Related Applications Research Articles

Penerapan Peran Media Relations Dalam Meningkatkan Citra Universitas Muhammadiyah Jakarta

One of the main activities of Public Relations itself is media relations. Media relations, or relations with mass media, is a strategic means for universities to improve the image of UMJ. Media relations allows the University of Muhammadiyah Jakarta to build public awareness about their programs, activities, and achievements. Through publications in mass media and online media, they can reach a wider audience and introduce the University of Muhammadiyah Jakarta to the public. This study aims to understand and analyze in depth the application of media relations in improving the image of the University of Muhammadiyah Jakarta. The image of the university is one of the things that is quite important in attracting prospective students, supporting development efforts carried out by university residents in terms of research and publications, and strengthening cooperation networks with existing stakeholders. Furthermore, interviews will be conducted with public relations staff of the University of Muhammadiyah Jakarta regarding public relations work practices through existing Media Relations. As a result, media relations has a very significant role in improving the image of an institution, especially in the context of today's digital era. Digital transformation has brought major changes in various aspects of life, including education. With registration, payments, and online classes all going digital, the existence of an institution without a digital presence can make it lag far behind the competition.

Controlling nonlinear collapse of ellipticity and orientation of a co-variant vector optical field.

A vector optical field with inhomogeneous spatial polarization distribution offers what we believe to be a new paradigm to form controllable filaments. However, it is challenging to steer multiple performances (e.g. number, orientation, and interval) of filaments in transparent nonlinear media at one time. Herein, we theoretically self-design and generate a kind of believed to be novel ellipticity and orientation co-variant vector optical field to interact with Kerr medium to solve this issue. The collapsing behaviors of such a new hybrid vector optical field reveal that, by judiciously adjusting the inherent topological charge and initial phase of incident optical field, we are able to give access to stable collapsing filamentation with tunable numbers, orientations and interval. Additionally, the collapsing patterns presented are immune nearly to the extra random noise. The relevant mechanism behind the collapse of the vector optical field is elucidated as well. The findings in this work may have huge potential in optical signal processing, laser machining, and other related applications.

Vascularization strategies for human skin tissue engineering via 3D bioprinting

The skin is composed of many cells that are organized into different layers and connected by dense and complex vascular networks. This creates a dynamic microenvironment in which cells interact within the matrix. Significant advancements have been made in this field over the past decade, and various strategies have been developed for accelerating and enhancing skin regeneration. The primary challenge for successful skin grafts is the integration of the functional vasculature, which can supply essential nutrients and oxygen to cell-laden structures and damaged native tissues. An inadequate vascular network can lead to ischemia, which can cause slow wound healing—particularly in the case of chronic skin conditions. Therefore, blood vessel formation remains one of the most significant obstacles that skin tissue engineering must overcome to create vascularized skin tissue substitutes with specific living cells. Technological advances can augment effective vascularization. The three-dimensional (3D) bioprinting platform is a promising technology that allows precise deposition of living cells and bioactive materials. The application of this technology to skin tissue engineering can provide solutions for augmenting pre-vascularization in engineered in vitro skin models and in vivo skin substitutes. This review presents the significance of skin vascularization in in vitro modeling and in vivo wound healing. Various strategies and related applications involving 3D bioprinting technology are introduced for the biofabrication of enhanced vascularized skin in vitro and in vivo, followed by a discussion of their limitations and future research directions.

Following the Writer’s Path to the Dynamically Coalescing Reactive Chains Design Pattern

Two recent studies addressed the problem of reducing transitional turbulence in applications developed in C# on .NET. The first study investigated this problem in desktop and Web GUI applications and the second in virtual and augmented reality applications using the Unity3D game engine. The studies used similar solution approaches, but both were somewhat embedded in the details of their applications and implementation platforms. This paper examines these two families of applications and seeks to extract the common aspects of their problem definitions and solution approaches and codify the problem-solution pair as a new software design pattern. To do so, the paper adopts Wellhausen and Fiesser’s writer’s path methodology and follows it systematically to discover and write the pattern, recording the reasoning at each step. To evaluate the pattern, the paper applies it to an arbitrary C#/.NET GUI application. The resulting design pattern is named Dynamically Coalescing Reactive Chains (DCRC). It enables the approach to transitional turbulence reduction to be reused across a range of related applications, languages, and user interface technologies. The detailed example of the writer’s path can assist future pattern writers in navigating through the complications and subtleties of the pattern-writing process.

Synthetic Aperture Radar Image Despeckling Based on a Deep Learning Network Employing Frequency Domain Decomposition

Synthetic aperture radar (SAR) images are inevitably interspersed with speckle noise due to their coherent imaging mechanism, which greatly hinders subsequent related research and application. In recent studies, deep learning has become an effective tool for despeckling remote sensing images. However, preserving more texture details while removing speckle noise remains a challenging task in the field of SAR image despeckling. Furthermore, most despeckling algorithms are designed specifically for a specific look and seriously lack generalizability. Therefore, in order to remove speckle noise in SAR images, a novel end-to-end frequency domain decomposition network (SAR−FDD) is proposed. The method first performs frequency domain decomposition to generate high-frequency and low-frequency information. In the high-frequency branch, a mean filter is employed to effectively remove noise. Then, an interactive dual-branch framework is utilized to learn the details and structural information of SAR images, effectively reducing speckles by fully utilizing features from different frequencies. In addition, a blind denoising model is trained to handle noisy SAR images with unknown noise levels. The experimental results demonstrate that the SAR−FDD achieves good visual effects and high objective evaluation metrics on both simulated and real SAR test sets (peak signal-to-noise ratio (PSNR): 27.59 ± 1.57 and structural similarity index (SSIM): 0.78 ± 0.05 for different speckle noise levels), demonstrating its strong denoising performance and ability to preserve edge textures.

Artificial Neural Network Applications for Energy Management in Buildings: Current Trends and Future Directions

ANNs have become a cornerstone in efficiently managing building energy management systems (BEMSs) as they offer advanced capabilities for prediction, control, and optimization. This paper offers a detailed review of recent, significant research in this domain, highlighting the use of ANNs in optimizing key energy systems, such as HVAC systems, domestic water heating (DHW) systems, lighting systems (LSs), and renewable energy sources (RESs), which have been integrated into the building environment. After illustrating the conceptual background of the most common ANN architectures for controlling BEMSs, the current work dives deep into relative research applications, thereby exhibiting their methodology and outcomes. By summarizing the numerous impactful applications during 2015–2023, this paper categorizes the predominant ANN-based techniques according to their methodological approach, specific energy equipment, and experimental setups. Grounded in the different perspectives that the integrated studies illustrate, the primary focus of this paper is to evaluate the overall status of ANN-driven control in building energy management, as well as to offer a deep understanding of the prevailing trends at the building level. Leveraging detailed graphical depictions and comparisons between different concepts, future directions, and fruitful conclusions are drawn, and the upcoming innovations of ANN-based control frameworks in BEMSs are highlighted.

Distributed Sequential Detection for Cooperative Spectrum Sensing in Cognitive Internet of Things.

The rapid development of wireless communication technology has led to an increasing number of internet of thing (IoT) devices, and the demand for spectrum for these devices and their related applications is also increasing. However, spectrum scarcity has become an increasingly serious problem. Therefore, we introduce a collaborative spectrum sensing (CSS) framework in this paper to identify available spectrum resources so that IoT devices can access them and, meanwhile, avoid causing harmful interference to the normal communication of the primary user (PU). However, in the process of sensing the PUs signal in IoT devices, the issue of sensing time and decision cost (the cost of determining whether the signal state of the PU is correct or incorrect) arises. To this end, we propose a distributed cognitive IoT model, which includes two IoT devices independently using sequential decision rules to detect the PU. On this basis, we define the sensing time and cost functions for IoT devices and formulate an average cost optimization problem in CSS. To solve this problem, we further regard the optimal sensing time problem as a finite horizon problem and solve the threshold of the optimal decision rule by person-by-person optimization (PBPO) methodology and dynamic programming. At last, numerical simulation results demonstrate the correctness of our proposal in terms of the global false alarm and miss detection probability, and it always achieves minimal average cost under various costs of each observation taken and thresholds.

Context-detail-aware United Network for Single Image Deraining

Images captured outdoors are often affected by rainy days, resulting in a severe deterioration in the visual quality of the captured images and a decrease in the performance of related applications. Therefore, single image deraining has attracted attention as a challenging research topic. Nowadays, there are two common deraining architectures in single image deraining. The first one is to restore the rain-free image by deducting rain streaks learned by the model from the rain image, but the background structure is easily mistaken for rain streaks and subtracted. The other one is to directly learn the clean background structure through the model using rain images, but it is difficult to completely remove the rain streaks due to the complexity of the information in images with rain. Therefore, current methods cannot balance rain streak removal and rain-free image background restoration in a single architecture and achieve good results. To address this issue, we propose a novel framework, namely, Context-Detail-Aware United Network (CDaUNet), which combines the above two architectures in this study. More specifically, we divide the restoration of the background structure of rain-free images and the learning of rain streaks into two independent sub-networks. The proposed Structure-Aware Rain Removal Network (SaRRN) is to learn the background structure in images to reconstruct clean rain-free images, whereas Detail-Aware Rain Streak Learning Network (DaRLN) is proposed to learn the details of rain streaks in images. Finally, we fuse the results generated by the two sub-networks through our designed Dual Architecture Fusion Network (DAFN) to reconstruct original rain images to effectively fuse the results of the two sub-networks. The experimental results show that CDaUNet achieves satisfactory performance in comparison with the state-of-the-art approaches included in rain streak removal and rain-free image structure restoration architectures on both synthetic and real image datasets, confirming the effectiveness of our method.

On the dynamics of soliton solutions for the nonlinear fractional dynamical system: Application in ultrasound imaging

This article examines the third-order non-linear fractional Westervelt model by using a newly developed integration tool termed the new extended direct algebraic approach. The different wave structures to the considered model are secured in various forms, including bright, dark, and combo solitons. The application of wave structures is advantageous in the examination of sound wave propagation and high amplitude phenomena in the fields of medical imaging and therapy. These solutions are effective in facilitating ultrasound propagation in tissue, underwater acoustics, acoustic cavitation, acoustic levitation, and other related applications. The internal tissue of human beings can be visualized and studied through the use of ultrasound imaging technologies in the field of medicine. This technology possesses numerous uses in both industrial and medicinal sectors. There has been a growing interest in fractional nonlinear partial differential equations due to their ability to describe various complex occurrences and exhibit more dynamic architectures of localized wave solutions. On employing accurate parameter values, multiple graphical representations are generated to provide the visual depiction of the acquired results. The results of this study indicate that the chosen methodology effectively improves nonlinear dynamical processes. The findings indicate that the selected methodology demonstrates efficacy, feasibility, and versatility when applied to intricate systems across several domains, with a special emphasis on ultrasonic imaging. The findings indicate that the system exhibits a potentially significant abundance of soliton structures.

Useful blunders: Can automated speech recognition errors improve downstream dementia classification?

Objectives:We aimed to investigate how errors from automatic speech recognition (ASR) systems affect dementia classification accuracy, specifically in the “Cookie Theft” picture description task. We aimed to assess whether imperfect ASR-generated transcripts could provide valuable information for distinguishing between language samples from cognitively healthy individuals and those with Alzheimer’s disease (AD). Methods:We conducted experiments using various ASR models, refining their transcripts with post-editing techniques. Both these imperfect ASR transcripts and manually transcribed ones were used as inputs for the downstream dementia classification. We conducted comprehensive error analysis to compare model performance and assess ASR-generated transcript effectiveness in dementia classification. Results:Imperfect ASR-generated transcripts surprisingly outperformed manual transcription for distinguishing between individuals with AD and those without in the “Cookie Theft” task. These ASR-based models surpassed the previous state-of-the-art approach, indicating that ASR errors may contain valuable cues related to dementia. The synergy between ASR and classification models improved overall accuracy in dementia classification. Conclusion:Imperfect ASR transcripts effectively capture linguistic anomalies linked to dementia, improving accuracy in classification tasks. This synergy between ASR and classification models underscores ASR’s potential as a valuable tool in assessing cognitive impairment and related clinical applications.

Nonideal Compressible Fluid Dynamics of Dense Vapors and Supercritical Fluids

The gas dynamics of single-phase nonreacting fluids whose thermodynamic states are close to vapor-liquid saturation, close to the vapor-liquid critical point, or in supercritical conditions differs quantitatively and qualitatively from the textbook gas dynamics of dilute, ideal gases. Due to nonideal fluid thermodynamic properties, unconventional gas dynamic effects are possible, including nonclassical rarefaction shock waves and the nonmonotonic variation of the Mach number along steady isentropic expansions. This review provides a comprehensive theoretical framework of the fundamentals of nonideal compressible fluid dynamics (NICFD). The relation between nonideal gas dynamics and the complexity of the fluid molecules is clarified. The theoretical, numerical, and experimental tools currently employed to investigate NICFD flows and related applications are reviewed, followed by an overview of industrial processes involving NICFD, ranging from organic Rankine and supercritical CO2 cycle power systems to supercritical processes. The future challenges facing researchers in the field are briefly outlined.

Mirror-image trypsin digestion and sequencing of D-proteins.

The development of mirror-image biology systems and related applications is hindered by the lack of effective methods to sequence mirror-image (D-) proteins. Although natural-chirality (L-) proteins can be sequenced by bottom-up liquid chromatography-tandem mass spectrometry (LC-MS/MS), the sequencing of long D-peptides and D-proteins with the same strategy requires digestion by a site-specific D-protease before mass analysis. Here we apply solid-phase peptide synthesis and native chemical ligation to chemically synthesize a mirror-image version of trypsin, a widely used protease for site-specific protein digestion. Using mirror-image trypsin digestion and LC-MS/MS, we sequence a mirror-image large subunit ribosomal protein (L25) and a mirror-image Sulfolobus solfataricus P2 DNA polymerase IV (Dpo4), and distinguish between different mutants of D-Dpo4. We also perform writing and reading of digital information in a long D-peptide of 50 amino acids. Thus, mirror-image trypsin digestion in conjunction with LC-MS/MS may facilitate practical applications of D-peptides and D-proteins as potential therapeutic and informational tools.

Exploring the effect of pre-service teachers’ digital competence on paperless behavior

<p>Pre-service teachers must implement paperless behavior in line with institutional policies, technological developments, and environmentally friendly issues. Pre-service teacher candidates are expected to have digital competence in skills, abilities, and knowledge to successfully use computers, their related applications, and software in teaching and educational practice. It is predicted to affect their paperless behavior because digital activities reduce paper use. This study aimed to analyze the influence of prospective teachers’ digital competencies on their paperless behavior. The research is based on the theory of planned behavior (TPB) using a quantitative approach involving 372 pre-service teachers at the Faculty of Economics, Universitas Negeri Semarang (UNNES). Sampling was done by purposive sampling with specific criteria. The results of the study show that digital competence has an influence on paperless behavior by 43.1%. These findings add insight into the role of digital competence in an environmentally friendly learning process. This research suggests increasing students’ digital competence so that students’ paperless behavior rises in line with environmental issues.<strong></strong></p>

Simple color appearance model (sCAM) based on simple uniform color space (sUCS).

A new color appearance model named sCAM has been developed, including a uniform color space, sUCS. The model has a simple structure but provides comprehensive functions for color related applications. It takes input from either XYZ D65 or signals from an RGB space. Their accuracy has been extensively tested. sUCS performed the best or second-best to the overall 28 datasets for space uniformity and the 6 datasets for hue linearity comparing the state of the art UCSs. sCAM also performed the best to fit all available one- and two-dimensional color appearance datasets. It is recommended to have field tests for all color related applications.

Lattice Boltzmann simulation of droplet solidification processes with different solid-to-liquid density ratios

Droplet solidification is widespread in nature and industry. The difference in droplet density before and after solidification has a significant impact on the solidification characteristics of the droplet. In this research, a lattice Boltzmann model considering that difference is developed to simulate the droplet solidification process and is validated by comparing the simulation results with the analytical solutions and the experimental data. The triple contact line variation, solidification time, and final droplet profile are analyzed with a special emphasis on the effect of solid-to-liquid density ratio. As the density ratio increases, the triple contact line tends to go up but the droplet height decreases, leading to a shorter solidification time. In the final droplet profile, a tip or plateau is observed when the density ratio is smaller or larger than unity and the plateau radius increases at a larger density ratio. The effects of Stefan number (St), contact angle (CA), and Bond number (Bo) under different density ratios on the solidification process are further investigated. The increasing St and Bo, or decreasing CA reduces the solidification time. The Bo and CA mainly affect the solidification process by modifying the initial droplet profile. This work can provide a better understanding of the droplet solidification mechanisms and related applications.

Parametric and Nonparametric Machine Learning Techniques for Increasing Power System Reliability: A Review

Due to aging infrastructure, technical issues, increased demand, and environmental developments, the reliability of power systems is of paramount importance. Utility companies aim to provide uninterrupted and efficient power supply to their customers. To achieve this, they focus on implementing techniques and methods to minimize downtime in power networks and reduce maintenance costs. In addition to traditional statistical methods, modern technologies such as machine learning have become increasingly common for enhancing system reliability and customer satisfaction. The primary objective of this study is to review parametric and nonparametric machine learning techniques and their applications in relation to maintenance-related aspects of power distribution system assets, including (1) distribution lines, (2) transformers, and (3) insulators. Compared to other reviews, this study offers a unique perspective on machine learning algorithms and their predictive capabilities in relation to the critical components of power distribution systems.

On simplified methods for determining the elastic constants of layered polymer composites

The paper considers the standard layer-by-layer method for calculating composite structures. The main problems of the method are highlighted and the approaches which allow to simplify the calculation are analyzed. It is shown that the transition to trace can be used to restore the elastic constants of a monolayer on the basis of only one experiment. Simplified tensor transformations for rotating the coordinate system are considered. The conclusion about the limits of applicability of the simplified relations for different materials is made.

Blind video quality assessment based on Spatio-Temporal Feature Resolver

As a method to measure video quality, blind video quality assessment (BVQA) plays an important role in video related applications. Feature extraction, as a vital component of BVQA, significantly impacts the performance and speed of the method. In the process of designing BVQA method, we make the feature extraction process independent of the whole quality evaluation method. First, a Spatio-Temporal Feature Resolver (STFR) is obtained by training. Then, the STFR is employed to directly extract the spatio-temporal features of the video sequences. Finally, the extracted features are mapped to quality scores using Support Vector Regression (SVR). STFR only needs to be trained in an undistorted video sequence, and can be directly applied to video sequences of various scenes, which has a universality. To evaluate the effectiveness of the proposed method, the experimental results on four published video quality databases show that the proposed BVQA method not only achieves more accurately than the existing BVQA methods, but also exhibits significant competitiveness in computational speed.

IGF2BP3-mediated enhanced stability of MYLK represses MSC adipogenesis and alleviates obesity and insulin resistance in HFD mice

Mesenchymal stem cells (MSCs) hold immense potential as multipotent stem cells and serve as a primary source of adipocytes. The process of MSC adipogenesis plays a crucial role in maintaining systemic metabolic homeostasis and has garnered significant attention in tissue bioengineering. N6-methyladenosine (m6A), the most prevalent RNA modification, is known to regulate cell fate and disease. However, the precise involvement of m6A readers in MSC adipogenesis remains unclear. In this study, we investigated the impact of IGF2BP3, a prominent m6A reader, on MSC adipogenesis. Our findings revealed a decrease in IGF2BP3 expression during the natural adipogenic differentiation of MSCs. Furthermore, IGF2BP3 was found to repress MSC adipogenesis by augmenting the levels of MYLK, a calcium/calmodulin-dependent kinase. Mechanistically, IGF2BP3 interacted with MYLK mRNA in an m6A-dependent manner, extending its half-life and subsequently inhibiting the phosphorylation of the ERK1/2 pathway, thereby impeding the adipogenic differentiation of MSCs. Additionally, we successfully achieved the overexpression of IGF2BP3 through intraperitoneal injection of adeno-associated virus serotype Rec2, which specifically targeted adipose tissue. This intervention resulted in reduced body weight and improved insulin resistance in high-fat diet mice. Overall, our study provides novel insights into the role of IGF2BP3 in MSC adipogenesis, shedding light on adipocyte-related disorders and presenting potential targets for related biomedical applications.

Carbon nitride with water soluble ability: Enhanced oxygen species interphase mass transfer for homogenous photocatalytic water purification

Heterogeneous catalysis has been considered a promising way for waste purification, however the catalytic performances of the heterogeneous-based systems are still unsatisfactory, mainly due to the limited active sites exposure and sluggish reactants mass transfer. In this study, we propose a conceptual carbonitride-based quasi-homogeneous catalytic system, exhibiting remarkable photocatalytic activity toward organic pollutants degradation and pathogenic bacteria inactivation. We verify that downsizing carbonitride into nanodot level and modifying the material with hydrophilic groups can endow it with water soluble ability, forming a quasi-homogeneous catalytic system to maximize the active sites exposure and minimize the reactants mass transfer resistance for high-efficient water purification. As a result, the as-established quasi-homogeneous photocatalytic system exhibits abundant surface-active sites and reactants mass transfer efficiencies that allows remarkable O2 reduction for ROS generation. In addition, the treated water containing the functionalized carbonitride is found capable of driving the fast flocculate of suspended substance, which helps to build a well water decontamination and suspended substance removal water purification nexus for practical applications. The work demonstrated here sheds new light in designing high-efficient homogeneous catalytic system for energy and environmental related applications.