAX modeling is reliable for explaining the physiological response of Hass avocado fruits due to external factors. In future research, it is intended to extrapolate this stochastic modeling procedure to measure the effect of dynamic loads on the respiratory metabolism of fruits during transportation, where there is a considerable loss in the quality of fresh products. [ABSTRACT FROM AUTHOR]

The sample and hold circuit (SHC), as a key module of the analog-to-digital converters (ADCs), directly affects the sampling speed and accuracy of the entire data acquisition system. Using TSMC 0.18 μm current-mode SHC for high-speed ADC was designed using CMOS technology. The proposed circuit combines a single amplifier and negative feedback structure to eliminate clock-feed through noise and high signal-to-noise distortion ratio (SNDR) from low power supply voltage and very low current consumption. Meanwhile, a differential structure is provided to reduce distortion caused by channel charge injection. Discussed design issues such as clock feedthrough noise, channel modulation effects, and power consumption. The design verification of the proposed current-mode SHC was completed through circuit layout simulation using CMOS model parameters. The results show that, from a 1.0 V supply and with a power consumption of 54.4 μW, the proposed circuit provides 71.2 dB SNDR, 11.53 bit effective number of bits, and a figure of merit of 1.8 fJ/conversion-step when the sampling frequency is 10 KS/S. [ABSTRACT FROM AUTHOR]