Miracle-dz/UniFAR-FTUs huggingface dataset:
Observed nighttime conductance alters modeled global hydrology and carbon budgets

This is a well written manuscript that makes a valuable scientiﬁc contribution highlight-ing the sensitivity of terrestrial ecosystem simulations to the parameterization of stomatal conductance. The authors use a novel dataset of nighttime stomatal conductance to inform simulations in a global land surface model showing that in some simulations global evapotranspiration is increased and runoff reduced. My main criticism is that the attribution of the effects in the simulations to nighttime conductance and transpiration is not entirely accurate. Figures 4 & 5 show that the modiﬁed model structures make by far the largest impact on daytime transpiration and not nighttime. And that the g0 and gmin simulations tend to over-predict daytime transpiration in July, the time when these simulations diverge the most from the control and the gnight simulations. This should be discussed in depth. Figure 5 also shows that the modiﬁed model structures only very slightly improve simulations of nighttime transpiration. In these simulations of modiﬁed stomatal conductance, the main effects result from changes in daytime stomatal conductance, not nighttime stomatal conductance. Though this result is touched on in the manuscript it is not well emphasised. The effects on daytime stomatal conductance and ﬂuxes should be more prominently discussed as it is the major ﬁnding from this work and it implies that future efforts should focus on better characterising minimum stomatal conductance during the day. Given this, the title is somewhat mis-leading as it implies that realistic nighttime conductance leads to changed nighttime transpiration which affects global ﬂuxes, the title should reﬂect the fact that it is really the change to minimum conductance that is affecting the global ﬂuxes.
An 85-MHz fourth-order programmable IIR digital filter chip

The authors describe the design and VLSI implementation of a single-chip 85-MHz fourth-order infinite impulse response (IIR) digital filter chip fabricated in 0.9- mu m CMOS technology. The coefficient and input data word lengths of the filter are 10 b each, and the output data word length is 15 b. The coefficients are fully programmable. The chip can be programmed to implement any IIR filter from first to fourth order or an FIR filter up to 16th order at sample rates up to 85 MHz. A total of seventeen 10*10 multiply-add modules are used in this chip. The chip contains 80000 devices in an active area of 14 mm/sup 2/. It dissipates 2.2 W at 85-MHz clock rate and performs over 1.5*10/sup 9/ multiply-add operations per second. The underlying filtering algorithm, chip architecture, circuit and layout design, speed issues, and test results are described. The results of an E-beam probing experiment on packaged chips at 100-MHz clock rates are also presented and discussed. >
Preparation and physicochemical characterization of binary and ternary ground mixtures of carvedilol with PVP and SLS aimed to improve the drug dissolution

Abstract The objective of the present study was to enhance the dissolution rate of carvedilol (CA), a poorly water-soluble antihypertensive drug, using a co-grinding method in the presence of polyvinylpyrrolidone (PVP) and sodium lauryl sulfate (SLS). Various ratios of CA:PVP:SLS were ground by a planetary ball mill. They were studied in terms of dissolution, solubility, and particle size. The solid state and morphology of the intact drug and prepared samples were also characterized using differential scanning calorimetry (DSC), infrared spectroscopy (IR), X-ray diffraction (XRD), and scanning electron microscope (SEM). According to the results, co-grinding in the presence of PVP and SLS significantly increased CA dissolution rate. DE60 (dissolution efficiency) obtained for the best ternary ground mixture (89.31%) was 3.4 and 4.5 times higher than that of the related physical mixture (PM) and the intact drug, respectively. Further, the solubility of this formulation was about 10 times higher compared to that of the intact CA. A direct correlation was also observed between the chamber rotation speed of the planetary mill within the range of 100–400 rpm and CA dissolution rate. Finally, DSC, IR, and XRD analysis ruled out any polymorphic changes and chemical interactions during the grinding process.
Development and validation of lncRNAs-based nomogram for prediction of biochemical recurrence in prostate cancer by bioinformatics analysis

Background: Early biochemical recurrence (BCR) was considered as a sign for clinical recurrence and metastasis of prostate cancer (PCa). The purpose of the present study was to identify a lncRNA-based nomogram that can predict BCR of PCa accurately. Materials and methods: Bioinformatics analysis, such as propensity score matching (PSM) and differentially expressed genes (DEGs) analyses were used to identify candidate lncRNAs for further bioinformatics analysis. LASSO Cox regression model was used to select the most significant prognostic lncRNAs and construct the lncRNAs signature for predicting BCR in discovery set. Additionally, a nomogram based on our lncRNAs signature was also formulated. Both lncRNAs signature and nomogram were validated in test set. GSEA was carried out to identify various gene sets which share a common biological function, chromosomal location, or regulation. Results: A total of 457 patients with sufficient BCR information were included in our analysis. Finally, a five lncRNAs signature significantly associated with BCR was identified in discovery set (HR=0.44, 95%CI: 0.27-0.72, C-index = 0.63) and validated in test set (HR=0.22, 95%CI: 0.09-0.56, C-index = 0.65). Additionally, the lncRNAs-based nomogram showed significant performance for predicting BCR in both discovery set (C-index = 0.74) and test set (C-index = 0.78). Conclusion: In conclusion, our lncRNAs-based nomogram is a reliable prognostic tool for BCR in PCa patients. In addition, the present study put forward the direction for the further investigation on the mechanism of PCa progression.
Concentration of ghrelin and leptin in serum and human milk in nursing mothers according to the type of feeding.

OBJECTIVE we assessed the relationship between serum and human foremilk and hindmilk concentrations of ghrelin and leptin in nursing mothers according to the type of feeding. METHODS this cohort design was carried out on 131 mother-newborn dyads admitted to a physiological puerperium ward. The independent variables were the type of feeding, full breastfeeding (FBF, 56.5%) and partial breastfeeding (PBF, 43.5%). The dependent variables were the concentration of total ghrelin (pg/ml) and leptin (ng/ml) in serum, foremilk and hindmilk at eight and 16 weeks. Fasting blood samples were obtained from the nursing mothers at four months for serum assays. Unpaired Student's t-test, Mann-Whitney U test, Pearson's correlation tests, coefficient of determination and linear regression were used. RESULTS the concentration of ghrelin and leptin in hindmilk was higher than that of foremilk in both groups at eight and 16 weeks. The concentration of ghrelin and leptin was higher in serum than in foremilk in both groups. These values showed a direct and significant linear correlation with the exception of ghrelin in the FBF group. The serum concentration of leptin in mothers explained 32% of the variance of its concentration in foremilk in the FBF and 13% in the PBF groups. CONCLUSION the hindmilk/foremilk gradient suggests an intake regulating mechanism during the fed. The concentration of ghrelin and leptin was higher in the serum than in foremilk and its correlation and determination coefficients could suggest plasma-milk transfer in addition to synthesis regulation by the mammary gland, adipose tissue or other organs.
Quantitatively determining total bacterial count of raw goat milk using dielectric spectra.

The objective of this study was to evaluate dielectric spectra as a means of quantitatively determining total bacterial count (TBC) of raw goat milk. The dielectric spectra, including dielectric constant (ε') spectra and dielectric loss factor (ε″) spectra, and TBC of 154 raw goat milk samples were measured using network analyzer and plate count methods, respectively. Owing to the poor linear relationship between TBC in logarithm and permittivities at a single frequency, chemometrics was used to reduce noise, identify outliers, select effective variables, and divide sample sets. Several linear models, such as multiple linear regression, ridge regression, and least absolute shrinkage and selection operator, were established to determine TBC based on the effective spectra of ε', ε″, and their combination (ε'+ε″). The results indicated that the models built using the spectra of ε'+ε″ and ε' had excellent TBC prediction performance. The best model was multiple linear regression based on ε'+ε″ spectra with the residual predictive deviation of 3.26. This study shows that the dielectric spectra had great potential to quantitatively and rapidly determine TBC of raw milk.
Effect of a mixture of flufenacet and isoxaflutole on population numbers of soil-dwelling microorganisms, enzymatic activity of soil, and maize yield

Abstract This study was aimed to evaluate the effect of a mixture of flufenacet + isoxaflutole on counts of microorganisms, ecophysiological diversity index (EP), colony development index (CD) and on the enzymatic activity of soil and maize growth. The experiment was conducted with sandy clay, to which the tested herbicide was administered in doses of: 0.25, 5.0, 10, 20, 40, 80 and 160 mg/kg. Soil without the addition of the mixture served as the control. Results demonstrated that the tested mixture contributed to a decrease in numbers of Azotobacter, organotrophic bacteria, actinobacteria and fungi. The negative effect of the herbicide could also be noticed in the case of the enzymatic activity of soil. Soil contamination contributed to suppressed activities of dehydrogenases, catalase, urease, alkaline phosphatase and arylsulfatase. In turn, the initial increase in the activity of β-glucosidase was followed by its decline observed with time. The flufenacet + isoxaflutole mixture affected also maize plant growth, reducing maize dry matter yield when used at doses from 5.0 to 160 mg/kg. In summary, it may be concluded that mixture evokes a negative effect on the microbiological and biochemical activity of soil and that their excess in the soil leads to plant decay as at the seeding stage.
MPCA META-HEURISTICS FOR AUTOMATIC ARCHITECTURE OPTIMIZATION OF A SUPERVISED ARTIFICIAL NEURAL NETWORK

Artificial neural networks (ANN) has been studied intensively, but there still are many unresolved issues. The search and definition of an optimal architecture remains a very relevant ANN research topic. The search space of neural network topology, each point rep- resents a possible architecture. Associating each point to a performance level relies on the a priori establishment of some optimality criterion. Here, a new meta-heuristics, multi-particle collision algorithm (MPCA) was applied to design an optimum architecture for a supervised ANN. The MPCA optimization algorithm emulates a collision process of multiple particles inspired in processes of a neutron traveling in a nuclear reactor. The multilayer perceptron (MLP) was the neural network adopted here, and backpropagation strategy was used for calculating of the weight of connections to the MLP-NN. The MLP-NN configured by this op- timal or inverse designs was applied to predict the seasonal mesoscale climate. The dataset for trainning is obtained from NCEP-NOAA reanalysis and from a metherological model. In order to reduce the dimension of the search space to find the optimized ANN, it is considered the following: three activation functions, up to three hidden layers, and up to 32 neurons per hidden layer. The comparison is performed between the ANN configuration obtained by automatic process and another configuration proposed by a human specialist.
The usage of hidden Markov models in the vision system of a mobile robot

This paper presents the development and experimental implementation of remote control of mobile robots using visually recognized bask hand gestures presented as input commands. The hand is segmented from the background by taking advantage of the unique coloration of human skin. Color histogramming techniques and Gaussian estimation are applied at the image preprocessing stage. To shorten the sampling period the Kalman filter and technique based on position prediction of the bounding box containing the hand are applied. The recognition module uses hidden Markov models. Some practical experiments conducted in laboratory are briefly discussed Preliminary results obtained are promising and show that, on the basis of the software developed as well as the Khepera robot equipped with a relatively cheap color camera, we can conduct several advanced experiments in naturally occurring circumstances in our laboratory.
On Air-Gap Approximations for Slotted Electrical Machine Models Assuming Radial Magnetic Fields

For modeling electrical machine characteristics with a high level of detail, one of the important aspects is to represent slotting effects on the air-gap magnetic field. In analytical model approaches, the stator and rotor slots typically influence the magnetic field indirectly as part of air-gap permeance. This work provides a comparison of state-of-art methods to approximate the air-gap permeance including slotting effects. The selected methods are compared with regard to spatial distributions of radial air-gap magnetic flux density and of exemplary flux linkages for a theoretical movable coil. As calculation basis, a cage induction machine at a static operating point is considered. The resulting magnetic flux density distributions illustrate the accuracy differences between the air-gap approximation approaches. Special focus lies on variations in the curve shapes, which are connected to rather generalizable method characteristics. Regarding the exemplary flux linkages, a majority of the approaches lead to strongly resembling results. In this context, computing efficiencies of the methods constitute a discussion point.
A Dominant Gate Insertion Algorithm Implementation for Logic Locking in IP Protection

Logic locking manages different logic gates insertion as key-gates along with the key-inputs after location calculation for IP protection. This paper presents a dominant gate algorithm to achieve the security standard of logic locking, which managed the Hamming Distance(HD) exceed 50% while exempt from the key sensitization concurrently. It shows a comprehensive defense ability from the perspective of the general security standard. The results prove that it can simultaneously maintain the mutual interference of the key-inputs and maximize the ambiguity of the output on various benchmark circuits with a reasonable area overhead below 5%.
Research on the Steady Operation Optimization Model of Natural Gas Pipeline Considering the Combined Operation of Air Coolers and Compressors

Based on the mutual coupling effect among the compressor, the air cooler and pipes in the system of natural gas pipeline, innovatively with the goal of minimum energy consumption, this paper established a combined operation optimization model of the air cooler and compressor through the optimization of the switching scheme of compressors and air coolers, which can greatly reduce the production energy consumption of the pipeline system. Moreover, when the air temperature is taken as an optimization variable, the most proper temperature to start the air cooler of each compressor station can be worked out to guide the optimized operation of the pipeline, which is of high value for promotion and application. The case analysis of west–east natural gas pipeline II showed that among genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) algorithm that are used to solve the optimization model, the genetic algorithm is the fastest, and the simulated annealing algorithm the slowest, but the optimization results of the simulated annealing algorithm is the best, in which the reduced production energy consumption accounted for 33.77%, testifying the practicability and creativity of the optimization model.
Improving Load Forecasting Process for a Power Distribution Network Using Hybrid AI and Deep Learning Algorithms

Load forecasting is useful for various applications, including maintenance planning. The study of load forecasting using recent state-of-the-art hybrid artificial intelligence (AI) and deep learning (DL) techniques is limited in South Africa (SA) and South African power distribution networks. This paper proposes a novel hybrid AI and DL South African distribution network load forecasting system. The system comprises of modules that handle the collection of the loading data from the field, analysis of data integrity using fuzzy logic, data preprocessing, consolidation of the loading and the temperature data, and load forecasting. The load forecasting results are then used to inform maintenance planning. The load forecasting is conducted using a hybrid AI/DL load forecasting module. A novel comparative study of recent state-of-the-art AI techniques is also presented to determine the best technique to deploy in this module when forecasting South African power redistributing customers’ loads. The impact of the inclusion of weather parameters and loading data clean up on the load forecasting performance of a hybrid AI technique, optimally pruned extreme learning machines (OP-ELM), and a deep learning technique, long short-term memory (LSTM), is also investigated. These techniques are compared with each other and also with a commonly used powerful hybrid AI technique, adaptive neuro-fuzzy inference system (ANFIS). LSTM was found to achieve higher load forecasting accuracies than ANFIS and OP-ELM in forecasting the two distribution customers’ loads in this paper. Only the LSTM models’ performance improved with the inclusion of temperature in their development.
Sizing Optimization and Operational Strategy of Hres (PV-WT) using Differential Evolution Algorithm

The instability of energy resources and corresponding cost of the system are the main two problems for designing the hybrid solar-wind power generation systems. The configuration of the system must have a high reliability on the power supply availability but with a minimum cost. The purpose of this paper is to find the most optimum or balanced configuration between technical reliability and total annual cost for the PV module number, the wind turbine number, and the battery number. The appropriate strategy of load management is needed by adjusting the potential energy resource to the load power demand. Loss of Power Supply Probability (LPSP) is a method to determine the ratio of power generation unavailability by the system configuration which used as technical analysis. Annualized Cost of System (ACS) is a method to determine the total annualized cost of the project lifetime which used as economic analysis. The result from the simulation showed that the Differential Evolution (DE) algorithm can be an alternative method to find the best configuration with a low number of LPSP and ACS. Since DE has a better efficacy and faster time to find global optimum than other algorithms.
Consensus control with a constant gain for discrete-time binary-valued multi-agent systems based on a projected empirical measure method

This paper studies the consensus control of multiagent systems with binary-valued observations. An algorithm alternating estimation and control is proposed. Each agent estimates the states of its neighbors based on a projected empirical measure method for a holding time. Based on the estimates, each agent designs the consensus control with a constant gain at some skipping time. The states of the system are updated by the designed control, and the estimation and control design will be repeated. For the estimation, the projected empirical measure method is proposed for the binary-valued observations. The algorithm can ensure the uniform boundedness of the estimates and the mean square error of the estimation is proved to be at the order of the reciprocal of the holding time ( the same order as that in the case of accurate outputs ). For the consensus control, a constant gain is designed instead of the stochastic approximation based gain in the existing literature for binary-valued observations. And, there is no need to make modification for control since the uniform boundedness of the estimates ensures the uniform boundedness of the agentsʼ states. Finally, the systems updated by the designed control are proved to achieve consensus and the consensus speed is faster than that in the existing literature. Simulations are given to demonstrate the theoretical results.
PID Control Considerations for Analog-Digital Hybrid Low-Dropout Regulators (Invited Paper)

This paper briefly introduces the developments of the fully-integrated low-dropout regulators (LDOs) at the beginning. Then, we use the classic proportional-integral-derivative (PID) control theory to discuss and categorize the existing LDOs, and present our design considerations for analog-digital hybrid LDOs in an intuitive way.
A New Mobile Application for Encrypting SMS / Multimedia Messages on Android

Mobile communication devices have become popular tools for communicating among people. This paper is a launcher for sending/receiving secured SMS/Multimedia files using Blowfish algorithm. A smart mobile application on android platform is introduced as an application that helps the user to encrypt the message (SMS/Multimedia files) before it is transmitted over the mobile network. The new idea of the program is to transmit encrypted messages and multimedia files or any other type of files via mobile networks or the internet as an alternative mean. Utilizing the internet takes place through a secured hosting website with a user name and password integrated in the program. To maintain intensive security, a private key encrypts the files and another private key encrypts file name. The transferring media is maintained online in the absence of mobile network coverage.
Avoiding Currents Overshoot in IPOP DAB System

Constant progress in power electronics and growing participation of renewable sources in a total energy market results in much interest of Energy Storages (ES) as well. Their popularity implies increasing power ranges what often may be assured by parallel connection of DC/DC ES converters for example in an Input Parallel Output Parallel (IPOP) manner. On the other hand, varying voltage of the ES results in various power range of the system, thus in given conditions all parallel operating converters are not necessarily required and may even worsen the overall efficiency. Then, a dynamic connection or disconnection (engaging and dismissing) of parallel devices is required regarding to actual power demands and ES’s voltage level. When the Dual Active Bridge (DAB) converter is used as the DC/DC topology it may be easily performed by setting zero signals for all transistors of the whole converter. Unfortunately, repetitive engaging and dismissing the whole converter module stresses its components such high frequency transformer’s and power mosfets. This may result in harmful EMF introduction and affects overall reliability. This paper presents a simple method to improve process within a two-module IPOP DAB system in a manner where currents overshoots are reduced. It is achieved by adequate modulation modification and proper perfilter application. The idea is finally validated by simulation.