Receiver Sensitivity Research Articles

Construction of a risk prediction model for postoperative deep vein thrombosis in colorectal cancer patients based on machine learning algorithms

BackgroundColorectal cancer is a prevalent malignancy of the digestive system, with an increasing incidence. Lower extremity deep vein thrombosis (DVT) is a frequent postoperative complication, occurring in up to 40% of cases.ObjectiveThis research aims to develop and validate a machine learning model (ML) to predict the risk of lower limb deep vein thrombosis in patients with colorectal cancer, facilitating preventive and therapeutic measures to enhance recovery and ensure safety.MethodsIn this retrospective cohort study, we collected data from 429 colorectal cancer patients from January 2021 to January 2024. The medical records included age, blood test results, body mass index, underlying diseases, clinical staging, histological typing, surgical methods, and postoperative complications. We employed the Synthetic Minority Oversampling Technique to address imbalanced data and split the dataset into training and validation sets in a 7:3 ratio. Feature selection was performed using Random Forest (RF), XGBoost, and Least Absolute Shrinkage and Selection Operator algorithms (LASSO). We then trained six machine learning models: Logistic Regression (LR), Naive Bayes (NB), Gaussian Process (GP), Random Forest, XGBoost, and Multilayer Perceptron (MLP). The model’s performance was evaluated using metrics such as area under the Receiver Operating Characteristic curve, accuracy, sensitivity, specificity, F1 score, and confusion matrix. Additionally, SHAP and LIME were used to enhance the interpretability of the results.ResultsThe study combined Random Forest, XGBoost algorithms, and LASSO regression with univariate regression analysis to identify significant predictive factors, including age, preoperative prealbumin, preoperative albumin, preoperative hemoglobin, operation time, PIKVA2, CEA, and preoperative neutrophil count. The XGBoost model outperformed other ML algorithms, achieving an AUC of 0.996, an accuracy of 0.9636, a specificity of 0.9778, and an F1 score of 0.9576. Moreover, the SHAP method identified age and preoperative prealbumin as the primary determinants influencing ML model predictions. Finally, the study employed LIME for more precise prediction and interpretation of individual predictions.ConclusionThe machine learning algorithms effectively predicted postoperative lower limb deep vein thrombosis in colorectal cancer patients. The XGBoost model demonstrated strong potential for improving early detection and treatment in clinical settings.

Evaluation of a Novel Smart Capsule Bacterial Detection System Device for Diagnosis of Small Intestinal Bacterial Overgrowth.

There is a large unmet need for alternative, non-invasive, and accurate diagnosis of small intestinal bacterial overgrowth (SIBO). The smart capsule bacterial detection system (SCBDS) device contains a targeted sampling technology and an onboard SCBDS assay to detect metabolically active bacteria in the small intestine. Here, we evaluated the agreement of SCBDS assay with duodenal aspiration/culture exvivo in a multicenter clinical study. Duodenal aspiration was performed in subjects with gastrointestinal symptoms suggestive of SIBO. Aspirated fluid was sent to local and central microbiology labs to evaluate the agreement for detecting bacteria with SCBDS assay compared to the total bacterial count (TBC) reference standard. The performance of SCBDS assay was evaluated using a receiver operator curve, sensitivities, and specificities. Aspirates from 66 patients were collected and analyzed for TBC and SCDBS assay. The overall agreement between the two assays was 82%-92% across 3 clinical sites. The SCBDS assay had a sensitivity of 67%-100% and a specificity of 90%-97% using either ≥ 103 or 105 CFU mL-1 cutoff. Additionally, there was a good correlation (r = 0.82) for the TBC culture between the local and central labs. The SCBDS assay showed a high level of agreement with TBC and improved performance compared to other non-invasive tests. These results demonstrate the potential utility of SCBDS device to aid SIBO diagnosis as a simple and non-invasive tool that merits further clinical validation.

Analysis of Bacterial Metabolites in Breath Gas of Critically Ill Patients for Diagnosis of Ventilator-Associated Pneumonia—A Proof of Concept Study

Bacterial infection of the lower respiratory tract frequently occurs in mechanically ventilated patients and may develop into life-threatening conditions. Yet, existing diagnostic methods have moderate sensitivity and specificity, which results in the overuse of broad-spectrum antibiotics administered prophylactically. This study aims to evaluate the suitability of volatile bacterial metabolites for the breath-based test, which is used for diagnosing Ventilator-Associated Pneumonia (VAP). The in vitro experiments with pathogenic bacteria most prevalent in VAP etiology (i.e., Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa) were performed to identify bacteria-derived metabolites using a specially designed cultivation system enabling headspace sampling for GC-MS analysis. Thirty-nine compounds were found to be significantly metabolized by tested species and, therefore, selected for monitoring in the exhaled breath of critically ill, mechanically ventilated (MV) patients. The emission of volatiles from medical respiratory devices was investigated to estimate the risk of spoiling breath results with exogenous pollutants. Bacterial metabolites were then evaluated to differentiate VAP patients from non-infected MV controls using Receiver Operating Characteristic (ROC) analysis, with AUC, sensitivity, and specificity calculated. Nine bacterial metabolites that passed verification through a non-parametric ANOVA test for significance and LASSO penalization were identified as key discriminators between VAP and non-VAP patients. The diagnostic model achieved an AUC of 0.893, with sensitivity and specificity values of 87% and 82.4%, respectively, being competitive with traditional methods. Further validation could solidify its clinical utility in critical care settings.

Differentiation between invasive ductal carcinoma and ductal carcinoma in situ by combining intratumoral and peritumoral ultrasound radiomics

BackgroundThis study aimed to develop and validate an ultrasound radiomics model for distinguishing invasive ductal carcinoma (IDC) from ductal carcinoma in situ (DCIS) by combining intratumoral and peritumoral features.MethodsRetrospective analysis was performed on 454 patients from Chengzhong Hospital. The patients were randomly divided in accordance with a ratio of 8:2 into a training group (363 cases) and validation group (91 cases). In addition, 175 patients from Yanghu Hospital were used as the external test group. The peritumoral ranges were set to 2, 4, 6, 8, and 10 mm. Mann–Whitney U-test, recursive feature elimination, and a least absolute shrinkage and selection operator were used to in the dimension reduction of the radiomics features and clinical knowledge, and machine learning logistic regression classifiers were utilized to construct the diagnostic model. The area under the curve (AUC) of the receiver operating characteristics, accuracy, sensitivity, and specificity were used to evaluate the model performance.ResultsBy combining peritumoral features of different ranges, the AUC of the radiomics model was improved in the validation and test groups. In the validation group, the maximum increase in AUC was 9.7% (P = 0.031, AUC = 0.803) when the peritumoral range was 8 mm. Similarly, when the peritumoral range was only 8 mm in the test group, the maximum increase in AUC was 4.9% (P = 0.005, AUC = 0.770). In this study, the best prediction performance was achieved when the peritumoral range was only 8 mm.ConclusionsThe ultrasound-based radiomics model that combined intratumoral and peritumoral features exhibits good ability to distinguish between IDC and DCIS. The selection of peritumoral range size exerts an important effect on the prediction performance of the radiomics model.

Capacity Optimization of the Next-Generation Passive Optical Networks Based on Genetic Algorithm

This paper proposes an analytical and a numerical models for Next Generation Passive Optical Network (NGPON) by combining Gigabit-PON (GPON) and 10 Gigabit asymmetrical PON (XGPON) technologies. This allows for a larger number of subscribers to be accommodated on the network, up to 256. A capacity optimization procedure based on Genetic Algorithm (GA) techniques is then proposed and analyzed. The uplink and downlink capacities are maximized by optimizing various link parameters, such as the number of Optical Network Units (ONUs), the average optical transmitter power, the receiver sensitivity and the network operating margins. Simulation results shown that, among others, the Distributed-feedback laser (DBF) average power, the Positive-Intrinsic-Negative (PIN) sensitivity and the network margin are the key parameters entrusted to the GA technique in order to maximize the downlink capacity. In contrast, the Fabry Perot (FP) average power, the Avalanche Photodiode (APD) sensitivity and network margin are found to be the most influential parameters optimized by GA to maximize uplink capacity. Significantly, it is demonstrated that the GA technique, when used in optimizing NGPON capacity, enabling 256 subscribers and offering a data rates up to 71.02 Gb/s and 390.56 Gb/s in the upstream and downstream directions, respectively.

Security enhancement for NOMA-PON with 2D cellular automata and Turing pattern cascading scramble aided fixed-point extended logistic chaotic encryption

The non-orthogonal multiple access-passive optical network (NOMA-PON) is facing the dual security threats of primary user interference and unauthorized third-party user eavesdropping, so efficient data security enhancement techniques are crucial. To solve these problems, we propose a fixed-point extended (FE)-logistic chaotic mapping to reduce the computational complexity while employing a two-dimensional (2D) cellular automata (CA) and Turing pattern (TP) cascading scramble (CA-TPCS) encryption algorithm to further improve the sensitivity of the NOMA-PON system. The CA-TPCS consists of 2D-CA dynamic bit encryption and Turing symbol substitution (TSS). By using FE chaos to construct 2D-CA and adopting index sort to extract the TSS matrix, dynamic diffusion of bits and scrambling of a 2D symbol matrix are achieved. To ensure the key privacy, we employ a dual key mechanism, and uplink data is introduced as the private key. To verify the feasibility of the proposed method, a simulation validation is built on a 17.6 Gb/s power division multiplexing-orthogonal frequency division multiplexing (PDM-OFDM) NOMA-PON system transmitted over 25 km standard single mode fiber (SSMF). The results show that the proposed scheme has no effect on the optimal power allocation rate (PAR) values and the values are all 3. Meanwhile, the receiver sensitivity gains of 0.2 and 0.3 dB are obtained for high-power and low-power users after encryption. The ciphertext has good diffusion and statistical properties, and the key space is flexibly controlled by the FE precision f, the length l of the transmitted bit, and the size T of the TP, with the value of 22f+l+T×T. The results show that the proposed scheme is not only very compatible with PDM technology but also can realize the dual defense of internal aggression and external aggression. It has a good application prospect in the future NOMA-PON.

Abstract 4135726: Prospective validation and implementation pilot study of an emergency department heart failure risk stratification tool: STRIDE-HF

Background: We previously found the STRIDE-HF emergency department (ED) risk tool accurately predicted risk of a 30-day serious adverse event (SAE), including 30-day mortality, cardiopulmonary resuscitation, intra-aortic balloon pump insertion, intubation, new dialysis, myocardial infarction, or coronary revascularization. Research question: We sought to prospectively validate STRIDE-HF and describe safety of the deployed model in a clinical pilot. We hypothesized that performance would decrease slightly with prospective evaluation and that real-time risk display would contribute to safe disposition decisions. Goals: 1) To prospectively validate KPCARES-HF across 21 community EDs among ED patients with acute heart failure (AHF) from January 1, 2023 – December 31 st , 2023; and 2) to describe outcomes among patients predicted to be very low risk and discharged home in the clinical pilot. Methods: For prospective validation, we report 30-day mortality rates by risk strata. We assess model performance using area under the receiver operator curve (AUROC) and sensitivity at a key clinical threshold. We describe rates of 30-day SAE among patients predicted to be very low risk and discharged after ED or observation unit care in the clinical pilot. Results: There were 13,274 ED patients in the prospective validation; median age was 76, 50.8% were female, and 44.5% were non-White. We found 11.4%, 24.8%, 31.9%, and 31.9% of patients were very low, low, moderate, and high risk, respectively, while 21.6%, 15.2% and 63.0% were discharged, observed, and admitted, respectively. Among discharged patients, 29.2% and 13.3% were moderate or high risk, respectively, and 30-day mortality rates among these patients were 2.2% and 8.4%, respectively. Among 8,363 admitted patients, 27.7% were very low or low risk. Conclusions: STRIDE-HF maintained high predictive accuracy with prospective validation in this diverse, multi-center cohort. Our findings suggest that use of STRIDE-HF might help better align risk with admission decision and can be safely used to assist providers in identifying patients who may be stable for outpatient care.

Low hardware-complexity IQ skew tolerant finite field and time domain hybrid adaptive equalization for 400G/800G-ZR coherent transmission

In 400G/800G-ZR transmission systems afflicted by substantial inter-symbol interference (ISI) stemming from chromatic dispersion (CD), frequency-domain adaptive equalizers (FD-AEQs) are preferred to time-domain AEQs (TD-AEQs) as the former have relatively lower computation load. However, the reliance of FD-AEQs on computationally expensive fast Fourier transform (FFT)/inverse fast Fourier transform (IFFT) operations poses a major obstacle in the development of low-power 400G/800G-ZR digital coherent transceivers. Furthermore, to mitigate the significant performance degradation due to receiver in-phase and quadrature (IQ) skew, the employment of twice the number of filters becomes imperative to enable individual adjustments of the I and Q tributaries, nearly doubling the number of necessary FFT/IFFT operations and computation load. This prohibitively high computation load poses a formidable obstacle to the realization of low-power and cost-effective transceivers. To address this issue, we introduce a novel low hardware-complexity IQ skew tolerant finite field (FF) and TD hybrid AEQ for 400G/800G-ZR coherent transmission. By optimizing the AEQ design and the Fermat number transform (FNT) based linear convolution scheme, the proposed hybrid AEQ can achieve a remarkable reduction in hardware complexity by more than 70%, compared with the existing IQ skew tolerant FD-AEQ, while preserving the same receiver sensitivity. Both numerical simulations and experiments demonstrate that the performance of the low complexity hybrid AEQ is the same as the FD-AEQ with much higher complexity. The proposed comprehensive and hardware efficient hybrid AEQ scheme is very appealing for 400G/800G-ZR coherent receivers which need to compensate for large ISI and IQ skew under stringent power constraints.

Power domain superimposed modulation method based on dimensional level transformation and 5D constellation shaping.

This paper proposes a power domain superposition modulation method based on dimensional level transformation and five dimensions (5D) constellation shaping. Stacking a regular triangular pyramid and enhancing it in terms of dimensions achieves the geometric shaping of the 5D constellation. Based on this, signal superposition in the power domain is achieved through dimensional transformation for data modulation. Experimental verification was conducted on a 2 km seven-core fiber in an IM/DD system. Compared to the traditional 2D-32QAM, this method can achieve a 0.7 dB gain at high received optical power and a 0.4 dB receiver sensitivity gain at a BER of 3.8 × 10-3.

Security improvement for TWDM-PON utilizing blowfish cryptography

Security issues with optical access networks (OANs) are increasing as the number of users has dramatically grown. To address this problem, a full-system division multiplexing in both the time and wavelength domains has been utilized in a passive optical network second-generation (TWDM-NG-PON2) system. A secure approach to transfer data was presented by implementing the blowfish algorithm for image encryption and decryption in the OAN. Simulation results indicate effective realization of a bidirectional fiber link at 40 km with a splitting ratio of 1:256. The minimum accepted receiver sensitivity is −30.9dBm for uplink and −33.9dBm for downlink, and a symmetric high capacity of 160 Gbps rate has been obtained. The proposed algorithm in TWDM-PON offers high security with large bandwidth and an advanced long-haul network that provides effective resource usage and reinforced network authenticity.

Integrative hybrid deep learning for enhanced breast cancer diagnosis: leveraging the Wisconsin Breast Cancer Database and the CBIS-DDSM dataset

The objective of this investigation was to improve the diagnosis of breast cancer by combining two significant datasets: the Wisconsin Breast Cancer Database and the DDSM Curated Breast Imaging Subset (CBIS-DDSM). The Wisconsin Breast Cancer Database provides a detailed examination of the characteristics of cell nuclei, including radius, texture, and concavity, for 569 patients, of which 212 had malignant tumors. In addition, the CBIS-DDSM dataset—a revised variant of the Digital Database for Screening Mammography (DDSM)—offers a standardized collection of 2,620 scanned film mammography studies, including cases that are normal, benign, or malignant and that include verified pathology data. To identify complex patterns and trait diagnoses of breast cancer, this investigation used a hybrid deep learning methodology that combines Convolutional Neural Networks (CNNs) with the stochastic gradients method. The Wisconsin Breast Cancer Database is used for CNN training, while the CBIS-DDSM dataset is used for fine-tuning to maximize adaptability across a variety of mammography investigations. Data integration, feature extraction, model development, and thorough performance evaluation are the main objectives. The diagnostic effectiveness of the algorithm was evaluated by the area under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity, specificity, and accuracy. The generalizability of the model will be validated by independent validation on additional datasets. This research provides an accurate, comprehensible, and therapeutically applicable breast cancer detection method that will advance the field. These predicted results might greatly increase early diagnosis, which could promote improvements in breast cancer research and eventually lead to improved patient outcomes.

Ultralow-noise preamplified optical receiver using conventional single-wavelength transmission

Conventional optical amplifiers that use stimulated emission suffer from the generation of excess noise, thus limiting the performance in many applications. The phase-sensitive optical parametric amplifier, relying on the use of a nonlinear material for amplification, is an exception that can approach a noise figure of 0 dB. Its implementation in optical communication links has, however, been cumbersome due to increased complexity both in the transmitter and the receiver, effectively limiting the use of such amplifiers in practice. Here, we propose and demonstrate an implementation of a transmission system with exceptional performance in terms of receiver sensitivity (0.9 photons per bit) using a standalone ultralow-noise phase-sensitively preamplified receiver and a conventional single-wave optical transmitter. This is a significant simplification compared to previous demonstrations and can transform such amplifiers from a curiosity to practical use for example in deep-space-to-earth communication links.

Simultaneous generation of dual 16-QAM-modulated millimeter-wave signals enabled by precoding-based optical I/Q modulation and single photodetector detection

Integration of quadrature-amplitude-modulation (QAM) modulation and millimeter-wave (mm-wave) technology ensures a concise enhancement of transmission capacity and peak data rates in radio-over-fiber (RoF) systems. Nevertheless, with the continuous development of new-generation mobile applications, higher demands on access flexibility have also been formulated. We propose a novel, to the best of our knowledge, dual 16-QAM-modulated mm-wave signal generation scheme for the simultaneous generation and transmission of two independent 16-QAM signals with different carrier frequencies, enhancing the channel capacity, spectrum efficiency, and access flexibility of the RoF systems. In our proposed scheme, the generation of the optical dual 16-QAM-modulated mm-wave signals is implemented by a precoding-based optical in-phase/quadrature (I/Q) modulator operating at optical carrier suppression (OCS) mode, and their detection is implemented by a single photodetector (PD). At the transmitter side, two independent driving QAM vector radio-frequency (RF) signals with precoding, generated via digital signal process (DSP), are fed into two parallel Mach–Zehnder modulators (MZMs) of an I/Q modulator to implement OCS modulation. The I/Q modulator generates optical carriers carrying both information from different QAM signals simultaneously. After square-law detection in a single PD, we generate the desired dual 16-QAM-modulated mm-wave signals with photonic frequency-doubling and recover the original 16-QAM signals without information distortion. Based on our proposed scheme, we experimentally demonstrate the simultaneous generation and transmission of 2-Gbaud dual 16-QAM-modulated mm-wave signals with carrier frequencies of 26 GHz and 40 GHz, respectively. After transmission over 10-km single-mode fiber (SMF) link and 1.2-m wireless link, the dual 16-QAM signals can be successfully separated and demodulated by a common DSP. Bit-error ratios (BERs) of both recovered 16-QAM signals can reach the hard-decision forward-error-correction (HD-FEC) threshold of 3.8×10−3. Compared with the existing schemes, our scheme only requires one-half of the driving frequency to generate dual 16-QAM-modulated mm-wave signals with the same carrier frequency. The result indicates that our proposed scheme can lower bandwidth requirements for optoelectronics devices, along with implementing better spectral efficiency and receiver sensitivity, which is more applicable to the demands for increased system capacity and multi-frequency access flexibility in future systems.

MIMO Volterra polynomial equalizer for PDM ultrahigh-order QAM signals.

In this work, we proposed and experimentally demonstrated a multiple-input multiple-output (MIMO) Volterra polynomial equalizer (MVPE), in a probabilistic shaping (PS) polarization-division multiplexed (PDM) 4096-quadrature amplitude modulation (QAM) coherent optical transmission system. In comparison with the conventional single-input single-output (SISO) Volterra nonlinear equalizers (VNLEs), the proposed MVPE can equalize the in-phase (I) and quadrature (Q) components in both the X and Y polarizations simultaneously and effectively alleviate nonlinear distortions. In the PS-PDM-4096-QAM experiment, the normalized generalized mutual information (NGMI) reached the threshold of DVB-S2 low-density parity-check (LDPC) codes with a 20% overhead. Compared to the SISO VNLE scheme, the MVPE scheme improves receiver sensitivity by approximately 2.8 dB while maintaining nearly similar computational complexity.

Immunoregulatory role of AC007278.3 and HOTAIR long non-coding RNAs in lupus nephritis: potential biomarkers and therapeutic targets.

Long non-coding RNAs (lncRNAs) have emerged as crucial regulators in various biological processes, including immune regulation and autoimmune pathologies. However, their specific significance in modulating the cytokine network in systemic lupus erythematosus (SLE) remains largely unexplored. This study assessed the expression patterns of immune-related lncRNAs, HOTAIR, and AC007278.3, along with their related protein-coding genes, TNF-α and IL18RAP, in nephritic SLE patients. Additionally, the potential of selected genes as diagnostic biomarkers for SLE was evaluated. Blood samples were obtained from SLE patients (n = 30) and age-sex-matched healthy controls (HCs) (n = 60). Subsequently, RNA was isolated from peripheral blood mononuclear cells (PBMCs), and cDNA was synthesized to analyze the expression levels of the target genes using real-time PCR. The correlation analysis between the relative expressions of different genes was examined in both the patient and HC groups. The diagnostic potential of the lncRNAs was determined by calculating the Area Under the Curve of the Receiver Operating Characteristics (AUC of ROC), Cut-off, sensitivity, and specificity. Our results indicated a significant upregulation of lncRNAs AC007278.3 (fold change [FC] = 14.13, p-value < 0.0001) and HOTAIR (FC = 14.1, p-value < 0.0001). Correspondingly, their associated target genes, TNF-α and IL18RAP, were also overexpressed in patients (FC = 2.66 and FC = 5.18, respectively, p-value < 0.001). Notably, a strong positive correlation was observed between IL18RAP and AC007278.3 in SLE patients. Moreover, the AUC of ROC analyses underscored the diagnostic efficacy of AC007278.3 alone and combined with HOTAIR, yielding values of 0.89 and 0.86, respectively. These findings highlight the potential immunoregulatory roles of lncRNAs AC007278.3 and HOTAIR, emphasizing their significance as promising diagnostic biomarkers and potential therapeutic targets for SLE. Additionally, they provide valuable insights into the molecular mechanisms underpinning the disease's pathogenesis.

Low-complexity end-to-end deep learning framework for 100G-PON

End-to-end learning allows communication systems to achieve optimal performance compared with conventional blockwise structure design. By modeling the channel with neural networks and training the transmitter and receiver on this differentiable channel, the whole system can be jointly optimized. However, in existing schemes, channel modeling methods, such as the generative adversarial network and long short-term memory network, have complex architectures and cannot track channel changes, leading to less effective end-to-end learning. Meanwhile, the complexity of neural networks deployed at the transmitter and receiver is too high for practical applications. In this work, we propose an efficient and low-complexity end-to-end deep learning framework and experimentally validate it on a 100G passive optical network. It uses a noise adaptation network to model channel response and noise distribution and employs offline pretraining and online tracking training to improve the efficiency and accuracy of channel modeling. For the transmitter, it consists of a pattern-dependent look-up table (PDLUT) based on a neural network (NN-PDLUT) with a single convolutional layer. Further, the receiver is also an NN with a single convolutional layer; thus, the end-to-end signal processing is extremely simple. The experimental results show that end-to-end learning improves the receiver sensitivity by 0.85 and 1.59 dB compared with receiver-only equalization based on Volterra nonlinear equalization (VNLE) and joint equalization based on a PDLUT and a feed-forward equalizer, respectively. Moreover, the number of multiply–accumulate operations consumed by the transmitter and receiver in the end-to-end learning scheme is reduced by 75.7% compared with VNLE-based receiver-only equalization.

Construction and analysis of the EMC evaluation model for vehicular communication systems

With the development of wireless communication technologies, vehicle-mounted wireless communication systems are changing with each passing day, and their EMC has become the key to constraining vehicles to adapt to complex electromagnetic environments. Aiming at the limitations of existing EMC evaluation methods or models for wireless communication systems and the actual needs of EMC evaluation and analysis for vehicular communication systems, a five-level novel evaluation model including working environment, signal spectrum, receiver sensitivity, antenna isolation and communication performance is constructed considering the completeness and accuracy of EMC evaluation for vehicular communication systems. The performance of the constructed model is validated using the vehicular communication system of an armored vehicle as an example. The model can evaluate whether there is interference between the working environment and the signal spectrum of the vehicle-mounted radio station. error in the reduction of the receiver sensitivity between calculation and measurement is 5.8%. The calculated isolation of the vehicle-mounted antenna is in good agreement with the measurements. The modulation mode and coding mode of the vehicle-mounted digital communication system with better performance are simulated and analyzed. When the receiver sensitivity is reduced by 6 dB, the vehicle communication distance is reduced by 50%. The simulation and measurement results show that the proposed model is suitable for the evaluation of electromagnetic compatibility of vehicular communication systems for armored vehicles.

Modulation transfer protocol for Rydberg RF receivers

We propose and demonstrate a modulation transfer protocol to increase the detection sensitivity of a Rydberg RF receiver to fields out of resonance from the transition between Rydberg levels. This protocol is based on a phase modulation of the control field used to create the Electromagnetically Induced Transparency signal. The nonlinear wave mixing of the multi-component coupling laser and the probe laser transfers the modulation to the probe laser, which is used for the RF-field detection. The measurements compare well with semi-classical simulations of atom–light interaction and show an improvement in the RF bandwidth of the sensor and an improved sensitivity of the response to weak fields.

IMPACT OF REAL-LIFE ENVIRONMENTAL EXPOSURES ON REPRODUCTION: A contemporary review of machine learning to predict adverse pregnancy outcomes from pharmaceuticals, including DDIs.

Clinical drug trials often do not include pregnant people due to health risks; therefore, many medications have an unknown effect on the developing fetus. Machine learning QSAR models have been used successfully to predict the fetal risk of pharmaceutical use during pregnancy. Those undergoing pregnancy are often excluded from clinical drug trials due to the risk that participation would pose to their health and the health of the developing fetus. However, they often require pharmaceuticals to manage health conditions that, if left untreated, could harm themselves or the fetus. This can mean that such individuals take one or more pharmaceuticals during pregnancy, many of which have unknown reproductive effects. Machine learning models have been used to successfully predict a number of reproductive toxicological outcomes for pharmaceuticals, including transplacental transfer, US Food and Drug Administration safety rating, and drug interactions. Models use quantitative chemical and structural features of active compounds as inputs to make predictions concerning the outcome of interest using computational algorithms. Models are validated and evaluated rigorously with metrics such as accuracy, area under the receiver operator curve, sensitivity, and precision. Results from these models can be a potential source of valuable information for pregnant people and their medical providers when making decisions regarding therapeutic drug use. This review summarizes current machine learning applications to make predictions about the risk and toxicity of medication use during pregnancy. Our review of the recent literature revealed that machine learning quantitative structure-activity relationship models can be used successfully to predict the transplacental transfer and the US Food and Drug Administration pregnancy safety category of pharmaceuticals; such models have also been employed to predict drug interactions, though not specifically during pregnancy. This latter topic is a potential area for future research. In this review, no single algorithm or descriptor-calculation software emerged as the most widely used, and their performances depend on a variety of factors, including the outcome of interest and combination of such algorithms and software.

Analyzing the Indirect Effects of Lightning on Unmanned Aerial Vehicle Navigation Receivers

To investigate the interference effects of lightning on unmanned aerial vehicle (UAV) navigation receivers, an analytical approach for the evaluation of indirect lightning effects is suggested. The blocking mechanism of lightning pulses on the receiver is analyzed using the vector method. A behavioral-level simulation model of the receiver’s radio frequency (RF) front-end is constructed, and a lightning pulse injection simulation is performed based on the advanced design system (ADS). The simulation results indicate that lightning pulses induce blocking interference on the RF front-end of the navigation receiver, reducing its sensitivity and dynamic range. This interference is attributed to gain compression and degradation of the noise figure in the low-noise amplifier (LNA). To further validate the simulation results, a pin injection test is carried out to examine the influence of lightning pulses with different peak voltages on receiver sensitivity. The test results indicate a notable reduction in the receiver sensitivity when the lightning pulse peak reaches 250 V. The sensitivity trends across various test panels are consistent, and the damage thresholds are similar. These findings confirm the accuracy of the simulation results.