Numerous Applications Research Articles

Fixed/predefined-time projective synchronization for a class of fuzzy inertial discontinuous neural networks with distributed delays

In this article, fixed-time projective synchronization (FTPS) and predefined-time projective synchronization (PTPS) for a class of discontinuous fuzzy inertial neural networks (FINNs) with distributed delays are considered. To this end, regarding differential inclusion theories and the Lyapunov stability method, more precise and improved setting times of FTPS are systematically constructed. Furthermore, a more general and novel predefined-time stability lemma is established to make the synchronized time more flexible and can be an accurate value in advance. Meanwhile, some effective feedback controllers and new criteria are established to guarantee the proposed FINNs achieve FTPS and PTPS, respectively. Finally, the corresponding numerical simulations and image encryption application are offered to confirm the obtained results.

A continuous ultra-narrow impulse synchronizer using a monolithic field programmable gate array for fast deployment and scalability.

Ultra-narrow pulses serve as critical components in numerous applications. These pulses have ultra-fast leading edges that typically function as precision trigger signals to synchronize various instruments. Ultra-narrow pulses inherently exhibit an ultra-wide bandwidth, gaining significant attention in diverse electronic systems encompassing communications, radar imaging, electronic warfare, and others. Although several techniques have been explored for generating ultra-narrow pulses, field programmable gate arrays (FPGAs) offer a promising alternative in terms of flexibility and integration. This study introduces a scalable delay pulse synchronizer method with a resolution of 23 ps. A programmable, successive, narrow pulse sequence operating at a 1-GHz repetition frequency is implemented within a monolithic FPGA. The performance of the proposed method is evaluated using an existing board with a general commercial FPGA in the laboratory. This new method presents a convenient and efficient approach of achieving ultra-narrow pulse synchronization, being applicable across various fields.

Electrospun nanofibers synthesis enhancing the activity of energy storage devices/electrochemical and biosensor applications

AbstractNanofibers have been synthesized by many methods due to its numerous applications. This article provides a thorough introduction to electrospinning here, including its history, various production techniques, common materials, and potential uses, most notably in energy storage. Electrospinning, a simple and low‐cost process, can be used to effectively manufacture nanofibers. While the method of electrospinning has been known for over a half‐century, current understanding of the process and the parameters that determine the qualities of the fibers produced by it is quite limited. This is extremely advantageous for energy devices including electrochemical (bio) sensors that give increased solar‐electric energy conversion efficiency. Apart from energy, other applications related to drug delivery, filter media and membrane like are also discussed. This article covers current advances in electrospun nanofibers, with an emphasis on their synthesis and applications.

Review on the Discovery of New Benzimidazole Derivatives as Anticancer Agents: Synthesis and Structure-activity Relationship (2010-2022)

Background: Benzimidazole (Benz-fused bicyclic ring system) is the most versatile class of heterocyclic compounds due to its numerous applications in industrial and synthetic organic chemistry because of its many biological actions. Benzimidazole analogs have been used to discover various medical problems, such as cancer, bacterial infections, fungi infections, etc. Researchers are studying nitrogencontaining hybrid heterocyclic compounds because they provide a broad range of therapeutic potential and have minimal side effects. Objective: The current literature review emphasizes recent developments in the design of new benzimidazole derivatives as possible anticancer agents with their relationship between structure and activity, which will give insight into the future design of more active benzimidazole molecules. Results: The present review consists of synthetic protocols for the synthesis of benzimidazole derivatives along with their pharmacological potentials and structure-activity relationship in correlation with synthetic molecules to provide a depth view of the work done on benzimidazole. Conclusion: It would be significant for further research in developing better drug molecules representing a potent derivative of medicinal agents.

Using natural language processing to increase prediction and reduce subgroup differences in personnel selection decisions.

The purpose of this research is to demonstrate how using natural language processing (NLP) on narrative application data can improve prediction and reduce racial subgroup differences in scores used for selection decisions compared to mental ability test scores and numeric application data. We posit there is uncaptured and job-related constructs that can be gleaned from applicant text data using NLP. We test our hypotheses in an operational context across four samples (total N = 1,828) to predict selection into Officer Training School in the U.S. Air Force. Boards of three senior officers make selection decisions using a highly structured rating process based on mental ability tests, numeric application information (e.g., number of past jobs, college grades), and narrative application information (e.g., past job duties, achievements, interests, statements of objectives). Results showed that NLP scores of the narrative application generally (a) predict Board scores when combined with test scores and numeric application information at a level of correlation equivalent to the correlation between human raters (.60), (b) add incremental prediction of Board scores beyond mental ability tests and numeric application information, and (c) reduce subgroup differences between racial minorities and nonracial minorities in Board scores compared to mental ability tests and numeric application information. Moreover, NLP scores predict (a) job (training) performance, (b) job (training) performance beyond mental ability tests and numeric application information, and (c) even job (training) performance beyond Board scores. Scoring of narrative application data using NLP shows promise in addressing the validity-adverse impact dilemma in selection. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Utilization of Capacitor Bank for Electromagnetic Pulse Crimping

Capacitors play an important role in an electrical system. They have numerous applications in the field of lasers, fast X-ray, neutron sources, electromagnetic pulse generators, electron beam accelerators, plasma generation and electromagnetic welding of materials. One of the applications of high voltage capacitors as energy storage and discharge devices is in the crimping of cables using the electromagnetic pulse forming technique. This research paper investigates the use of a high-voltage energy storage capacitor with a capacitance of 208 microfarads and rated voltage of 44 kV to perform Electromagnetic Pulse Crimping (EMPC) of lugs for 70 mm2 and 120 mm2 cables. The capacitor operates at 12 kV and is discharged to produce a high current that generates a strong magnetic field of up to 20 Tesla, which is utilized for joining the cable conductor and the lug. The study examines the performance of the capacitor under different operating values such as voltage, capacitance, and discharge time and investigates the effect of these parameters on the termination quality. The paper presents the experimental results of the crimping, which include the contact resistance, weld strength, microstructure, and defects of the welded joints. The results indicate that the capacitor-based electromagnetic pulse crimping produces welds with high strength and minimal defects. Additionally, the paper discusses the future scope of research in this area and the potential applications of such capacitors in other welding techniques. The findings of this research paper provide valuable insights for engineers and researchers working in the field of energy storage, high-voltage applications and welding.

Measuring and classifying IP usage scenarios: a continuous neural trees approach

Understanding user behavior via IP addresses is a crucial measure towards numerous pragmatic IP-based applications, including online content delivery, fraud prevention, marketing intelligence, and others. While profiling IP addresses through methods like IP geolocation and anomaly detection has been thoroughly studied, the function of an IP address—e.g., whether it pertains to a private enterprise network or a home broadband—remains underexplored. In this work, we initiate the first attempt to address the IP usage scenario classification problem. We collect data consisting of IP addresses from four large-scale regions. A novel continuous neural tree-based ensemble model is proposed to learn IP assignment rules and complex feature interactions. We conduct extensive experiments to evaluate our model in terms of classification accuracy and generalizability. Our results demonstrate that the proposed model is capable of efficiently uncovering significant higher-order feature interactions that enhance IP usage scenario classification, while also possessing the ability to generalize from the source region to the target one.

Pneumatically tunable adherence of elastomeric soft hollow pillars with non-circular contacts

Dynamically tunable interfacial dry adhesion plays a significant role in numerous biological functions and industrial applications. Among various strategies, pneumatics-activated adhesive devices draw much attention due to their distinct advantages such as fast speed, reliable performance, large adhesion tunability and easily accessible materials. To understand and predict adhesion strength of pneumatics-activated adhesives, it is necessary to examine their interfacial mechanics that is nonlinearly coupled with the large deformation of the devices under pressure. However, previous studies have only focused on axisymmetric cases in which the outline of the contact area is circular, whereas the tunable adherence of non-circular contact controlled by pneumatics remains elusive. In this work, through a combination of experiments and simulations, we study the effect of non-circular contact geometry on tunable dry adhesion of pressure-activated soft hollow pillars. Specifically, elliptical, square, and rectangular contact shapes are considered and their effects on tunable adhesion of the soft hollow pillars are compared to that of circular contact geometry thoroughly. The results show that soft hollow pillars with elliptical, square, and rectangular contact surfaces demonstrate rich interfacial delamination behaviors that depend on the contact outline geometry and internal pressure. Among all contact geometries, elliptical contact has the highest adhesion tunability yet requires lowest activating pressure owing to the non-uniform curvature distribution of the contact outline. However, when the eccentricity increases, the elliptical contact has reduced tunability of adhesion caused by the contact of opposing sides of the sidewall upon buckling. For square and rectangular contacts, they have the lowest adhesion tunability and need higher activating pressure than those of circular and elliptical contact since the 90-degree edges of the sidewall prohibit buckling instability. Our findings greatly broaden the design space of pneumatics-activated adhesive devices by adding the contact geometry of the soft hollow pillars as a new design parameter, which can provide valuable guidance for tunable adhesive design for various applications in manufacturing and robotics.

Anomaly detection using deep learning based model with feature attention

<span lang="EN-US">Anomaly detection is a difficult problem with numerous industrial applications, such as analyzing the quality of objects using images. Anomaly detection is the process of identifying outliers in a given dataset. Recently, machine learning approaches to computer vision problems have outperformed classical state-of-the-art approaches. Anomaly detection problems can be solved using supervised approaches. However, labelled datasets are hard to obtain. Thus, many researchers have taken an unsupervised approach to solving the problem of anomaly detection. In this study, we use an adversarial auto encoder model as a base model and create a custom model to detect anomalies in images and videos. The model was trained exclusively on normal data. The modified national institute of standards and technology database (MNIST) dataset achieved an area under curve (AUC) score of 0.872 for anomaly detection, while the University of California San Diego (UCSD) anomaly dataset (Video dataset) achieved an AUC score of 0.74 for Ped1 and 0.87 for Ped2. To calculate the anomaly score, the concept of attention weights is combined with the reconstruction loss, and the proposed method outperformed other similar methods designed for the same problem. However, the usefulness of the proposed model was demonstrated through the detection of anomalies, and the model is still being improved for use in real-world situations.</span>

Diagnosis of Multicomponent Faults in SRM Drives Based on Auxiliary Current Reconstruction Under Soft-Switching Operation

Switched reluctance motors (SRMs) are considered a potential candidate for utilization in numerous safety-critical applications including electric vehicles (EVs) and more/all electric aircraft (MEA), etc., where the reliability of the drive system is critically important. An SRM drive system suffers from various electrical, mechanical, and sensor faults during operation. The existing fault detection schemes investigated each fragile component separately and proposed different approaches for each fault, which not only make the system complex but also less reliable for real-time implementation in safety-critical applications. Thus, a more sophisticated algorithm to detect and discriminate each of these faults in real time becomes attractive. A simple fault detection and isolation scheme for multiple faults in SRM drive systems is reported in this paper. The investigated faults include open- and short-circuit faults in power switches, open-phase winding, and zero-output current sensors faults. An auxiliary phase current for each phase was reconstructed from the dc-link and the faults are identified and discriminated effectively through the digital analysis of the fundamental current and the auxiliary current in real-time. The feasibility of the proposed scheme is verified by the extensive simulation and experiments conducted on a three-phase 12/8 SRM drive system.

Blockchain: Applications, Challenges, and Opportunities in Consumer Electronics

One of the most beneficial and original solutions for cybersecurity is blockchain. Although the technology has only been on the market for a short time, it has already effectively replaced economic transaction systems in a number of businesses in its early stages and is capable of modernizing various business models in numerous consumer electronic applications. It is critical for planners and decision makers to thoroughly evaluate the suitability of Blockchain for their particular use cases and commercial applications, even as it promises a secure distributed framework for sharing, exchanging, and integrating information across users and third parties. Blockchain should only be deployed if it is suitable, offers security, and provides better opportunities to drive revenue and save costs. In this paper, you will get an overview of this technology that can be used to achieve security in consumer electronic applications. We also discuss the difficulties and potential benefits of using blockchain technologies in consumer electronics applications.

Study of synchronous RF pulsing in dual frequency capacitively coupled plasma

Low-pressure multi-frequency capacitively coupled plasmas (CCPs) are used for numerous etch and deposition applications in the semiconductor industry. Pulsing of the radio-frequency (RF) sources enables control of neutral and charged species in the plasma on a millisecond timescale. The synchronous (i.e. simultaneous, in-phase) pulsing of both power sources in a dual frequency CCP is examined in this article. Due to the low gas pressure, modeling has been done using the electrostatic particle-in-cell/Monte Carlo collision method. The objective of this work is to investigate the sensitivity of the plasma properties to small changes in timing during synchronous pulsing of the two RF sources. It is demonstrated that small deviations in the on and off times of the two RF sources can lead to major changes in the plasma characteristics. This high sensitivity is of concern for process repeatability but can be utilized to enable better control of the dynamics of plasma-surface interaction. In the simulations, the pulsing parameters (on and off times and ramp rates) are varied and the temporal evolution of plasma characteristics such as electron density (ne ), species current at the electrode, and electron temperature are examined. It is demonstrated that if the low-frequency (LF) source is turned off a few μs before (or after) the high-frequency source, ne during the off-state is significantly higher (or lower) due to the frequency coupling effect. Similarly, turning on the LF source with a small delay results in a sharp increase in the plasma density when the RF sources are turned on.

Accuracy and Pitfalls in the Smartphone-Based Audiometry Examination.

Approximately 466 million people suffer from hearing loss worldwide, with Indonesia ranking fourth in Southeast Asia. However, conventional pure-tone audiometry is not yet available in many areas because of its high cost. Numerous available smartphone-based audiometry applications are potential alternative screening tools for hearing loss, especially in Indonesia. This study examined the findings on the validation of smartphone-based audiometry applications to assess hearing functions available in Indonesia. Based on the established eligibility criteria, this study was conducted by browsing the relevant literature validating smartphone-based audiometry applications in Indonesia. Relevant study data, such as the author, year, location, implementation procedures, and outcomes, were extracted and summarized. This systematic review found 17 relevant and eligible publications. Of the six applications tested, 5 were found to have good validity, such as uHearTM, Audiogram MobileTM, AudCalTM, Hearing TestTM e-audiologia, and WuliraTM. All smartphone-based audiometry was tested only for the air conduction threshold and was influenced by several factors. Because smartphone-based audiometry is inexpensive, simple, and more accessible than conventional audiometric testing, it can be useful as a screening modality or alternative approach to assess hearing function. Unfortunately, smartphone-based audiometry cannot replace conventional audiometry in diagnosing hearing impairment.

Enhance or Leave It: An Investigation of the Image Enhancement in Small Object Detection in Aerial Images

Recent years of object detection (OD), a fundamental task in computer vision, have witnessed the rise of numerous practical applications of this sub-field such as face detection, self-driving, security, and more. Although existing deep learning models show significant achievement in object detection, they are usually tested on datasets having mostly clean images. Thus, their performance levels were not measured on degraded images. In addition, images and videos in real-world scenarios often involve several natural artifacts such as noise, haze, rain, dust, and motion blur due to several factors such as insufficient light, atmospheric scattering, and faults in image sensors. This image acquisition-related problem becomes more severe when it comes to detecting small objects in aerial images. In this study, we investigate the small object identification performance of several state-of-the-art object detection models (Yolo 6/7/8) under three conditions (noisy, motion blurred, and rainy). Through this inspection, we evaluate the contribution of an image enhancement scheme so-called MPRNet. For this aim, we trained three OD algorithms with the original clean images of the VisDrone dataset. Followingly, we measured the detection performance of saved YOLO models against (1) clean, (2) degraded, and (3) enhanced counterparts. According to the results, MPRNet-based image enhancement promisingly contributes to the detection performance and YOLO8 outperforms its predecessors. We believe that this work presents useful findings for researchers studying aerial image-based vision tasks, especially under extreme weather and image acquisition conditions

Blockchain Technology: A Framework for Endless Applications

Over the last decade, Blockchain Technology has evolved from being a peer-to-peer implementation of digital currency into a framework for numerous applications. That is, blockchain is widely recognized as the technology behind cryptocurrencies such as Bitcoin, but recent research indicates that it can be exploited way more than that. Among the most notable characteristics of the Blockchain Technology are system decentralization, data immutability, transaction transparency, and user privacy. Thus, it becomes apparent that it can significantly benefit many consumer applications, where trust and security are crucial aspects of the implementation. In this paper, we review and present, in a tutorial manner, blockchain's fundamental postulates and characteristics, and we introduce a novel point of view, where we visualize a layered representation of the blockchain infrastructure, so as to analyze it as a framework. Thinking of Blockchain Technology as a framework may assist consumers to better understand its capabilities and limitations. Additionally, we review emerging blockchain-based applications that span across many different scientific fields. We also discuss the relative merits and restrictions. Namely, we have identified applications in finance, smart contracts, internet of things, e-voting systems, storage and data protection, reputation systems, healthcare, and transportation, indicating that the Blockchain Technology is becoming a framework for endless applications.

Fault Tree Analysis Including Component Dependencies

Fault Tree (FT) analysis is not only the most common technique used in engineering practice for the estimation of system reliability, but is also a key tool shared between designers, analysts, and regulators for the safe operation and licensing purposes. In spite of its long lasting success, traditional FT analysis presents significant limitations in modeling a wide range of features frequently encountered in modern systems. The most critical of these is the assumption of failure events independence, which is often not justified by the realistic behavior of the engineering system, undermining modeling accuracy. This article introduces a novel methodology for the analysis of FTs allowing for component dependencies and dynamic features. The proposed approach, based on the use of binary decision diagrams, is demonstrated using a simple numerical application for verification. Its applicability and computational feasibility is discussed in details.

Machine learning-based model inference for spectral response of photonic crystals

Photonic Crystals (PhCs) are materials with a periodic arrangement of dielectric or metallic components that can manipulate the flow of photons. Conventional techniques for computing the spectrum response of these artificial structures are time-consuming, laborious, and susceptible to human errors. This paper presents a novel approach incorporating Machine Learning (ML) in forming PhC-based periodic structures. The structures are designed from the Two-Dimensional (2D) PhCs having air holes in a dielectric material. These crystalline structures work on the Guided Mode Resonance (GMR) principle and find their use in numerous applications, including optical filters in Near Infrared Range (NIR). However, the conventional methods for analyzing the output spectra of PhCs need to be revised due to the complexities in designing compound optical filters. Therefore, an automated process employing ML is required. As a result, in our work, we form 2D crystalline structures using the Finite-Difference Time Domain (FDTD) method and predict various spectral responses of PhCs via ML-based linear regression. The proposed mathematical models provide efficient results and considerably less simulation efforts and time compared to the traditional manual methods.

A Review - Bamboo Industry

Abstract: The article highlights bamboo as a versatile material with numerous applications across various industries. Its widespread availability, rapid growth, and favorable physical and chemical properties make it a convenient and easily accessible resource globally. Bamboo, being one of the oldest traditional building materials, has a rich history of use in construction. Bamboo belongs to the grass family and is renowned for being the fastest-growing plant in the world. Its industrial applications span a wide range, including food production, wood substitute, pulpand paper production, medicinal products, cottage industries, and charcoal production

Production and Optimization of L-glutaminase from Halophilic Fusarium solani-melongenae Strain CRI 24 under Submerged and Solid State Fermentation

L-glutaminase is a unique enzyme with catalytic activity and the ability to modulate glutamine levels, making it a valuable enzyme with numerous potential applications. L-glutaminase triggers a distinctive reaction by converting L-glutamine into glutamic acid while releasing ammonia concurrently. This enzymatic process holds potential applications across diverse industries, notably in food and pharmaceuticals. The primary objective of the present research was to identify and isolate a fungal strain proficient in L-glutaminase production from soil found in maritime environments. The physical and nutritional conditions were optimized for maximum synthesis of L-glutaminase under solid state fermentation (SSF) and submerged fermentation conditions (SmF). The isolated organism was identified as Fusarium solani-melongenae strain CRI 24 by morphological and 18S rRNA analysis. The optimum carbon source under SmF and SSF was found to be starch (0.2% w/v). Wheat bran as solid substrate among others showed optimum enzyme activity. On the seventh day of incubation, at pH 8.0 and 0.7% L-glutamine concentration under SSF and SmF, the highest enzyme activity was detected. The greatest enzyme activity in SSF was seen at a moisture content of 10%. Fusarium solani-melongenae species produced the enzyme under optimal conditions and 4.20 and 4.73-fold increase (from 0.8 U/mL to 3.61 U/mL and from 0.781 U/mL to 3.69 U/mL) was achieved after optimization in submerged and in solid state fermentation, respectively. The selective isolation and optimization processes described in this work are a promising technique for the industrial production of L-glutaminase and can be applied in the pharmaceutical and food industries.

High‐order models for hydro‐mechanical coupling problems in multiscale porous media

AbstractAn accurate prediction of nonlinear hydro‐mechanical (HM) coupling in subsurface structures with pronounced heterogeneity at multiple spatial scales is still an open topic and crucial for numerous engineering applications, for example, hydraulic fracturing and enhanced geothermal systems. In this study, novel high‐order multi‐scale asymptotic solutions are developed to accurately capture the locally oscillating characteristics of gas flow and solid displacement fields at multiple scales. First, the formal macro‐meso two‐scale asymptotic expansion is performed to establish the homogenized solutions and macro‐meso high‐order models which can predict the HM coupling problems at the macroscale and mesoscale. By directly expanding the cell functions defined at the mesoscale to the microscopic levels, the high‐order two‐scale expansion for the mesoscopic cell functions is built, and the upscaled relations for the flow parameters and constitutive coefficients from the microscale to the mesoscale and macroscale are developed correspondingly. The multi‐scale low/high‐order models are established by combining the high‐order expansion of all cell functions at the mesoscale and the developed macro‐meso two‐scale models. The main contributions are that the present approaches follow the reverse thought processes of reiteration homogenization methods, and offer a very innovative and efficient way to establish high‐accuracy high‐order models for the nonlinear HM coupling problems in the heterogeneous porous medium with any number of spatial scales. The effectiveness and accuracy of the present solution are validated by several representative cases with different constitutive coefficient contrasts. The results demonstrate that the high‐order solutions provide an accurate prediction for gas transport and solid deformation of heterogeneous porous media with multi‐scale configurations.