Search Results (60)

The growing demand for agile and reliable Unmanned Aerial Vehicles (UAVs) has spurred the advancement of advanced control strategies capable of ensuring stability and precision under nonlinear and uncertain flight conditions. This work addresses the challenge of accurately tracking UAV position by proposing a neural-network-based approach designed to replicate the behavior of classical control systems. A complete nonlinear model of the quadcopter was derived and linearized around a hovering point to design a traditional proportional derivative (PD) controller, which served as a baseline for training a nonlinear autoregressive exogenous (NARX) artificial neural network. The NARX model, selected for its feedback structure and ability to capture temporal dynamics, was trained to emulate the control signals of the PD controller under varied reference trajectories, including step, sinusoidal, and triangular inputs. The trained networks demonstrated performance comparable to the PD controller, particularly in the vertical axis, where the NARX model achieved a minimal Mean Squared Error (MSE) of $7.78\times {10}^{-5}$ and an $R^2$ value of 0.9852. These results confirm that the NARX neural network, trained via supervised learning to emulate a PD controller, can replicate and even improve classical control strategies in nonlinear scenarios, thereby enhancing robustness against dynamic changes and modeling uncertainties. This research contributes a scalable approach for integrating neural models into UAV control systems, offering a promising path toward adaptive and autonomous flight control architectures that maintain stability and accuracy in complex environments. Full article

The swift expansion of China’s automotive manufacturing industry has spurred a constant rise in the demand for automotive parts production and distribution, making the optimization of distribution routes in complex environments a crucial research topic. Efficiently optimizing these routes not only boosts production efficiency and cuts costs for automotive manufacturers but also enhances supply chain management and advances sustainable development. This study focuses on the optimization of automotive parts distribution routes under a multi-manufacturer collaboration framework. An optimization model is proposed to minimize the total operational costs within a joint distribution system, incorporating an improved Ant Colony Optimization (ACO) algorithm to formulate an effective solution approach. The model considers complex factors such as dynamic demand, time-window constraints, and periodic distribution. A PIVNS algorithm integrating a virtual distribution center with an enhanced variable neighborhood search is designed to efficiently address the problem. The efficacy of the proposed model and algorithm is substantiated through extensive experiments grounded in real-world case studies. The results confirm the high computational efficiency of the proposed approach in solving large-scale problems, which significantly reduces distribution costs while improving overall supply chain performance. Specifically, the PIVNS algorithm achieves an average travel distance of 2020.85 km, an average runtime of 112.25 s, a total transportation cost of CNY 12,497.99, and a loading rate of 86.775%. These findings collectively highlight the advantages of the proposed method in enhancing efficiency, reducing costs, and optimizing resource utilization. Overall, this study provides valuable insights for logistics optimization in automotive manufacturing and offers a significant reference for future research and practical applications in the field. Full article

The Ma’anshan section of the lower Yangtze River features a complex multi-anabranching system, where the river divides into several branches around mid-channel sandbars, with distinct point bars alternately developing along both banks. Within this morphologically active system, Zhengpu Harbor suffered severe operational disruptions by accelerated siltation at its approach channel, primarily due to its vulnerable location downstream of the expanding Niutun River point-bar on the left bank. To systematically diagnose the mechanisms of siltation, this study integrates multi-method investigations: decadal-scale morphodynamic analysis using long-term bathymetric surveys, numerical modeling to quantify engineering impacts on flow dynamics, and multiple linear regression analysis for the contributions of key influencing factors. The result identifies three primary drivers of siltation, collectively responsible for 70% of the sediment accumulation, including the rightward shift of the thalweg in the Ma’anshan left branch, reduced flow diversion of the left Branch of Central bar, and the expansion of the Niutun River point bar. River engineering structures, such as bridges, contribute approximately 12%, while changes in upstream flow-sediment supply account for approximately 18%. To mitigate siltation at Zhengpu Harbor’s approach channel, this study proposes targeted engineering interventions to enhance local hydrodynamic conditions. The spur dikes were designed to enhance the morphological stabilization of the Central bar head to regulate flow distribution. A diversion channel could also be excavated at the tail of the Niutun River shoal, and emergency dredging was recommended at the harbor front. Numerical modeling indicates that these measures will increase flow velocity by over 0.1 m/s at the harbor front, mitigating the siltation situation. The study concludes that the proposed engineering measures can reduce annual siltation by approximately 30% under normal-year hydrological conditions, demonstrating their feasibility in mitigating siltation trends in multi-anabranching river systems. This research provides a reference for addressing siltation issues in harbors within complex anabranching river systems. Full article

Noise, vibration and harshness analyses are of great interest for the latest developments of the gearboxes of electric vehicles. Gearboxes are now the main source of vibrations, since electric powertrains are much quieter than internal combustion engines. Traditionally, the simulation of the non-linear gear dynamics is studied by first performing a series of preliminary static analyses to compute the static transmission error (STE). The STE (i.e., in the form of varying mesh stiffness) is then accepted as the system’s excitation source to compute the dynamic transmission error (DTE). This paper presents a novel approach to analyze the non-linear dynamics of gears which does not require any preliminary static analyses. The method consists of a frequency–domain approach based on the Harmonic Balance Method (HBM) and the Alternating Frequency–Time (AFT) scheme, allowing for much faster simulations when compared to the widely used direct–time integration (DTI). The contact between the teeth is modeled as intermittent and penalty based with a varying gap. The time–varying gap between the teeth is initially approximated to a step function that guarantees the design contact ratio. The methodology introduced is tested on a lumped parameter model of a spur–gear pair already proposed and simulated in the literature. The results obtained with the novel approach are compared with the DTI simulation of the model as a reference. The excellent match between the different approaches validates the reliability of developed methodology. Full article

The world is still facing an old challenge, e.g., environmental change. In many nations, including developing countries such as Indonesia, spurring economic growth is considered the best way to overcome many things. Indonesia is moving the center of the Indonesian Capital City (IKN). By opening up new space through the consideration of equitable economic development toward a green environment, this study is designed to investigate the impact of economic growth on environmental quality and vice versa. The object of analysis is directed at Penajam Paser Utara (PPU) as the new IKN center and four other IKN buffer areas in East Kalimantan, including (1) Paser, (2) Balikpapan, (3) Samarinda, and (4) Kutai Kartanegara. This study uses panel data regression and linear trends. The data elaborated is economic growth based on Regional Domestic Product (GRDP), which is proxied, and environmental quality, as reflected by the Environmental Quality Index (IKLH), during the 2017–2023 period. Referring to the method implemented, there is significant positive causality between the two in the selected IKN areas. Through the linear trend model, it is found that there is a tendency for changes in the data analyzed based on constant time. This study can be an instrument for practical policy making and a breakthrough in the development of a scientific discipline that studies the relationship between economic growth and environmental quality in two directions. Full article

This paper analyses the indigenization or Koreanization of Protestantism in South Korea in the late 20th century through a study of an original messianic and millenarian movement, Victory Altar. The group was founded in 1981 by Cho Hee-Seung in the biblical tradition, with references to Korean spiritual traditions as well. Its most salient feature is the self-consecration of Cho Hee-Seung as “Victor Christ and God”, the unique universal Messiah. In order to show the correlation between this spiritual movement, Protestantism, and Korean culture, I survey the recent history of South Korea and its staunch nationalism largely spurred by Protestant missionaries at the turn of the 20th century. I then present the core teachings of Cho the Messiah: the biological immortality of neohumans, the Hebrew genealogy of the Koreans thanks to the saga of the Lost Tribe of Dan from Israel to Korea, and his major vows to protect South Korea. An assessment of the heritage of Protestantism in this movement is then offered through the perspective of post-colonialism since Victory Altar sees itself and its Messiah/God as far superior to the God and Messiah of the Western powers that brought Christianity to Korea without really understanding it. Full article

The digital economy is the future direction of global development, and its innovation-driven effect has become the focus of scholars. This study takes China’s Shanghai and Shenzhen A-share listed manufacturing enterprises from 2012 to 2022 as research samples and uses a text-analysis method to measure the comprehensive index of the regional digital economy. Based on the dual dimensions of quantity and quality, we empirically test the actual impact of the digital economy on manufacturing innovation. The results show the following: (1) The internal components of the digital economy include data elements, digital technology, and digital infrastructure, which have a significant role in promoting manufacturing innovation. (2) There are significant differences in the release of digital economy innovation dividends among enterprises with different growth stages, property rights, and technology endowments. (3) From the macro-regional level, the rational allocation of R&D personnel and R&D funds is an effective way for the digital economy to enable manufacturing innovation. In addition, from the micro-enterprise level, factor-combination and information-optimization effects are also effective ways for the digital economy to drive manufacturing innovation. (4) Further, the impact of the digital economy on manufacturing innovation is nonlinear, with changes in internal composition. This study helps to break through the obstacles of the digital economy to achieve innovation empowerment and provides a theoretical reference for the manufacturing industry to continue to spur innovation vitality. Full article

Theano, TensorFlow, Keras, Torch, PyTorch, and other software frameworks have remarkably stimulated the popularity of deep learning (DL). Apart from all the good they achieve, the danger of such frameworks is that they unintentionally spur a black-box attitude. Some practitioners play around with building blocks offered by frameworks and rely on them, having a superficial understanding of the internal mechanics. This paper constitutes a concise tutorial that elucidates the flows of signals and gradients in deep neural networks, enabling readers to successfully implement a deep network from scratch. By “from scratch”, we mean with access to a programming language and numerical libraries but without any components that hide DL computations underneath. To achieve this goal, the following five topics need to be well understood: (1) automatic differentiation, (2) the initialization of weights, (3) learning algorithms, (4) regularization, and (5) the organization of computations. We cover all of these topics in the paper. From a tutorial perspective, the key contributions include the following: (a) proposition of R and S operators for tensors—rashape and stack, respectively—that facilitate algebraic notation of computations involved in convolutional, pooling, and flattening layers; (b) a Python project named hmdl (“home-made deep learning”); and (c) consistent notation across all mathematical contexts involved. The hmdl project serves as a practical example of implementation and a reference. It was built using NumPy and Numba modules with JIT and CUDA amenities applied. In the experimental section, we compare hmdl implementation to Keras (backed with TensorFlow). Finally, we point out the consistency of the two in terms of convergence and accuracy, and we observe the superiority of the latter in terms of efficiency. Full article

Liquidambar formosana Hance is renowned for its rich leaf color and possesses notable advantages, such as robust adaptability, strong resistance to diseases and pests, and rapid growth, making it a preferred choice for urban greening and carbon sequestration forest initiatives. The completion of whole-genome sequencing of L. formosana has spurred an increased interest in exploring the molecular mechanisms underlying seasonal changes in leaf color, marking a significant focus in L. formosana breeding research. However, there is currently a lack of stable reference genes suitable for analyzing the expression patterns of functional genes in L. formosana exhibiting varying leaf colors. This study selected five L. formosana varieties with significant differences in leaf colors. Through the RT-qPCR analysis, and evaluation using BestKeeper, geNorm, NormFinder, Delta Ct, and RefFinder, the expression stability of 14 candidate reference genes was examined. Consequently, two reference genes (LifEF1-α and LifACT) with stable expression, suitable for RT-qPCR of L. formosana with diverse leaf colors, were identified. The stability of these selected reference genes was further validated by examining the LifbHLH137 gene, which promoted the biosynthesis of anthocyanins. This advancement facilitated molecular biology and genetic breeding investigations of L. formosana, providing essential data for the precise quantification of functional genes associated with leaf color variation. Full article

For people from communities experiencing poverty and oppression, education, particularly higher education, is a means to ensure upward socioeconomic mobility. The access to and attainment of education are issues of social and economic justice, built upon foundational experiences in primary and secondary settings, and impacted by students’ cultural and socio-political environments. 6. The 2020 murder of George Floyd, the Black Lives Matter movement, ongoing discourse around immigration, and COVID-19-related hate targeting people of Asian American descent prompted national calls to dismantle social and systemic racism, spurring diversity, equity, inclusion, and accessibility (DEIA) initiatives, particularly in education. However, these efforts have faced opposition from teachers who have told students that all lives matter, and racism does not exist in many American classrooms Loza. These comments negate students’ experiences, suppress cultural and identity affirmation, and negatively impact student wellness and academic performance. Forged in this polarized environment, two longtime community organizers and educators, an indigenous person living away from her ancestral lands and a multiracial descendant of Japanese Americans interned during WWII, whose identities, experiences, and personal narratives shape the course of their work in and outside of the physical classroom, call on fellow educators to exercise y (2018) component of the archeology of self, a “profound love, a deep, ethical commitment to caring for the communities where one works”, by adopting a framework to encourage this profound love in students, acting not just as a teacher, but as a sensei. The word sensei is commonly understood in reference to a teacher of Japanese martial arts. The honorific sensei, however, in kanji means one who comes before, implying intergenerational connection. Sensei is an umbrella expression used for elders who have attained a level of mastery within their respective crafts—doctors, teachers, politicians, and spiritual leaders may all earn the title of sensei. The sensei preserves funds of knowledge across generations, passing down and building upon knowledge from those who came before. The Student Empowerment through Narrative, Storytelling, Engagement, and Identity (SENSEI) framework provides an asset-based, culturally affirming approach to working with students in and beyond the classroom. The framework builds on tools and perspectives, including Asset-based Community Development (ABCD), the Narrative Theory, Yosso’s cultural community wealth, cultural continuity, thrivance, community organizing tenets, and storytelling SENSEI provides a pedagogy that encourages students to explore, define, and own their identities and experiences and grow funds of knowledge, empowering them to transform their own communities from within. The SENSEI framework begins by redefining a teacher as not simply one who teaches in a classroom but rather one who teaches valuable life lessons that transcend colonial conceptualizations of the teacher. In colonized contexts, teachers function to maintain hegemony and assert dominance over marginalized populations. In the SENSEI framework, teachers are those who disrupt colonial patterns and function to reclaim the strengths and voices of the communities they serve. In the SENSEI framework, students are not relegated to those enrolled in classrooms. As with a sensei, a student exists to counter hegemony by embracing and enacting their cultural wealth Educators must help counter harmful narratives and encourage students to identify the strengths that lie within themselves and their communities. Collective forms of narrative that value identity can ensure the continuity of a community or a people. The stories of students’ histories, traditional practices, and resilience can help disrupt harms, many that have lasted for generations, so they may not just survive, but thrive. Full article

As the construction industry places increasing emphasis on environmental conservation and sustainability, this trend has spurred profound research into the optimization of door and window performance. One of the critical components of windows is their frames. Over the past several decades, the design of window frames has undergone significant innovations, ranging from introducing new materials to novel design concepts. The performance of window frames is typically influenced by materials, structural design, and the surrounding environment. Consequently, this paper analyzes the common window frame materials in Chinese civil buildings through investigation. It explores commonly used types of window frames available in the market, focusing on their materials and structural designs. It analyzes issues observed during their usage, integrates findings from existing research, and discusses the performance of window frame materials. Additionally, it explores improvement strategies to meet the evolving demands of contemporary and future architectural doors and windows, providing valuable reference points for designers. Finally, approaching the discussion from a sustainable development perspective, the paper evaluates the environmental impact of wood, aluminum alloy, polymer, and composite window frame materials. It emphasizes that wood- and aluminum-clad wood windows represent sustainable options with versatile applications in diverse scenarios. Full article

This paper presents a wideband approach for L5 and S-band integer-N phase-locked loop (PLL) targeting Indian Regional Navigation Satellite System (IRNSS) applications. A reference spur reduction technique using a $G_m-C$ filter is proposed. The reference spur is improved by 7 dB when compared with one without any $G_m-C$ filter. The wideband integer-N PLL is designed and fabricated in UMC 65-nm CMOS process. The $G_m-C$ filter block consumes 200 $\mathsf{\mu }$A current. The wideband voltage-controlled oscillator (VCO) oscillates from $1.6$ GHz to $3.2$ GHz having a tuning range (TR) of $40\%$, achieving a best and worst phase noise of ≈−122 dBc/Hz and ≈$-116$ dBc/Hz at a 1 MHz offset, respectively. Full article

The flow dynamics adjacent to spur dikes exhibit turbulence and complexity, often resulting in the formation of scouring pits in the riverbed nearby. In regions downstream characterized by robust riverbed mobility, the vulnerability of riprap spur dikes stems from the instability of the upper riprap induced by these scour pits. Current research on scour pits primarily focuses on singular runoff conditions, with a limited exploration into the formation and traits of these scour pits under the combined influence of runoff and tidal currents. This study delves into the formation process and features of scour pits adjacent to submerged riprap spur dikes shielded by flexible mattresses, considering the impact of both runoff and tidal forces, using flume model tests in the tidal zone of the lower Yangtze River as a reference. Our findings reveal that the scour pits at the forefront and rear of riprap spur dikes undergo cyclic scouring and silting influenced by the runoff and tide current’s duration and intensity. The maximum scour depth observed ranges from 60% to 90% of that during runoff alone, contingent upon the ratio of maximum flow velocity at flood tide and ebb tide (denoted as e). This law can be quantitatively elucidated through the concept of the average effect of flow on the riverbed scouring and silting in a unit time (denoted as E). A formula to calculate the maximum scour depth of riprap spur dikes under both runoff and tidal current scenarios, along with a slope formula describing the maximum scour depth relative to the spur dike toe are proposed in this study. These formulations offer versatility across varying flow conditions. Subsequently, we establish an evaluation index pertinent to the safety operation of spur dikes based on the latter formula. This research contributes to a more comprehensive understanding of scour pit dynamics adjacent to spur dikes, especially under combined runoff and tidal influences. The proposed formulae and evaluation index hold promise in enhancing the assessment and maintenance practices for these critical riverbank structures. Full article

A low-power and low-jitter 1.2 GHz Integer-N PLL (INPLL) is designed in a 65 nm standard CMOS process. A novel high-gain sampling phase detector (PD), which takes advantage of a transconductance (Gm) cell to boost the gain, is developed to increase the phase detection gain by ~100× compared to the Phase-Frequency Detectors (PFDs) used in conventional PLLs. Using this high detection gain, the noise contribution of the PFD and Charge Pump (CP), reference clock, and dividers on the PLL output is minimized, enabling low output jitter at low power, even when using low-frequency reference clocks. To provide a sufficient frequency locking range, an auxiliary frequency-locked loop (AFLL) is embedded within the INPLL. An integrated Lock Detector (LD) helps detect the INPLL locked state and disables the AFLL to save on power consumption and minimize its impact on the INPLL jitter. The proposed INPLL layout measures 700 µm × 350 µm, consumes 350 µW, and exhibits an integrated phase noise (IPN) of −37 dBc (from 10 kHz to 10 MHz), equivalent to 2.9 ps rms jitter, while keeping the spur level 64 dBc lower, resulting in jitter figure of Merit ($Fo{M}_{jitter}$) ~−236 dB. Full article

In the automotive industry, system-on-chips are crucial for managing weak radio waves from space, known as satellite signals. Integer-N phase-locked loops have played a vital role in the operation of system-on-chips in recent history. Their clock frequencies are carefully designed to prevent electromagnetic interference. However, as global navigation satellite system becomes more prevalent, integer-N phase-locked loops face new challenges in generating clocks within the shrinking frequency bands due to large frequency steps determined using a reference clock. To address it, replacing integer-N phase-locked loops with fractional-N phase-locked loops is required. This topic has not been discussed extensively, but it is a practical issue that requires consideration due to its potential impact on development costs. This is why we developed an attachable fractional divider. Our developed divider can efficiently transform integer-N phase-locked loops into fractional-N phase-locked loops, achieving low jitter degradation of 0.35 ps_rms and a low fractional spur of −69.3 dBc. Thanks to its attachable design, it expedites time-to-market. Regarding mass production, ensuring immunity to process, voltage, and temperature variations is a significant concern. We introduce the circuit techniques employed in the developed fractional divider for immunity to process, voltage, and temperature variations. Subsequently, we provide a comprehensive set of measurement results. The frequency differences over process variations in fractional-N mode is 6.14 ppm. Power supply and temperature dependances are extremely small in spread-spectrum clocking mode. This article illustrates that the developed fractional divider enhances both time-to-market and product reliance. Full article