Search Results (1,065)

The application of robotic arms in the industrial field is continuously becoming greater and greater. The impact force generated by a robotic arm in a gripping operation leads to vibration and wear. To address this problem, this paper proposes a trajectory planning method based on the improved Slime Mould Algorithm. An interpolation curve under the joint coordinate system is constructed by using seven non-uniform B-spline functions, with time and impact force as the optimization objectives and angular velocity, angular acceleration, and angular acceleration as the constraints. The original algorithm introduces Bernoulli chaotic mapping to increase the diversity of the population, adaptively adjusts the feedback factor, improves the crossover operator to accelerate the global convergence, and combines the original algorithm with an improved artificial bee colony search strategy guided by the global optimal solution, adding a quadratic interpolation method to increase the diversity of the population and to accelerate the global convergence speed. Combined with the improved artificial swarm search strategy guided by the global optimal solution, the quadratic interpolation method is added to enhance the local utilization ability. The simulation and real-machine experimental results show that the improved algorithm shortens the movement time of the robotic arm, reduces the joint impacts, minimizes the vibration and wear, and prolongs the service life of the robotic arm. Full article

The increasing demand for higher resolution and faster machinery in silicon wafer inspection is driven by the rise in electronic device production and the decreasing size of microchips. This paper presents the design and implementation of a device capable of accurately measuring the surface of silicon wafers using the stitching technique. We propose an optical system design for measuring the surface profile, specifically targeting the roughness and nanotopography of a silicon wafer. The device achieves a lateral resolution of 7.56 μm and an axial resolution of 1 nm. It can measure a full 300-mm wafer in approximately 60 min, acquiring around 400 million data points. The technique utilized is a wavefront phase sensor, which reconstructs the surface shape using two images displaced a certain distance from the conjugate plane in the image space of a 4f system. The study details the calibration process and provides a method for converting local measurement coordinates to global coordinates. Quantitative phase imaging was obtained by using the wave front intensity image algorithm. The conclusive results validate the method different metrics over a wafer with bonded dies. In addition, the device demonstrates the ability to distinguish different dies that are thinned along with die-to-wafer bonding onto a carrier wafer to obtain the difference in coplanarity between the die and its surroundings as well as to detect defects during the die-to-wafer bonding. Lastly, the residual stress in the thin film deposited over the die is obtained using the Stoney model. Full article

In Japan, the decline in the number of agricultural workers and the aging of the workforce are problems, and there is a demand for more efficient and labor-saving work. Furthermore, in order to correct the rising price of fertilizer and the increasing burden on the environment caused by fertilizer, there is a demand for more efficient fertilization. Therefore, we aim to develop an electric soil sampling robot that can run autonomously using Global Navigation Satellite System (GNSS) information. GNSS and the Inertial Measurement Unit (IMU) are used as navigation sensors. The work machine is a crawler type that reduces soil compaction. In addition, a route map was generated in advance using the coordinate values of the field, with soil sampling positions set at 10 m intervals. In the experiment, the robot traveled along the route map and stopped automatically. The standard deviation of the standard deviation of lateral error was about 0.032 m, and the standard deviation of the interval between soil sampling positions was also less than 0.05 m. Therefore, it can be said that the accuracy is sufficient for soil sampling. It can also be said that even higher density sampling is possible by setting the intervals for soil sampling at finer intervals. Full article

Amid the global wave of digital transformation, advancing the sustainable growth of the real economy has emerged as a key strategic priority. Drawing on panel data from 30 provinces, municipalities, and autonomous regions in China (excluding Tibet, Hong Kong, Macao, and Taiwan) between 2013 and 2021, this study utilizes fixed effects and mediation effect models to investigate both the direct and indirect pathways through which the digital economy drives the sustainable development of the real economy. The results indicate that (1) the digital economy exerts a significant direct positive influence on the real economy, demonstrating its role in spurring growth and innovation while injecting fresh momentum into sustainable development. (2) It also indirectly facilitates the real economy’s sustainability by promoting the coupling and coordination of the manufacturing and service sectors, emphasizing the importance of industrial synergy in achieving sustainable economic growth. (3) Regional analysis reveals that the digital economy’s positive direct effect on the real economy is particularly evident in North China and the Southeast and Southwest regions. Furthermore, in the Southeast and Southwest, the mediation effect of industrial coupling and coordination further strengthens the sustainability of the real economy. This study offers theoretical insights into the integration of the manufacturing and service industries and provides practical guidance for advancing the United Nations’ 2030 Agenda for Sustainable Development. It also highlights policy recommendations for China to build a modern industrial system and achieve high-quality economic growth. Full article

Green hydrogen (GH₂) is emerging as a key driver of global energy transition, offering a sustainable pathway to decarbonize energy systems and achieve climate objectives. This review critically examines the state of GH₂ research production technologies and their integration into renewable energy systems, supported by a bibliometric analysis of the recent literature. Produced via electrolysis powered by renewable energy, GH₂ shows significant potential to decarbonize industries, enhance grid stability, and support the Power-to-X paradigm, which interlinks electricity, heating, transportation, and industrial applications. However, widespread adoption faces challenges, including high production costs, infrastructure constraints, and the need for robust regulatory frameworks. Addressing these barriers requires advancements in electrolyzer efficiency, scalable fuel cell technologies, and efficient storage solutions. Sector-coupled smart grids incorporating hydrogen demonstrate the potential to integrate GH₂ into energy systems, enhancing renewable energy utilization and ensuring system reliability. Economic analyses predict that GH₂ can achieve cost parity with fossil fuels by 2030 and will play a foundational role in low-carbon energy systems by 2050. Its ability to convert surplus renewable electricity into clean energy carriers positions it as a cornerstone for decarbonizing energy-intensive sectors, such as industry, transportation, and heating. This review underscores the transformative potential of GH₂ in creating a sustainable energy future. By addressing technical, economic, and policy challenges and through coordinated efforts in innovation and infrastructure development, GH₂ can accelerate the transition to carbon-neutral energy systems and contribute to achieving global climate goals. Full article

Global Navigation Satellite System/Acoustic (GNSS/A) underwater positioning technology is attracting more and more attention as an important technology for building the marine Positioning, Navigation, and Timing (PNT) system. The random error of the tracking point coordinate is also an important error source that affects the accuracy of GNSS/A underwater positioning. When considering its effect on the mathematical model of GNSS/A underwater positioning, the Total Least-Squares (TLS) estimator can be used to obtain the optimal position estimate of the seafloor transponder, with weak consistency and asymptotic unbiasedness. However, the tracking point coordinates and acoustic ranging observations are inevitably contaminated by outliers because of human mistakes, failure of malfunctioning instruments, and unfavorable environmental conditions. A robust alternative needs to be introduced to suppress the adverse effect of outliers. The conventional Robust TLS (RTLS) strategy is to adopt the selection weight iteration method based on each single prediction residual. Please note that the validity of robust estimation depends on a good agreement between residuals and true errors. Unlike the Least-Squares (LS) estimation, the TLS estimation is unsuitable for residual prediction. In this contribution, we propose an effective RTLS_Eqn estimator based on “total residuals” or “equation residuals” for GNSS/A underwater positioning. This proposed robust alternative holds its robustness in both observation and structure spaces. To evaluate the statistical performance of the proposed RTLS estimator for GNSS/A underwater positioning, Monte Carlo simulation experiments are performed with different depth and error configurations under the emulational marine environment. Several statistical indicators and the average iteration time are calculated for data analysis. The experimental results show that the Root Mean Square Error (RMSE) values of the RTLS_Eqn estimator are averagely improved by 12.22% and 10.27%, compared to the existing RTLS estimation method in a shallow sea of 150 m and a deep sea of 3000 m for abnormal error situations, respectively. The proposed RTLS estimator is superior to the existing RTLS estimation method for GNSS/A underwater positioning. Full article

The One Health approach integrates human, animal, and environmental health to address complex challenges like emerging zoonotic diseases and antimicrobial resistance (AMR). Singapore’s dense urban environment, biodiversity, and role as a global hub make it vulnerable to these health threats, necessitating a robust and coordinated One Health framework. This paper reviews Singapore’s One Health strategy, focusing on governance, surveillance, cross-sector partnerships, and public health infrastructure. A structured literature review, including peer-reviewed articles and grey literature, identified key strengths and gaps. Strengths include interagency coordination through the One Health Coordinating Committee, advanced surveillance systems like CDLENS and SIDPIC, and key institutions such as the National Centre for Infectious Diseases (NCID) and the National Public Health Laboratory (NPHL). However, gaps remain in multi-sector engagement, data-sharing mechanisms, and public awareness of One Health principles. To address these challenges, this paper recommends enhancing multi-sector collaboration, strengthening data-sharing networks, and increasing public education on One Health. Investments in preventive medicine, cross-border capacity-building, and leveraging artificial intelligence for predictive analytics are essential for bolstering Singapore’s health security. By addressing these gaps, Singapore can enhance its preparedness and serve as a global leader in One Health implementation. Full article

This study examines the renewed interest in Industrialised Building (IB) adoption in Australia amid the housing crisis, addressing the gap between potential and implementation. Drawing on a systematic review of 171 peer-reviewed articles (1998–2024), we examine how the interplay between micro-level decision-making, meso-level organisational routines, and macro-level institutional arrangements shapes global IB adoption patterns, with implications for the Australian context where limited research exists. Our analysis highlights that successful IB adoption depends on coordinated alignment across systemic levels, with government policies and sustainability initiatives emerging as key global drivers. However, adoption barriers differ by market maturity; Australia faces unique challenges, such as economic constraints, limited stakeholder collaboration, and misaligned institutional frameworks, despite advancements in technology and innovation. The findings advance construction innovation literature by presenting a theoretically grounded framework to address IB adoption barriers and enablers. In the Australian context, realising IB’s potential requires co-evolution across micro, meso, and macro levels, driven by workforce upskilling, stakeholder collaboration, and adaptive regulations to transform construction practices. Full article

The Global Positioning System (GPS) has revolutionized navigation in modern society. However, the susceptibility of GPS signals to interference and obstruction poses significant navigational challenges. This paper introduces a GPS-denied method based on scene image coordinates instead of real-time GPS signals. Our approach harnesses advanced image feature-recognition techniques, employing an enhanced scale-invariant feature transform algorithm and a neural network model. The recognition of prominent scene features is prioritized, thus improving recognition speed and precision. The GPS coordinates are extracted from the best-matching image by juxtaposing recognized features from the pre-established image database. A Kalman filter facilitates the fusion of these coordinates with inertial measurement unit data. Furthermore, ground scene recognition cooperates with its aerial counterpart to overcome specific challenges. This innovative idea enables heterogeneous collaboration by employing coordinate conversion formulas, effectively substituting traditional GPS signals. The proposed scheme may include military missions, rescues, and commercial services as potential applications. Full article

The re-emergence of the Nipah virus (NiV) in Kerala, India, following the tragic death of a 14-year-old boy, underscores the persistent threat posed by zoonotic pathogens and highlights the growing global public health challenge. With no vaccine or curative treatment available, and fatality rates as high as 94% in past outbreaks, the Nipah virus is a critical concern for health authorities worldwide. Transmitted primarily through contact with fruit bats or consumption of contaminated food, as well as direct human-to-human transmission, NiV remains a highly lethal and unpredictable pathogen. The World Health Organization has classified Nipah as a priority pathogen due to its alarming potential to cause widespread outbreaks and even trigger the next pandemic. Recent outbreaks in India and Bangladesh, occurring with seasonal regularity, have once again exposed the vulnerability of public health systems in containing this virus. This study explores the epidemiology, ecological factors driving transmission, and the public health response to NiV, emphasizing the role of zoonotic spillovers in pandemic preparedness. As the global community grapples with an increasing number of emerging infectious diseases, the Nipah virus stands as a stark reminder of the importance of coordinated surveillance, rapid containment measures, and the urgent development of novel strategies to mitigate the impact of this re-emerging threat. Full article

To address the challenges faced by fuel cell hybrid electric tractors (FCHETs) equipped with a battery and supercapacitor, including the complex coordination of multiple energy sources, low power allocation efficiency, and unclear optimal energy consumption, this paper proposes two energy management strategies (EMSs): one based on hierarchical instantaneous optimization (HIO) and the other based on multi-dimensional dynamic programming with final state constraints (MDDP-FSC). The proposed HIO-based EMS utilizes a low-pass filter and fuzzy logic correction in its upper-level strategy to manage high-frequency dynamic power using the supercapacitor. The lower-level strategy optimizes fuel cell efficiency by allocating low-frequency stable power based on the principle of minimizing equivalent consumption. Validation using a hardware-in-the-loop (HIL) simulation platform and comparative analysis demonstrate that the HIO-based EMS effectively improves the transient operating conditions of the battery and fuel cell, extending their lifespan and enhancing system efficiency. Furthermore, the HIO-based EMS achieves a 95.20% level of hydrogen consumption compared to the MDDP-FSC-based EMS, validating its superiority. The MDDP-FSC-based EMS effectively avoids the extensive debugging efforts required to achieve a final state equilibrium, while providing valuable insights into the global optimal energy consumption potential of multi-energy source FCHETs. Full article

The rapid proliferation of vehicles globally presents significant challenges to road transportation efficiency and safety, including accidents, emissions, energy utilization, and road management. Autonomous vehicle platooning emerges as a promising solution within intelligent transportation systems, offering benefits like reduced fuel consumption and emissions, and optimized road use. However, implementing autonomous vehicle platooning faces obstacles such as stability under disturbances, safety protocols, communication networks, and precise control. This paper proposes a novel control strategy coordinated Kalman observer–Linear Quadratic Regulator (CKO-LQR) to ensure platoon formation stability in the presence of disturbances. The disturbances considered include vehicle movements, sensor noise, and communication delays, with the leading vehicle’s movement serving as the commanding signal. The proposed controller maintains a constant inter-gap distance between vehicles despite the disturbances utilizing a coordinated Kalman observer to estimate preceding vehicle movements. A comparative analysis with conventional PID controllers demonstrates superior performance in terms of faster settling times and robustness against disturbances. This research contributes to enhancing the efficiency and safety of autonomous vehicle platooning systems. Full article

Energy is often described as the lifeblood of a nation’s economy, and the world energy trilemma calls for collaboration and innovative solutions at the national level. This is where Education for Sustainable Development (ESD) plays a crucial role, helping integrate the achievement of the United Nations Sustainable Development Goals (SDGs) while addressing the challenges posed by the energy trilemma. Europe’s strong commitment to transitioning to sustainable energy is evident in its response to geopolitical changes and climate targets. Notably, the Baltic States have taken decisive action in response to the war in Ukraine, choosing to completely halt electricity imports from Russia and Belarus. This shift was supported by increased energy imports via interconnectors from Finland, Sweden, and Poland, with electricity imports rising to 13,053 GWh—an increase of 2.6% in 2023 compared to the previous year. Latvia, which holds the highest green energy potential in the Baltic Sea region, has nevertheless lagged behind its Baltic counterparts in terms of implementation. In 2021, Latvia ranked third among European Union (EU) countries for renewable energy share in final energy consumption, with 42.1%, significantly higher than the EU average of 21.8%. However, further progress is needed to meet Latvia’s 2030 target of 14% renewable energy use in transport. The Baltic States aim to produce 98–100% of their electricity from renewable sources by 2050. The Baltic States should be regarded as a unified energy system, with a coordinated strategy for achieving sustainable energy development through collaboration and joint planning. This analysis highlights the complexities of managing energy markets amidst global and regional challenges, emphasizing the importance of well-designed public interventions to secure long-term benefits. The study concludes with a call for enhanced interagency cooperation to reform ESD and create a new interdisciplinary sector dedicated to “Sustainable Development”. Full article

A global prescribed performance control method is proposed for a class of uncertain high-order odd-rational-power nonlinear systems (a chain of integrators whose power is the ratio of odd integers) with actuator faults, where the high-order odd-rational powers and the parameters of actuator faults are unknown. A new coordinate transformation on the state and tracking errors is introduced based on the tangent function and its inverse function, resulting in a global low-complexity prescribed performance controller. The proposed controller does not require any knowledge of system nonlinearities or powers, and it also does not require the time derivatives of virtual control signals without using any filters, which implies the controller is of low complexity. Finally, two simulation examples, including a practical high-maneuver flight control example, are given to demonstrate the effectiveness of our method. Full article

Standard safety assessments of civil engineering systems are conducted using safety factors. An alternative method to this approach is the assessment of the engineering system using reliability analysis of the structure. In reliability analysis of the structure, both the uncertainty of the load and the properties of the materials or geometry are explicitly taken into account. The uncertainties are described in a probabilistic manner. After defining the ultimate and serviceability limit state functions, we can calculate the failure probability for each state. When assessing structural reliability, it is useful to calculate measures that provide information about the influence of random parameters on the failure probability. Classical measures are vectors, whose coordinates are the first partial derivatives of reliability indices evaluated in the design point. These values are obtained as a by-product of the First-Order Reliability Method. Furthermore, we use Sobol indices to describe the sensitivity of the failure probability to input random variables. Computations of the Sobol indices are carried out using the classic Monte Carlo method. The aim of this article is not to define new sensitivity measures, but to show the advantages of using structural reliability and sensitivity analysis in everyday design practice. Using a simple cantilever beam as an example, we will present calculations of probability failure and local and global sensitivity measures. The calculations will be performed using COMREL modules of the STRUREL computing environment. Based on the results obtained from the sensitivity analysis, we can conclude that in the case of the serviceability limit state, the most significant influence on the results is exerted by variables related to the geometry of the beam under consideration. The influence of changes in Young’s modulus and load on the probability of failure is minimal. In further calculations, these quantities can be treated as deterministic. In the case of the ultimate limit state, the influence of changes in the yield strength is significant. The influence of changes in the load and length of the beam is significantly smaller. The authors present two alternative ways of designing with a probabilistic approach, using the FORM (SORM) and Monte Carlo simulation. The approximation FORM cannot be used in every case in connection with gradient determination problems. In such cases, it is worth using the Monte Carlo simulation method. The results of both methods are comparable. Full article