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Background and Aims: Lynch syndrome (LS) carries a substantial lifetime risk of colorectal cancer which is currently mitigated by biennial colonoscopy surveillance. Paramount to the surveillance programme is the removal of adenomas before malignant transformation but there is an associated service burden and morbidity of repeated endoscopy. We investigated if faecal immunochemical testing (FIT) for faecal haemoglobin has the diagnostic performance to replace colonoscopy. Methods: In this retrospective cohort study, patients due to undergo planned surveillance for LS between November 2020 and April 2022 were sent two FIT kits prior to colonoscopy. Test diagnostic performance of colorectal cancer (CRC), advanced and non-advanced adenoma detection was calculated for single and double FIT strategies. A faecal-Hb of 10 µg Hb/g was considered positive. Results: In total, 78 patients, with 45 (57.7%) female, median age 52 years (IQR 41–63), completed at least one FIT and colonoscopy. The median time from FIT to colonoscopy was 47 days. A single FIT was positive in 7/30 cases of adenoma (2/3 advanced, 5/27 non-advanced). A total of 64 (82.1% of FIT1T returners) completed a second FIT. Using the greatest of the two FITs (FIT2TMAX) 8/26 (2/3 advanced, 4/23 non-advanced), patients with adenomas were identified. There were no cases of CRC. The sensitivity for adenoma detection was 23.3% and 23.1%, respectively. Conclusions: In patients with LS awaiting colonoscopy, FIT has a low sensitivity for detecting adenomas and advanced adenomas. This is not improved by the addition of a second FIT test. [ABSTRACT FROM AUTHOR]

Background: There is a dearth of studies investigating the efficacy of hydroxychloroquine in the treatment of either anogenital lichen sclerosus or extragenital lichen sclerosus, a condition that, if left untreated, could lead to a greater degree of scarring and malignant transformation. Objective: This study aims to analyze the demographic characteristics, clinicopathological features, treatment response, and outcomes of patients diagnosed with either anogenital or extragenital lichen sclerosus who received hydroxychloroquine therapy. Methods: A retrospective analysis was conducted involving 70 patients diagnosed with lichen sclerosus who underwent treatment with hydroxychloroquine at our institution between 2018 and 2023. Results: Among the cohort, 67 patients were female, and 3 were male. Extragenital lichen sclerosus was diagnosed in 23 patients, with 16 exhibiting concomitant morphea overlap. Itching was the predominant clinical presentation (67%). A notable proportion of patients (36%) had a connective tissue disorder, prompting hydroxychloroquine therapy. Among the 30 patients treated solely for lichen sclerosus, 21 demonstrated response and 9 had no response. From a broader comparison of response to hydroxychloroquine, the overall anogenital response rate was 84.6% as opposed to 50% in extragenital lichen sclerosus. The median time to initial response was 4 months. Adverse effects, predominantly mild, were observed in 10 (14.3%) patients. Limitation: This study is constrained by its retrospective nature and reliance on data from a single center, resulting in a limited sample size. Conclusion: Hydroxychloroquine demonstrates promise as a therapeutic option for anogenital lichen sclerosus because of its favorable response rates and low incidence of adverse effects. However, further investigations, including larger‐scale or prospective studies, are imperative to ascertain its definitive efficacy. [ABSTRACT FROM AUTHOR]

The air temperature is a vital measuring variable widely applied in science and engineering as it is considered one vital part of the lives of plants and animals. From the point of view of time series analysis and data-driven modeling, changes in daily 24h air temperature can be described as one time-series data. Compared to relatively complicated models, simple, efficient and of even better performance are advantages of data-driven-based concise modeling methods, especially for embedded applications which usually have limited resources such as low RAM and low processing power. This study takes the perspective of data-driven modeling and time-series analysis to present one succinct and efficient solution based on discrete cosine transform (DCT)-extended modeling for the hourly air temperature forecast (HTF) in engineering and embedded applications. Characteristics of the DCT lie in optimal de-correlation and energy compaction. Orienting upon extended-DCT modeling, one concise least-squares (LS)-extended DCT predictive algorithm for the HTF is introduced. Then one Mbed-based embedded system design utilizing the proposed LS-extended DCT predictive algorithm as well as theoretical analyses and fast computation of arbitrary length DCT is presented. Verification results indicate the suitability of the proposed predictive algorithm. Only the temperature data without other parameters being straightly employed in forecast modeling is one major advantage of the proposed data-driven DCT-based HTF method, which benefits it to be suitable for engineering and embedded applications. [ABSTRACT FROM AUTHOR]

Massive multiple input multiple output (MIMO) technology stands as a cornerstone in the evolution of 5G networks. Using an abundance of degrees of freedom, MIMO systems exploit complex spatial characteristics to improve performance. Despite its potential, designing effective 5G MIMO communication systems faces major challenges, primarily in system modeling and channel estimation. Dynamic and complex propagation environments present significant obstacles in accurately representing system behaviour and estimating channel parameters. In order to estimate the channel in the wireless network an improved deep learning based Residual recurrent neural network (ResRNN) is implemented in this paper. Firstly, the least square estimator is used to estimate or calculate the historic channel responses of a pilot block. With these collective responses of the channel, we train the responses using the new deep learning based ResRNN, in which the weight of channel parameters are selected optimally using Improved pelican optimization algorithm (IPOA). Using the IPOA-based ResRNN hybrid precoder the present channel responses can be evaluated or estimated. By varying the length of pilot sequence and by varying the size of antennas at the transmitter and receiver, the metrics such as the bit error rate (BER), mean square error and throughput for (signal to noise ratio) SNR are evaluated. Simulation results show the developed method outperforms the conventional methods by attaining a BER of 10–5 with SNR is 30 dB wireless channel access. The performance of the proposed IPOA-DERNN method is compared with the conventional channel estimation methods. [ABSTRACT FROM AUTHOR]

Abstract Background Hepatocyte death and a systemic inflammatory response are the outcome of a complex chain of events mediated by numerous inflammatory cells and chemical mediators. The point of this study was to find out if tadalafil and/or Lepidium sativum (L. sativum) could help people who have been exposed to carbon tetrachloride (CCL4) and are experiencing acute moderate liver failure. This was especially true when the two were used together. Method and materials To cause mild liver failure 24 h before sacrifice, a single oral dosage of CCL4 (2.5 mL/kg b.w.) (50% in olive oil) was utilized. Furthermore, immunohistochemical expression of nuclear factor kappa B (NF-κB) as well as histological abnormalities were performed on liver tissue. Results The results showed that tadalafil and/or L. sativum, especially in combination, performed well to cure acute mild liver failure caused by CCL4. This was demonstrated by a decrease in NF-κB expression in the liver tissue and an improvement in organ damage markers observed in the blood and liver tissues. Furthermore, such therapy reduced interleukin1 beta (IL-1β) and tumor necrosis factor-alpha (TNF-α) levels in the liver tissue. It’s worth noting that the tested combination resulted in greater liver improvement. Conclusions According to the findings, tadalafil and L. sativum, particularly in combination, have the ability to protect the liver from the negative effects of CCL4 exposure. Because of its capacity to improve liver function, restore redox equilibrium, and decrease inflammatory mediators, it is a prospective option for mitigating the negative effects of common environmental pollutants such as CCL4.

Background and Aims: Lynch syndrome (LS) carries a substantial lifetime risk of colorectal cancer which is currently mitigated by biennial colonoscopy surveillance. Paramount to the surveillance programme is the removal of adenomas before malignant transformation but there is an associated service burden and morbidity of repeated endoscopy. We investigated if faecal immunochemical testing (FIT) for faecal haemoglobin has the diagnostic performance to replace colonoscopy. Methods: In this retrospective cohort study, patients due to undergo planned surveillance for LS between November 2020 and April 2022 were sent two FIT kits prior to colonoscopy. Test diagnostic performance of colorectal cancer (CRC), advanced and non-advanced adenoma detection was calculated for single and double FIT strategies. A faecal-Hb of 10 µg Hb/g was considered positive. Results: In total, 78 patients, with 45 (57.7%) female, median age 52 years (IQR 41–63), completed at least one FIT and colonoscopy. The median time from FIT to colonoscopy was 47 days. A single FIT was positive in 7/30 cases of adenoma (2/3 advanced, 5/27 non-advanced). A total of 64 (82.1% of FIT1T returners) completed a second FIT. Using the greatest of the two FITs (FIT2TMAX) 8/26 (2/3 advanced, 4/23 non-advanced), patients with adenomas were identified. There were no cases of CRC. The sensitivity for adenoma detection was 23.3% and 23.1%, respectively. Conclusions: In patients with LS awaiting colonoscopy, FIT has a low sensitivity for detecting adenomas and advanced adenomas. This is not improved by the addition of a second FIT test.

Due to the high correlation between Effective Angular Momentum (EAM) and Length of Day (LOD) data, and the wide application of LOD prediction, this study proposes to combine EAM data with Global Navigation Satellite System (GNSS) LOD data to obtain a more accurate LOD series and attempt to provide a reasonable formal error for the EAM dataset. Firstly, tidal corrections are applied to the LOD data. A first-order difference method is proposed to identify outliers in GNSS LODR (tidal corrected LOD) data, and the EAM data are converted into LODR data using the Liouville equation. Then, the residual term and the fitted term are obtained by least squares fitting. Finally, the fitted residual terms of GNSS LODR and EAM LODR are combined by using the Kalman combination method. In this study, EAM data from the German Research Centre for Geosciences (GFZ) (2019–2022), as well as LOD data from Wuhan University (WHU) and Jet Propulsion Laboratory (JPL), are used for the Kalman combination algorithm experiment. In the Kalman combination, we consider weighted combination based on formal error. However, none of the computing centers provide an uncertainty estimation for the EAM dataset. Therefore, we simulate the combination experiment of LOD and EAM with formal error ranging from 0 to 100 us. The experiment shows that using reasonable formal error for the EAM dataset can improve the accuracy of LOD. Finally, when the formal error of EAM is 2–5 times that of the GNSS LOD formal error, i.e., the EAM formal error is between 10 and 30 us, the accuracy of the combined LOD can be improved by 10–20%. [ABSTRACT FROM AUTHOR]

The network teaching evaluation of sports professional technical courses has positive significance for the sustainable development of education. And how to establish an effective evaluation model is the key part. The research introduces the creation of in-depth learning scenarios (LS) into the network teaching of sports professional technical courses, and then constructs a new network teaching mode of sports professional technical courses. The Particle Swarm Optimization algorithm - Attention-Long Short-Term Memory network (PSO-Attention-LSTM) Chinese Emotion Classification Model (ECM) is constructed to classify the online evaluation text to realize the evaluation of online teaching. This model combines the improved PSO, Attention and LSTM classification models. The optimal number of hidden layer nodes for LSTM model is about 100, and the optimal data size for batch processing is 25. The overall error rate of online teaching teachers of male and female sports professional skill courses is 10.1%, and the overall error rate of online teaching teachers of general and advanced sports professional skill courses is 12.8%. The application effect of the creation of in-depth learning scene in the network teaching of physical education technical courses is shown. When the classification threshold is 0.6 and 0.8 respectively, the AUC of PSO-Attention-LSTM Chinese ECM is 0.821 and 0.809 respectively. The research institute has put forward that the online teaching platform of sports professional technical courses has extremely high practical application value and has been unanimously praised by network users. [ABSTRACT FROM AUTHOR]

Introduction- Diabetes mellitus (DM) is a very common disorder with distressing effects on metabolism causing various complications including bone disorders. The aim of the study was to assess the occurrence of osteoporosis in diabetics along with other factors, to closely observe the relation of diabetes with risk of fractures & osteoporosis. Material and method- DM patients visiting outpatient clinic of GMC (Government medical college), Srinagar, J&K (Jammu & Kashmir) were recruited in the study along with healthy volunteers as control. Lumbar spine (LS) and femoral neck (FN) T-score was assessed by DXA (dual x-ray absorptiometry) scan and then WHO interpretation of T-score was done to evaluate risk of osteoporosis. Data was analyzed and “P≤ 0.05 was considered as statistically significant.” Result- Mean difference in age (between diabetics and control group) and association of gender with diabetes was found non-significant. The BMI (body mass index), FBS (fasting blood sugar) and HbA1c (glycosylated hemoglobin) levels were significantly higher in diabetics than non-diabetics. Mean LS T-score was significantly lower and FN T-score was non-significantly lower in diabetics than non-diabetics. LS and FN T-score of diabetic males was significantly more than diabetic females. Risk of osteoporosis was found significantly more in diabetic females than diabetic males. Conclusion- The Study recommends screening of DM population for bone health regardless of age and sex. Further for prevention and better management of osteoporosis in DM patients, clinicians must counsel them regarding adequate nutrient intake for good glycaemic control and bone health. [ABSTRACT FROM AUTHOR]

The components of EAM are strongly correlated with LOD and play an important role in UT1-UTC and LOD prediction. However, the EAM dataset is prone to be noisy. In this study, we propose a hybrid method to reduce the noise of the EAM data and improve the accuracy of UT1-UTC and LOD predictions. We use the EOP data to denoise the EAM data, and use Kalman filtering to denoise the 1–6 days forecast of EAM. Then, we use the denoised EAM dataset to improve the UT1-UTC and LOD prediction. The denoised EAM dataset improved the prediction of UT1-UTC within 10 days by 20%. In addition, we found that by introducing two additional periodic (23.9 days and 91.3 days) components for the least-squares fitting, the accuracy of UT1-UTC and LOD prediction in the range of 30–80 days is significantly improved. In more than 430 UT1-UTC and LOD prediction experiments conducted during 2021–2022, the improvements in the 1–6 days forecast were significant. For the 6th day, 30th day, and 60th day, the MAE of UT1-UTC was 0.1592, 2.9169, and 6.7857 ms, respectively, corresponding to improvements of 31.35, 12.60, and 12.93%, respectively, when compared to predictions of Bulletin A. The MAE of LOD predictions on the 1st day, 6th day, 30th day, and 90th day was 0.0255, 0.0432, 0.1694, and 0.2505 ms, respectively, which improved by 26.09, 14.29, 6.36, and 3.76% when compared with our second EOPPCC method. [ABSTRACT FROM AUTHOR]

Immersive virtual reality technology (IVR) can create contextualized learning environments that learners cannot easily access. It is widely used in education. More and more researchers are paying attention to IVR’s influencing factors. However, most of these researches focus on the aspects of technology and environment, ignoring the aspects of learners themselves. Therefore, this paper explores the impact of learners’ attention level (AL) and learning style (LS) on learning behavior in IVR. Firstly, the AL data monitored by EEG equipment allows correlation and difference analysis to explore the relationship among AL, LS and learning performance (LP). Then, according to the video-recorded learning behavior data, the lag sequence analysis method is used to analyze the learning behavior sequence transformation of the high-concentration group and low-concentration group so that it can explore the problem learning behaviors of learners with different concentrations. The results show that: in the virtual learning environment (Vir-LE), there is a strong positive correlation between AL and LP. There is no significant difference in LP with different LS, but the AL of visual learners is higher than that of verbal learners. Through this experiment, it is helpful to eliminate the interference factors in the range of human subjective perception and improve the accuracy of the measurement of learning effect.

Generalized frequency division multiplexing (GFDM) is one of the candidate waveforms beyond 5G for wireless communication. Channel estimation is challenging in wireless transmission because of inherent interference. It is based on a pilot signal that uses Least Square (LS) and Normalized Least Mean Square (NLMS) to perform the estimation. This paper used the Artificial Neural Network (ANN) as a channel estimation method, considered a novel estimation process for the GFDM transceiver. The channel estimation based on ANN depends on the data set generated from LS estimation, which considers the proposed method is optimized to LS based on the backpropagation neural network. The ANN algorithm considered a fitness function to estimate the channel. Levenberg-Marquardt backpropagation (LM), Bayesian Regularization backpropagation (BR), and Scaled Conjugate Gradient backpropagation (SCG) training methods training by using the Matlab toolbox used to perform the estimation. BR training method gives the best performance than LS and other training methods at 22 neurons in hidden layers, which at 20dB give BER = 0.0369 that enhanced over LS by 0.08.

(1) Background: Sentinel lymph node biopsy is important in the search for metastases, especially in patients with malignant breast disease. Our study proposed new techniques to prevent complications such as possible postoperative seroma formation, pain or hypoesthesia of the axillary cord and medial arm surface, as well as motor deficits, to avoid disabling outcomes and presents initial data from our experience with the sentinel lymph node biopsy technique. (2) Methods: We mainly used two radioactive tracer detection techniques and a new technique using a radiotracer called Sentimag-magtrace. The positive lymph node was located and removed to perform histologic analysis. In our study, we evaluate 100 patients who underwent breast cancer surgery. (3) Results: We calculated the identification rates of the different methods of sentinel lymph node detection and found that it was 88.9% using radioactive tracers vs. 89.5% using the magnetic tracer technology (Sentimag). (4) Conclusions: Thus, this technique avoids radiation exposure for both patients and health care providers, and can reduce costs and time. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this work, two machine learning (ML)‐based structures for joint detection‐channel estimation in OFDM systems are proposed and extensively characterized. Both ML architectures, namely Deep Neural Network (DNN) and Extreme Learning Machine (ELM) are developed for provide improved data detection performance and compared with the conventional matched filter (MF) detector equipped with the minimum mean square error (MMSE) and least square (LS) channel estimators. The bit‐error‐rate (BER) performance vs computational complexity trade‐off is analyzed, demonstrating the superiority of the proposed DNN‐OFDM and ELM‐OFDM detectors methodologies. [ABSTRACT FROM AUTHOR]

Due to the high correlation between Effective Angular Momentum (EAM) and Length of Day (LOD) data, and the wide application of LOD prediction, this study proposes to combine EAM data with Global Navigation Satellite System (GNSS) LOD data to obtain a more accurate LOD series and attempt to provide a reasonable formal error for the EAM dataset. Firstly, tidal corrections are applied to the LOD data. A first-order difference method is proposed to identify outliers in GNSS LODR (tidal corrected LOD) data, and the EAM data are converted into LODR data using the Liouville equation. Then, the residual term and the fitted term are obtained by least squares fitting. Finally, the fitted residual terms of GNSS LODR and EAM LODR are combined by using the Kalman combination method. In this study, EAM data from the German Research Centre for Geosciences (GFZ) (2019–2022), as well as LOD data from Wuhan University (WHU) and Jet Propulsion Laboratory (JPL), are used for the Kalman combination algorithm experiment. In the Kalman combination, we consider weighted combination based on formal error. However, none of the computing centers provide an uncertainty estimation for the EAM dataset. Therefore, we simulate the combination experiment of LOD and EAM with formal error ranging from 0 to 100 us. The experiment shows that using reasonable formal error for the EAM dataset can improve the accuracy of LOD. Finally, when the formal error of EAM is 2–5 times that of the GNSS LOD formal error, i.e., the EAM formal error is between 10 and 30 us, the accuracy of the combined LOD can be improved by 10–20%.

Abstract Broadband Power Line Communication (BB-PLC) technology enables data transmission for smart grid applications. Nevertheless, channel equalizers are required in the receiver to estimate and compensate for the nonlinear time-variant impulse response and noise interference effects introduced by the BB-PLC channel. In this paper, we append the Particle Swarm Optimization (PSO) algorithm and its proposed improved version to the commonly used Least-Square (LS) and Linear Minimum Mean Square Error (LMMSE) algorithms to advance four new blocktype pilot-aided hybrid channel estimation algorithms for low-voltage Orthogonal Frequency Division Multiplexing (OFDM)-based BB-PLC systems. Extensive numerical simulation results for four different M-QAM formats (M = 8, 16, 32, 64) show that the proposed algorithms significantly improve the performance of the traditional LS and LMMSE estimators, at least for the parameters of the BB-PLC system studied in this work. In addition, the computational load complexity of the PSO-inspired LMMSE algorithm is lower compared to the conventional LMMSE estimator.

Many studies have been published to address the growing issues in wireless communication systems. Space-Time Block Coding (STBC) is an effective and practical MIMO-OFDM application that can address such issues. It is a powerful tool for increasing wireless performance by coding data symbols and transmitting diversity using several antennas. The most significant challenge is to recover the transmitted signal through a time-varying multipath fading channel and obtain a precise channel estimation to recover the transmitted information symbols. This work considers different pilot patterns for channel estimation and equalization in MIMO-OFDM systems. The pilot patterns fall under two general types: comb and block types, with a proper arrangement suitable to the multiple transmit antennas. The two main channel estimation methods, LS and MMSE, are compared by evaluating performance in terms of Bit Error Rate (BER) to analyze the performance of pilot-aided channel estimation for 2x2 and 4x4 MIMO arrangements utilizing LTE parameters and the effects of modifying different numbers of OFDM subcarriers under different channel models It has been discussed, a 4x4 system performs better than a 2x2 system in terms of BER with an acceptable amount of additional complexity.

Xinggao sheep are a breed of Chinese domestic sheep that are adapted to the extremely cold climatic features of the Hinggan League in China. The economically vital reproductive trait of ewes (litter size, LS) and productive traits of lambs (birth weight, BWT; weaning weight, WWT; and average daily gain, ADG) are expressed in females and later in life after most of the selection decisions have been made. This study estimated the genetic parameters for four traits to explore the genetic mechanisms underlying the variation, and we performed genome-wide association study (GWAS) tests on a small sample size to identify novel marker trait associations (MTAs) associated with prolificacy and growth. We detected two suggestive significant single-nucleotide polymorphisms (SNPs) associated with LS and eight significant SNPs for BWT, WWT, and ADG. These candidate loci and genes also provide valuable information for further fine-mapping of QTLs and improvement of reproductive and productive traits in sheep. [ABSTRACT FROM AUTHOR]

The performance of OFDM system depends on the how efficient channel estimation is, which directly impactsits bit error rate (BER). In this research work, the novel technique of pseudo-pilot aided OFDM system is usedits channel estimation with least square and Recursive Least Squares approach. In proposed technique, for channel estimation Pseudo-pilot technique is used in OFDM system over AWGN fading channels. A hybridization technique has been implemented to alleviate the error rate in the signals. The hybrid optimization approach is implemented on the estimated output for the optimum solution. The combination of the two optimization techniques is particle swarm optimization (PSO) and Moth Flame Optimization (MFO) method that is used for optimization of the performance rate. To get more optimum solutions (reducing BER) OFDM system is trained with neural network. The performance of the proposed techniques and the weighted scheme are compared and verified using computerized simulation carried out using Matrix Laboratory software. The hybrid approach is able to achieve low BER of the network. The BER of PSO is 1.004 × 10−7 whereas for hybrid optimization (PSO + MFO) BER is 6.275 × 10−8 at 18 dB SNR. After training of the whole system is done with BPNN which will further reduce the BER while increasing SNR. By using BPNN, BER will further reduce to 1.243 × 10−8 at 18 dB SNR. It means hybrid optimization is done to optimize the performance of the channel and reduce the BERs, which will help to increase the channel estimation process as well as channel capacity and further reduces the losses while transmission from one end to the other end. [ABSTRACT FROM AUTHOR]

Generalized frequency division multiplexing (GFDM) is one of the candidate waveforms beyond 5G for wireless communication. Channel estimation is challenging in wireless transmission because of inherent interference. It is based on a pilot signal that uses Least Square (LS) and Normalized Least Mean Square (NLMS) to perform the estimation. This paper used the Artificial Neural Network (ANN) as a channel estimation method, considered a novel estimation process for the GFDM transceiver. The channel estimation based on ANN depends on the data set generated from LS estimation, which considers the proposed method is optimized to LS based on the backpropagation neural network. The ANN algorithm considered a fitness function to estimate the channel. Levenberg-Marquardt backpropagation (LM), Bayesian Regularization backpropagation (BR), and Scaled Conjugate Gradient backpropagation (SCG) training methods training by using the Matlab toolbox used to perform the estimation. BR training method gives the best performance than LS and other training methods at 22 neurons in hidden layers, which at 20dB give BER = 0.0369 that enhanced over LS by 0.08. [ABSTRACT FROM AUTHOR]

Today, GPS-free drone localization is increasingly gaining attention in various applications, but it faces significant accuracy challenges in three-dimensional (3D) space due to various impairments. This study investigates the effects of carrier frequency offset (CFO), phase noise (PN), and down-tilted base station (BS) antennas on drone positioning and tracking. Additionally, we explore the impact of inter-site distance (ISD) and BS density on drone position estimation accuracy. In our methodology, we consider a flying drone equipped with a single transmission antenna and BSs configured with 4 × 4 antennas under specific impairments. We first analyze the effects of these impairments on the signal's covariance matrix. Then, using the MUSIC algorithm, we estimate the azimuth and elevation angles, which serve as the basis for drone localization using the Least Squares (LS) method across all BSs. Finally, the estimated positions feed into an Extended Kalman Filter (EKF) for tracking. Our results present a sequential analysis of the impact of all impairments on the off-diagonal covariance matrix, on the Angle of Arrival (AOA) estimation and 3D drone localization. We use simulations to demonstrate how hardware impairments affect 3D drone localization accuracy under varying ISD and BS densities. [ABSTRACT FROM AUTHOR]

Competing Interests: Conflicts of interest Author Afvari serves as a paid consultant of Unity Biotechnology. Dr LaChance is a PI for a research grant from Pfizer looking to explore the role of the JAK/STAT pathway in connective tissue disease. Authors Lim, Nguyen, and Kanwar and Drs Kraus and Shahriari have no conflicts of interest to declare.

The components of EAM are strongly correlated with LOD and play an important role in UT1-UTC and LOD prediction. However, the EAM dataset is prone to be noisy. In this study, we propose a hybrid method to reduce the noise of the EAM data and improve the accuracy of UT1-UTC and LOD predictions. We use the EOP data to denoise the EAM data, and use Kalman filtering to denoise the 1–6 days forecast of EAM. Then, we use the denoised EAM dataset to improve the UT1-UTC and LOD prediction. The denoised EAM dataset improved the prediction of UT1-UTC within 10 days by 20%. In addition, we found that by introducing two additional periodic (23.9 days and 91.3 days) components for the least-squares fitting, the accuracy of UT1-UTC and LOD prediction in the range of 30–80 days is significantly improved. In more than 430 UT1-UTC and LOD prediction experiments conducted during 2021–2022, the improvements in the 1–6 days forecast were significant. For the 6th day, 30th day, and 60th day, the MAE of UT1-UTC was 0.1592, 2.9169, and 6.7857 ms, respectively, corresponding to improvements of 31.35, 12.60, and 12.93%, respectively, when compared to predictions of Bulletin A. The MAE of LOD predictions on the 1st day, 6th day, 30th day, and 90th day was 0.0255, 0.0432, 0.1694, and 0.2505 ms, respectively, which improved by 26.09, 14.29, 6.36, and 3.76% when compared with our second EOPPCC method.

This paper aims to improve the channel estimation (CE) in the indoor visible light communication (VLC) system. We propose a system that depends on a comparison between Deep Neural Networks (DNN) and Kalman Filter (KF) algorithm for two optical modulation techniques; asymmetrically clipped optical-orthogonal frequency-division multiplexing (ACO-OFDM) and direct current optical-orthogonal frequency division multiplexing (DCO-OFDM). The channel estimation can be evaluated by changing the rate of errors in the received bits, where increased error means a performance decrease of the system and vice versa. Receiving less errors at the receiver indicates improved channel estimation and system performance. Thus, the main aim of our proposal is decreasing the error rate by using different estimators. Using the simulation results with the metric parameter of bit error rate (BER) aims to determine the improvement ratio between different systems. The proposed model is trained with OFDM signal samples with labels, where the labels represent the received signal after applying OFDM travelling across the medium. At a BER = 10–3 with DCO-OFDM, the DNN outperforms KF with 1.6 dB (7.6%) at the bit energy per noise (E b / N o) axis. Also, for ACO-OFDM at BER = 10–3, the DNN achieves better results than KF by about 1.3 dB (8.12%) at the (E b / N o). At different values of M in QAM, the DNN outperforms KF for ACO-OFDM by average improvement of ~ 1 dB (~ 11.5%). [ABSTRACT FROM AUTHOR]

Broadband Power Line Communication (BB-PLC) technology enables data transmission for smart grid applications. Nevertheless, channel equalizers are required in the receiver to estimate and compensate for the nonlinear time-variant impulse response and noise interference effects introduced by the BB-PLC channel. In this paper, we append the Particle Swarm Optimization (PSO) algorithm and its proposed improved version to the commonly used Least-Square (LS) and Linear Minimum Mean Square Error (LMMSE) algorithms to advance four new blocktype pilot-aided hybrid channel estimation algorithms for lowvoltage Orthogonal Frequency Division Multiplexing (OFDM)- based BB-PLC systems. Extensive numerical simulation results for four different M-QAM formats (M = 8, 16, 32, 64) show that the proposed algorithms significantly improve the performance of the traditional LS and LMMSE estimators, at least for the parameters of the BB-PLC system studied in this work. In addition, the computational load complexity of the PSO-inspired LMMSE algorithm is lower compared to the conventional LMMSE estimator. [ABSTRACT FROM AUTHOR]

(1) Background: Sentinel lymph node biopsy is important in the search for metastases, especially in patients with malignant breast disease. Our study proposed new techniques to prevent complications such as possible postoperative seroma formation, pain or hypoesthesia of the axillary cord and medial arm surface, as well as motor deficits, to avoid disabling outcomes and presents initial data from our experience with the sentinel lymph node biopsy technique. (2) Methods: We mainly used two radioactive tracer detection techniques and a new technique using a radiotracer called Sentimag-magtrace. The positive lymph node was located and removed to perform histologic analysis. In our study, we evaluate 100 patients who underwent breast cancer surgery. (3) Results: We calculated the identification rates of the different methods of sentinel lymph node detection and found that it was 88.9% using radioactive tracers vs. 89.5% using the magnetic tracer technology (Sentimag). (4) Conclusions: Thus, this technique avoids radiation exposure for both patients and health care providers, and can reduce costs and time.

Clear cell carcinoma (CCC) of the diaphragm is rare, with an origin that is reported to be associated with malignant transformation of extraperitoneal endometriosis. Lynch syndrome (LS) is an autosomal dominant hereditary cancer syndrome caused by germline pathogenic variants in one of the DNA mismatch repair (MMR) genes, MLH1, MSH2, MSH6 and PMS2. Women with LS have a significantly increased lifetime risk of endometrial and ovarian cancer. CCC is a common histology of endometriosis- and LS-associated malignancy. The present study describes the case of a 51-year-old woman with an intra-abdominal mass found during a routine physical examination. The patient had undergone total hysterectomy and bilateral adnexectomy for atypical endometrial hyperplasia (AEH) and ovarian endometriosis, respectively, 3 years previously. Enhanced computed tomography showed a mass on the surface of the liver. Laparoscopic examination of the abdominal cavity revealed a tumor on the underside of the right diaphragm, which was then surgically excised. Pathological examination of the excised tumor, along with immunohistochemistry, led to a diagnosis of CCC. Since LS was suspected due to the genetic family history of the patient, microsatellite instability analysis was performed on the diaphragmatic tumor, and the results were positive. Immunohistochemistry was performed for MMR proteins in AEH and CCC cells, both of which revealed loss of MSH2 and MSH6 expression. Following detailed genetic counseling, genetic testing of MMR genes was performed, revealing a germline pathogenic variant in MSH2 (c.1000C>T, p.Gln344*), thus confirming the diagnosis of LS. To the best of our knowledge, this is the first case report of concurrent diaphragmatic CCC and LS. Patients with LS and endometriosis are at risk of developing ovarian cancer or intra-abdominal malignant tumors. In addition, immunohistochemistry screening for MMR proteins should be considered in patients with AEH and a family history of LS-related cancer, to enable early clinical intervention in cases of endometrial cancer. [ABSTRACT FROM AUTHOR]

Objective: Examine the impact of COVID-19 pandemic on the outcomes in patients with CLTI or DFI. Background: Patients with CLTI and/or DFI are at risk of amputations if not treated in a timely manner. Methods: We compared the outcomes in patients with CLTI or DFI during 2 periods; Period 1[ P 1] (15/03/2019-31/05/2019) and period 2[ P 2] (15/03/ 2020-31/05/2020- corresponding to COVID-19 pandemic). Results: One hundred thirty-nine patients were treated in P1 [mean age 70 years (±11), Male:Female = 102:37] whereas 95 patients were treated in P2 [mean age 67 (±12), Male:Female = 64:31]. The 2 cohorts were matched regarding Rutherford category (P = 0.25) and GLASS classification (P = 0.38). Notably, the time from onset of symptom to clinical presentation was significantly longer [31 (1-105) days vs 27 (0–78) days, (P = 0.017)], whereas the time from presentation to first intervention was significantly shorter [3 (0–61) days vs 5 (0–65) days, (P = 0.013)] in P2 compared to P1. There was a significantly higher white cell count (P = 0.014) and CRP (P = 0.004) on admission in P2. Having treatment for CLTI or DFI in P2 was an independent predictor of worse primary patency rate and freedom from major adverse limb events. At 90 days, amputation-free survival and limb salvage were noticeably worse in P2 compared to P1 (amputation-free survival was 80% and 87% whereas limb salvage was 64% and 72% in P2 and P1, respectively). Conclusions: Patients with CLTI and DFI experienced a significantly delayed presentation with features of sepsis on admission in P2. Treatment in P2 was a predictor of worse primary patency and freedom from major adverse limb events and therefore close and long follow-up is advisable. [ABSTRACT FROM AUTHOR]

Optical Wireless Communication (OWC) is a new trend in communication systems to achieve large bandwidth, high bit rate, high security, fast deployment, and low cost. The basic idea of the OWC is to transmit data on unguided media with light. This system requires multi-carrier modulation such as Orthogonal Frequency Division Multiplexing (OFDM). This paper studies optical OFDM performance based on Intensity Modulation with Direct Detection (IM/DD) system. This system requires a non-negativity constraint. The paper presents a framework for wireless optical OFDM system that comprises (IM/DD) with different forms, Direct Current biased Optical OFDM (DCO-OFDM), Asymmetrically Clipped Optical OFDM (ACO-OFDM), Asymmetrically DC-biased Optical OFDM (ADO-OFDM), and Flip-OFDM. It also considers channel coding as a tool for error control, channel equalization for reducing deterioration due to channel effects, and investigation of the turbulence effects. The evaluation results of the proposed framework reveal enhancement of performance. The performance of the IM/DD-OFDM system is investigated over different channel models such as AWGN, log-normal turbulence channel model, and ceiling bounce channel model. The simulation results show that the BER performance of the IM/DD-OFDM communication system is enhanced while the fading strength is decreased. The results reveal also that Hamming codes, BCH codes, and convolutional codes achieve better BER performance. Also, two algorithms of channel estimation and equalization are considered and compared. These algorithms include the Least Squares (LS) and the Minimum Mean Square Error (MMSE). The simulation results show that the MMSE algorithm gives better BER performance than the LS algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we have developed and implemented Minimum Mean Square Channel Estimation with Compressive Sensing (MMSE-CS) algorithm in MIMO-OFDM systems. The performance of this algorithm is analyzed by comparing it with Least Square channel estimation with compressive sensing (LS-CS), Least Square (LS) and Minimum Mean Square Estimation (MMSE) algorithms. It is observed that the performance of MMSE-CS in terms of Bit Error Rate (BER) metric is definitely better than LS-CS and LS algorithms and it is at par with MMSE algorithm. Moreover the role of compressive sensing theory in channel estimation is accentuated by the fact that in MMSE-CS algorithm only a very small number of channel coefficients are sensed to recreate the transmitted data faithfully as compared to MMSE algorithm.

Low-dimensional chaotic maps are used for encrypting images because of their simplicity and low implementation cost. However, such chaotic systems are less secure and are slow in encryption speed as compared to multi-dimensional chaotic systems. This paper proposes the combination of spatial domain and frequency domain for image encryption. The proposed combination uses Four-dimensional chaotic map Lorenz System (LS) for key generation and alternate logistic structure for permutation-diffusion process. Initially, Lorenz System (LS) generates the security key using matrices color image's R, G, and B components. Secondly, one dimensional and two dimensional chaotic structures are used to generate a chaos matrix i.e. the two logistic maps are iterated alternately for diffusing the matrix. Finally, the diffused plain image matrix is encoded into the phase and amplitude of the composite function which is encrypted with white noise distribution by using the fast Fourier transform. The simulated outcomes describe that the proposed approach increases the key space value, encryption speed and resists brute force attacks. [ABSTRACT FROM AUTHOR]

Human emotion is often a reflection of one's behaviour that leads to awareness of one's mental health. Emotion detection on Twitter users specifically gains attention as it generates information that is useful in the field of psychology and linguistics. However, most existing works related to the subject analysed the emotion of a tweet based on the number of keywords or phrases in it, thus resulting in false annotations and eventually false judgement of the user's emotion. This paper proposes a two-step model with high and reliable accuracy to detect the emotion of Twitter users based on the semantic meaning of the tweets they posted. The first step classified the emotion of each tweet using four different deep learning techniques. The second step detected the emotion of the user based on the proposed statistical post-level features and a boosting technique. Then, Kappa's agreement score method was implemented to validate both models. The first step achieved 0.9482 in accuracy while the second step achieved 0.9683 in accuracy. To further validate such highly accurate result, a case study was conducted, and a web-based system was developed to analyse the emotion of college students during the COVID-19 pandemic. Additionally, our analysis corresponded to most of the current reports on the pandemic which further proved the reliability of the developed model. [ABSTRACT FROM AUTHOR]

Channel estimation (CE) plays a crucial role in establishing a wireless link, specifically at the receiver node. Most of the receivers that estimate the channel is in the presence of AWGN. However, these schemes perform expressively worse when the impulsive noise is added in AWGN which is introduced by manmade sources (pressure cooker, motorbike, electric supply) as well as natural noises (earthquakes and thundering). The major contribution of this research is to analyze the channel estimation schemes in the Gaussian mixture model (GMM) environment. The performance of channel estimation schemes has been compared in terms of mean square error (MSE) and bit error rate (BER). Four channel estimation schemes e.g., MMSE, DFT, correlation- based methods like Gauss-Seidel (GS) and Successive Over-Relaxation (SOR), are studied and analyzed. The study reveals that the correlation scheme based on the method of SOR is more effective as compared to the methods of DFT, MMSE and GS because of faster convergence rate along with the minimum number of iteration. SOR shows sustainable results up to the probability of an impulsive element of 5 Percent. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of encirclement rotating tracking for surface formation targets by multiple agents. Unlike most existing studies in which the agent trajectory is considered as a circle with a prescribed radius, the ellipse trajectories of agents changing with formation targets are addressed in this paper. The agents can only measure the bearing information of targets with unknown bounded velocity and acceleration. A least-square (LS) estimator is presented to localize each target and estimate the geometric center of the formation. Then, the target formation is approximated by an ellipse and the corresponding extended ellipse target model is established. To control agent behavior more conveniently, the motion of agent is decomposed into rotation and translation. On this basis, a finite-time control protocol is proposed for agents to travel along the desired trajectory around the extended target within finite time. Finally, a numerical experiment is performed to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, two techniques of channel estimation are studied. For both, a statistical estimation of the channel in the time domain is discussed. Assuming that the transmission is performed on a flat fading channel, the two estimation methods MMSE and LS are tested and compared. A reduction of the complexity of the MMSE method by simplifying the estimation matrix is put to the test to find a compromise between complexity and performance. The results presented are the error rate of symbol SER and the quadratic mean of the error MSE as a function of the signal-to-noise ratio SNR. [ABSTRACT FROM AUTHOR]