Earticle

본 논문은 X-Band 애플리케이션에 활용하기 위한 4x4 마이크로스트립 어레이 안테나 최적화 설계와 제작에 대한 연구 결과를 제시하고 있다. 일반적으로 마이크로스트립 패치와 어레이 안테나 설계 파라미터는 기판의 종류와 패치의 사이즈를 조정함으로써 원하는 공진 주파수와 이득을 얻을 수 있다. 이 연구는 X-Band 공진 특성과 이득을 향상시키기 위해 최적의 기판 재료로 설계된 4x4 어레이 안테나 설계와 제작을 통한 특성 측정 결과를 소개하였다. 설계된 안테나의 크기는 10mm(W)x7.4mm(W)x0.79(H)mm 이다. 사용된 유전체 기판은 Rogers RO 4350B(εr=3.54)이며 안테나 급전 방식은 안테나의 사이즈를 줄이기 위해 인셋피드(Inset-fed) 방식이 적용되었다. 제안된 구조의 단일패치와 4x4 어레이 안테나는 설계 중심 주파수 10.3GHz에서 안정된 SWR(<1.5) 특성과 18.5dBi의 이득으로 예측되었다. 시뮬레이션 결과를 바탕으로 안테나가 제작되었고 측정 결과도 시뮬레이션 결과와 유사한 특성을 나타내는 것을 보았다. 본 연구에서 제안된 안테나는 군사, 위성 통신, 생물의학 분야를 포함한 X-Band 애플리케이션에 활용될 수 있다.

This paper details research on the optimized design and fabrication of a 4x4 microstrip array antenna intended for X-Band applications. The study focuses on achieving the desired resonance frequency and gain by modifying the microstrip patch and array antenna parameters, including substrate type and patch size. It presents results from designing and fabricating a 4x4 array antenna with optimum substrate materials to enhance X-Band resonance characteristics and gain. The antenna dimensions are 10mm(W)x7.4mm(L)x 0.79mm(H), with an Rogers RO 4350B dielectric substrate (εr=3.54) and an inset-fed feeding method to minimize antenna size. Both the single patch and 4x4 array antennas demonstrated stable SWR (<1.5) and a gain of 18.5dBi at the target frequency of 10.3GHz in simulations. The fabricated antenna showed performance consistent with simulation results. This antenna design is suitable for X-Band applications, including military, satellite communications, and biomedical fields.

With the progress of IT technology, it has become possible to reduce consumer costs in the power market by using energy storage systems (ESS) in smart grids. Traditional algorithms proposed to solve optimization of ESS problems are difficult to apply to dynamic situations, hence adaptable and relatively simple designs such as deep reinforcement learning (DRL) techniques have begun to be used instead. In this study, a Markov decision process is designed to determine the charging and discharging amounts within a certain range to extend the lifespan of the ESS. Furthermore, DRL techniques such as deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and soft actor-critic (SAC) were trained, and their performances were compared for analysis.

With advancements in information technology, traffic signal control has become a crucial component of smart transportation systems, and research based on reinforcement learning (RL) for this purpose is being actively conducted. However, tuning the weights of a multi-objective reward function remains a challenging task. This paper proposes an algorithm that leverages a large language model (LLM) to dynamically adjust the weights of the RL reward function in real time, enabling efficient traffic signal control at intersections. We compare the performance of dynamic weight adjustment via LLM and evaluate the signal control efficiency of the proposed model under various weather conditions.

Erratic rainfall due to climate change in the Kenya's Arid and Semi-Arid Lands, particularly Makueni County, threatens rain-fed maize production and food security. This study explores deficit irrigation to optimize water use, aiming to improve water productivity, stabilize yields and enhance resilience, aligning with Sustainable Development Goals on water management and climate action. The Soil-Water-Atmosphere-Plant (SWAP) model is used for estimating soil moisture/transpiration and managing irrigation and drainage across diverse climatic and environmental contexts from 2013 to 2022. We derived the Soil Moisture Deficit Index (SMDI) and Crop Water Stress Index CWSI to assess drought conditions and optimize irrigation schedules. Several numerical experiments were conducted to validate and test SWAP in balancing water use and productivity at the field-scale. The SMDI-based irrigation interval (7-day) consistently outperformed in achieving water productivity up to 25.2 kg ha^-1 mm^-1 compared to the results (22.5 kg ha^-1 mm^-1) of CWSI. SMDI showed better crop yields and water savings, especially in the dry years, while CWSI performed better in the wetter years but consumed more water. The 7-day interval with SMDI offered the best balance between water efficiency and productivity. Although SMDI requires advanced soil moisture monitoring, it proved more efficient than CWSI, which may be suitable for less-resourced areas.

In order to support the mission of the institution in which they are attached, academic libraries provide services in both manual and digital but COVID -19 pandemic that spanned between March and September, 2020 has changed the scenario. With particular reference to Nigeria, about 249,606 cases were confirmed and in order to curb the scourge of this deadly disease, physical academic activities were prevented by Nigeria Centre for Disease Control (NCDC). With this development, innovative tools became indispensable tools for successful delivery of library services in Nigerian academic libraries. Whether or not these tools are still in use for reformation of library service during post- Covid era remains unclear, hence, need for this study. This study examined librarians' use of innovative tools for information dissemination in Nigerian academic libraries during the post-Covid era using a descriptive survey design. Data were obtained both in quantitative and qualitative formats from one hundred and forty-four librarians as respondents. A total enumeration sampling technique was adopted because the population was minimal. Findings of the study revealed that innovative tools such as videoconferencing, WhatsApp, teleconferencing, Facebook, LinkedIn, and web-based learning applications are still in use by librarians for the dissemination of information during the post-Covid era. These tools are useful and beneficial to librarians during the post-COVID era, as they facilitate easy participation and engagement of library users in various discussions. Inadequate funding and lack of advanced technology skills were also identified as major impediments to the successful use of innovative tools for information dissemination. As a result, it was suggested that academic libraries throughout Nigeria prioritize staff training on the necessary digital skills needed to cope in this advanced technology era.

To standardize the recipes for healthy fast food market potentiality, a sensory acceptability analysis, instrumental texture analysis and nutritional evaluation were performed on Korean style mung bean pancake (MPC) and modified MPC containing squid meat and soybean. Optimal ingredient formulation was revealed to be 34% mung bean, 49% pork and 17% vegetables for traditional MPC, and 21% pork, 66% squid meat and 13% soybean for modified MPC, using response surface methodology. Flavor and hardness correlated highly with overall acceptability, rather than appearance and color of traditional MPC. Higher squid levels raised adhesiveness, springiness and resilience of modified MPC, but the higher soybean levels decreased these textural attributes. Protein, lipid and total calories of modified MPC were lower than those of traditional MPC. Degree of gelatinization of modified MPC was superior to traditional MPC.

Broiler transport in hot climates is associated with higher heat stress, mortality, and meatquality defects when high temperature and humidity combine with uneven ventilation to create hot, humid thermal core zones during prolonged feed and water deprivation. This review synthesizes mitigation options across pre-transport preparation, in-transit management, and post-transport recovery, focusing on practices feasible on farms and in processing plants and using a decision-relevant framing that prioritizes controlling total deprivation time, limiting within-load heat and humidity accumulation, and providing a recovery microclimate after arrival. Pre-transport priorities include planning withdrawal as total deprivation time, minimizing catching and loading delays, and maintaining crate hygiene to reduce fecal contamination and in-crate air-quality challenges under warm, humid conditions. Dietary additives may support muscle energy buffering and oxidative status. During transit, risk may be lowered by ventilation-aware loading and stacking, checking air distribution across the load, and adjusting crating density based on weather and journey duration. Misting should be used only when airflow is adequate because adding water without ventilation can raise humidity and weaken evaporative cooling. After transport, lairage should be treated as an active recovery step with clear microclimate targets before slaughter.

This study explores the effects of dietary tryptophan (Trp) supplementation on stress mitigation and production parameters in aging laying hens housed under high-density conditions. A total of 700 Hy-line laying hens, aged 70 weeks, were used in the experiment. The hens were divided into four groups, receiving diets supplemented with 0%, 0.25%, 1%, and 2% Trp over a four-week period. The study aimed to evaluate the impact of Trp on Hen-Day Egg Production (HDEP), egg mass, feed conversion ratio (FCR), and a range of physiological and biochemical stress indicators. The results indicated a quadratic response in HDEP and egg mass, with optimal production achieved at 1% Trp supplementation. Egg weight was linearly decreased by Trp supplementation. The FCR was quadratically affected, with lower FCR achieved at 0.25% and 1% Trp supplementation. The content of white blood cells, heterophiles, lymphocytes, and monocytes in blood was linearly reduced by supplementation of Trp. A linear decrease in the content of red blood cells, hemoglobin, and hematocrit was observed with the supplementation of Trp. The concentration of triglyceride was linearly decreased, and an increasing quadratic response was observed up to the level of 1% Trp inclusion and decreased thereafter. The content of glucose in blood was linearly increased by supplementation of Trp. the concentration of immunoprecipitation and lactate dehydrogenase was linearly decreased with supplementation of Trp. The concentration of blood corticosterone was higher in laying hens fed 0 and 0.25% of Trp compared with 1 and 2% supplementation. The concentration of blood serotonin was higher in laying hens fed 0.25 and 2% of Trp compared with 0% supplementation. In week 4, an increasing linear response was observed by Trp inclusion for yolk color, shell strength, and shell thickness. The study concludes that 1% Trp supplementation not only enhances productivity and egg quality but also contributes to reduced stress for laying hens.

With the growing demand for hybrid UAVs, efficient energy management systems (EMS) are becoming increasingly essential. This study proposes a Deep Q-network-based EMS for hybrid UAVs powered by an internal combustion engine and a battery. The EMS optimizes engine efficiency by maintaining operation within its most effective range while the battery supplies additional power as needed. Simulations under dynamically changing conditions demonstrate that the EMS efficiently distributes energy between sources, ensuring reliable power delivery and significantly improving overall efficiency. The proposed system presents a promising approach to enhancing the performance of hybrid UAVs.

Intelligent Reflecting Surfaces (IRS) offer a promising solution for enhancing sum rates in wireless networks by dynamically adjusting signal reflections to optimize propagation paths. When combined with Non-Orthogonal Multiple Access (NOMA), which enables multiple users to share the same frequency band, significant improvements in spectral efficiency can be achieved. However, as the number of users increases in IRS-NOMA systems, ensuring consistently high data rates for all users becomes challenging due to coverage limitations and inefficient power allocation in static network configurations, leading to performance degradation in multi-user scenarios. To address these limitations, we propose a novel IRS-NOMA cooperative system designed to optimize sum rates through an intelligent power allocation algorithm, nearby users, and IRS to assist the base station in delivering signals and expanding network coverage. The proposed system operates in two phases: during the first phase, the base station transmits signals directly to users and indirectly through the IRS. In the second phase, nearby users assist in relaying signals to enhance coverage and reliability. The proposed system adopts a cascaded channel model to accurately capture the interactions between the base station, IRS, and users. By leveraging our optimization algorithm, the proposed system ensures efficient resource allocation, achieving superior spectral efficiency and fairness among users compared to traditional models. Numerical results validate the effectiveness of the proposed system, demonstrating its potential for next-generation IoT networks.

Edge caching in the Internet of Vehicles (IoV) can reduce backhaul strain and content access delay. However, due to the constant changes in vehicle requests, offloading applications to edge servers is crucial for efficiently anticipating and caching popular content. Additionally, conventional data-sharing techniques are inadequate for this task due to their inability to preserve the privacy of vehicular users (VU). To overcome these issues, we propose a cooperative proactive content caching system incorporating Asynchronous federated learning and Deep reinforcement learning named PCAD that leverages the strengths of Dueling Deep Q-Networks and Prioritized Experience Replay in vehicular edge computing. PCAD lowers the latency of content access by prefetching contents that are popular beforehand caching them on edge nodes and cutting the waiting time for every vehicle to complete training as well as uploading local models before updating the global model. Additionally, we investigate intelligent caching decisions based on content prediction. Comprehensive experimental evaluations indicate that our proposed approach significantly outperforms existing benchmark caching techniques. More specifically, our suggested approach works better than DDQN, c- -greedy, and PCAD without DRL methods and the cache hit rate improves by approximately 4.25%, 11.23%, and 25.82%, respectively, as the cache capacity hits 400 MB.

Dilithium was selected as one of NIST standard Post Quantum Digital Signature algorithms and is undergoing standardization as a Module Lattice Digital Signature Algorithm (ML-DSA). However, until now research on optimization in embedded environments has primarily been conducted on ARM architectures, which are the basic benchmark targets. To prepare for future quantum secure Internet of Things environments, performance optimization on resource-constrained must be considered. Thus, in this paper, for the first time, we propose an optimized implementation of Dilithium in the 16-bit MSP430 environment, a low-resource device. We redesign the state-of-the-art optimization strategies for Dilithium to suit the MSP430 environment. By taking full advantage of MSP430’s hardware multiplier in the NTT-based polynomial multiplication, we achieve 73.0% and 80.1% of performance improvement for NTT and NTT?1 compared to those in the reference implementation, which contributes about 5.5%?7.0%, 15.3%?17.5%, and 7.5%?10.0% of performance improvement compared to Dilithium’s public reference implementation for keypair generation, signing, and verification, respectively.

We introduce a deep reinforcement learning-based approach for smart city planning, designed to determine the optimal timing for constructing various smart city components such as apartments, base stations, and hospitals over a specified development period. Utilizing the Dueling Deep Q-Network (DQN), the proposed method aims to maximize the city’s population while maintaining a predetermined happiness level of residents in the smart city. This optimization is achieved through strategic construction of smart city components, considering that both the total population and happiness levels are influenced by the interplay between housing, communication, transportation, and healthcare infrastructures, as well as the population ratio. Specifically, we present two distinct formulations of the Markov Decision Process (MDP) for smart city planning to illustrate the practicality of applying reinforcement learning across different scenarios.

Purpose: It is essential to regulate nursing to protect the population's health. As regulation constantly changes in response to societal trends, periodic reviews of nursing regulations become imperative. Therefore, we developed a legal framework by extracting essential elements for nursing regulation and explored its potential application. Methods: This study consisted of two parts. First, the legal framework for nursing regulation was developed through reviewing literature that mentions the content that can be included in nursing regulations, and through a content validity assessment by five experts. Second, this legal framework was applied to the nursing laws of China, Hong Kong, Japan, and Taiwan to confirm the suitability of the framework. Results: In the first part of the study, the legal framework for nursing regulation consists of seven categories (purpose, definitions, standards for practice, license acquisition and registration to practice, regulatory body, protection of the legal authority of nurses, offenses/penalties, and disciplinary procedures) and 17 items was developed. As a result of applying this framework to nursing laws in four countries, the average utilization rate for all 17 items was 68.4%. The matching scores between the framework and the law were over 60% for all four laws. Conclusion: Regulations, especially in the form of legislation, must be carefully considered because laws involve enforcement and potential penalties. This study is significant for identifying essential nursing regulation elements and offering a practical reference tool, expected to be widely utilized in future nursing policy and regulatory research.

This research improved the growth potential of Bifidobacterium animalis subsp lactis strain JNU306, a commercial medium that is appropriate for large-scale production, in yeast extract, soy peptone, glucose, L-cysteine, and ferrous sulfate. Response surface methodology (RSM) was used to optimize the components of this medium, using a central composite design and subsequent analyses. A second-order polynomial regression model, which was fitted to the data at first, significantly lacked fitness. Thus, through further analyses, the model with linear and quadratic terms plus two-way, three-way, and four-way interactions was selected as the final model. Through this model, the optimized medium composition was found as 2.8791% yeast extract, 2.8030% peptone soy, 0.6196% glucose, 0.2823% L-cysteine, and 0.0055% ferrous sulfate, w/v. This optimized medium ensured that the maximum biomass was no lower than the biomass from the commonly used blood-liver (BL) medium. The application of RSM improved the biomass production of this strain in a more cost-effective way by creating an optimum medium. This result shows that B. animalis subsp lactis JNU306 may be used as a commercial starter culture in manufacturing probiotics, including dairy products.

This study focuses on modeling mobile 3D game engine and suggesting modified skinned-mesh schema based on JSR-184 in order to improve the performance in terms of memory consumption and time complexity. Most of the 3D games have used OpenGL-ES low-level APls, which may limit portability and fast developing time. Hence, the 3D mobile game engine providing high-level APls which works on GSM (Global System for Mobile Communication) phones on J2ME, is proposed here in order to optimize the performance for Java environment abiding JSR-184 standard. To prove performance enhancement, skinned-mesh schema on JSR-184 engine is modified and tested. The experimental results are shown.

The market demand of display devices is drastically increasing in the information technology age. The research on OLED (Organic Light Emitting Diodes) display with the luminescence in itself is being more paid attention than LCD (Liquid Crystal display) with the light source from the back. The vapor deposition process is most essential in manufacturing OLED display. The temperature distribution of the linear cell in this process is closely related to securing the uniformity of organic materials on the substrate. This work analyzed the temperature distribution depending on the intervals between the crucible and the heating band as well as on the amount of the heat flux from the heating band. Moreover, the roles of the water jacket and the configuration of the cover within the linear cell were examined through the temperature analysis for six configurations of the linear cell. Under the above temperature analysis, the variations in the intervals and the amount of the heat flux were considered to have an effect on building the uniform temperature distribution within the crucible. It is predicted that the water jacket and the adequate configuration of the cover will prevent the blowout and clogging phenomena, respectively. The results can be used as the fundamental data for designing the optimal linear cell.

Estimating the location of a target in search-and-rescue operations is quite challenging when the target is not responding. Therefore, in this paper, we investigate the passive target localization problem using mobile unmanned aerial vehicles (UAVs), where multiple mobile UAVs receive the time-difference-of-arrival (TDOA) measurements from the source UAV. Unlike traditional TDOA for static UAVs, the problem becomes more challenging when considering the mobility of UAVs. Therefore, we propose a novel TDOA model for target localization with mobile UAVs. We also measure the performance limit inequality between its Cramer-Rao lower bound (CRLB) and the mean-squared error (MSE).

Background: Standing frames are a common intervention for children with cerebral palsy (CP), yet there is a lack of standardized dosing recommendations, impeding the enhancement of treatment outcomes in this population. Objects: This paper aims to optimize dosing strategies for standing frame programs in children with CP. It evaluates effective durations and frequencies for using standing frames to improve gait, hip joint integrity, functional activities, joint range of motion, and muscle tone. The goal is to provide evidence-based clinical recommendations to guide practitioners in treating pediatric CP patients. Methods: A comprehensive research was conducted across seven databases, yielding 23 studies meeting inclusion criteria. Strength of evidence was assessed using established tools. Clinical recommendations were formulated based on the amalgamation of existing evidence. Results: The paucity of evidence-based dosing recommendations for children with CP supported standing device is highlighted in this review. Key findings suggest that standing frames implemented 5 days per week demonstrate positive effects on gait (45 minutes/day, 3 times/week), hip joint integrity (60 minutes/day), functional activities (60 minutes/day in 30° to 60° of bilateral hip abduction), joint range of motion (60 minutes/day), and muscle tone (30 minutes/day). Conclusion: This systematic review of the treatment regimens for children with CP is providing useful insights to the dosing strategies of standing frames. The evidence supports a 30-60 minutes per day and 3-5 days a week intervention with specified durations for optimal outcomes. In enhancing the effectiveness of standing frames, as well as promoting evidence-based practices in the management of children with CP, these clinical recommendations offer guidance for practitioners.

We propose a method for optimizing multiple-drone pursuers’ performance in handling attacks from Kamikaze multiple-drone evaders on a battlefield. The central aspect of this problem is to minimize damage produced by evaders towards a defended area guarded by multiple-pursuers. We propose a communication strategy among pursuers where each pursuer can communicate with each other to decide which evaders should be chased and immobilized by each pursuer. We simulate the proposed method in a dynamic 3D environment. The simulation results conclude that our proposed method performs better than the commonly used algorithm for solving this kind of problem.