Ideal Balance Research Articles

Zero-oxygen balanced fused 1,2,3,4-tetrazine (TNF) as a high-performance energetic material

A straightforward synthesis of 2,9-bis(trinitromethyl)bis([1,2,4]triazolo)[1,5-d:5′,1′-f][1,2,3,4]tetrazine(TNF), achieving an ideal zero oxygen balance in the tetrazine–triazole framework with excellent overall performance.

Optimizing H.266/VVC Intra Coding with a Genetic Algorithm: Balancing Speed and Quality

The growing need for high-definition video material requires improvements in video encoding systems that maximize encoding performance while simultaneously improving compression efficiency. This paper presents a novel genetic algorithm-based intra-coding optimization method for the H.266Versatile Video Coding (VVC) standard. One of the biggest problems in video compression is finding the ideal balance between encoding speed and video quality, which is what our approach aims to solve. Our suggested method makes use of the strong search capabilities of the evolutionary algorithm to choose the best Multi-Type Tree (MTT) partitions and coding tools from the wide range of possibilities present in H.266VVC. The wellness assessment work that guides this choice method combines criteria for perceptual appraisal of video quality and measures for coding productivity appraisal.

SteelGuard-yolo: Steel Surface Defect Detection Network Based on Improved YOLOv5s

Steel is playing an increasingly important role in industry, and the detection of defects on its surface is also of great significance. The complex and diverse defects on the steel surface bring great challenges to the detection. In this paper, we propose a SteelGuard-yolo based steel surface defect detection model, whose main role is to improve the existing algorithms for detecting steel surface defects. First, we design the C2f module with weight aggregation and introduce the BiFormer attention mechanism to improve the feature extraction capability of the model. Second, we design a new up-sampling structure and introduce the Multi-Scale Dilated Attention (MSDA) module to effectively improve the feature fusion capability of the model. Finally, we introduce the Simam attention mechanism and use EIoU as a new loss function to improve the robustness and accuracy of the model.SteelGuard-yolo has a powerful multi-scale feature fusion capability and achieves an ideal balance between latency and accuracy. The algorithm proposed in this paper is tested on the NEU-DET dataset and achieves an average accuracy of 69.0%, which compares favorably with most one- and two-stage detection algorithms.

An Efficient Hybrid Optimizer for Resource Reuse in a Cloud Environment

In a cloud context, merging complimentary numerous virtual machines (VMs) on an existing physical machine (PM) is the primary method for optimizing physical resources. One well-known area of research concentrates on making better use of VM migration resources when taking into account the dynamically changing resource demands of VMs. Finding the ideal balance between the complexity and performance of the VM migration optimization is the problem here. On the one hand, effective resource reuse is achieved through VM migration planning, and on the other, VM migration frequency is decreased to improve migration efficiency. On the other hand, a cloud data centre’s enormous PM and VM population typically makes migration planning more challenging, which impedes the VM migration decision-making process. By reducing the number of VM migration options to make VM migration planning easier and address these issues, this study recommend a hybrid Ant Colony and Genetic Algorithm (AGO) resource pool architecture. Then, establishing this model as a base, we develop the hybrid resource-reuse optimization method, which maximizes resource utilization with a minimal number of VM migrations. Finally, we evaluate hybrid AGO using simulation testing and real-world trials on a working cloud platform. Compared to similar methods, the findings show that hybrid AGO increases average resource utilization by 15%, reduces the use of PMs by 15%, and decreases the average number of migrations by 30%.

Ideal/balance point clustering algorithm

Generally clustering is one of the largest used ML techniques to group data points of similar type together. Mostly while grouping data points based upon few popular traditional clustering algorithms like KNN, K-means etc. only the magnitudes of those data points are considered. The deviation of those data points from a particular point said to be the center point or the balance point of the entire dataset is not considered that much, but Here in this Ideal Balance Point clustering algorithm the degree of deviation of all the data points from the balance point is primarily considered. By using such a methodology, we may be able to form clusters of data points based on their degrees of deviation from the Ideal Balance Point of the entire dataset. The Ideal Balance Point of the dataset is actually the centroid of the plane on which the datapoints are distributed. Here in the Ideal Balance Point clustering algorithm the user may also manually feed-in the Ideal Balance Point for the dataset based on the requirement or as per the practical idealness of the circumstance and nature of the dataset. Generally, this idea is derived from the theory that an object has all its’ mass equally distributed on the centroid. Hence the plane formed by the distribution of the data points is considered as an object and the centroid of it is assumed to be the point where the entire data points are directed into, but the ideal balance point for the data may also differ as per real life scenario, thus the feature of feeding in the balance point is also given in this Ideal Balance Point clustering algorithm.

Automated detection and quantification of pavement cracking around manhole

Damage detection plays an important role in pavement health monitoring and inspection. Unfortunately, research about damage detection of the special component of pavement structures, such as pavement manhole covers, is relatively few. A new pipeline for the detection and quantification of damage around the pavement manhole covers is proposed in this research. In this pipeline, the Attention-enhanced Manhole Detection Model (AMDM) is proposed to detect manhole covers. AMDM achieves an ideal balance between accuracy and speed by eliminating redundant structures and incorporating an attention mechanism. The BCSM (Boundary-enhanced Crack Segmentation Model) is proposed to segment the damage around the manhole cover, and the boundary loss function is used to enhance the fine segmentation ability of the model on the boundary. The MAP (Mean Average Precision) of the manhole covers detection model is 96.68%, and the MIOU (Mean Intersection Over Union) of the crack segmentation model is 89.73%. Attributing to this efficient and accurate pipeline, a reasonable damage evaluation method is proposed in the end, which is based on statistical data and engineering experience. Overall, not only this research will contribute an automatic and cost-effective method to the detection and evaluation of damage around the manhole cover, but also inspire the detection and evaluation of other special components of civil engineering structures.

Effect of Heat Treatment Process on Microstructure and Mechanical Properties of High-Carbon H13 Steel

This paper investigated the mechanical properties and microstructures of different samples of H13 steel after they underwent various heat treatment processes. It provided a detailed analysis of the microstructure and mechanical properties under different processes and approached the topic from a theoretical perspective. The phase composition of each sample remained unchanged after undergoing different heat treatment processes. Despite the vacuum gas quenching (H1) sample being guaranteed a hardness of 58.47 HRC, its toughness fell below expectations at a mere 46.75 J. Notably, the microstructure of the sample which underwent the H1 process and the cryogenic (H2) treatment exhibited a finer grain size and higher toughness compared to the sample which only underwent the H1 process without the cryogenic treatment. Its toughness was 70.19 J, but its hardness slightly decreased to 57.47 HRC. Following the application of oil quenching and cryogenic treatment (H3), the hardness of the sample significantly increased, reaching a remarkable 58.38 HRC. Additionally, the sample exhibited good impact resistance, with a measurement of 74.25 J. Before the H2 process, the sample which underwent the spheroidizing annealing process (H4) had a higher hardness compared to the sample without spheroidizing annealing. At the same time, when comparing the above four samples, the sample that underwent the H4 process exhibited the best toughness, with a value of 86.94 J, while still maintaining a hardness of 57.85 HRC; thus, it achieved an ideal balance between strength and toughness. Therefore, the optimal heat treatment process for high-carbon H13 steel was spheroidizing annealing followed by vacuum gas quenching and then cryogenic treatment.

Adaptive Multi-Objective Algorithm for the Sustainable Electric Vehicle Routing Problem in Medical Waste Management

This paper addresses the complex issue of managing medical waste transportation using electric vehicles, with the goal of minimizing both energy consumption and the risks associated with hazardous waste. A multi-objective mixed-integer linear programming model is introduced, incorporating practical factors such as time windows, partial recharge policy, load-dependent discharge, infection risk, and trips to waste disposal facilities. Our proposed method, a combination of the multi-objective evolutionary algorithm using decomposition (MOEA/D) with adaptive large neighborhood search (ALNS) and local search (LS) techniques, is referred to as MOEA/D-ALNS. This method demonstrates superior performance compared with the non-dominated sorting genetic algorithm, NSGA-II, modified MOEA/D and MOEA/D-LNS in benchmark instances with realistic assumptions. Our experimental results revealed an inverse correlation between energy consumption and risk objectives. Sensitivity analyses showed that eliminating time-window constraints results in more energy-efficient and safer routes while maintaining a slightly lower battery energy level can strike an ideal balance between energy consumption, risk, and battery health. This research contributes to the understanding of infectious medical waste management with its consideration of electric vehicles and waste disposal. It lays a solid foundation for future studies aiming to improve the sustainability and efficiency of medical waste routing practices.

Organizational Determinants and Compliance Behavior to Shape Information Security Plan

In the advanced field of Information and Communication Technology (ICT) within modern corporate frameworks, the pressing issue of non-compliance becomes increasingly crucial. Achieving the ideal balance—where one fosters consistent employee commitment without resorting to overly harsh penalties for possible violations—presents a complex problem. Such a nuanced relationship calls for a synchronized coordination among the company’s underlying factors, the principles of the Information Security Plan (ISP), and overarching compliance mandates. As companies step into a period where digital environments are in constant flux, the importance of securing information systems rises to a critical level. Against this backdrop, compliance stands out as a vital component, functioning as a stringent safeguard in the ongoing mission to protect precious digital assets—a mission comprehensively detailed within the ISP. This in-depth academic study sets out to rigorously explore and scrutinize the diverse opinions and beliefs of committed employees and insightful management concerning unwavering company alignment with the ISP. This is accomplished by defining a construct that centers on key dimensions: Organizational Culture, Personal Attitudes, Actors, Behavioral Intentions, and Motivational Dynamics. Eleven Hypotheses are outlined and represent the materialisation of the model. This model form a starting point from which future empirical exploration will be able to take place, propelling us towards a deeper understanding of the phenomena under scrutiny.
 
 Received: 15 September 2023 / Accepted: 23 October 2023 / Published: 5 November 2023

Prospects of Halal Green Tourism in Brunei Darussalam

In recent years, there has been a growing interest among academics worldwide in the concepts of halal and green tourism, particularly focusing on sustainability. This trend has created both opportunities and challenges for tourism stakeholders, as there is a strong interest from tourists in both halal and green tourism experiences. Brunei Darussalam, with about 80% of its land covered in primary tropical forest and a significant portion devoted to permanent forests and forest reserves, offers numerous green tourist attractions despite its small size. To gain a comprehensive understanding of the concepts and relevant aspects of halal-green tourism, this study explores the existing literature and theories related to the topic. Using a qualitative approach and secondary data collection, the study provides insights into halal-green tourism in Brunei. Furthermore, a SWOT analysis of the Brunei tourism industry is presented based on the information gathered. The data indicates that Brunei offers an ideal balance of environmentally friendly and halal-friendly services, making it an attractive destination for halal-green tourism. The SWOT analysis serves as a valuable guideline for relevant authorities and policymakers in planning the future development of the country’s tourism industry. Additionally, local travel agencies can utilize this analysis to promote and enhance halal green tourism in Brunei.
 Keywords: Brunei, green tourism, halal green tourism, halal tourism, SWOT analysis

169 Minimizing Sow Feeding Cost While Enhancing Production

Abstract Strategies to minimize sow feeding cost while enhancing production are important to the overall success of an operation. Feed to the sow breeding herd accounts for approximately 15% of the total feed use and cost within a production system. Thus, a 10% improvement in sow feed efficiency can represent 0.05 points in feed efficiency for the whole herd. While advancements in knowledge around sow nutrition have been gradual, improvements in sow reproductive performance, productivity, and body composition have been substantial, yielding a remarkably efficient contemporary sow. In fact, efficiency may be key to the success of implementation of strategies that minimize feed cost and at the same time enhance production in the sow herd. In terms of minimizing sow feed usage, an important strategy is to implement feeding levels according to a body condition score in gestation to prevent over-feeding and an over-conditioned herd. It is also crucial to assess feeding levels to open sows, either in wean-to-estrus interval or in the open pool, to avoid feed wastage with ad libitum feeding. Regular feed boxes calibration in gestation and feeder adjustments in lactation are critical to allow an adequate feeding level to sows, while preventing feed wastage. In terms of minimizing feed cost, it is important to review the energy, lysine, and phosphorus requirements of sows on a daily intake basis and account for average herd weight. That provides the opportunity to estimate the ideal balance between nutrient density and feeding level to minimize overall feed cost. In addition, it is critical to understand the impact of energy density and bulk density on overall feed cost. As reduced energy and bulk density ingredients are incorporated into the feed, feeding levels should be increased and could potentially offset feed costs savings. In a dynamic economic landscape, the strategies summarized here may aid in the goal of finding opportunities to minimize sow feeding cost but at the same time provide nutritional support that enables sows to fully express their production potential.

Convergence points of optimal herbage accumulation and intake rate by sheep grazing tall fescue

AbstractPasture management oriented towards sustainable intensification and stability of yield must consider the ideal balance between the mass of leaves removed by grazing and the mass left as residual. The former supports high animal production and the later supports an optimum soil–plant carbon balance. This study aimed to evaluate the optimal herbage level from both plant and animal perspectives, through the adjustment of instantaneous herbage accumulation rate as estimated by the Gompertz curve model and short‐term intake rate of sheep. Weekly tall fescue (Schedonorus arundinaceus) herbage accumulation was measured during seven growth periods with initiation dates in October, November and December 2015, and March, April, August and September 2016 and with the initiation dates arranged in a randomized complete block design with four replicates. Total herbage mass, leaf lamina mass, pseudo‐stem + sheath mass, senescent mass, and sward surface height were measured each week. The results demonstrated that a pre‐grazing sward surface height of 22.3 cm of tall fescue promoted the maximum short‐term intake rate of sheep and the optimum herbage accumulation rate in the spring and autumn periods. In general, the convergent point occurred after the maximum instantaneous leaf lamina accumulation rate and before the maximum instantaneous accumulation rate of pseudo‐stem + sheath and senescent material. We suggest the existence of a convergent point in which both primary and secondary production of pastoral ecosystems could be fostered.

Counseling on the Use of Lime (Citrus aurantifolia) as Pharmaceutical Preparations in the Making of Mouthwash and hand Sanitizer

Health services, including in the form of health counselling and traditional medicine, are one of the most elementary health service programs considering the level of the ratio of the population to the centre, health service units and health workers who do not meet the ideal balance, so a breakthrough and smart steps are needed in responding to this. I was counselling on the Use of Lime (Citrus aurantifolia) as a Pharmaceutical Preparation in the Making of Mouthwash (Mouthwash) and Hand Sanitizer ( Antibacterial Hand Sanitizer) with the aim of improving the degree of health in the mouth and using Lime as a hand sanitiser. The methods of these activities are identification of the target objectives of the training, tools and materials used, good communication methods that are easy to understand, visualisation, evaluation, and feedback so that it can be concluded that counselling on the use of Lime as a pharmaceutical preparation in the manufacture of Mouthwash and Hand Sanitizer is a concern for healthy living in the surrounding community for the better. Thus creating a healthy and prosperous society.

"I Just Want You to Hear That Term": Characterizing Language Used in Fetal Cardiology Consultations.

The way clinicians communicate with parents during pregnancy about congenital heart disease (CHD) can significantly influence parental understanding of and psychological response to the diagnosis. A necessary first step to improving communication used in fetal cardiology consultations is to understand and describe the language currently used, which this paper aims to do. Nineteen initial fetal cardiology consultations with parents were audio-recorded, transcribed verbatim, and coded by two independent coders. A codebook was inductively developed and applied to all transcripts. The finalized coding was used to characterize fetal cardiologists' language. We identified four discourse styles employed in fetal cardiology consultations: small talk, medical, plain, and person-centered. Plain language was used to define and emphasize the meaning of medical language. Person-centered language was used to emphasize the baby as a whole person. Each consultation included all four discourse styles, with plain and medical used most frequently. Person-centered was used less frequently and mostly occurred near the end of the encounters; whether this is the ideal balance of discourse styles is unknown. Clinicians also used person-centered language (as opposed to disease-centered language), which is recommended by medical societies. Future studies should investigate the ideal balance of discourse styles and the effects of clinician discourse styles on family outcomes, including parents' decision-making, psychological adjustment, and quality of life.

PERANCANGAN TATA LETAK PENYIMPANAN BAJU DI GUDANG BEHERO

One of the strategic operational choices that affects the long-term efficacy of the business's operations is layout. Finding the ideal balance between space expenses and material handling costs is the goal of warehouse planning. The class-based dedicated storage strategy is used in this study, which classifies products into three, four, or five categories depending on how frequently they are picked up. The suggested clothes layout adheres to the Class Based Storage approach, in which clothing is arranged according to popularity, similarity, size, and other factors.Fast moving products are stored on the first and second shelves, which are located in the front. The shelf has three levels, with the middle level being used for S and M sizes because they are the most popular and are therefore easier for staff to access. Because the bottom shelf is simpler to reach than the top shelf and can be picked up by employees without the use of tools, size L and XL have an average sales level there. Jumbo size products (XXL, XXXL, and XXXXL) are positioned on the third (top) shelf since they have the lowest sales rates.

Balancing Learning Model Privacy, Fairness, and Accuracy With Early Stopping Criteria.

As deep learning models mature, one of the most prescient questions we face is: what is the ideal tradeoff between accuracy, fairness, and privacy (AFP)? Unfortunately, both the privacy and the fairness of a model come at the cost of its accuracy. Hence, an efficient and effective means of fine-tuning the balance between this trinity of needs is critical. Motivated by some curious observations in privacy-accuracy tradeoffs with differentially private stochastic gradient descent (DP-SGD), where fair models sometimes result, we conjecture that fairness might be better managed as an indirect byproduct of this process. Hence, we conduct a series of analyses, both theoretical and empirical, on the impacts of implementing DP-SGD in deep neural network models through gradient clipping and noise addition. The results show that, in deep learning, the number of training epochs is central to striking a balance between AFP because DP-SGD makes the training less stable, providing the possibility of model updates at a low discrimination level without much loss in accuracy. Based on this observation, we designed two different early stopping criteria to help analysts choose the optimal epoch at which to stop training a model so as to achieve their ideal tradeoff. Extensive experiments show that our methods can achieve an ideal balance between AFP.

The relationship between Innovation and Sustainability in Organizations: towards the ideal balance between evolution and preservation

En un mundo altamente dinámico, las organizaciones enfrentan el desafío de la innovación debido al entorno competitivo caracterizado por cambios constantes y rápidos. En este contexto, la preservación de los recursos y la sociedad se define como eje central para garantizar un futuro sostenible. Es aquí donde toma una alta relevancia el balance que se debe lograr desde las habilidades gerenciales, para llevar a nuevos niveles los procesos, los productos y los sistemas de las compañías, procurando siempre la mejora continua y a la vez, logrando incluir en sus procesos temas tan importantes como el impacto ambiental, la inclusión y el desarrollo de sociedades y comunidades relacionadas a estos. Para ello, se propone revisar sistemáticamente los artículos científicos en la base de datos Scopus para los años 2017-2022, con la identificación de artículos con mayor impacto para este análisis, de los cuales 5 documentos se relacionan dada su relevancia e impacto en el desarrollo de las secciones de este artículo. A partir del análisis temático, las contribuciones se centran en 3 áreas principales de investigación, que son: potencial de innovación de las empresas; innovación para modelos de negocio sostenibles; innovación sostenible; La mayoría de los artículos se dedican a las dos primeras áreas de investigación; También entienden el desarrollo sostenible en los campos social, ambiental y económico.

Rare earth Eu3+ /Tb3+ -doped hollow microspheres (LaPO4 ) and core-shell nanocomposite luminescent materials with different transmittance (SiO2 @LaPO4 Eu3+ ) preparation and research on luminescence performance.

For the manufacture of hollow nanospheres that had different shapes, three distinct templates-urea, carbon microspheres, and polyethylene glycol 20,000-were used. The relationship between microspheres with various hollow structures and their luminescent properties were investigated. Furthermore, the effects of annealing temperature and the proportion of rare earth Eu3+ /Tb3+ ions in the reaction were investigated using the structural characteristics of the microspheres and fluorescent materials. The fluorescent materials were wrapped on the outer surface of the microspheres. The ideal balance between the best structure and superior luminescent performance was found, to create reasonably good white light.

Mapping the nutritional landscape in the yellow mealworm: testing the nutrient-mediated life-history trade-offs.

Animals must acquire an ideal amount and balance of macronutrients to optimize their performance, health and fitness. The nutritional landscape provides an integrative framework for analysing how animal phenotypes are associated with multiple nutritional components. Here, we applied this powerful approach to examine how the intake of protein and carbohydrate affects nutrient acquisition and performance in the yellow mealworm (Tenebrio molitor) reared on one of 42 synthetic foods varying in protein and carbohydrate content. Tenebrio molitor larvae increased their food consumption rate in response to nutrient dilution, but this increase was not sufficient to fully compensate for the dilution. Diluting the food nutrient content with cellulose reduced the efficiency of post-ingestive nutrient utilization, further restricting macronutrient acquisition. Tenebrio molitor larvae utilized macronutrients most efficiently at a protein to carbohydrate (P:C) ratio of 1.77:1, but became less efficient at imbalanced P:C ratios. Survivorship was high at high protein intake and fell with decreasing protein intake. Pupal mass and growth rate exhibited a bell-shaped landscape, with the nutritional optima being located around protein-biased P:C ratios of 1.99:1 to 2.03:1 and 1.66:1 to 2.86:1, respectively. The nutritional optimum for development time was also identified at high P:C ratios (1.66:1 to 5.86:1). Unlike these performance traits, lipid content was maximized at carbohydrate-biased P:C ratios of 1:3.88 to 1:3.06. When given a food choice, T. molitor larvae self-composed a slightly carbohydrate-biased P:C ratio of 1:1.24, which lies between the P:C ratios that maximize performance and lipid content. Our findings indicate the occurrence of a nutrient-mediated trade-off between performance and energy storage in this insect.

FPGA-Based Machine Learning: Platforms, Applications, Design Considerations, Challenges, and Future Directions

Field-Programmable Gate Arrays (FPGAs) have emerged as a promising platform for accelerating machine learning tasks due to their high parallelism, low latency, and hardware customization ability. In this paper, the authors provide an overview of popular FPGA platforms for machine learning and compare the tradeoffs among FPGAs, GPUs, and CPUs for machine learning. The authors also present specific applications of machine learning based on FPGAs, including those in autonomous driving and healthcare. Additionally, the paper explores FPGA design considerations, such as architecture, resource utilization, and power consumption. Nonetheless, obstacles persist in the realm of FPGA-based machine learning that require attention. Identifying the ideal balance between adaptability and performance, considering factors such as space, energy usage, and latency, is still challenging. As the capabilities of FPGAs expand, there is a significant need for devices that have a smaller footprint, reduced power consumption, and minimized delays. The paper emphasizes the necessity of ongoing research in the field of FPGA-based machine learning to address these issues and continue enhancing the performance and effectiveness of machine learning systems.