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Polyamide composite reverse osmosis (RO) membranes occupy an important position in water treatment. However, membrane fouling, especially biofouling, can lead to a significant decrease in membrane permeability. Therefore, reducing biological contamination is a significant and important property of an RO membrane. In this article, a hypothesis on the development of a new kind of RO membrane for antibacterial purposes was prepared by the modification of gallic acid (GA) and silver nanoparticles (AgNPs). Then, experiments were carried out to verify the hypothesis, getting a modified RO membrane with the composite of GA@AgNPs. The water flux of the GA@AgNPs RO membrane was 31.1 L·m ·h , which was 46.7% higher than that of the original membrane, while the rejection rate of salt remained at 93.8–97.6%. Moreover, the GA@AgNPs RO membranes exhibited outstanding antibacterial properties with more than 99.9% antibacterial efficiency against both and . Our work provides a new idea for solving the problem of biofouling RO membranes.

This paper presents a new and mathematical method for online signature validation based on machine learning. In this way, the average values of the factors are taken into account to ensure validity. Here, seven different types of features used are x coordinates, y coordinates, time stamp, pen up and down, azimuth, height and pressure. Three new features are extracted from it, i.e., (displacement, velocity and acceleration) using the correlated extraction process to obtain dynamic feature of signature. These features are extracted from the popular dataset SVC2004. The extracted feature is then passed to various classifiers named as Naive Bayes, random forest, J48, MLP, logistic regression and PART. The result of genuine and forge signatures is obtained in terms of precision, true positive rate, false positive rate, F -score, etc. The obtained result is then compared with the existing method with respect to false acceptance rate and false rejection rate.

Online signature verification is most popular in the field of biometrics and forensics. Due to its popularity and recent demand, the major challenges are to improve its performance and complexity. This paper presents a novel approach for online signature validation based on local weight learning. The different features like, x coordinate, y coordinates time stamp, pen up and down, azimuth, height, pressure, displacement, velocity and acceleration are extracted from online signature. The extracted features are then passed to locally weighted learning classifier algorithms. Our experimentation is performed on local weight learning classifier of a machine learning method. Locally weighted learning classifier is experimentally found to be effective having False acceptance rate and False rejection rate as 1.18 and 0.02, respectively. The proposed methods gives better performance when compared with other well-known existing models for online signature verification. This result demonstrates that the method is suitable for a real-time system.The popular SVC2004 dataset is used in the experiments, which confirms the effectiveness of the proposed method in simultaneously achieving lower false positive and false negative rate.

A commercial nanofiltration (NF) membrane was modified using poly(sodium 4-styrenesulfonate) (PSS) to improve the nitrate rejection from groundwater. Fourier transform infrared spectroscopy, thermogravimetric analysis, zeta potential, and water contact angle analyses were performed, showing that PSS was successfully coated onto the membrane with the surface negative charge density being enhanced. The results of nitrate removal tests showed that the best PSS concentration was 1.5 mg/L, with the nitrate rejection rate of 88.8% and the permeate flux of 27.0 L/m 2  h. The effect of initial nitrate concentration and solution pH on the nitrate removal performance of the modified NF membrane was investigated. The results indicate that the modified NF membrane can improve nitrate removal from actual groundwater, with little membrane permeate flux loss. Graphical abstract

The disturbance rejection rate (DRR) is an inherent problem of the seeker. The additional line-of-sight (LOS) angular velocity information of the seeker caused by the DRR will affect the attitude of the aircraft through the guidance system, thus forming a parasitic loop in the guidance and control system of the aircraft, which has a great influence on the guidance accuracy. In this study, the influence of the DRR of the roll–pitch seeker on the stable tracking of a maneuvering target is explored. First, the tracking principle of the roll–pitch seeker is analyzed and the conditions for completely isolating the disturbance of the aircraft attitude are deduced. Then, the expression of the frame error angle is derived, a semi-strap-down stable control closed-loop scheme is established, and the DRR transfer function is derived by adding different disturbance torque models. Finally, the simulation of stability tracking characteristics is carried out. The results show that when the aircraft attitude is disturbed at a low frequency or the target is maneuvering at a low frequency, the DRR caused by the spring torque has a great influence on the tracking angle of the two frames, the line of-sight rate accuracy of the optical axis output and the detector error angle. On the contrary, the damping torque DRR plays a leading role in tracking accuracy.

The rise of nanofiltration technologies holds great promise for creating more effective and affordable techniques aiming to remove undesirable pollutants from wastewaters. Despite nanofiltration’s promising potential in removing antineoplastic drugs from liquid matrices, the limited information on this topic makes it important to estimate the rejection rates for a larger number of compounds, particularly the emerging ones, in order to preview the nanofiltration performance. Aiming to have preliminary estimations of the rejection rates of antineoplastic drugs by nanofiltration, 54 antineoplastic drugs were studied in 5 nanofiltration membranes (Desal 5DK, Desal HL, Trisep TS-80, NF270, and NF50), using a quantitative structure-activity relationship (QSAR) model. While this methodology provides useful and reliable predictions of the rejections of compounds by nanofiltration, particularly for hydrophilic and neutral compounds, it is important to note that QSAR results should always be corroborated by experimental assays, as predictions were confirmed to have their limitations (especially for hydrophobic and charged compounds). Out of the 54 studied antineoplastic drugs, 29 were predicted to have a rejection that could go up to 100%, independent of the membrane used. Nonetheless, there were 2 antineoplastic drugs, fluorouracil and thiotepa, for which negligible removals were obtained (<21%). This study’s findings may contribute (i) to the selection of the most appropriate nanofiltration membranes for removing antineoplastic drugs from wastewaters and (ii) to assist in the design of effective treatment approaches for their removal.

A smart door lock system based on IoT is presented which uses fingerprint biometric authentication, ESP32 microcontroller and Blynk IoT platform to provide a secure, user friendly and remote controllable access control solution. The proposed architecture replaces traditional locks with a real time biometric system that gives instant feedback through onboard display (OLED) and buzzer and remote monitoring and control through a mobile app. A new fail-safe mechanism is implemented: after 3 failed fingerprint attempts the system will lock out for 15 seconds and send instant alert to the authorized user’s smartphone. Performance test of the prototype shows fingerprint recognition time of around 1.0 second and door unlock time of 5 seconds, so it’s convenient to use. The system has a very low False Acceptance Rate (FAR) of 1.32% which means strong resistance to unauthorized access. The False Rejection Rate (FRR) is higher (around 26.32%) due to user error such as improper finger placement – so a usability issue to be addressed. The device can store up to 3 fingerprint profiles and gives visual/audible alert for all access events. This integration of IoT with biometric security not only enhances physical security but also user convenience, a modern smart-lock solution for smart home automation.

Latex phase blending and crosslinking method was used in this research work to produce nitrile butadiene rubber-graphene oxide (NBR-GO) membranes. This fabrication technique is new and yields environmentally friendly membranes for oil-water separation. GO loading was varied from 0.5 to 2.0 part per hundred-part rubber (pphr) to study its effect on the performance of NBR-GO membrane. GO was found to alter the surface morphology of the NBR matrix by introducing creases and fold on its surface, which then increases the permeation flux and rejection rate efficiency of the membrane. X-Ray diffraction analysis proves that GO was well dispersed in the membrane due to the non-existence of GO fingerprint diffraction peak at 2θ value of 10–12° in the membrane samples. The membrane filled with 2.0 pphr GO has the capability to permeate 7688.54 Lm−2 h−1 water at operating pressure of 0.3 bar with the corresponding rejection rate of oil recorded at 94.89%. As the GO loading increases from 0.5 to 2.0 pphr, fouling on the membrane surface also increases from Rt value of 45.03% to 87.96%. However, 100% recovery on membrane performance could be achieved by chemical backwashing.

We focus on a queueing model in which the sizes of arriving jobs are stochastically dependent and each job may be denied service with a probability determined by the queue size (active management). Both of these effects are known to occur in computer networking and many other real-world realizations of queueing systems. For such a model, we perform a thorough transient and stationary analysis of the job departure process and the job rejection process. The results include theorems on the expected number of jobs that depart within a specified time interval, the departure intensity at a given time, the stationary departure rate, the expected number of jobs rejected within a specified interval, the transient rejection intensity and the stationary rejection rate. Sample numerical calculations are provided for illustration. They include various settings of the level of dependence between jobs, job rejection probabilities, and system load, as well as their impact on the departure and rejection processes.

This paper proposes a high-performance embedded system for offline Urdu handwritten signature verification. Though many signature datasets are publicly available in languages such as English, Latin, Chinese, Persian, Arabic, Hindi, and Bengali, no Urdu handwritten datasets were available in the literature. So, in this work, an Urdu handwritten signature dataset is created. The proposed embedded system is then used to distinguish genuine and forged signatures based on various features, such as length, pattern, and edges. The system consists of five steps: data acquisition, pre-processing, feature extraction, signature registration, and signature verification. A majority voting (MV) algorithm is used for improved performance and accuracy of the proposed embedded system. In feature extraction, an improved sinusoidal signal multiplied by a Gaussian function at a specific frequency and orientation is used as a 2D Gabor filter. The proposed framework is tested and compared with existing handwritten signature verification methods. Our test results show accuracies of 66.8% for ensemble, 86.34% for k-nearest neighbor (KNN), 93.31% for support vector machine (SVM), and 95.05% for convolutional neural network (CNN). After applying the majority voting algorithm, the overall accuracy can be improved to 95.13%, with a false acceptance rate (FAR) of 0.2% and a false rejection rate (FRR) of 41.29% on private dataset. To test the generalization ability of the proposed model, we also test it on a public dataset of English handwritten signatures and achieve an overall accuracy of 97.46%.