Offset Strategy Research Articles

Identification of optimal cutting parameters regarding lacquer wettability of CNC processed MDF panels by artificial neural network

ABSTRACT The primary objective of this study was to evaluate the surface roughness and lacquer wettability of medium-density fiberboard (MDF) panels processed on computer numerical control (CNC) machine. Then, it is aimed to determine the optimal CNC cutting parameters by modeling the data obtained from the experiments with ANN. For these purposes, two cutting tool types, two toolpath strategies, three spindle speed and four cutting depth values were used as CNC cutting parameters. The roughness and lacquer wettability tests were performed on the surfaces of MDF specimens after CNC machining. Using the obtained experimental data, the ANN models with the highest predictive ability were determined and the optimum CNC cutting parameters were defined according to surface roughness and lacquer wettability. The smoothest surfaces were obtained for the spiral router bit with offset toolpath strategy, 22,500 rpm spindle speed and 1.6 mm cutting depth, and for the straight router bit with raster toolpath strategy, 21,500 rpm spindle speed and 1.7 mm cutting depth. The optimal cutting parameters that gave the best lacquer wettability were an offset toolpath strategy, 20,500 rpm spindle speed and 1.5 mm cutting depth for spiral router, and a raster toolpath strategy, 20,500 rpm spindle speed and 3.1 mm cutting depth for straight router.

Compressive Mechanical Properties and Energy Absorption of Cubic Spherical Hollow Cellular Material Based on the Rod and Curved Surface Structure

Cellular materials, such as lattices and honeycombs, were widely used for structural protection owing to their excellent mechanical properties. By combining the excellent characteristics of lattice and honeycomb curved surface structures, this study designed a cell called Cubic Spherical Hollow (CSH), which had the advantages of orthogonal isotropy and better spatial connectivity. Inspired by the micro-structural arrangement of bamboo fiber and conch shell, we designed the new CSH cellular materials through the interlayer offset strategy. Then, the effects of interlayer offset on the mechanical properties and compression deformation behavior were explored experimentally and simulatively. Compared with other cellular materials, CSH cellular materials exhibited excellent mechanical properties with higher specific strength and specific modulus. The arrangement offsets of the CSH cells between different layers led to a change in the deformation mechanism from a rod’s compressive mode to a combination of a rod’s compressive mode and curved surface’s bending mode. The initial peak stress and plateau stress decreased with the increased arrangement offsets of the CSH since the extruded protrusions of the structure filled the holes and reduced the lateral expansion behavior of the material in the pressurized environment. This phenomenon also reduced the transverse expansion and prevented the sudden change in stress in the constrained space, indicating that the structural deformation was stable and had excellent cushioning and energy absorption properties. In this study, the cellular material design strategy opens a new avenue for energy absorption.

Drained peatlands rewetting as a promising trend for carbon offsets in Russia: legal and economic aspects

“Zero” greenhouse gas (GHG) emissions is one of the most pressing climate protection goals worldwide. The most important questions for the Russian Federation are about the significance and role of the country’s ecological systems in achieving “carbon neutrality”: creating conditions to reduce emissions and increase the GHG absorption by ecosystems, the implementation of natural climate projects to protect the climate in the country, and recognition climate outcomes of such projects at the national and international levels. This article analyzes legal and economic aspects of the emerging trend of Russian drained temperate peatlands rewetting as a new carbon offset strategy. We discuss the regulatory context and evaluate non-climate benefits. Drained peatlands rewetting holds promise for substantial GHG reduction in the country. However, further development of the methodology and alignment with relevant international standards is required for international recognition of the national carbon offset projects focused on drained peatlands rewetting.

Achieving low-porosity AlSi10Mg cladding layers for the additive repair of aluminum alloy parts by directed energy deposition

Directed Energy Deposition (DED) has broad application prospects for repairing damaged aluminum alloy parts such as piston aero-engine casing. However, due to the low laser absorptivity and the complex thermal cycle process, it is challenging to obtain aluminum alloy cladding layers with high-quality morphology and low-porosity by DED method. In this work, a series of individual stages of DED process are studied during single-tracks overlapping and layers stacking. Correspondingly, the within layer scanning strategy, inter-layer stacking strategy, and process parameters are examined carefully. Using the proposed DED processing flow that combines substrate preheating with intermittent process, the deposited layers without balling and collapse are successfully achieved. Furtherly, the bi-directional scanning strategy for single-tracks overlapping within layer is proposed to compensate the thickness nonuniformity caused by heat accumulation. The inter defects identified as “lack of fusion” and “gas pores” are observed in the cladding layers with uniform surface. By analyzing the pore formation mechanism and considering the energy distribution and re-melting, the multi-layer offset stacking strategy is proposed to inhibit the porosity to 0.25 %. Combined with the optimized process parameters, the porosity is reduced up to 0.09 %, ensuring the density of the DED deposition coating. This work provides new pathway and direct guidance for fabricating high-quality AlSi10Mg cladding layers during the DED repairing of valuable aluminum alloy parts.

Hunting the Red Bear: Satellite Reconnaissance and the ‘Second Offset Strategy’ in the Late Cold War

Cold War historians have argued that reconnaissance satellites were a stabilizing force in superpower relations because they created transparency and enabled arms control treaties. But this is an incomplete view of the role of reconnaissance satellites in U.S. national security. Newly declassified documents reveal that beginning in the late 1970s, reconnaissance satellites were a key component of the ‘Second Offset Strategy.’ With this new defense framework, the Pentagon endeavored to use advanced technologies to secure qualitative advantages over the numerically superior Red Army. A new class of reconnaissance satellites provided the means of tracking and targeting Soviet units in near-real-time to enable precision strikes deep behind enemy lines. This newfound ability to use satellites to pinpoint Soviet forces helped facilitate the U.S. shift towards a more flexible nuclear and conventional military strategy in the late 1970s. By this time, the same satellites that were policing arms control agreements were also secretly a key component of a competitive U.S. conventional and nuclear strategy to ensure that NATO would prevail in a superpower conflict. Moreover, this emerging military support role of reconnaissance satellites catalyzed the development of weapons designed to attack them, thereby precipitating space arms competition in the late Cold War.

GFENet: a lightweight network for efficient detection of abnormal objects in transmission line corridors

Abstract Abnormal objects in transmission line corridors pose a grave hazard to the security of the power system. The intelligent edge monitoring system, driven by a lightweight model, offers a promising approach. However, due to the limitations of the lightweight networks in addressing various uncertainties of anomalous objects, effectively balancing the efficiency and accuracy of existing methods presents a significant challenge. Therefore, a lightweight network called GFENet is proposed, which is designed to effectively address missed and false detections from the fluctuation in similarity of inter-class features and operational characteristics, as well as the diversity in intra-class shapes and scales under complex conditions. Firstly, learnable and efficient channel attention is proposed. This mechanism utilizes hybrid pooling fusion and weighted learning adjustment strategy to expand the receptive field, thereby capturing the distinctive visual features of the object. Next, we introduce Feature Pyramid Network and Path Aggregation Network to facilitate multi-scale feature interactions. Then, an efficient dynamic head is proposed, which employs a keypoint offset strategy to achieve scale, spatial, and task awareness. This enhances the understanding of object structure and shape without increasing computational costs. Finally, the experimental results on self-built dataset demonstrate that the GFENet can virtually balance network lightweighting and accuracy, significantly enhancing the ability to detect foreign object intrusions in complex environments.

Floating wind-integrated PV system yield analysis considering AHSE dynamics & solar azimuth effects

Floating Photovoltaic (FPV) systems face dual challenges of structural reliability and high electricity costs in offshore deployments, hindering their widespread adoption. Integrating FPV with Floating Offshore Wind Turbines (FOWT) offers a promising solution to these challenges. A hybrid wind-solar system is proposed, leveraging mathematical modelling and simulation, with FPV system inheriting dynamic states from FOWT and employing a shared DC bus for power generation. Key findings reveal reduced power variation as solar elevation angle increases, with photovoltaic efficiency declining near the complementary angle of PV tilt offset. The FPV system demonstrates resilience to platform movements, ensuring over 97% efficiency even under significant wave heights. Additionally, greater pitch motion enhances power complementary effects between wind and solar energy. A proposed tilt offset control strategy effectively compensates for power fluctuations, particularly at lower wind-solar azimuth angles and higher solar elevation angles. Real-world based simulation in the Taiwan Strait validates these findings, emphasizing the potential of wind-solar coupled platforms for optimizing renewable energy performance in coastal regions.

An Emissions Offset Strategy to Accomplish 2 °C Long-Term Mitigation Goals in the European Union

Regional carbon budgeting in policymaking is underutilized despite its importance for achieving global climate goals, particularly the Paris Agreement’s target of limiting global warming to 2 °C by 2050. In this work, we present the model PLEDGES, a novel system dynamic-based simulation tool that focuses on the European Union region to equitably distribute carbon budgets among the Member States and activate emissions offset strategies to manage unexpected deviations from the EU27 carbon budget. The emissions trading dynamic is based on the “Gains from Trade” approach. The tool also calculates the cost of the offset strategies based on the use of the abatement cost curves for the Member States. Using a case study of the recent increase in carbon emissions in Germany in response to reduced Russian gas supplies, different emissions scenarios for Germany’s quota redistribution among the Member States are explored. The study reveals varied cost implications of between 30–60 Eur/ton CO2eq to offset the emissions increase across other Member States. Final recommendations include promoting cross-border collaboration at the EU27 level.

基于EMSDBO算法农业植保无人机三维航迹多目标优化研究

Both cruising ability and safety should be considered in the 3D inspection path planning of agricultural unmanned aerial vehicles (UAVs). Specific to a complex working environment, the 3D inspection environment of agricultural UAVs was simulated through terrain modeling and threat modeling. First, the dynamic constraints of flight approaching rate and response time were added to the threat cost, and the 3D mission space model and flight path cost function were constructed considering the influence of UAVs’ turning performance. Second, the offset estimation strategy, variable spiral search strategy, quasi-reverse learning strategy and dimension-by-dimension mutation strategy were introduced into the dung beetle optimizer (DBO) algorithm to improve the global optimization ability and convergence rate of the algorithm. By establishing a three-dimensional trajectory planning model for unmanned aerial vehicles, the trajectory planning is transformed into a multi-objective function optimization problem, and an improved algorithm is used to solve the three-dimensional trajectory planning of unmanned aerial vehicles. The fitness is evaluated by considering the objective function of trajectory cost, terrain cost, and danger level, and the trajectory planning is iteratively optimized. The results indicate that the proposed improved dung beetle algorithm for trajectory planning has lower overall cost and stability in adapting to different complex terrain environments.

Investigating Drivers Impacting Carbon Stock and Carbon Offset in a Large-Scale Rubber Plantation in the Middle South of Thailand.

A large-scale rubber plantation in Southern Thailand is expected to capture a significant amount of carbon dioxide from emissions through carbon sinks in the vegetation and soil. The goal of this research is to create a carbon offset assessment for rubber plantations lasting for 30 years using a voluntary market contract approach. To evaluate the area of large-scale rubber plantations, this study evaluated major growing regions in five provinces in the middle-south region of Thailand (Nakhon Si Thammarat, Phatthalung, Songkhla, Satun and Trang) using an integrated RS-GIS technique that incorporated biomass allometric equations, soil series databases, and object-based classification. The classification of rubber plantation areas and the mapping of rubber stand ages were conducted to estimate the above-ground biomass of the rubber tree. Texture analysis was used in the rubber classification process, and normalised difference vegetation index (NDVI) was combined with texture analysis to separate vegetation areas from other land cover. Four groups of varying ages (1-6, 7-13, 14-20 and 21-30 years old) were evaluated for their capacity to generate carbon offsets. The equations of voluntary market contract revenue according to the contract method of the CCX were applied for this case study. This evaluation was used to estimate their annual value, total and net incomes in the carbon market price regarding the RGGI Allowance (RGA). Carbon offset income was then used to estimate the potential income (over a 30-year period) of the life of the contract. The results showed that the carbon stock potential of rubber plantations depended on the age of the trees and the soil carbon stock. The total carbon stock in the rubber plantations varied from 249.73 to 301.48 Mg C/ha (or equivalently 916.49 to 1,106.44 Mg CO2e/ha). Furthermore, the potential net income of the contract was estimated to be between USD5,378.32 and USD5,930.38 Mg CO2e/ha over a 30-year period according to the voluntary market contract revenue. These results suggest that the large agricultural land plot policy could create opportunities for carbon offsetting. The policy of large-scale rubber areas could be used as a tool and mechanism for farmers who are considering participating in carbon-crediting mechanisms. Then, farmers could use voluntary market contracts as a guide and foundation for their decision-making. The carbon offset credit strategy could assist Thailand in achieving its climate goals of transitioning to a low-carbon agriculture sector.

Estimation of Shape Error with Monitoring Signals

Recently, extensive research has actively been conducted in relation to intelligent manufacturing systems. During the machining process, various factors, such as geometric errors, vibrations, and cutting force fluctuations, lead to shape errors. When a shape error exceeds the tolerance, it results in improper assembly or functionality issues in the assembled part. Predicting shape errors before or during the machining process helps increase production efficiency. In this paper, we propose a methodology that uses monitoring signals and on-machine measurement (OMM) results to predict machining quality in real time. We investigate the correlation between monitoring signals and OMM results and then construct a machine learning model for shape error estimation. The developed model implements a tool offset compensation strategy. The performance of the proposed method is evaluated under various sliding window sizes and the compensation weights. The experimental results confirmed that the proposed algorithm is effective for obtaining a uniform machining quality.

Energy flexibility of commercial buildings for demand response applications in Australia

Demand response (DR) is widely recognized as an important mechanism in the Australian electricity market, though large-scale uptake in commercial buildings is yet to occur, in part due to the difficulty of characterising the resource. This paper describes a bottom-up physics-based approach to characterise the DR potential of three types of commercial buildings (schools, offices, and data centres) under a global set-point temperature offset strategy. Representative models are calibrated with energy meter data and parametric analysis is used to assess sensitivity to different building, operating and system parameters. Parametric equations are provided for relative DR potentials as functions of temperature and time of day. School buildings were found to have the highest relative DR potential (∼40–45%) for ambient temperatures over 30 °C, followed by data centres (∼20–30%) and offices (∼20%). Location has the strongest relative influence for school and office buildings and equipment energy intensity for data centres. The Australia-wide combined DR potential for school, office and data centres is estimated to be between 551 and 647 MW. Office buildings have the highest aggregate potential at between 1.5 and 1.7 times that of school buildings and between 9 and 11 times that of data centres.

Stochastic-offset-enhanced restricted slice excitation and 180° refocusing designs with spatially non-linear ΔB0 shim array fields.

Developing a general framework with a novel stochastic offset strategy for the design of optimized RF pulses and time-varying spatially non-linear ΔB0 shim array fields for restricted slice excitation and refocusing with refined magnetization profiles within the intervals of the fixed voxels. Our framework uses the decomposition property of the Bloch equations to enable joint design of RF-pulses and shim array fields for restricted slice excitation and refocusing with auto-differentiation optimization. Bloch simulations are performed independently on orthogonal basis vectors, Mx, My, and Mz, which enables designs for arbitrary initial magnetizations. Requirements for refocusing pulse designs are derived from the extended phase graph formalism obviating time-consuming sub-voxel isochromatic simulations to model the effects of crusher gradients. To refine resultant slice-profiles because of voxelwise optimization functions, we propose an algorithm that stochastically offsets spatial points at which loss is computed during optimization. We first applied our proposed design framework to standard slice-selective excitation and refocusing pulses in the absence of non-linear ΔB0 shim array fields and compared them against pulses designed with Shinnar-Le Roux algorithm. Next, we demonstrated our technique in a simulated setup of fetal brain imaging in pregnancy for restricted-slice excitation and refocusing of the fetal brain. Our proposed framework for optimizing RF pulse and time-varying spatially non-linear ΔB0 shim array fields achieve high fidelity restricted-slice excitation and refocusing for fetal MRI, which could enable zoomed fast-spin-echo-MRI and other applications.

The Cold War Computer Arms Race

The Cold War computer arms race illustrates the military’s role in strategic competition. The Soviets bought and stole, versus creating computer technology themselves. A U.S.-led coalition integrated economic, diplomatic, and information mechanisms, embargoing computer technology to disadvantage the Soviets. President Ronald W. Reagan’s offset strategy integrated military power, openly demonstrating computer-infused weapons lethality that jeopardized Soviet quantitative military advantage. President Reagan’s use of the computer arms race shows a way to conduct and integrate a strategic competition campaign of deterrence that includes coercive diplomacy with diplomatic efforts that can deter China and Russia while encouraging them to reverse harmful foreign and domestic policies.

Target Soybean Leaf Segmentation Model Based on Leaf Localization and Guided Segmentation

The phenotypic characteristics of soybean leaves are of great significance for studying the growth status, physiological traits, and response to the environment of soybeans. The segmentation model for soybean leaves plays a crucial role in morphological analysis. However, current baseline segmentation models are unable to accurately segment leaves in soybean leaf images due to issues like leaf overlap. In this paper, we propose a target leaf segmentation model based on leaf localization and guided segmentation. The segmentation model adopts a two-stage segmentation framework. The first stage involves leaf detection and target leaf localization. Based on the idea that a target leaf is close to the center of the image and has a relatively large area, we propose a target leaf localization algorithm. We also design an experimental scheme to provide optimal localization parameters to ensure precise target leaf localization. The second stage utilizes the target leaf localization information obtained from the first stage to guide the segmentation of the target leaf. To reduce the dependency of the segmentation results on the localization information, we propose a solution called guidance offset strategy to improve segmentation accuracy. We design multiple guided model experiments and select the one with the highest segmentation accuracy. Experimental results demonstrate that the proposed model exhibits strong segmentation capabilities, with the highest average precision (AP) and average recall (AR) reaching 0.976 and 0.981, respectively. We also compare our segmentation results with current baseline segmentation models, and multiple quantitative indicators and qualitative analysis indicate that our segmentation results are better.

Neostructural Realism’s Explanation on Russia-Ukraine War

This article aims to explain why NATO keeps expanse on Ukraine, and why Russia responses it through invasion now. By using qualitative methode, library research, and the neostructural realism theory founded that since the dissolution of Soviet Union, NATO has been no longer appropriate abstracted as an alliance but an unipole security coalition, then the NATO expansion on Ukraine can be seen as the continuity of U.S.’ containment policy to Russia, and adding its coalition partners for unilateral action legitimacy in order to maintain its hegemon position which undermine Russia’s Eurasia great power vision and its crucial national security. Meanwhile the uneven distribution of power elements between U.S. and Russia as the macro international structure constraint Russia invasion but the bipolar world through Russia-China unlimited strategic partnership, and the shifting of U.S. offset strategy to Russia on hypersonic missile as the micro structure encourage Russia to take invasion now.

A Multiple Mechanism Enhanced Arithmetic Optimization Algorithm for Numerical Problems.

The Arithmetic Optimization Algorithm (AOA) is a meta-heuristic algorithm inspired by mathematical operators, which may stagnate in the face of complex optimization issues. Therefore, the convergence and accuracy are reduced. In this paper, an AOA variant called ASFAOA is proposed by integrating a double-opposite learning mechanism, an adaptive spiral search strategy, an offset distribution estimation strategy, and a modified cosine acceleration function formula into the original AOA, aiming to improve the local exploitation and global exploration capability of the original AOA. In the proposed ASFAOA, a dual-opposite learning strategy is utilized to enhance population diversity by searching the problem space a lot better. The spiral search strategy of the tuna swarm optimization is introduced into the addition and subtraction strategy of AOA to enhance the AOA's ability to jump out of the local optimum. An offset distribution estimation strategy is employed to effectively utilize the dominant population information for guiding the correct individual evolution. In addition, an adaptive cosine acceleration function is proposed to perform a better balance between the exploitation and exploration capabilities of the AOA. To demonstrate the superiority of the proposed ASFAOA, two experiments are conducted using existing state-of-the-art algorithms. First, The CEC 2017 benchmark function was applied with the aim of evaluating the performance of ASFAOA on the test function through mean analysis, convergence analysis, stability analysis, Wilcoxon signed rank test, and Friedman's test. The proposed ASFAOA is then utilized to solve the wireless sensor coverage problem and its performance is illustrated by two sets of coverage problems with different dimensions. The results and discussion show that ASFAOA outperforms the original AOA and other comparison algorithms. Therefore, ASFAOA is considered as a useful technique for practical optimization problems.

Sequential LMBA Design Technique for Improved Bandwidth Considering the Balanced Amplifiers off-State Impedance

This work proposes a design technique to improve the bandwidth of sequential load-modulated balanced amplifiers (SLMBAs). It is theoretically demonstrated that a finite off-state impedance of the balanced power amplifiers (BPAs) degrades the overall SLMBA performance, especially between the two efficiency peaks, i.e., when the BPAs start to operate. Moreover, it is shown that this issue is especially important when high-power devices are considered. The new proposed design technique optimizes the load trajectory of the BPAs, by properly designing their output matching networks, considering the actual off-state impedance. The method avoids the use of the conventional narrowband compensation offset lines strategy, being able to obtain optimum efficiency and output power performance when the BPAs are turned on across wide bandwidths. The proposed design technique is explained and validated with a practical 1.3–2.1-GHz 200-W GaN HEMT-based SLMBA prototype.

Advanced inter-spacecraft offset frequency setting strategy for the Taiji program based on a two-stage optimization algorithm.

For space-based gravitational wave (GW) detection, the continuity of detection data acquisition is crucial to the inversion of wave sources and the realization of scientific goals. To control the inter-spacecraft beat-note frequency in an appropriate range for continuous gravitational wave detection and to reduce the upper bound of the beat-note frequency for improving the detection capability, a two-stage optimization algorithm is proposed to solve the offset frequency setting strategy in the Taiji program. The optimization objectives are the maximum offset frequency duration and minimum upper bound of the beat-note frequency. Considering all feasible phase-locked schemes, Doppler frequency shift, and the bandwidth of the phasemeter, a series of offset frequency setting strategies satisfying the conditions was obtained. The solution results show that the upper bound can be reduced to 16MHz and, in this case, the offset frequency changes nine times with a minimum and maximum offset frequency duration of 90days and 713days, respectively. If the Doppler frequency shift is constrained, the minimum upper bound can be reduced to 14MHz. When the minimum duration is increased, the minimum upper bound is increased. These results show that, by varying the offset frequency a limited number of times, the data continuity requirements of the Taiji program can be satisfied, and the phasemeter development difficulty and detection capability can be balanced, and may provide a reference for the phasemeter design, the setting of phase-locking schemes, and inter-spacecraft offset frequency in the Taiji program.

The democratic offset: Contestation, deliberation, and participation regarding military applications of AI

Authoritarian regimes’ unrestricted collection of citizens’ data might constitute an advantage regarding the development of some types of AI, and AI might facilitate authoritarian practices. This feedback loop challenges democracies. In a critical continuation of the Pentagon’s Third Offset Strategy, I investigate a possible Democratic Offset regarding military applications of AI focussed on contestation, deliberation, and participation. I apply Landemore’s Open Democracy, Hildebrandt’s Agonistic Machine Learning, and Sharp’s Civilian-Based Defence. Discussing value pluralism in AI ethics, I criticise parts of the literature for leaving the fundamental ethical incompatibility of democracies and authoritarian regimes unaddressed. I am focussing on the duty to disobey illegal orders derived from customary international humanitarian law (IHL) and the standard of ‘meaningful human control’, which is central to the partially outdated debate about lethal autonomous weapon systems (LAWS). I criticize the standard of ‘meaningful human control’ following two pathways: First, the ethical and legal principles of just war theory and IHL should be implemented in military applications of AI to submit human commands to more control, in the sense of technological disaffordances. Second, the debate should focus on the societal circumstances for personal responsibility and disobedience to be trained and exerted in deliberation and participation related to military applications of AI, in the sense of societal affordances. In a larger picture, this includes multi-level stakeholder involvement, robust documentation to facilitate auditing, civilian-based defence in decentralized smart cities, and open-source intelligence. This multi-layered approach fosters cognitive diversity, which might constitute a strategic advantage for democracies regarding AI.