Input Range Research Articles

Abstract 3675: Design of high-performance tumor-informed minimal residual disease (MRD) panels from low FFPE tumor input

Abstract Background: Minimal residual disease (MRD) testing can detect cancer recurrence months to years earlier than the current standard of care, enabling earlier treatment of recurrence and improved patient outcomes. Tumor-informed MRD assays typically utilize formalin-fixed paraffin-embedded (FFPE) tumor tissue, which is available in limited quantities for some patients, for example, following core needle biopsy (CNB), after neoadjuvant treatment or when patients need multiple tests from the same tumor sample. To determine the lower limit of tissue input, we evaluated our MRD assay performance across a range of extracted tumor volumes. Methods: Resected tumors and CNBs were sectioned, H&E stained, and macro-dissected. Tumor gDNA was extracted, quantified, prepared into libraries and sequenced. Sequenced libraries were aligned and evaluated for depth of coverage, variation of coverage, and duplication rate. Somatic calling was performed on matched tumor and normal samples. Up to 1000 target sites were selected for high-depth targeted sequencing of the tumor and normal gDNA for confirmation of somatic variant calls. The positive predictive value (PPV) was computed as the percent of putative somatic variant sites that were present in the tumor capture library and absent in the normal capture library. Results: Extracted tumor volumes varied by almost two orders of magnitude, from 0.06mm3 (equivalent to needle core or fine needle aspirate biopsies) to 5mm3 (achievable with resected tumor). gDNA amount varied linearly (3.6ng to 1549ng) with tumor tissue input, indicating the low tissue to paraffin ratio did not have an adverse effect on yield. Tumor gDNA inputs into library prep ranged from 2.5ng to 100ng. DNA amounts above 100ng into library prep had no discernable benefit. Below 10 ng, depth of coverage and the coefficient of variation in coverage indicated poor-quality libraries. Samples with ~10 ng of gDNA input into library prep showed depth of coverage comparable to higher inputs and saturated the achievable library complexity. Additionally, PPV of somatic calling was consistent across the range of gDNA inputs from 10-100 ng, demonstrating equivalent assay performance. Conclusion: Tumor-informed MRD assays have immense potential for increasingly sensitive treatment response and recurrence monitoring that can inform better treatment decisions. FFPE tumor tissue is a critical input into MRD assays but is a limited resource. This study supports a minimum DNA input of 10 nanograms for a single attempt at extraction, corresponding to a tissue volume of 0.2mm3 or a single 10µm slide with a 20mm2 area, representing one of the lowest tissue input requirements for an MRD assay. Low FFPE tissue requirements expand the patient population that benefit from MRD testing by utilizing samples that have low tumor content, are post-neoadjuvant therapy or do not meet the tumor volume requirements of competing MRD offerings. Citation Format: Matt LaBella, Ashley Acevedo, Ravi Patel, Kiefer Haug, Sangita Ganesh, Elise Buser, Nafei Xu, Shalee Carlson, Kyle Trettin, Sarah Ratzel, Kieko Hara, Pavlos Msaouel, Kanishka Sircar, Chad Tang, Dale Muzzey, Genevieve Gould. Design of high-performance tumor-informed minimal residual disease (MRD) panels from low FFPE tumor input [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3675.

Abstract 323: Optimization of a liquid biopsy assay to accommodate low cfDNA samples and increase throughput

Abstract Molecular testing that interrogates cell free DNA (cfDNA) from plasma has significant potential value across the cancer care continuum, including early cancer detection, driver mutation identification, and disease recurrence monitoring. To achieve this potential, liquid biopsy assays must be optimized to address multiple challenges. For one, cfDNA quantity in the blood is highly variable, with different amounts present depending on malignancy and stage of progression. This requires optimization to detect low concentrations of circulating tumor DNA (ctDNA) and to accommodate variable tumor fractions. In addition, minimizing assay costs will improve test accessibility and adoption. To address these issues, Genece Health is optimizing their lung cancer liquid biopsy assay to accommodate minimal cfDNA input into library preparation. Furthermore, Genece is testing algorithms that tolerate reduced read depths from low-pass whole genome sequencing (LP-WGS) while maintaining high performance. The Genece lung cancer liquid biopsy assay profiles fragment end-motif and size (FEMS) and fragment coverage signals using LP-WGS (Illumina) and proprietary machine learning algorithms. This study first tested the frequency of end motifs in pooled ctDNA after modifying library preparation (IDT) to reduce reagent volumes from 1X down to 0.5X, 0.25X, and 0.1X of manufacturer specification. A range of cfDNA input from 0.25 ng to 6 ng was tested with each library preparation volume. In addition, LP-WGS read coverage from 5X to 1X was evaluated to assess impact on end motif frequency and assay sensitivity. Performance characterization was further evaluated with a cohort of 50 clinical lung cancer and 100 healthy donor plasma samples tested at 1X and 0.25X library reagent volume. We observed an expected increase in library complexity at higher cfDNA input and library reagent volume. Assay performance was minimally impacted by variable cfDNA input and library reagent volume. However, reducing sequencing read depth is tolerated down to 1.25X depth before increasing the variability of end motif frequencies. These observations are corroborated in the 150 clinical sample cohort. This data shows that by altering standard library preparation conditions and reducing unique read depth, liquid biopsy assays can accommodate a wider range of cfDNA, while concurrently decreasing overall costs. Citation Format: Michael Salmans, Dasom Kim, Andrew Carson, Mengchi Wang, Byung In Lee, Bryan Leatham. Optimization of a liquid biopsy assay to accommodate low cfDNA samples and increase throughput [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 323.

Abstract 7008: NEBNext® E5hmC-seqTM: Direct detection of 5-hydroxymethylcytosine at single base resolution

Abstract DNA methylation is a critical epigenetic regulator of gene expression during development and in diseases such as cancer. The modified cytosines, 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC), can be detected by sequencing libraries generated using existing methods: NEBNext® EM-seq™ or bisulfite sequencing. However, these methods cannot differentiate between 5mC and 5hmC. Interest in the role of 5hmC in regulating gene expression and as a cancer biomarker has increased, resulting in the need for robust methods for the identification of specific 5hmC sites. Methods currently exist to enable discrimination of 5mC and 5hmC (for example, oxBS-seq and TAB-seq), however these are based on modifications of bisulfite sequencing, and suffer from reduced data quality due to fragmentation and loss of DNA. Here we describe an enzymatic method that enables specific detection of 5hmC, termed NEBNext® Enzymatic 5hmC-seq (E5hmC-seqTM). E5hmC-seq libraries were generated for 0.1 ng to 200 ng DNA isolated from human brain and E14 mouse embryonic stem cells. Libraries were prepared using NEBNext® Ultra IITM reagents followed by two enzymatic steps to detect 5hmC. In the first step, 5hmCs are glucosylated, which protects them from subsequent deamination by APOBEC. In contrast, cytosines and 5mCs are deaminated resulting in their conversion to uracil and thymine, respectively. This conversion allows discrimination of 5hmC from cytosine and 5mC. Finally, libraries were PCR amplified using NEBNext® Q5U® and sequenced on the Illumina platform. The E5hmC-seq libraries have similar characteristics to EM-seq libraries, including expected insert sizes due to intact DNA molecules, low duplication rates and minimal GC bias. Moreover, E5hmC-seq libraries were well-correlated between inputs and replicates at higher sequencing depths. Fully hydroxymethylated T4147 phage DNA was used as an internal control, with 98-99% of cytosines identified as 5hmC. 5mC and 5hmC levels were also profiled during E14 cell differentiation for a period of 10 days. Interestingly, 5hmC levels decreased whereas 5mC levels increased particularly during the first five days of differentiation. LC-MS/MS quantification of this same DNA mirrored the changes observed by sequencing. E5hmC-seq libraries provide accurate measurements of 5hmC across a wide input range with expected insert sizes and minimal GC bias. Additionally, subtracting E5hmC-seq data from EM-seq data, which detects both 5mC and 5hmC, enables the precise location of individual 5mC and 5hmC sites. The ability to discriminate between 5mC and 5hmC will allow key insights to be made into the role of cytosine modifications in development and cancer. Citation Format: Garrett M. Cammarata, Daniel J. Evanich, V. K. Chaithanya Ponnaluri, Vaishnavi Panchapakesa, Ariel Erijman, Matt A. Campbell, Nan Dai, Bradley W. Langhorst, Louise Williams. NEBNext® E5hmC-seqTM: Direct detection of 5-hydroxymethylcytosine at single base resolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7008.

A scalable and cost-efficient rRNA depletion approach to enrich RNAs for molecular biology investigations.

Transcriptomics analyses play pivotal roles in understanding the complex regulatory networks that govern cellular processes. The abundance of rRNAs, which account for 80%-90% of total RNA in eukaryotes, limits the detection and investigation of other transcripts. While mRNAs and long noncoding RNAs have poly(A) tails that are often used for positive selection, investigations of poly(A)- RNAs, such as circular RNAs, histone mRNAs, and small RNAs, typically require the removal of the abundant rRNAs for enrichment. Current approaches to deplete rRNAs for downstream molecular biology investigations are hampered by restrictive RNA input masses and high costs. To address these challenges, we developed rRNA Removal by RNaseH (rRRR), a method to efficiently deplete rRNAs from a wide range of human, mouse, and rat RNA inputs and of varying qualities at a cost 10- to 20-fold cheaper than other approaches. We used probe-based hybridization and enzymatic digestion to selectively target and remove rRNA molecules while preserving the integrity of non-rRNA transcripts. Comparison of rRRR to two commercially available approaches showed similar rRNA depletion efficiencies and comparable off-target effects. Our developed method provides researchers with a valuable tool for investigating gene expression and regulatory mechanisms across a wide range of biological systems at an affordable price that increases the accessibility for researchers to enter the field, ultimately advancing our understanding of cellular processes.

Assay Validation of Cell-Free DNA Shallow Whole-Genome Sequencing to Determine Tumor Fraction in Advanced Cancers

Blood-based liquid biopsy is increasingly used in clinical care of patients with cancer, and fraction of tumor-derived DNA in circulation (tumor fraction; TFx) has demonstrated clinical validity across multiple cancer types. To determine TFx, shallow whole-genome sequencing of cell-free DNA (cfDNA) can be performed from a single blood sample, using an established computational pipeline (ichorCNA), without prior knowledge of tumor mutations, in a highly cost-effective manner. We describe assay validation of this approach to facilitate broad clinical application, including evaluation of assay sensitivity, precision, repeatability, reproducibility, pre-analytic factors, and DNA quality/quantity. Sensitivity to detect TFx of 3% (lower limit of detection) was 97.2% to 100% at 1× and 0.1× mean sequencing depth, respectively. Precision was demonstrated on distinct sequencing instruments (HiSeqX and NovaSeq) with no observable differences. The assay achieved prespecified 95% agreement of TFx across replicates of the same specimen (repeatability) and duplicate samples in different batches (reproducibility). Comparison of samples collected in EDTA and Streck tubes from single venipuncture in 23 patients demonstrated that EDTA or Streck tubes were comparable if processed within 8 hours. On the basis of a range of DNA inputs (1 to 50 ng), 20 ng cfDNA is the preferred input, with 5 ng minimum acceptable. Overall, this shallow whole-genome sequencing of cfDNA and ichorCNA approach offers sensitive, precise, and reproducible quantitation of TFx, facilitating assay application in clinical cancer care.

Control-IQ Technology Use in Individuals With High Insulin Requirements: Results From the Multicenter Higher-IQ Trial.

Control-IQ technology version 1.5 allows for a wider range of weight and total daily insulin (TDI) entry, in addition to other changes to enhance performance for users with high basal rates. This study evaluated the safety and performance of the updated Control-IQ system for users with basal rates >3 units/h and high TDI in a multicenter, single arm, prospective study. Adults with type 1 diabetes (T1D) using continuous subcutaneous insulin infusion (CSII) and at least one basal rate over 3 units/h (N = 34, mean age = 39.9 years, 41.2% female, diabetes duration = 21.8 years) used the t:slim X2 insulin pump with Control-IQ technology version 1.5 for 13 weeks. Primary outcome was safety events (severe hypoglycemia and diabetic ketoacidosis (DKA)). Central laboratory hemoglobin A1c (HbA1c) was measured at system initiation and 13 weeks. Participants continued using glucagon-like peptide-1 (GLP-1) receptor agonists, sodium-glucose transport protein 2 (SGLT-2) inhibitors, or other medications for glycemic control and/or weight loss if on a stable dose. All 34 participants completed the study. Fifteen participants used a basal rate >3 units/h for all 24 hours of the day. Nine participants used >300 units TDI on at least one day during the study. There were no severe hypoglycemia or DKA events. Time in range 70-180 mg/dL was 64.8% over the 13 weeks, with 1.0% time <70 mg/dL. Hemoglobin A1c decreased from 7.69% at baseline to 6.87% at 13 weeks (-0.82%, P < .001). Control-IQ technology version 1.5, with wider range of weight and TDI input and enhancements for users with high insulin requirements, was safe in individuals with T1D in this study.

Deep Learning Integrating Scale Conversion and Pedo‐Transfer Function to Avoid Potential Errors in Cross‐Scale Transfer

AbstractPedo‐transfer functions (PTFs) relate soil/landscape static properties to a wide range of model inputs (e.g., soil hydraulic parameters) that are essential to soil hydrological modeling. Combining PTFs and hydrological models is a powerful strategy allowing the use of soil/landscape static properties for the generalization of large‐scale modeling. However, since the spatial scales of soil hydraulic parameters required for model inputs and soil/landscape static properties are often not identical, cross‐scale transfer is required, which can be a significant source of errors. Here, we investigate uncertainties in cross‐scale transfer and develop an approach that avoids them. The proposed method uses the convolutional neural network (CNN) as a cross‐scale transfer approach to directly map soil/landscape static properties to soil hydraulic parameters across different spatial scales. The proposed CNN approach is applied under two different estimation strategies to invert the hydraulic parameters of a soil‐water balance model and subsequently the quality of the parameters is assessed. Both synthetical and real‐world results around the conterminous United States indicate that in general the employed end‐to‐end strategy is superior to the two‐step strategy. The CNN‐based integrated model successfully reduces potential errors in cross‐scale transfer and can be applied to other areas lacking information on hydraulic parameters or observations. The proposed method can be extended to improve parameter estimation in earth system models and enhance our understanding of key hydrological processes.

A Rail-to-Rail Operational Amplifier for Transimpedance Optoelectronic Conversion

The transimpedance conversion circuit is an important part of the fluorescent optical fiber temperature sensor, which is used for rare earth fluorescence detection. Transimpedance conversion circuits can benefit from the wide input range, high gain, low input offset voltage, and high common-mode rejection ratio of rail-to-rail operational amplifiers. In this paper, a constant transconductance rail-to-rail operational amplifier is designed and implemented based on the 0.18 μm CMOS process. According to the simulation results, the transconductance change rate of the operational amplifier is 4.8%, and its gain is 140 dB. The input offset voltage of this operational amplifier is 0.41 μV. Both the power supply rejection ratio and the common-mode rejection ratio have values above 140 dB, with the common-mode rejection ratio reaching as high as 165.4 dB. The transimpedance conversion circuit consists of the operational amplifier designed in this paper. The test results show that the operational amplifier has a certain application value in the transimpedance amplification circuit.

A linear cross-coupled gate-driven quasi-floating bulk low-power wide input range transconductor.

High linearity for a wider input voltage range and low-power operation of the operational transconductance amplifier (OTA) are indispensable parameters for health care applications, which require high quality and accurate signal conditioning. However, achieving low-power operation along with high linearity at low supply voltages is challenging for OTA using conventional low-power and linearization techniques. This paper proposes an OTA based on a cross-coupled gate-driven quasi-floating bulk (CGDQFB) MOSFET and source-degenerated linearization techniques, which works at a supply of ±0.5V. The post-layout simulations of the proposed OTA are performed in the 180nm standard CMOS process, which shows a transconductance of 0.321 μS, an output impedance of 331 MΩ, an input impedance of 897 GΩ, a DC gain of 40.54 dB, a gain-bandwidth of 0.145MHz, a total harmonic distortion (THD) of 51.09 dB for an input voltage range of 626.7 mV at a frequency of 100.39Hz, and a power consumption of 0.45 μW. The proposed OTA shows an input common mode range of -0.34 to 0.4V, an output voltage swing of -0.34 to 0.34V, a common mode rejection ratio of 91.08 dB, and a consumption area of 23 965.92 µm2. Furthermore, with 200 Monte Carlo iterations, the proposed OTA shows variability for gain and THD of 0.003 and 0.036, respectively. The proposed CGDQFB OTA is a suitable contender for conditioning bio-signals used in health care applications.

Experimental Investigation of Characteristics and Development of Nonlinearity Models for FR1-Range Radio-Frequency Amplifiers

Experimental studies of nonlinear properties of low-power radio-frequency amplifiers similar to those used in 4G/5G mobile communication equipment of the FR1 frequency range are carried out. The measurements of the characteristics of the amplifiers are performed by using the double-frequency testing technique at frequencies of the n7 band (2500–2570 / 2620–2690 MHz), which is allocated in Belarus for 4G mobile communication systems, and the n78 band (3300–3800 MHz), which is planned to be used in Belarus for 5G mobile communications. Based on the results of the measurements of double-frequency characteristics of the amplifiers, their single-tone amplitude characteristics, as well as two-tone characteristics and values of the dynamic range of 3-, 5-, 7-, and 9th order intermodulation in the first harmonic zone, high-order polynomial models of transfer characteristics of the investigated amplifiers are synthesized. The synthesized models are suitable for use in a wide dynamic range of input signals in case of simultaneous modeling of nonlinear effects of all kinds that pose a danger to radio reception in a complex electromagnetic environment created in the frequency bands of mobile (cellular) communications: both “subtle” effects (intermodulation) and “rough” effects (desensitization, cross-modulation). When using the technology of discrete nonlinear analysis of the behavior of radio equipment in a complex electromagnetic environment, the obtained models provide high efficiency of simulation and quantitative analysis of nonlinear processes and radio interference occurring in 4G/5G/6G radio equipment and networks in a complex electromagnetic environment.

A hybrid amplifier topology for low‐noise direct‐coupled front ends

SummaryHybrid amplifiers combine discrete transistors with operational amplifiers (OAs) achieving superior characteristics compared with commercially available OAs, while keeping simple structures and a reduced number of parts. A single‐ended amplifier topology based on a junction field‐effect transistor (JFET) is proposed as a means of achieving very low input noise levels, both in current and voltage. It is direct‐coupled and allows to place a high‐pass filter at its input to preserve input range, or at its output to optimize noise. The proposed topology results in a low input capacitance and low input‐current noise, making it suitable for applications where multiple amplifiers are connected in parallel to achieve ultra‐low noise levels. The amplifier's gain is set by a resistor ratio, being suitable for instrumentation front ends. The circuit analysis and design equations are provided to adapt it for different applications and, as an example, an amplifier with a gain of 40 dB and a bandwidth from DC to 1 MHz was designed, built, and tested. The dominant noise contribution of the amplifier was the JFET (a JFE2140 device), featuring an input noise voltage of , that is reduced to when two amplifiers are connected in parallel.

1 V Electronically Tunable Differential Difference Current Conveyors Using Multiple-Input Operational Transconductance Amplifiers.

This paper presents electronically tunable current conveyors using low-voltage, low-power, multiple-input operational transconductance amplifiers (MI-OTAs). The MI-OTA is realized using the multiple-input bulk-driven Metal Oxide Semiconductor transistor (MIBD-MOST) technique to achieve minimum power consumption. The MI-OTA also features high linearity, a wide input range, and a simple Complementary Metal Oxide Semiconductor (CMOS). Thus, high-performance electronically tunable current conveyors are obtained. With the MI-OTA-based current conveyor, both an electronically tunable differential difference current conveyor (EDDCC) and a second-generation electronically tunable current conveyor (ECCII) are available. Unlike the conventional differential difference current conveyor (DDCC) and second-generation current conveyor (CCII), the current gains of the EDDCC and ECCII can be controlled by adjusting the transconductance ratio of the current conveyors. The proposed EDDCC has been used to realize a voltage-to-current converter and current-mode universal filter to show the advantages of the current gain of the EDDCC. The proposed current conveyors and their applications are designed and simulated in the Cadence environment using 0.18 μm TSMC (Taiwan Semiconductor Manufacturing Company) CMOS technology. The proposed circuit uses ±0.5 V of power supply and consumes 90 μW of power. The simulation results are presented and confirm the functionality of the proposed circuit and the filter application. Furthermore, the experimental measurement of the EDDCC implemented in the form of a breadboard connection using a commercially available LM13700 device is presented.

Event-Triggered Relearning Modeling Method for Stochastic System with Non-Stationary Variable Operating Conditions

This study presents a novel event-triggered relearning framework for neural network modeling, designed to improve prediction precision in dynamic stochastic complex industrial systems under non-stationary and variable conditions. Firstly, a sliding window algorithm combined with entropy is applied to divide the input and output datasets across different operational conditions, establishing clear data boundaries. Following this, the prediction errors derived from the neural network under different operational states are harnessed to define a set of event-triggered relearning criteria. Once these conditions are triggered, the relevant dataset is used to recalibrate the model to the specific operational condition and predict the data under this operating condition. When the predicted data fall within the training input range of a pre-trained model, we switch to that model for immediate prediction. Compared with the conventional BP neural network model and random vector functional-link network, the proposed model can produce a better estimation accuracy and reduce computation costs. Finally, the effectiveness of our proposed method is validated through numerical simulation tests using nonlinear Hammerstein models with Gaussian noise, reflecting complex stochastic industrial processes.

Validation of computational fluid dynamics of shake flask experiments at moderate viscosity by liquid distributions and volumetric power inputs

Computational fluid dynamics (CFD) has recently become a pivotal tool in the design and scale-up of bioprocesses. While CFD has been extensively utilized for stirred tank reactors (STRs), there exists a relatively limited body of literature focusing on CFD applications for shake flasks, almost exclusively concentrated on fluids at waterlike viscosity. The importance of CFD model validation cannot be overstated. While techniques to elucidate the internal flow field are necessary for model validation in STRs, the liquid distribution, caused by the orbital shaking motion of shake flasks, can be exploited for model validation. An OpenFOAM CFD model for shake flasks has been established. Calculated liquid distributions were compared to suitable, previously published experimental data. Across a broad range of shaking conditions, at waterlike and moderate viscosity (16.7 mPa∙s), the CFD model's liquid distributions align excellently with the experimental data, in terms of overall shape and position of the liquid relative to the direction of the centrifugal force. Additionally, the CFD model was used to calculate the volumetric power input, based on the energy dissipation. Depending on the shaking conditions, the computed volumetric power inputs range from 0.1 to 7 kW/m3 and differed on average by 0.01 kW/m3 from measured literature data.

A 1.5MSPS, 120 dB SFDR, ±10 V input range SAR ADC with sampling nonlinearity compensation and inherent 2-b coarse ADC for MSBs decision

This paper presents a high-precision, successive-approximation-register (SAR) analog-to-digital converter (ADC) with maximum input range of ±10 V for industry applications, where the wide-range input signal is sampled directly on part of the capacitive digital-to-analog converter (CDAC) via high-voltage (HV) sampling switches. An inherent 2-b coarse ADC is designed to avoid charge leakage under large input signal, which is reused for nonlinearity compensation from the sampling switch's resistance, while the nonlinearity due to clock feedthrough is compensated by a binary capacitor array. A capacitance reduction structure is used in the CDAC with redundant bits to save the CDAC's area. Fabricated in a 180-nm CMOS process with HV option, the ADC achieves 102-dB SNDR and 120-dB SFDR under 1.5 MSPS throughput. The overall power consumption without BGR and reference buffer is 34.4 mW under ±11-V bipolar HV supplies for sampling network and 1.8-V supply for the core circuits, corresponding to a FoMs of 175.4 dB, respectively. The entire chip occupies 7.2-mm2 area.

Characteristics of an AC rotating gliding arc discharge in NH3 and air atmospheres

Plasmas have emerged as a promising technology for the utilization of NH3 as a carbon-free fuel for direct plasmas-assisted combustion and hydrogen production. This study aims to explore the electrical and optical emission characteristics of the rotating gliding arc (RGA) discharge in NH3 and air swirling flows over a wide range of nominal power inputs. The electrical characteristics were measured using voltage–current probes, while a spectrometer was employed to assess the spectral characteristics. Additionally, a synchronized high-speed camera equipped with a dual-scope objective was utilized to capture transient phenomena of the arc in the optical emission spectrum. Both the spark-type and glow-type discharges were observed in RGA. Interestingly, the spark-type discharge frequency exhibited a non-monotonic variation with increasing nominal power inputs for both NH3 and air, while the glow-type discharge frequency displayed a monotonous upward trend. Regarding the spectral emission characteristics in NH3, a dramatic transition of the arc emission spectrum from Hα to NH2* was observed as nominal power inputs increased. The Hα and NH2* emissions that are dominated in the spark-type and glow-type discharges, respectively, and the intensity ratio of Hα and NH2* emission shows evident correlations with discharge current and electric field strength. In summary, this study represents the first investigation into the electrical and spectral characteristics of RGA in NH3.

Comments on the Numerical Models for Correlating Weld Metal Composition to Microstructure and Properties

There has been a steady rise in the use of computational tools to model or describe weld microstructure and properties from data sets containing a wide range of inputs and outputs measured by experiments. The tools range in sophistication from simple two-factor correlations in spreadsheets to artificial neural networks.

A High ENOB 14-Bit ADC without Calibration

This paper presents an implementation of a 14-bit 2.5 MS/s differential Successive-Approximation-Register (SAR) analog-to-digital converter (ADC) to be used for sensing multiple analog input signals. A differential binary-weighted with split capacitance charge-redistribution capacitive digital-to-analog converter (CDAC) utilizing the conventional switching technique is designed, without using any calibration mechanism for fast power-on operation. The CDAC capacitor unit has been optimized for improved linearity without calibration technique. The SAR ADC has a differential input range 3.6 Vpp, with a SNDR of 80.45 dB, ENOB of 13.07, SFDR of 87.16 dB and dissipates an average power of 0.8 mW, while operating at 2.5 V/1 V for analog/digital power supply. The INL and DNL is +0.22/−0.34 LSB and +0.42/−0.3 LSB, respectively. A prototype ADC has been fabricated in a conventional CMOS 65 nm technology process.

Design and implementation of intelligent‐oriented electronic communication power monitoring system

AbstractIn order to improve the monitoring effect of electronic communication power supply, this paper applies the intelligent communication signal processing method to the monitoring system, and deeply analyzes the basic principle of OTA. Moreover, from the perspective of improving linearity, a signal attenuation OTA is designed in this paper, and its linearity has been greatly improved. In addition, this paper designs a cross‐coupled POTA that can not only ensure high linearity but also achieve tunable translinearity. The tunable POTA circuit has simple structure, wide linear input range, tunable translinearity and easy implementation, and can meet its performance requirements in FPAA arrays. Through data analysis, it can be seen that the intelligent‐oriented electronic communication power monitoring system proposed in this paper has a good data monitoring effect. This study provides an effective approach for the development of electronic communication power monitoring technology, improves the signal processing efficiency of electronic communication power monitoring, and also provides some reference for the improvement of signal strength in electronic communication technology.

Enhancing labor productivity as a key strategy for fostering green economic growth and resource efficiency

Amidst a time when uncontrolled economic growth has frequently harmed the environment, it is crucial to reassess our strategy toward economic progress. The necessity to tackle climate change, resource depletion, and environmental deterioration demands a profound transition towards ecologically sound and sustainable economic development. This study examines the crucial significance of labor productivity in promoting sustainable economic growth and the effective utilization of resources in Asia, Europe, and South America from 1990 to 2020. To accomplish this, we utilized the Data Envelopment Analysis (DEA) methodology to examine a range of input and output characteristics thoroughly. These parameters included labor productivity, renewable energy usage, material efficiency, Green GDP, carbon footprint, and water footprint. The results of our study demonstrate significant regional variations in the efficient utilization of labor and resources to promote sustainable economic development. The findings of the DEA model emphasize that countries with higher labor productivity are more capable of pursuing an environmentally benign and sustainable financial path. Moreover, our research demonstrates a substantial association between enhanced labor productivity and diminished carbon and water footprints. This highlights the importance of labor productivity as a fundamental element for maximizing resource efficiency. In addition, we propose policy suggestions that motivate and improve worker efficiency as a practical strategy to accomplish both economic growth and environmental sustainability.