Earticle

Since the dawn of modern forestry, optimization of forest operations has been at the forefront of maintaining profitable organizations. One of, if not the most expensive aspect of a boreal softwood operation is road construction which has a direct relationship with the ability to extract timber efficiently. Matthews (1942) expressed that extraction distance and road spacing optimization could be enhanced by mathematical equations; however, experience and a knowledge of the land is not replaceable. The experience Matthews (1942) referred to can undoubtably be associated with the countless variables that any given parcel of land can contribute to reducing the accuracy of “armchair, slide ruler” management. This study was conducted in the Jellicoe area of the Nipigon Forest in Ontario, Canada. Two cut blocks were each divided into four separate distance compartments. The first compartment, 0-300 meters being the assumed optimal extraction area from the landing, with three consecutive compartments of 100-meter increases that follow (301-400 meters, 401-500 meters, and 501- 600 meters). Timber extraction productivity (m3 PMH-1) variables (volume extracted, area, and PMH) were recorded to observe variation between distance compartments and within the associated production metrics. The hypothesis that spruce-pine-fir timber extracted by grapple skidder in a mostly dry site with minimal slope in a northwestern Ontario boreal forest will maintain a comparable level of productivity up to 400 meters could not be rejected as no statistical difference was found between the 0-300 and 301-400 compartments.

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.

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).

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.

Post-activation performance enhancement (PAPE) is widely applied as a warm-up strategy to enhance performance. Although various rest intervals have been proposed following PAPE, an optimal recovery duration has not been clearly established. In addition, most previous studies applying PAPE have focused only on comparing individual variables rather than on multidirectional analysis of skill-related physical fitness factors. The purpose of this study was to identify the optimal rest interval following upper- and lower-body PAPE through a comprehensive analysis of skill-related physical fitness in elites. Seven male elites participated in a crossover design under four groups: control (CON), 3-minute rest (3RG), 8-minute rest (8RG), and 12-minute rest (12RG) groups following PAPE. PAPE was induced using resistance exercises at 85% of one-repetition maximum (1RM), consisting of the bench press for the upper body and squat for the lower body. Skill-related physical fitness was assessed using measures of power, agility, balance, speed, coordination, and reaction time. Data were analyzed using SPSS version 24.0. One-way repeated-measures ANOVA with LSD. The significance level was set at .05. Lower-body power was greater in the 3RG and 12RG conditions compared with CON, whereas no significant differences were observed in upper-body power. Upper-body agility improved in both 3RG and 12RG relative to CON, while lower-body agility did not differ across conditions. Dynamic balance improvements were primarily evident in 3RG compared with CON. Speed did not differ significantly across conditions following upper-body PAPE. Coordination increased in 3RG compared with CON after upper-body PAPE, and in all rest conditions (3RG, 8RG, and 12RG) relative to CON after lower-body PAPE. Reaction time showed no significant differences across conditions. These findings suggest that the optimal rest interval following PAPE varies according to body segment and fitness component. However, when considering all skill-related fitness measures, a 3-minute rest interval may represent the optimal recovery duration in elites.

The duration of an inpatient stay affects hospital administration and improves hospital effectiveness in terms of controlling expenses and raising patient standards. It also assists in identifying the correlations among illnesses requiring hospitalization. For our study, we took 24,150 records from of the Open Data database pertaining to inpatient admissions in 2023. We used a number of methods, including Neural Networks, Deep Learning, Linear Regression, and Support Vector Machines, to predict the Length of Stay (LOS). We converted the data to numerical form for predictive purposes, dividing the dataset into 70% for training and 30% for testing. We assessed the model's performance using Root Mean Squared Error (RMSE) and split the forecast into four LOS categories: 0-2, 3-4, 5-7, and 8 days or more. The study also employed the Apriori algorithm to identify illness association rules that could impact LOS estimates. The results showed that identifying illness correlations is one element that might aid in enhancing the capacity to predict LOS.

High-resolution land cover maps are essential in fields such as forest resource management, urban green space planning, and environmental protection. In recent years, Unmanned Aerial Vehicles (UAVs) have increasingly become influential in land cover mapping due to their flexibility, low cost, and fast data acquisition capability. However, accurately classifying high-resolution image data collected by UAVs remains a challenge due to the complexity of the data and the substantial computational resources required for processing. To address this problem, this study combines UAV remote sensing data with Object-Based Image Analysis (OBIA) to optimize feature selection to improve the accuracy of land cover classification and provide more reliable data support. In this study, combinations of four feature types were evaluated using a Decision Tree (DT) algorithm in eight scenarios. The results showed that a comparison with spectral features alone and the combination of other feature types can significantly improve the classification accuracy. Height features contribute the most to enhancing the classification results, followed by spectral and geometric features, while the contribution of texture features is relatively limited. In addition, the optimal feature combination selected by the Recursive Feature Elimination (RFE) method further validates its effectiveness in improving land cover classification results. Finally, the best feature combination achieved a classification accuracy of 72.00% and a Kappa coefficient of 0.6543, proving the effectiveness of the feature selection and optimization strategy.

This research investigates how heat treatment influences the physical, mechanical, and chemical characteristics of betung bamboo (Dendrocalamus asper). Two methods of heat treatment are applied: Oil Heat Treatment (OHT) and Air Heat Treatment (AHT), conducted at temperatures of 180°C, 200°C, 220°C, and 240°C, with durations of 1, 2, and 3 h. The study evaluates changes in color, weight, density, equilibrium moisture content (EMC), compressive strength, and crystallinity using X-ray Diffraction (XRD) analysis. Findings reveal that heat treatment causes significant modifications to the bamboo's physical, mechanical, and chemical attributes. Notably, OHT and AHT lead to substantial color changes, reflected in variations of L*, a*, and b* values depending on the temperature and treatment time. Weight and density decrease significantly after heat treatment, especially at 240°C for 3 hours. Additionally, equilibrium moisture content rises following treatment, while compressive strength diminishes. XRD analysis highlights alterations in the crystallinity and crystal size of the bamboo after treatment. In summary, heat treatment impacts the bamboo's characteristics, making it more suitable for industrial use as an improved raw material.

Respiratory diseases are one of the major causes of death worldwide. Therefore, research on respiratory disease classification using respiratory data is considered an important task. Previous studies mainly focused on respiratory disease classification using 2D feature extraction methods such as spectrograms and MFCCs. However, these methods have drawbacks such as long classification time and decreased accuracy as the number of respiratory disease types increases. To address this issue, we propose a solution that combines data with different dimensions to improve the performance of respiratory disease classification. We utilize the gammatone based spectrogram feature extraction method along with raw 1D respiratory data. By combining these two approaches, we can achieve both fast classification speed from 1D time-series models and high classification accuracy from 2D feature extraction methods. Our proposed respiratory disease classification study consists of four stages: data preprocessing, combined data generation, construction of a respiratory disease classification model, and decision-making for respiratory disease diagnosis. We validate our approach using a TCN (Temporal Convolutional Network) model and achieve a high respiratory disease classification accuracy of 98.93%. Moreover, our proposed method significantly reduces the training time for classification by more than four times compared to previous methods, thus demonstrating its superiority.

Optimization is important for processes in all parts of our lives. By modeling existing processes, it can help optimize existing processes. We specifically modeled the library book lending process in the university. It was modeled using ARIS express, a free modeling tool for business process analysis and management and we measured the time and automation of the book lending process before and after modeling. Due to the introduction of the automation system, the level of automation generally increased, while the alternation of application systems has decreased. All these courses were conducted with the advice of staff and students at the University of Graz in Austria.

In recent years, big data has usefully been deployed by organizations with the aim of getting a better prediction for the future. Moreover, knowledge management systems are being used by organizations to identify and create knowledge. Here, the output from analysis of big data and a knowledge management system are used to develop a new model with the goal of minimizing the cost of implementing new recognized processes including staff training, transferring and employment costs. Strategies are proposed from big data analysis and new processes are defined accordingly. The company requires various skills to execute the proposed processes. Organization’s current experts and their skills are known through a pre-established knowledge management system. After a gap analysis, managers can make decisions about the expert arrangement, training programs and employment to bridge the gap and accomplish their goals. Finally, deduction graph is used to analyze the model.