Modern Applications Research Articles

Chromosome Engineering: Technologies, Applications, and Challenges.

Chromosome engineering is a transformative field at the cutting edge of biological science, offering unprecedented precision in manipulating large-scale genomic DNA within cells. This discipline is central to deciphering how the multifaceted roles of chromosomes-guarding genetic information, encoding sequence positional information, and influencing organismal traits-shape the genetic blueprint of life. This review comprehensively examines the technological advancements in chromosome engineering, which center on engineering chromosomal rearrangements, generating artificial chromosomes, de novo synthesizing chromosomes, and transferring chromosomes. Additionally, we introduce the application progress of chromosome engineering in basic and applied research fields, showcasing its capacity to deepen our knowledge of genetics and catalyze breakthroughs in therapeutic strategies. Finally, we conclude with a discussion of the challenges the field faces and highlight the profound implications that chromosome engineering holds for the future of modern biology and medical applications.

Tensors in High-Dimensional Data Analysis: Methodological Opportunities and Theoretical Challenges

Large amounts of multidimensional data represented by multiway arrays or tensors are prevalent in modern applications across various fields such as chemometrics, genomics, physics, psychology, and signal processing. The structural complexity of such data provides vast new opportunities for modeling and analysis, but efficiently extracting information content from them, both statistically and computationally, presents unique and fundamental challenges. Addressing these challenges requires an interdisciplinary approach that brings together tools and insights from statistics, optimization, and numerical linear algebra, among other fields. Despite these hurdles, significant progress has been made in the past decade. This review seeks to examine some of the key advancements and identify common threads among them, under a number of different statistical settings.

Seamless Optimization of Wavelet Parameters for Denoising LFM Radar Signals: An AI-Based Approach

Linear frequency modulation (LFM) signals are pivotal in radar systems, enabling high-resolution measurements and target detection. However, these signals are often degraded by noise, significantly impacting their processing and interpretation. Traditional denoising methods, including wavelet-based techniques, have been extensively used to address this issue, yet they often fall short in terms of optimizing performance due to fixed parameter settings. This paper introduces an innovative approach by combining wavelet denoising with long short-term memory (LSTM) networks specifically tailored for LFM signals in radar systems. By generating a dataset of LFM signals at various signal-to-noise Ratios (SNR) to ensure diversity, we systematically identified the optimal wavelet parameters for each noisy instance. These parameters served as training labels for the proposed LSTM-based architecture, which learned to predict the most effective denoising parameters for a given noisy LFM signal. Our findings reveal a significant enhancement in denoising performance, attributed to the optimized wavelet parameters derived from the LSTM predictions. This advancement not only demonstrates a superior denoising capability but also suggests a substantial improvement in radar signal processing, potentially leading to more accurate and reliable radar detections and measurements. The implications of this paper extend beyond modern radar applications, offering a framework for integrating deep learning techniques with traditional signal processing methods to optimize performance across various noise-dominated domains.

Evaluating the Oral Method in Foreign Language Teaching: Historical Perspectives, Modern Applications, and Pedagogical Insight

This article explores the Oral Method in foreign language teaching, focusing on its historical significance, core principles, and relevance in contemporary education. Originally developed by Harold Palmer and A.S. Hornby, the Oral Method emphasizes verbal repetition, situational learning, and structured grammar acquisition as foundational tools for language proficiency. Through an analysis of its strengths—such as pronunciation accuracy, fluency development, and practical application—and its limitations, including restricted learner autonomy and cognitive engagement, this study highlights the method’s enduring value. The article also discusses the potential of integrating the Oral Method with communicative and task-based approaches, leveraging digital tools and personalized techniques to enhance its effectiveness for diverse learners. Ultimately, the Oral Method’s adaptable principles make it a valuable component of a comprehensive language teaching framework.

The backpropagation algorithm implemented on spiking neuromorphic hardware

The capabilities of natural neural systems have inspired both new generations of machine learning algorithms as well as neuromorphic, very large-scale integrated circuits capable of fast, low-power information processing. However, it has been argued that most modern machine learning algorithms are not neurophysiologically plausible. In particular, the workhorse of modern deep learning, the backpropagation algorithm, has proven difficult to translate to neuromorphic hardware. This study presents a neuromorphic, spiking backpropagation algorithm based on synfire-gated dynamical information coordination and processing implemented on Intel’s Loihi neuromorphic research processor. We demonstrate a proof-of-principle three-layer circuit that learns to classify digits and clothing items from the MNIST and Fashion MNIST datasets. To our knowledge, this is the first work to show a Spiking Neural Network implementation of the exact backpropagation algorithm that is fully on-chip without a computer in the loop. It is competitive in accuracy with off-chip trained SNNs and achieves an energy-delay product suitable for edge computing. This implementation shows a path for using in-memory, massively parallel neuromorphic processors for low-power, low-latency implementation of modern deep learning applications.

Research on the integrated application of machine learning in Unity

Abstract. In recent years, the introduction of Machine Learning Agents has enabled more individuals to apply machine learning within the renowned Unity game engine. As application development has become increasingly complex, traditional artificial intelligence (AI) struggles to keep pace with the growing demands of modern applications. However, machine learning offers new avenues for developing smarter, more dynamic game characters. This paper aims to provide a comprehensive review of the various applications of machine learning within the Unity ecosystem, including game AI, intelligent character behavior, and reinforcement learning. Through a literature review and case studies, this paper explores the specific applications of machine learning algorithms in the Unity engine and analyzes the current state of development in different fields. The research findings indicate that the application of machine learning in Unity exhibits numerous developmental trends and leaves ample room for further application development in its strong areas. This technology can be innovatively applied across many fields, from implementing advanced NPCs and third-person game agents to anti-cheat mechanisms, from image recognition-based health systems to the design of complex constrained environments. The integrated application model of machine learning and Unity3D will bring more innovation and possibilities to future application development.

Impact of Temperature and Substrate Type on the Optical and Structural Properties of AlN Epilayers: A Cross-Sectional Analysis Using Advanced Characterization Techniques

AlN, with its ultra-wide bandgap, is highly attractive for modern applications in deep ultraviolet light-emitting diodes and electronic devices. In this study, the surface and cross-sectional properties of AlN films grown on flat and nano-patterned sapphire substrates are characterized by a variety of techniques, including photoluminescence spectroscopy, high-resolution X-ray diffraction, X-ray photoelectron spectroscopy, ultraviolet photoelectron spectroscopy, and Raman spectroscopy. The results indicate that different sapphire substrates have minimal impact on the photoluminescence spectrum of the epitaxial films. As the temperature increased, the radius of curvature of the AlN films increased, while the warpage decreased. The AlN films grown on nano-patterned substrates exhibited superior quality with less surface oxidation. During the growth of AlN thin films on different types of substrates, slight shifts in the energy bands occurred due to differences in the introduction of carbon-related impurities and intrinsic defects. The Raman shift and full width at half maximum (FWHM) of the E2(low), A1(TO), E2(high), E1(TO), and E1(LO) phonon modes for the cross-sectional AlN films varied with the depth and temperature. The stress state within the film was precisely determined with specific depths and temperatures. The FWHM of the E2(high) phonon mode suggests that the films grown on nano-patterned substrates exhibited better crystalline quality.

An Old Babylonian Algorithm and Its Modern Applications

In this paper, an ancient Babylonian algorithm for calculating the square root of 2 is unveiled, and the potential link between this primitive technique and an ancient Chinese method is explored. The iteration process is a symmetrical property, whereby the approximate root converges to the exact one through harmonious interactions between two approximate roots. Subsequently, the algorithm is extended in an ingenious manner to solve algebraic equations. To demonstrate the effectiveness of the modified algorithm, a transcendental equation that arises in MEMS systems is considered. Furthermore, the established algorithm is adeptly adapted to handle differential equations and fractal-fractional differential equations. Two illustrative examples are presented for consideration: the first is a nonlinear first-order differential equation, and the second is the renowned Duffing equation. The results demonstrate that this age-old Babylonian approach offers a novel and highly effective method for addressing contemporary problems with remarkable ease, presenting a promising solution to a diverse range of modern challenges.

Evolutionary optimisation of pixelated IFA inspired antennas

As wireless communication systems increasingly require compact and efficient antennas, conventional antenna design methods are proving difficult to meet the rigorous demands of modern applications. For this purpose, this study introduces a methodology which uses pixelated Inverted-F Antenna (IFA) inspired designs optimised through genetic algorithms to enhance performance in constrained spatial environments. As pixels the antenna features the exemplary use of Einstein Hat-shaped tiles, enabling the antenna to efficiently utilize space. A with the proposed method optimised antenna is compared to traditional IFA designs and shows improved properties like enhanced antenna gain and efficiency as well as smaller reflection coefficient offering a promising solution for future compact antenna systems in the Internet of Things and beyond. Finally, a prototype was manufactured and the scattering parameters and antenna gain were measured within an anechoic chamber.

Novel hybrid coating material with triple distinct healing bond for fat oil and grease deposition control in the sewer system

Hybrid materials with distinct self-healing bonds are a prominent category of materials, demanding in various modern applications. Developing self-healable hybrid materials with desired self-healing properties at ambient conditions is highly desirable and challenging. Herein, we developed a novel zinc (II)–dimethylglyoxime–urethane-complex-based polyurethane elastomer (Zn-polyurethane hybrid material) with synergetic triple dynamic bonds, which exhibits spontaneous self-healing properties at 30 °C. Moreover, this material shows excellent thermal stability up to approximately 850 °C and is highly stable in water systems. This hybrid material has potential applications in water systems; in this study, we characterized it for fat, oil and grease (FOG) deposition management in sewer systems by applying it as a coating on concrete blocks. The findings indicate that following a 30-day leaching test conducted under controlled pH conditions on uncoated and Zn-polyurethane-coated concrete blocks, a reduction of over 80 % in calcium release was observed in the coated block compared to the uncoated concrete block. Additionally, in FOG deposit formation tests conducted on uncoated and coated concrete blocks for 30 days, a reduction of over 30 % in FOG deposit formation was noted in the coated sample.

Enabling modern bioenergy deployment in Nigeria to support industry and local communities

Nigeria intends to rank among the top 20 global economies by 2030 by focusing on industrialisation. However, limiting energy access may slow the rate of industrialisation. Bioenergy integration into Nigeria's energy mix can accelerate the industrialisation agenda due to the co-benefits it offers. We used a disaggregated approach to map agri-residue availability and identify knowledge gaps in agri-residue application to support modern and sustainable bioenergy integration into Nigeria's energy mix. Expert interviews with stakeholders from government departments, small- and large-scale industries, and feedstock producers were used to validate the biomass mapping. The output of the biomass mapping shows that residues from yam, sorghum, wheat, palm, cassava, rice, sugarcane, etc, have knowledge gaps in agri-residue application and they could support the industrialisation agenda of Nigeria. The output of the stakeholder engagement shows that fossil fuels are the main energy source for productive uses in Nigeria. Current waste management practices involve onsite burning and disposal on land. Bioenergy technologies currently deployed in Nigeria are predominantly anaerobic digestion and combustion. Stakeholders have a strong preference for electricity to be the predominant energy vector. However, awareness of modern bioenergy applications and technologies was limited even though Nigeria's Energy Masterplan supports the efficient use of biomass to generate clean heat, electricity and biofuel for industrial, transport and household applications. Based on these findings, we have developed a suite of novel bioenergy case studies to support biomass integration into Nigeria's energy system.

Parameters of Performance: A Deep Dive into Liquid-to-Air CDU Assessment

The rapid growth in data center workloads and the increasing complexity of modern applications have led to significant contradictions between computational performance and thermal management. Traditional air-cooling systems, while widely adopted, are reaching their limits in handling the rising thermal footprints and higher rack power densities of next-generation servers, often resulting in thermal throttling and decreased efficiency, emphasizing the need for more efficient cooling solutions. Direct-to-chip liquid cooling with cold plates has emerged as a promising solution, providing efficient heat dissipation for high-performance servers. However, challenges remain, such as ensuring system stability under varying thermal loads and optimizing integration with existing infrastructure. This comprehensive study digs into the area of data center liquid cooling, providing a novel, comprehensive experimental investigation of the critical steps and tests necessary for commissioning coolant distribution units (CDUs) in direct-to-chip liquid-cooled data centers. It carefully investigates the hydraulic, thermal, and energy aspects, establishing the groundwork for Liquid-to-Air (L2A) CDU data centers. A CDU’s performance was evaluated under different conditions. First, the CDU’s maximum cooling capacity was evaluated and found to be as high as 89.9 kW at an approach temperature difference (ATD) of 18.3 °C with a 0.83 heat exchanger effectiveness. Then, to assess the cooling performance and stability of the CDU, a low-power test and a transient thermohydraulic test were conducted. The results showed instability in the supply fluid temperature (SFT) caused by the oscillation in fan speed at low thermal loads. Despite this, heat removal rates remained constant across varying supply air temperatures (SATs), and a partial power usage effectiveness (PPUE) of 1.042 was achieved at 100% heat load (86 kW) under different SATs. This research sets a foundation for improving L2A CDU performance and offers practical insights for overcoming current cooling limitations in data centers.

Tunable Graphene‐Based Absorber Using Nanoscale Grooved Metal Film at Telecommunication Wavelengths

Graphene‐based absorbers have various modern applications across industries due to their exceptional properties. Some common applications include: thermal management and energy storage. Herein, the design and simulation of a broadband tunable absorber based on graphene with perfect absorption spectra in the near‐infrared region are reported. The proposed structure consists of an MgF2 layer and golden disc surrounded by L‐shaped golden arms placed on single layer of graphene. The structure guarantees polarization‐insensitive (PI) performance under normal incident due to the symmetrical design. The investigation of the PI of the structure reveals almost similar absorption for oblique incident angles up to 55° for TM and up to 60° for TE polarization. The desirable resonance wavelength is achievable by tuning the geometrical parameters. By changing the chemical potential of graphene, the absorption and bandwidth of absorber are controllable. A full width at half maximum of 330 nm is another superiority of this absorber. These considerable aspects of the proposed structure make it practical for varieties of applications such as cloaking, sensing, switching, and so on.

Analyzing the performance of metaheuristic algorithms in speed control of brushless DC motor: Implementation and statistical comparison.

A brushless DC (BLDC) motor is likewise called an electrically commutated motor; because of its long help life, high productivity, smaller size, and higher power output, it has numerous modern applications. These motors require precise rotor orientation for longevity, as they utilize a magnet at the shaft end, detected by sensors to maintain speed control for stability. In modern apparatuses, the corresponding, primary, and subsidiary (proportional-integral) regulator is broadly utilized in controlling the speed of modern machines; however, an ideal and effective controlling strategy is constantly invited. BLDC motor is a complex system having nonlinearity in its dynamic responses which makes primary controllers in efficient. Therefore, this paper implements metaheuristic optimization techniques such as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Accelerated Particle Swarm Optimization (APSO), Levy Flight Trajectory-Based Whale Optimization Algorithm (LFWOA); moreover, a chaotic map and weight factor are also being applied to modify LFWOA (i.e., CMLFWOA) for optimizing the PI controller to control the speed of BLDC motor. Model of the brushless DC motor using a sensorless control strategy incorporated metaheuristic algorithms is simulated on MATLAB (Matrix Laboratory)/Simulink. The Integral Square Error (ISE) criteria is used to determine the efficiency of the algorithms-based controller. In the latter part of this article after implementing these mentioned techniques a comparative analysis of their results is presented through statistical tests using SPSS (Statistical Package for Social Sciences) software. The results of statistical and analytical tests show the significant supremacy of WOA on others.

Magnesium alloys in the automotive industry: Alloying elements and their impact on material performance

This study aims to systematically review and evaluate the application of magnesium (Mg) within the automotive sector, with a focus on enhancing fuel efficiency and supporting environmental sustainability. The review emphasizes the latest advancements in cast magnesium alloys, wrought magnesium alloys, and functional magnesium materials, including the development of novel Mg-based materials. The environmental impact of utilizing magnesium in automotive components is also critically analyzed. Recent innovations, particularly in the fields of nanocomposites and alloying, have significantly enhanced the mechanical properties of magnesium alloys, such as increased strength and improved grain refinement, making them suitable for high-temperature applications. The findings suggest that the competitive pricing and enhanced properties of magnesium and its alloys are key drivers for their widespread adoption in automotive manufacturing. The paper provides a detailed discussion on the current trends in magnesium alloy applications, with particular focus on the role of alloying elements and strategies to address associated challenges. This comprehensive review outlines the recent technological advancements in magnesium materials and their relevance to modern engineering applications in the automotive industry.

Performance of Drying Oil Modified Chinese Lacquer and Its Gilding Effect

This study explores the modification of traditional Chinese lacquer by incorporating boiled tung oil (BTO), boiled linseed oil (BLO), and turpentine oil (TO) to enhance its properties for gold leafing applications. Current traditional lacquers are limited by slow drying times and inconsistent surface quality, making their performance suboptimal for decorative gilding. The research addresses these gaps by investigating how varying oil types and concentrations (10%, 30%, and 50%) affect the lacquer’s drying time, viscosity, leveling properties, and overall gilding performance. Results indicate that TO-modified lacquer exhibits the best overall performance, showing the fastest drying time, highest glossiness, and smallest color variation, while BTO provides the smoothest surface and BLO ensures the best adhesion. These results demonstrate that the careful selection of oil type and concentration significantly improves lacquer’s functionality for gold leafing, offering a more efficient and aesthetically superior alternative to unmodified lacquers. This study provides valuable insights for optimizing traditional lacquer formulations for modern applications in gilding and decorative finishes.

Enhancing System Performance with Observability Data Analytics and Cost Savings

Modern applications are increasingly complex, built using microservices architectures, spread across cloud environments, and deployed globally. Maintaining system performance, reliability, and security is no easy task. Observability, powered by data analytics, helps engineers monitor, understand, and optimize these systems efficiently. Beyond operational benefits, observability leads to significant cost savings by reducing downtime, optimizing infrastructure, and improving development cycles. This article dives into how observability transforms system performance and how it results in real-world savings for businesses

Enhanced Data Security Using 5x5 Hill Cipher with Modular 53

This research presents an optimized approach to the Hill Cipher encryption and decryption algorithm using a 5x5 matrix and modular 53 arithmetic. The traditional Hill Cipher, a well-known symmetric key algorithm, typically utilizes smaller matrices and modular arithmetic, which may not provide sufficient security for contemporary applications. By expanding the key matrix to a 5x5 structure and adopting a larger modulus of 53, the complexity and security of the cipher are significantly enhanced. The study details the methodology for constructing and implementing the 5x5 key matrix, as well as the processes for encryption and decryption under the modular 53 system. The computational efficiency and security improvements achieved through this optimization are analyzed. Comparative assessments with the conventional Hill Cipher demonstrate that the enhanced approach offers superior resistance against cryptographic attacks while maintaining manageable computational requirements. The results of this research indicate that the proposed optimized Hill Cipher can serve as a robust encryption method suitable for securing sensitive data in various modern applications. This study contributes to the field of cryptography by providing a more secure and efficient variant of the classical Hill Cipher algorithm

Practical Reasoning in DatalogMTL

Abstract DatalogMTL is an extension of Datalog with metric temporal operators that has found an increasing number of applications in recent years. Reasoning in DatalogMTL is, however, of high computational complexity, which makes reasoning in modern data-intensive applications challenging. In this paper we present a practical reasoning algorithm for the full DatalogMTL language, which we have implemented in a system called MeTeoR. Our approach effectively combines an optimised (but generally non-terminating) materialisation (a.k.a. forward chaining) procedure, which provides scalable behaviour, with an automata-based component that guarantees termination and completeness. To ensure favourable scalability of the materialisation component, we propose a novel seminaïve materialisation procedure for DatalogMTL enjoying the non-repetition property, which ensures that each rule instance will be applied at most once throughout its entire execution. Moreover, our materialisation procedure is enhanced with additional optimisations which further reduce the number of redundant computations performed during materialisation by disregarding rules as soon as it is certain that they cannot derive new facts in subsequent materialisation steps. Our extensive evaluation supports the practicality of our approach.

Design a new scheme for image security using a deep learning technique of hierarchical parameters

Abstract With the continued exponential growth of digital images, concerns about the security and confidentiality of visual data have increased. In this session, a new developed approach was presented for image security and confidentiality by taking advantage of deep learning (DL) technology and producing data hierarchies. Due to the development taking place in the field of images and the large circulation of them through modern applications, it has become necessary to maintain their security. DL technology was used to encrypt and decrypt images, and based on hierarchical variables to complicate the encryption process. Convolutional neural networks are used in automatic learning to extract hierarchical features from an image, and to ensure adaptability, the model is trained on a variety of images. In order to encrypt the image, multi-layered hierarchical processes are used, and there are layers added during the work for complexity and to thwart attacks. Manipulating the layers of the neural network in a hierarchical manner to benefit from the outputs of the layers in feedback reflects the importance of the contributions here. Likewise, scattering the columns and rows of the image in a descending or ascending manner increases the efficiency of the contribution in this study. The use of hierarchical parameters facilitates encryption and decryption for authorized users. The evaluation of the research was conducted using established picture metrics and compared to pre-existing encryption techniques. The experimental findings substantiated the efficacy of the proposed approach in upholding image security, with the inclusion of hierarchical information further bolstering its ability to thwart attacks. Consequently, it emerges as a very promising strategy for ensuring image security. The proposed method is a significant advancement in creating an image security strategy using DL and a hierarchical variable creation process. The study provides a good and adaptable solution to evolving image security challenges in the digital age.