Miracle-dz/UniFAR-FTUs huggingface dataset:
Phasor Estimation by EMD-Assisted Prony

The use of synchronized measurement technology leads to a more reliable and secure operation of the power system. Phasor calculation is needed for all buses where a phasor measurement unit is installed, and fast and precise estimation is necessary for accurate monitoring, protection and control. The Prony algorithm is one promising method due to its capability to estimate phasor adaptively with changing frequency. The algorithm projects the signal on <inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula> exponentially damped sine functions, where <inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula> is the order of the Prony. However, the order under different conditions should be adapted automatically to reduce computation time. To adaptively specify the order, the empirical mode decomposition (EMD) method is proposed in this paper to be combined with the Prony and named EMD-Prony. The EMD decomposes a signal into finite single oscillatory modes, representing the number of modes in the signal. EMD is also used as a pre-processing step to filter noise from an input signal of Prony. Therefore, EMD is proposed here as an assistant of Prony in a phasor estimation. Finally, the proposed method is tested on the benchmark signals proposed in the IEEE standard, signals obtained from a simulated power system, and measured data from a real-world power system.
Mitogen-Activated Protein Kinase and Substrate Identification in Plant Growth and Development

Mitogen-activated protein kinases (MAPKs) form tightly controlled signaling cascades that play essential roles in plant growth, development, and defense response. However, the molecular mechanisms underlying MAPK cascades are still very elusive, largely because of our poor understanding of how they relay the signals. The MAPK cascade is composed of MAPK, MAPKK, and MAPKKK. They transfer signals through the phosphorylation of MAPKKK, MAPKK, and MAPK in turn. MAPKs are organized into a complex network for efficient transmission of specific stimuli. This review summarizes the research progress in recent years on the classification and functions of MAPK cascades under various conditions in plants, especially the research status and general methods available for identifying MAPK substrates, and provides suggestions for future research directions.
Antibacterial Activity of Endemic Artocarpus nobilis Thw Found in Sri Lanka

Aims: To evaluate antibacterial activity of aqueous, methanol, dichloromethane, and hexane extracts of Artocarpus nobilis Thw. leaves and stem bark against Escherichia coli and Staphylococcus aureus using agar well diffusion method. Methodology: Matured fully expanded leaves and stem bark parts of Artocarpus nobilis were collected, air-dried, and grounded. The extraction was obtained using a decoction extraction method. Antibacterial activity was performed against Staphylococcus aureus (ATCC® 25923TM) and Escherichia coli (ATCC® 25922TM) using agar well diffusion method and gentamicin was used as a positive control. The whole experiment was done in triplicates and the diameter of the inhibition zone (in mm) was measured and recorded. Results: Results showed that aqueous bark extract (EC50 4.286 mg/mL) showed the highest efficacy and potency against E. coli while methanol bark extract (EC50 4.427 mg/mL) showed the highest efficacy and potency against S. aureus. R² and P values for aqueous, methanol, dichloromethane, and hexane extracts indicated that there was a strong, statistically significant correlation (P ≤ 0.05) between concentration and zone of inhibition for all extracts of A. nobilis against E. coli and S. aureus. Conclusion: This study showed that aqueous and methanol bark extracts of Artocarpus nobilis have marked in vitro dose-dependent antibacterial activity against E. coli and S. aureus respectively. Further studies are necessary to ascertain the mechanism and the active constituents responsible for the antibacterial activity of the of plant parts of Artocarpus nobilis.
Heart Failure Treatments Such As Angiotensin Receptor/Neprilysin Inhibitor Improve Heart Failure Status and Glucose Metabolism

A recent study suggested that angiotensin receptor/neprilysin inhibitor (ARNI; sacubitril/valsartan) can improve functional capacity and cardiac reverse remodeling in patients with heart failure with reduced ejection fraction (HFrEF). Another study suggested that ARNI reduced glycated hemoglobin (HbA1c) in patients with diabetes and HFrEF; however, the details of its efficacy are unknown. We herein report a case of HFrEF with abnormal glucose metabolism in which ARNI was initiated. On the 7th day of admission (before the initiation of ARNI), blood tests showed an abnormal glucose metabolism as follows: fasting blood glucose 134 mg/dL; and fasting blood insulin 11.4 µU/mL (homeostasis model assessment of insulin resistance (HOMA-IR) index 3.77; homeostasis model assessment of β-cell function (HOMA-β), 57.8%). On the 23rd day after the initiation of ARNI, even though the patient was not using hypoglycemic drugs, his fasting blood glucose markedly decreased to 70 mg/dL without hypoglycemic symptoms, and his fasting blood insulin decreased to 5.4 µU/mL (HOMA-IR decreased to 0.93, HOMA-β increased to 277.7%). These results imply that ARNI has the potential to improve insulin resistance and the islet beta-cell function in patients with heart failure, in addition to the original effect of improving the hemodynamics, although the effect of improving the glucose metabolism is also considered to have been influenced by the improvement of the heart failure status and other drugs that the patient was taking.
Urban Security Challenges: A Review of Crime Prevention Strategies Adopted by Residents in Greater Port Harcourt City, Rivers State, Nigeria

The vulnerability to the insecurity of cities, and how best to address them, have been subject to considerable discussions. With the rising crime rate in cities in Nigeria, households have provided for their security, particularly with structures in the building, streets and neighbourhoods. Observation of streets and houses in Greater Port Harcourt City Area show high fences (some with the addition of razor wires), gates to streets manned by security personnel, use of CCTV, electrified gates and fences, burglary proofing of windows, doors and even roofs. This research reviews strategy adopted by residents in curbing crime across different neighbourhoods of Greater Port Harcourt City. The study utilizes a survey research methodology based on data obtained from four hundred and ninety–eight (498) respondents drawn from four neighbourhoods selected purposively (three from planned neighbourhoods in the inner city and one large neighbourhood in the peri-urban area). The study followed a multi-stage framework. Five streets in the inner city and fifteen streets in the peri-urban area were randomly selected in the study neighbourhoods. This was followed by the selection of twenty houses on each street using the systematic sampling method. In each house, one household head was randomly picked for questionnaire administration. The results revealed that the use of urban design elements to control crime between the inner-city neighbourhoods and the neighbourhoods in the urban periphery of the city differed significantly. The inner-city made greater use of natural surveillance to control crime whereas the peri-urban area utilized target hardening. State actors and non-state actors were used for crime control across the neighbourhoods; however, income status was a key determinant of preference. The high income was favourable to the formal instrument of state power, the police while the low income preferred the non-state actors, the local vigilante. This study recommends an integrated crime prevention strategy for the entire city; adequate funding of security by the Government, the provision of amenities and street furniture, the use of neighbourhood plans that promotes defensible space in emerging areas, the introduction of technology and community policing in crime prevention will achieve far greater long-term security for the Greater Port Harcourt metropolis. Keywords: Urban security, Crime prevention, Neighbourhood, Greater Port Harcourt City.
Predicting Childhood Obesity Using Machine Learning: Practical Considerations

Previous studies demonstrate the feasibility of predicting obesity using various machine learning techniques; however, these studies do not address the limitations of these methods in real-life settings where available data for children may vary. We investigated the medical history required for machine learning models to accurately predict body mass index (BMI) during early childhood. Within a longitudinal dataset of children ages 0–4 years, we developed predictive models based on long short-term memory (LSTM), a recurrent neural network architecture, using history EHR data from 2 to 8 clinical encounters to estimate child BMI. We developed separate, sex-stratified models using 80% of the data for training and 20% for external validation. We evaluated model performance using K-fold cross-validation, mean average error (MAE), and Pearson’s correlation coefficient (R2). Two history encounters and a 4-month prediction yielded a high prediction error and low correlation between predicted and actual BMI (MAE of 1.60 for girls and 1.49 for boys). Model performance improved with additional history encounters; improvement was not significant beyond five history encounters. The combined model outperformed the sex-stratified models, with a MAE = 0.98 (SD 0.03) and R2 = 0.72. Our models show that five history encounters are sufficient to predict BMI prior to age 4 for both boys and girls. Moreover, starting from an initial dataset with more than 269 exposure variables, we were able to identify a limited set of 24 variables that can facilitate BMI prediction in early childhood. Nine of these final variables are collected once, and the remaining 15 need to be updated during each visit.
Numerical study of terahertz circular polarized substrate lens dipole antennas for polarization sensitive detectors

Wide band circular polarized (CP) antennas behind extended hemispherical lenses suitable for polarization sensitive THz detector and wireless communication have been designed and characterized using numerical simulation. Two novel, compact and CP dipole antennas are designed and studied for this purpose. CP property of planar antennas is achieved by geometrical modifications of antennas without any complicated feeding structure. Due to compact dimensions, wideband performance and CP behavior, these designs have applications in circular dichroism (CD) spectroscopy and terahertz detectors. This numerical study deals with polarization diversity with substrate lens, effect of off axis displacement on CP behavior of lens antenna which determines number of pixels for any application, far field patterns variation due to lens's internal reflection, directivity variation attributed to internal reflection and losses. Radiation efficiency variation of antenna by antenna dimensions is also studied carefully to design appropriate lens from application's point of view. Off axis performance of antenna on hemisphere lens is also studied and redesigning of antenna by some geometric modification has been carried out to improve far field patterns.
Adversarial Regressive Domain Adaptation Approach for Infrared Thermography-Based Unsupervised Remaining Useful Life Prediction

Infrared thermography provides abundant spatiotemporal degradation information, facilitating non-contact condition monitoring. Reducing domain shift between simulated and industrial infrared images is significantly desired for leveraging labeled simulated data to tackle practical insufficiency of run-to-failure samples. Recently, adversarial-based domain adaptation (DA) techniques have aroused broad concern in solving domain shifts. However, simultaneously aligning marginal and conditional distributions in cross-domain remaining useful life (RUL) prediction is rarely researched in adversarial-based DA. In this article, an adversarial regressive domain adaptation (ARDA) approach is, thus, put forward to address this challenge. First, a regressive disparity discrepancy is designed to describe the dissimilarity between distributions and derive the generalization bound for cross-domain prognostics. Guided by this bound, the ARDA effectively aligns marginal and conditional distributions by learning indistinguishable features and considering the relationship between samples and prediction tasks. Simulated and experimental infrared degradation image datasets are used to demonstrate the effectiveness and superiority of the proposed approach over existing methods for cross-domain RUL prediction.
Kalibrierung elektrochemischer Sensoren unter Nutzung genetischer Algorithmen

Eine neue Methode zur Kalibrierung elektrochemischer Sensoren wird vorgestellt. Nach einer Einführung zur Kalibriertechnik elektrochemischer Sensoren wird die Anwendung genetischer Algorithmen am Beispiel der Kalibrierung eines elektrochemischen Wasserstoffperoxid-Sensors gezeigt. Die Eigenschaften der genetischen Algorithmen werden erläutert und mögliche Anwendungen diskutiert. Das neue Kalibrierverfahren hat Vorteile im Vergleich mit der Regressionsanalyse.
3-D Parameterized Multipath Channel Estimation for Massive MIMO–OFDM Systems

In this article, we propose a three-dimensional (3-D) joint angle and delay estimation approach for massive multiple-input and multiple-output orthogonal frequency division multiplexing systems on the basis of the channel state information of a single base station, which is equipped with multiple antennas. By modeling the propagation channel by a finite number of multipath rays, we present a joint azimuth-angle, elevation-angle, and path delay estimation algorithm, in which a new version of subcarrier smoothing is developed for full-rank correlation matrix construction. The new algorithm offers closed-form parameter estimates and is applicable to arbitrary array configurations. The Cramér–Rao bound for the considered problem is derived as well. Simulation results with a typical outdoor millimeter-wave setup are finally presented to verify the efficacy of the proposed approach.
Enabling NSS Engineering Development Planning via Novel Knowledge Management Strategies, Tools, and Technologies

This paper studies the specific challenges presented by National Security Space (NSS) Development planning (DP) and explores how Knowledge Management (KM) can address the organizational and engineering challenges associated with our mission. We focus on both early successes and longer term strategic planning. This paper reviews the enabling KM tools and technologies that interconnect the multiorganizational activities associated with the engineering and DP of large complex technical programs that deliver operational capabilities. This paper illustrates the challenges associated with this engineering process, and how KM technology deployment would facilitate higher productivity of multiple stakeholders collaborating in such multi-year effort. The DP process is presented, and the KM requirements and deployment options are discussed. An initial plan is formulated to address the multiple challenges associated with integrating KM strategies and artifacts to NSS DP.
130‐nm BiCMOS design of low‐pass negative group delay integrated RL‐circuit

A design method of low‐pass (LP) negative group delay (NGD) integrated circuit (IC) implemented in 130‐nm BiCMOS technology is investigated. The LP‐NGD circuit is composed by RL‐network with BiCMOS high Ohmic unsalicided N+poly resistor and symmetrical high current spiral inductor. The design methodology of the investigated LP‐NGD circuit is explained by chip layout process. Then, the pre‐simulation is performed with the design rule check (DRC) and 225 μm × 215 μm layout versus schematic (LVS) approaches. The LP‐NGD design feasibility of the BiCMOS IC implementation is validated by AC frequency domain simulation with realistic component implementation constraints. As expected, the ideally calculated and simulated results show NGD of about −100 ps with 1.12 GHz cut‐off frequency and −6 dB attenuation. Moreover, the LP‐NGD function is also verified with transient analyses with Gaussian pulse and arbitrary waveform signals. As expected, despite the attenuation, the output signal leading and tailing edges appear in time‐advance of about −100 ps compared to the input ones.
Novel VLSI Architectures and Micro-cell Libraries for Subscalar Computations

Parallelism is the key to enhancing the throughput of computing structures. However, it is well established that the presence of data-flow dependencies adversely impacts the exploitation of such parallelism. This paper presents a case for a new computing paradigm namely subscalar digital arithmetic which is aimed at mitigating this issue. It proposes to break up atomic data and atomic operations thereon into sub-atomic data fragments and sub-atomic partial operations. Such a break-up exposes hitherto unexploited levels of parallelism by way of allowing overlap of operations even if data-dependent. Surprisingly this enhanced exploitation of latent parallelism comes with a favorable impact on the area-power characteristics of corresponding computing structures which is contrary to common sense. The paper also proposes a novel micro cell library with logic primitives at corresponding subscalar levels. The synthesized circuits for several sequential benchmarks show an order of magnitude improvement in their area-throughput figure-of-merit (FOM).
A Novel Electrochemical Sensing Platform for the Sensitive Detection and Degradation Monitoring of Methylene Blue

Methylene blue is a toxic dye that is extensively used as a colorant in textile industries. Industrial effluent containing methylene blue, when drained into water bodies without proper treatment, poses a serious threat to aquatic and human lives. In order to protect the biocycle, various methods have been established to detect and remove hazardous dyes from aqueous systems. Electrochemical methods are preferred, owing to their characteristic features of simplicity, portability, potential selectivity, cost effectiveness, and rapid responsiveness. Based on these considerations, an electrochemical sensor consisting of amino-group-functionalized, multi-walled carbon nanotubes (NH2-fMWCNTs) immobilized on a glassy carbon electrode (GCE) was developed for the sensitive detection of methylene blue in aqueous solutions. The performance of the designed sensor was analyzed by electrochemical impedance spectroscopy, cyclic voltammetry, and square wave voltammetry. The developed sensing tool demonstrated promising features of sensitivity, selectivity, stability, fast responsiveness, and the ability to work with a very small volume of the analyte, i.e., in microliters, for analysis. Amino groups rich in electrons provide a negative charge to multi-walled carbon nanotubes, which significantly enhances the electrocatalytic activity of NH2-fMWCNTs for cationic dyes such as methylene blue. Using the designed sensing platform, a linear calibration plot with a limit of detection of 0.21 nM was obtained for methylene blue under optimized conditions. The designed sensor was also employed to monitor the extent and kinetics of the degradation of methylene blue. Titania nanoparticles were used for photocatalytic degradation, and the kinetics of degradation was monitored by both UV-Visible spectroscopic and electrochemical methods. The results revealed more than 95% removal of methylene blue in a time span of just 30 min.
Recurrence of Equinus Foot in Cerebral Palsy following Its Correction—A Meta-Analysis

Background: Recurrence in cerebral palsy (CP) patients who have undergone operative or non-operative correction varies greatly from one study to another. Therefore, we conducted this meta-analysis to determine the pooled rate of equinus recurrence following its correction either surgically or non-surgically. Methods: Nine electronic databases were searched from inception to 6 May 2021, and the search was updated on 13 August 2021. We included all studies that reported the recurrence rate of equinus following its correction among CP patients. The primary outcome was recurrence, where data were reported as a pooled event (PE) rate and its corresponding 95% confidence interval (CI). We used the Cochrane’s risk of bias (RoB-II) tool and ROBINS-I tool to assess the quality of included randomized and non-randomized trials, respectively. We conducted subgroup analyses to identify the sources of heterogeneity. Results: The overall rate of recurrence was 0.15 (95% CI: 0.05–0.18; I2 = 88%; p < 0.01). Subgroup analyses indicated that the laterality of CP, study design, and intervention type were significant contributors to heterogeneity. The recurrence rate of equinus differed among interventions; it was highest in the multilevel surgery group (PE = 0.27; 95% CI: 0.19–0.38) and lowest in the Ilizarov procedure group (PE = 0.10; 95% CI: 0.04–0.24). Twelve studies had a low risk of bias, eight had a moderate risk, and nine had a serious risk of bias. Conclusion: The recurrence of equinus following its correction, either surgically or non-surgically, in CP patients is notably high. However, due to the poor quality of available evidence, our findings should be interpreted with caution. Future studies are still warranted to determine the actual risk of equinus recurrence in CP.
Improved Chan algorithm based optimum UWB sensor node localization using hybrid particle swarm optimization

The localization of Wireless sensor networks (WSNs) has been recognized as one of the most challenging problems to overcome. Thus, much work has been given to solving this difficult problem. In emergency services, navigational systems, civil/military surveillance etc., locating the signal source in a WSN is essential. A novel approach for sensor node localization using range-based localization methodology has been proposed to overcome this issue. The problem is expressed in the form of a maximum probability distribution function. The use of an RSSI-based Time Difference of Arrival (TDOA) measurement model, along with the Chan algorithm, is used to find the coordinates of unknown nodes has been proposed. With the help of ultra-wideband, this research aims to develop new and precise localization algorithms for wireless sensor networks (WSNs). This work offers localization using two-hybrid localization algorithms, i.e., ELPSO (Ensemble learning particle swarm optimization) and PSO-BPNN (Back-propagation neural network optimized by particle swarm optimization). Further, the error optimization accuracy has been compared between those algorithms using simulations. The proposed techniques consistently offer a better localization accuracy than the conventional algorithms available in the literature. The new localization methods with optimal techniques reduce the error value to a minimal distance. The distance value of localization error is nearly2.7cms compared to other designs from the literature. It is noted as significantly less.
Post-Operative Astigmatism Using Clear Corneal Tunnel Incision in Children with Developmental Cataract

Post-Operative Astigmatism Using Clear Corneal Tunnel Incision in Children with Developmental Cataract. of Ophthalmology Abstract Purpose: To evaluate the post-operative changes in astigmatism in Pseudophakic eyes of children using sutured clear corneal incisions. Methods : The charts of children who underwent surgery for developmental Cataract using clear corneal tunnel incision at 10’O clock and 2’O clock position were retrospectively reviewed. All the sutures from cornea were removed after 1 month of surgery. Refraction was done at 1 month, 3months 6 months and 1 year interval. Statistical analysis was done by SPSS version 16. Result: With the rule astigmatism was highest in Conclusion: Clear corneal incision at 10’O clock and 2’O clock did not significantly influence preoperative existing astigmatism. There was no significant change in astigmatism after 6 months & 1 year of surgery.