bluuebunny/crossref_metadata_2025 huggingface dataset:
THYLAKOID FORMATION 1 interacts with FLOWERING LOCUS T and modulates temperature‐responsive flowering in Arabidopsis
Authors: Susila Hendry , Gawarecka Katarzyna , Youn Geummin , Jurić Snježana , Jeong Hyewon , Ahn Ji Hoon
Published: August 2024

The intracellular localization of the florigen FLOWERING LOCUS T (FT) is important for its long‐distance transport toward the shoot apical meristem. However, the mechanisms regulating the FT localization remain poorly understood. Here, we discovered that in Arabidopsis thaliana, the chloroplast‐localized protein THYLAKOID FORMATION 1 (THF1) physically interacts with FT, sequestering FT in the outer chloroplast envelope. Loss of THF1 function led to temperature‐insensitive flowering, resulting in early flowering, especially under low ambient temperatures. THF1 mainly acts in the leaf vasculature and shoot apex to prevent flowering. Mutation of CONSTANS or FT completely suppressed the early flowering of thf1‐1 mutants. FT and THF1 interact via their anion binding pocket and coiled‐coil domain (CCD), respectively. Deletion of the CCD in THF1 by gene editing caused temperature‐insensitive early flowering similar to that observed in the thf1‐1 mutant. FT levels in the outer chloroplast envelope decreased in the thf1‐1 mutant, suggesting that THF1 is important for sequestering FT. Furthermore, THF1 protein levels decreased in seedlings grown at high ambient temperature, suggesting an explanation for its role in plant responses to ambient temperature. A thf1‐1 phosphatidylglycerolphosphate synthase 1 (pgp1) double mutant exhibited additive acceleration of flowering at 23 and 16°C, compared to the single mutants, indicating that THF1 and phosphatidylglycerol (PG) act as independent but synergistic regulators of temperature‐responsive flowering. Collectively, our results provide an understanding of the genetic pathway involving THF1 and its role in temperature‐responsive flowering and reveal a previously unappreciated additive interplay between THF1 and PG in temperature‐responsive flowering.
New faunistic data on Cunaxidae (Acari: Bdelloidea) of Asian Russia
Authors: Khaustov Alexander , Khaustov Vladimir
Published: August 2024

New faunistic data on mites of the family Cunaxidae of Asian Russia are provided. A total of 11 species were found, among them four species are described as new for science, namely, Neobonzia scutata sp. nov., Cunaxa palustris sp. nov., C. quinquesetosa sp. nov., and Cunaxoides magnificus sp. nov. Scirula impressa Berlese is recorded from Russia for the first time and redescribed; Cunaxoides trepidus Kuznetsov and Livshitz recorded from Asian Russia for the first time and redescribed; Armascirus fendai Kalúz and Vrabec and Cunaxoides lootsi Den Heyer are recorded from Russia for the first time; Cunaxa gordeevae Sergeyenko, C. anomala Khaustov and Kuznetsov, and Cunaxoides desertus Kuznetsov and Livshitz are recorded from Asian Russia for the first time.
Characterization of differences in seed endophytic microbiome in conventional and organic rice by amplicon-based sequencing and culturing methods
Authors: Khanal Sabin , Imran Muhammad , Zhou Xin-Gen , Antony-Babu Sanjay
Published: August 2024

The seed serves as the primary source for establishing microbial populations in plants across subsequent generations, influencing plant growth and overall health. Cropping conditions, especially farming practices, can influence the composition and functionality of the seed microbiome. Very little is known about the differences in seed microbiome between organic and conventional production systems. In this study, we characterized the endophytic microbial populations in seeds of rice grown under organic and conventional management practices through culture-dependent and -independent analyses. The V4 region of 16S rRNA was used for bacterial taxa identification, and the ITS1 region was used for the identification of fungal taxa. Our results revealed significantly higher Shannon and Simpson indices for bacterial diversity in the conventional farming system, whereas the fungal diversity was higher for observed, Shannon, and Simpson indices in the organic farming system. The cultivable endophytic bacteria were isolated and identified using the full-length 16S rRNA gene. There was no difference in culturable endophytic bacterial isolates in rice seeds grown under both conventional and organic farming systems. Among 33 unique isolates tested
in vitro
, three bacteria—
Bacillus
sp. ST24,
Burkholderia
sp. OR5, and
Pantoea
sp. ST25—showed antagonistic activities against
Marasmius graminum, Rhizoctonia solani
AG4, and
R. solani
AG11, the fungal pathogens causing seedling blight in rice.

IMPORTANCE

In this paper, we studied the differences in the endophytic microbial composition of rice seeds grown in conventional and organic farming systems. Our results demonstrate a greater bacterial diversity in conventional farming, while organic farming showcases a higher fungal diversity. Additionally, our research reveals the ability of seed bacterial endophytes to inhibit the growth of three fungal pathogens responsible for causing seedling blight in rice. This study provides valuable insights into the potential use of beneficial seed microbial endophytes for developing a novel microbiome-based strategy in the management of rice diseases. Such an approach has the potential to enhance overall plant health and improve crop productivity.
Origin of Optical Gain in Narrow ZnO Microrods with Whispering Gallery Modes
Authors: Tarasov A. P. , Zadorozhnaya L. A. , Kanevsky V. M.
Published: August 2024

Due to sufficiently high lasing thresholds, stimulated emission in relatively small ZnO microcrystal lasers is often considered to be fed by an inverted electron–hole plasma (EHP). In this study, the nature of optical gain in such emitters is investigated using ZnO microrods 1–6 µm in diameter synthesized by a modified thermal evaporation method and exhibiting whispering-gallery mode (WGM) lasing in the near ultraviolet range. It is demonstrated that optical gain in these objects is not a consequence of population inversion of the EHP at either low or room temperatures. Instead, the primary gain mechanism is the process of scattering of electron–hole pairs by free electrons. Unlike the case of large ZnO WGM microcavities, in small-diameter microrods this process turns out to be dominant over a wide temperature range.
A Class of RF-MEMS Switches with Low Pull-In Voltage
Authors: Tagliapietra Girolamo , Iannacci Jacopo
Published: August 2024

This paper presents a class comprising three series of Ohmic switches, elucidating their design principles, simulation outcomes and the findings derived from measuring the initial batch of manufactured samples. The design of the membranes employs meandered beams to achieve a small actuation voltage. Initial electro-mechanical simulations, conducted within the Ansys Workbench environment, projected actuation voltages ranging from 5 to 8 Volts, whereas the measured values were marginally higher. The electromagnetic behavior of these devices demonstrated a generally qualitative concordance with simulations performed in the Ansys HFSS environment, exhibiting satisfactory performance in terms of return loss ([Formula: see text][Formula: see text]dB) and isolation ([Formula: see text][Formula: see text]dB) across the 5–30[Formula: see text]GHz frequency spectrum.
A Survey on Deep Learning-Based Traffic Signal Control
Authors: Si Qinbatu , Yang Lirun , Bao Jingjing , Lin Yangfei , Bao Wugedele , Wu Celimuge
Published: August 2024

Intelligent Traffic Management is a crucial issue closely related to daily life and productivity, with traffic congestion being a complex and challenging problem faced by most cities. Traffic Signal Control (TSC) stands out as the most direct and effective method to tackle congestion. It aims to minimize travel time, enhance throughput, improve traffic safety, reduce emissions, and conserve energy by coordinating the direction and timing of vehicle movements at intersections. Traditional TSC methods mostly rely on simple rules, limited data, and expert knowledge, making them inadequate for increasingly complex traffic scenarios. In the context of TSC, an increasing number of researchers are turning to Deep Learning (DL) methods to address identification, decision-making, and optimization challenges. Although many reviews have examined the TSC problems and the application of Reinforcement Learning in this field, there remains a notable gap in comprehensive analyses of TSC utilizing a wider range of DL techniques, including Deep Reinforcement Learning, Federated Learning, and Meta-learning. This paper, building upon the basic concepts and traditional approaches of TSC, provides a detailed overview of the latest research advancements employing different DL methods for this issue. Experimental settings and evaluations are also introduced. Furthermore, to spark new interest in this research field, future works are proposed.
A Global-Features and Local-Features-Jointly Fused Deep Semantic Learning Framework for Error Detection of Machine Translation
Authors: Li Kangxi , Sun Dongyun
Published: August 2024

Error detection in machine translation is very important for users, but current methods often only focus on one of the local or global features, neglecting the fusion of the two, thereby reducing the detection effect. To address this issue, this study proposes a deep semantic learning framework based on deep learning mechanisms. This study utilizes convolutional neural networks to extract local features at the sentence level, and then uses recurrent neural networks to capture global features at the sentence sequence level. By integrating local features with global features, we have established a deep semantic learning framework. To evaluate the performance of this method, we conducted extensive detection experiments on multiple publicly available datasets, including translation error detection for words, grammar, and punctuation. Each detection metric was tested 2000 times, and 5 cyclic tests were conducted to reduce errors in deep semantic learning. The experimental results indicate that our method has made significant improvements in the error detection task of machine translation. Compared with the traditional methods, this method can better detect and repair errors in translation, and its stability and applicability are broader.
Fuzzy Multiple Attribute Decision-Making via Association Rules Mining
Authors: Jia Shuyan , Yang Haihong
Published: August 2024

With the advent of globalization, the social and economic environment has become complex and changeable, and the challenges faced by a decision-maker are full of uncertainty and ambiguity. Generally, the traditional decision-making method is too single and one-sided, and it will not have a great effect. As a result, researchers have begun to explore a variety of new decision-making methods. Among them, the association rule mining algorithm, which stands out, has been widely used in society. It has powerful data processing capabilities, can capture and process fuzzy information well, and reduces the omission of key decision-making factors. In this paper, FP-growth and triangular fuzzy number theory (FPGTFN) are applied to different datasets to verify the fuzzy decision theory. With multi-index and multi-index as important indicators, the fuzzy matrix iterates many times to calculate the highest weight. The optimal decision scheme is scheme 2, and the weight value is 0. 0756. The FPGTFN algorithm has better stability and less volatility in different datasets, and the decision results are more stable. FPGTFN has a smaller execution time, stable scheme output, and provides a better decision scheme. When the decision-maker has a certain expectation for the attribute values, the reference point can effectively avoid ignoring the correlation performance between the attributes, and can also reflect the actual profit and loss of the alternative scheme.
Enhancing Wine Classification Using PCA-IQPSO-SVM: A Comprehensive Optimization Approach
Authors: Zhu Fenhua , Hu Qing
Published: August 2024

In order to facilitate the high-quality advancement and digital innovation of the wine industry, a method based on principal component analysis (PCA) and improved quantum particle swarm optimization (IQPSO) to optimize support vector machine (PCA-IQPSO-SVM), was proposed to solve the wine classification problem. First, the feature extraction ability of PCA was used to reduce the input dimension of the model and improve the classification efficiency. At the same time, aiming at the problems that the quantum particle swarm optimization (QPSO) is easy to fall into local optimum and the convergence ability is decreased in the later stage of optimizing SVM, a variety of improvement strategies are used to improve QPSO to find the best parameters of SVM. The experimental results demonstrate that the model of PCA-IQPSO-SVM exhibits superior evaluation indices compared to other models. Moreover, the optimization efficiency of the PCA-IQPSO-SVM model is enhanced by 1.64% to reach an impressive 85.2%, showcasing its remarkable optimization effect. Simultaneously, this study provides a scientific approach for quality classification in the wine industry, thereby facilitating its high-quality development.
The Metabolic Cost Analysis of Human Walking with a Robotic Lower-limb Exoskeleton
Authors: Zhang Sen , Wang Wei , Wei Wei , Zhang Zhili , Ge Weimin
Published: August 2024

Lower limb exoskeletons are widely studied to provide assistance for both disabled and healthy person. The assisting efficacy would be more significant if the assisting strategy is more in line with the movement characteristics of different wearers. However, how to optimize the timing and magnitude of the assisting torque so as to provide different wearers with more personalized, comfortable and balanced assistance is still not well tackled. To address the issue, an assisting strategy with adjustable assisting timing and magnitude was designed in this paper. First, we designed a framework for smart human-exoskeleton collaboration in which machine learning methods and human-in-the-loop methodology are combined to optimize the assisting timing and magnitude. Second, an assisting strategy based on hip joints trajectory, which is embedded in the framework was designed to separately plan the assisting torque for hip flexion and extension. Third, the efficacy of the designed method was both verified with quantitative and qualitative results. Quantitative results showed that the designed method reduced the net metabolic cost of walking during load carriage by 2.68% on average. And questionnaire survey results showed that the designed assisting strategy provided subjects with more significant, comfortable and balanced assistance.
Session-based Recommendation Framework via Counterfactual Inference and Attention Network
Authors: Wang Zhenhao , Huang Bo
Published: August 2024

Session-based recommendation systems (SRS) can predict the next action of anonymous users based on their historical behavior sequence, while some features directly influence whether an interaction occurs, which leads to inaccurate recommendation results that can’t reflect the user’s real preferences. This causes confounding effects in SRS, which causes the recommendation system to misunderstand user preferences and recommend unsatisfied items to users. To address this problem, we propose a session-based recommendation framework via counterfactual inference and attention networks (SRS-CIAN). This framework introduces external attention mechanisms into session recommendation tasks and combines causal graphs for modeling while capturing information on items within the session. We use counterfactual inference to refactor counterfactual scenarios for handling the confounding effects. Through extensive empirical experiments on real-world datasets, we demonstrate that our approach surpasses several strong baselines for confounding effects.
Machine Learning-Driven GCC Loop Unrolling Optimization: Compiler Performance Enhancement Strategy Based on XGBoost
Authors: Shi Zhaoyi , Gao Jun , Guan Xin
Published: August 2024

In contemporary compilers, the determination of the loop unrolling factor is traditionally based on manually crafted heuristic rules. This approach heavily relies on human intuition, which limits its ability to achieve optimized performance across diverse architectures and can sometimes even lead to performance declines. Additionally, developers face challenges in achieving cross-platform compatibility, often necessitating extensive redesign efforts. In response, this study introduces a method leveraging the XGBoost algorithm to predict the optimal loop unrolling factor for compiler optimization, thereby aiming to replace human thinking with machine learning methods and standardize development processes. Initially, the study gathers data on the loop unrolling factors as determined by profile guided optimization technology, analyzes program-specific loop feature vectors and employs cross-validation, including the Pearson correlation coefficient and feature importance ranking, to construct a dataset. Subsequent use of XGBoost to train this dataset models the decision-making process for selecting the most effective loop unrolling factor. The final step involves integrating XGBoost’s trained decision tree model into GCC to calculate the optimal loop unrolling factor during actual compilation. Empirical results on the RISC-V platform indicate that this new method, when tested against the SPEC CPU 2006 benchmark suite, offers up to 6.18% improvement in performance over the existing heuristic approach. It provides a new method for loop unrolling in compilers, and provides an innovative guide for the application of machine learning in compilers.
Deep Learning-Based Human Action Recognition in Videos
Authors: Li Song , Shi Qian
Published: August 2024

In order to solve the problem of low accuracy and efficiency in video human behavior recognition algorithm, a deep learning video human behavior recognition algorithm is proposed, which is based on an improved time division network. This method innovates on the classical two-stream convolutional neural network framework, and the core is to enhance the performance of the time division network by implementing the sliding window sampling technique with multiple time scales. This sampling strategy not only effectively integrates the full time-series information of the video, but also accurately captures the long-term dependencies hidden in human behavior, which further improves the accuracy and efficiency of behavior recognition. Experimental results show that the method proposed in this paper has achieved good advantages in multiple data sets. On HMDB51, our method achieves 84% recognition accuracy, while on the more complex Kinetics and UCF101 datasets, it also achieves 94% and significant recognition results, respectively. In the face of complex scenes and changeable human body structure, the proposed algorithm shows excellent robustness and stability. In terms of real-time, it can meet the high requirements of real-time video processing. Through the validation of experimental data, our method has made significant progress in extracting spatiotemporal features, capturing long-term dependencies, and focusing on key information.
A High-Gain, High-Efficiency Transparent and Flexible Antenna for Vehicular Communication
Authors: An Kang , Chen Xiaolian , Nie Shuhong , Tolba Amr , Alfarraj Osama
Published: August 2024

A high-gain high-efficiency flexible and transparent film antenna in millimeter-wave is proposed in this paper. Comb structure which can be regarded as developed from the grid array structure is employed to mitigate the loss of the feeding network caused by the lossy metal mesh. The gain of the antenna has low sensitivity to the sheet resistance, which is promising in developing large-scale and high-performance transparent antenna. A large-scale ([Formula: see text]@28[Formula: see text]GHz) antenna is designed and fabricated using transparent metal mesh film with sheet resistance of 0.1[Formula: see text][Formula: see text]/sq and a thin PET film substrate. Bending measurement shows the antenna has excellent mechanical flexibility. A peak gain of 15.92[Formula: see text]dBi, radiation efficiency of 65.1%, optical transparency of 84% and 1,000-times bending are obtained. To the best of our knowledge, it is the highest gain and radiation efficiency flexible-transparent antenna reported thus far, which provides a glass-integrated antenna solution for vehicular communication.
Odd annular Bar-Natan category and 𝔤𝔩(1|1)
Authors: Necheles Casey , Wehrli Stephan
Published: August 2024

In this paper, we introduce two monoidal supercategories: the odd dotted Temperley–Lieb category [Formula: see text], which is a generalization of the odd Temperley–Lieb category studied by Brundan and Ellis in [Monoidal Supercategories, Commun. Math. Phys. 351 (2017) 1045–1089], and the odd annular Bar-Natan category [Formula: see text], which generalizes the odd Bar-Natan category studied by Putyra in [A [Formula: see text]-category of chronological cobordisms and odd Khovanov homology, Banach Center Publ. 103 (2014) 291–355]. We then show there is an equivalence of categories between them if [Formula: see text]. We use this equivalence to better understand the action of the Lie superalgebra [Formula: see text] on the odd Khovanov homology of a knot in a thickened annulus found by Grigsby and the second author in [An action of [Formula: see text] on odd annular Khovanov homology, Math. Res. Lett. 27(3) (2020) 711–742].
Notes on multiplier Hopf algebras and invariants of framed links and 3-manifolds
Authors: Yang Tao , Yetter David N.
Published: August 2024

In this paper, we generalize the well-known constructions of invariants of framed links and 3-manifolds based on Hopf algebras to multiplier Hopf algebras. Results include a structural characterization of ribbon multiplier Hopf algebras from which invariants of framed links arise from the usual functorial construction to the ribbon category of modules, and the extension of Hennings construction of invariants of framed links and 3-manifolds obtained from Hopf algebras to algebraic quantum groups equipped with an appropriate trace.
THE PREPARATION AND CHARACTERIZATION OF A LANTHANUM-BASED RARE EARTH CONVERSION COATING ON MAGNESIUM ALLOY AZ91D AND ITS DEGRADATION IN 3.5% NaCl SOLUTION
Authors: GAO JIELI , XU RUIZHI , YAN FUAN , FAN BAOMIN , WENG YUNXUAN , ZHOU YONG
Published: August 2024

Magnesium alloys have a huge market and broad prospects due to their high specific strength, good ductility, and strong thermal conductivity. However, the active chemical properties of elemental magnesium and the negative standard electrode potential result in magnesium alloys being highly susceptible to corrosion and failure. Chemical conversion treatment of magnesium alloy can improve its corrosion resistance by preparing a chemical conversion film on its surface. Rare earth conversion treatment has the advantages of effortless process, moderate price, no pollution, good corrosion resistance, and synergistic effect with inorganic or organic additives, which is in line with the pursuit of “green chemical” in industrial production. At present, there is little research and application on lanthanum-based rare earth conversion coatings (La-RECC). This paper selects rare earth element lanthanum (La) to prepare La-RECC on the surface of AZ91D magnesium alloy through chemical conversion treatment. The microstructure, chemical composition, and protective properties of La-RECC were discovered, as well as their interrelationships, revealing the degradation process of La-RECC in 3.5% NaCl solution. La-RECC is composed of La(OH)3 and Mg(OH)2, exhibiting a crystal cluster structure with cracks, and there are a large number of needle-like crystals on the crystal cluster of La-RECC, with a thickness of 2.14 [Formula: see text]m. The degradation process of La-RECC can be divided into three stages: Rapid degradation, slow degradation, and complete degradation. The complete degradation time of La-RECC in 3.5% NaCl solution is 82.5[Formula: see text]h.
ANOMALY STABILITY DETECTION METHOD OF MULTI-STATION PRECISION MACHINING MECHANICAL FIXTURES
Authors: LI SHAN
Published: August 2024

The stability test of multi-station precision machining fixtures can improve the safety of multi-station precision machining fixture. This paper puts forward a stability test method of multi-station precision machining fixtures based on the pressure pulsation amplitude test. The fundamental frequency characteristic detection of airflow pulsation is adopted to collect the stability characteristic parameters of multi-station precision machining machine clamps, and the wave number analysis model of airflow pulsation in the stable state of multi-station precision machining machine clamps is constructed. The time domain characteristic parameter analysis and spectrum gain control are adopted to realize the stability condition parameter analysis and spectrum characteristic decomposition of multi-station precision machining machine clamps. The end-face pulsation attenuation characteristic method of detection is followed to realize noise reduction of the stable air pulsation attenuation signal of multi-station precision machining fixture, and the rotor meshing frequency component of the stable air pulsation attenuation signal of multi-station precision machining fixture is extracted. The multi-scale wavelet air pulsation attenuation signal decomposition is adopted to realize the correlation spectrum characteristic detection of the signal, and the multi-dimensional space fusion is employed to realize signal characteristic clustering, thus realizing the stability detection of multi-station precision machining fixture. The test output shows that the proposed method has better convergence and strong feature clustering in the stability detection of multi-station precision machining machine fixtures, which improves the confidence level of multi-station precision machining machine fixture failure instability detection.
DRL-BASED KINETIC AND THERMAL ENERGY PROPERTY DETECTION FOR WELDED JOINT PERFORMANCE ANALYSIS
Authors: ANUSUYA M. , ASIF M. MOHAMMED , CHITTURI SRINIVAS , BALAJI E. , RAVICHANDRAN M. , YEDUKONDALU G. , SHANKAR E. , RAJARAM A.
Published: August 2024

Among many other sectors, the automobile industry relies heavily on resistance spot welding (RSW) to attach metal components. In order to determine if spot welded connections are weldable, this research develops a machine learning (ML) model and a parameter-less optimization method, and then suggests a data-driven welding analytics framework. It trains the ML model using data from experiments and simulations, which allows it to make accurate predictions about weld quality. In order to get the finest possible weld in the shortest amount of time, RL has been used to adjust factors like the FSW pin’s rotational and translational speeds. Due to their thorough exploration of the environment, these algorithms achieved extremely high accuracy rates, indicating that they are trustworthy in their ability to operate the agent optimally. However, there may be a large disparity in the precision with which different models provide predictions. Prioritizing the evaluation and selection of the most effective prediction model or models is of the highest significance. In addition, we build, test, and evaluate previous models of nugget width prediction using deep reinforcement learning (DRL). To begin with, we use bootstrapping to create sample distributions for all of the prediction models. Then, we evaluate their performances using statistical tests to see if there are any significant differences. This investigation shows that DRL which is built for RSW nugget width forecasting in this work, performs better than earlier models.
EFFECTS OF ISOMETRIC HOLD EXERCISE WITH PASSIVE TERMINAL KNEE EXTENSION IN RESTORING QUADRICEPS FUNCTION AFTER ACL RECONSTRUCTION — RESEARCH DESIGN AND METHODS
Authors: Raj Vijay Samuel , Mruthyunjaya  , Chandru Vijay
Published: August 2024

Background: Inhibition of the quadriceps muscle activity and reduced knee extension strength in terminal extension is observed in most cases following anterior cruciate ligament (ACL) injuries and reconstruction. Quadriceps lag (QL), if it persists, can lead to limitation of functional and performance skills. The loss of quadriceps power may be due to reduced voluntary activation of the quadriceps muscle due to arthrogenic muscle inhibition (AMI). The possibility of physiotherapeutic intervention to reduce the AMI must be considered to decrease the QL, and improve muscle strength and functional recovery. There is a need to develop a specific exercise addressing arthrogenic muscle inhibition (AMI), to achieve complete knee extension at the early phase of rehabilitation. The trial is aimed to evaluate the effect of IEPTE in patients with quadriceps lag following anterior cruciate ligament reconstruction (ACLR).

Methods: Randomized, Parallel Group Trial, including 36 patients ([Formula: see text] = 36) aged 18–50 years with ACLR, will be randomly allocated to isometric hold exercise with passive terminal knee extension (IEPTE) and ACL conventional exercise group (CG). The primary outcome for quadriceps lag and the secondary outcome of motor unit action potential (MUAP), pain and knee function will be evaluated. The IEPTE technique is developed by the author, who combined passive terminal knee extension with the isometric hold exercise to knee extensors. The effects of the exercise protocol will be evaluated using outcome measures at 0 (baseline), 2nd, 4th, 8th, 24th weeks. General linear mixed model for repeated measures with effect size tested using Cohen’s d will be used to analyze primary and secondary outcomes to investigate confounding interaction between groups and time. A statistically significant was set to [Formula: see text] < 0.05 (95% CI). The study results are expected to be completed by December 2023 and the results by 2024.

Discussion: The exercise developed addresses the AMI and achieves knee extension at the early phase of rehabilitation. If proven to be effective, the early activation of the quadriceps may improve the knee function. This developed exercise (IEPTE) thus can be used in the ACL rehabilitation program and results will help to explore further to frame new guidelines and protocol.
Analysing the Factors Influencing the Inclusive Development of Fisher Folk Concerning Southern Districts of Tamil Nadu
Authors: Agnes Pravina X. , Radhika R.
Published: August 2024

The state’s economy depends significantly on the work of fishermen. The purpose of the Development of Fisher folk is to create a community of fishermen that is fully developed in all areas, including education, health, social standing, and economic development. The many Fisher community’s development programs are designed to aid all men, women, youth, and children who work in fishing activities and reside in coastal areas by enhancing their access to education, healthcare, culture, and employment possibilities. The paper examines the factors influencing the inclusive development of fisher folk in southerly districts of Tamil Nadu. Through field surveys, fundamental knowledge is gathered. To compile a thorough profile of the socioeconomic circumstances of the households of the fishermen, an interview schedule has been created. By using convenience sampling, 200 respondents from Tamil Nadu’s southern districts, were included in the final sample set. Therefore, the results revealed that climatic change and inadequate technology significantly impact the Fisher communities’ inclusive development. The outcome of the results also shows the insignificant relation between inadequate facilities for storing the catch impact and inclusive development of fishing communities. Factors influencing inclusive development include socio-demographic characteristics, climate change, lack of occupational return, inadequate storage facilities, lack of knowledge, technology, and financial institutions. Age, marital status, education, and involvement do not significantly impact development. The findings also show that the development of the fishing community is unaffected by a lack of financial institutions, expertise, or awareness. The results confirmed that state assistance was inefficient in reaching the targeted society and emphasised the need for further planned government intervention. By creating additional capacity structure initiatives that maintain and provide continuous social defence and engage the coastal community through an innovative alertness campaign, the authorities may demonstrate their commitment to the full development of aquatic fishermen.
Can ICT Efficiency and Environmental Regulation have a Synergic Effect on Low-carbon Energy Technology Development?
Authors: Zhao Congyu , Wang Jianda , Tu Chuc Anh , Taghizadeh-Hesary Farhad
Published: August 2024

Low-carbon energy technology (LCET) holds the key to unlocking a harmonious coordination between energy consumption and environmental preservation, paving the way for a resilient and sustainable future. We consider the role of information and communication technology (ICT) efficiency and environmental regulation (ER) in promoting LCET based on the dataset in China during 2001–2019. We also explore the joint effect of ICT efficiency and ER on LCET. Moreover, the impact mechanisms are fully explored. The findings are as follows: (1) ICT efficiency and ER both have positive impacts on LCET, which means that they have individual low-carbon technological innovation effects. (2) The interaction of ICT efficiency and ER contributes to the rise of LCET. In other words, these two factors work synergistically to promote the development of LCET. (3) Heterogeneous effect suggests that the combined promotion effect of ICT efficiency and ER on LCET is more remarkable in regions with better economic development. (4) ICT efficiency affects LCET by encouraging R&D and fiscal spending on the environment, which means that R&D and fiscal spending on the environment are two transmission mechanisms between ER and LCET. The conclusions recommend that the government should promote ICT efficiency and ER to spur LCET development.
Structural and Electrical Property Studies of B-Site-Doped LaCrO3 Compound for Solid Oxide Fuel Cell Application
Authors: Soni Neha , Karma Nikita , Dubey Paras , Kaurav Netram
Published: August 2024

This work focuses on the detailed crystal structure and dielectric properties of LaCrO3 and Ni-doped LaCrO3 (LaCr[Formula: see text]Ni[Formula: see text]O[Formula: see text] compounds. Both the samples were synthesized via the auto-combustion route. The orthorhombic crystal structure with Pbnm space group of both the synthesized samples was confirmed using X-ray diffraction measurements. The average crystallite size of LaCr[Formula: see text]Ni[Formula: see text]O3compound is 52.71[Formula: see text]nm which was determined from the Debye Scherrer formula. In Ni-doped LaCrO3compound, the formation of Cr[Formula: see text] ions at the octahedral sites affects the dielectric properties. Enhancement in AC conductivity in LaCr[Formula: see text]Ni[Formula: see text]O3 compound is due to the hopping of charge carriers in the material.
Immersion and Invariance-Based Linear Tracking and Regulation Controller for Depth Position of an AUV
Authors: Desai Ravishankar Prakash , Manjarekar Narayan Suresh
Published: August 2024

This paper addresses the tracking control challenge in the diving motion system of a specific class of autonomous underwater vehicles (AUVs) characterized by a torpedo-like shape. A decoupled and reduced-order three degrees-of-freedom linearized diving motion model is employed for depth position control. A control law is synthesized using the immersion and invariance (I&I) technique to achieve the control objectives. The primary aim is to attain tracking by immersing a stable, lower-order target (second-order) dynamic system into a three-dimensional manifold, upon which the closed-loop system evolves. We address the regulation problem as a specialized instance of the tracking problem, with the reference input set as a predetermined known depth that requires regulation. The efficacy of the proposed control law is evaluated through simulation studies involving various scenarios. Robustness tests are conducted to assess the control law’s performance under modeling uncertainties and underwater disturbances. The computer simulation employs an AUV named MAYA, utilizing experimentally validated diving motion parameters. A comparative analysis is performed between the proposed control law and other benchmark controllers to gauge its performance. Additionally, the effectiveness of the proposed control law is confirmed by validating its application to the nonlinear model of the diving motion system.
An Efficient Multi-Agent Policy Self-Play Learning Method Aiming at Seize-Control Scenarios
Authors: Zhang Huaqing , Ma Hongbin , Zhang Xiaofei , Wang Li , Han Minglei , Chen Hui , Ding Ao
Published: August 2024

Aiming at the problem of multi-agent cooperative confrontation in seize-control scenarios, we design an efficient multi-agent policy self-play (EMAP-SP) learning method. First, a multi-agent centralized policy model is constructed to command the agents to perform tasks cooperatively. Considering that the policy being trained and its historical policies usually have poor exploration capability under incomplete information in self-play trainings, the intrinsic reward mechanism based on random network distillation (RND) is introduced in the self-play learning method. In addition, we propose a multi-step on-policy deep reinforcement learning (DRL) algorithm assisted by off-policy policy evaluation (MSOAO) to learn the best response policy in the self-play. Compared with DRL algorithms commonly used in complex decision problems, MSOAO has more efficient policy evaluation capability, and efficient policy evaluation further improves the policy learning capability. The effectiveness of EMAP-SP is fully verified in MiaoSuan wargame simulation system, and the evaluation results show that EMAP-SP can learn the cooperative policy of effectively defeating the Blue side’s knowledge-based policy under incomplete information. Moreover, the evaluations results in DRL benchmark environments also show that the best response policy learning algorithm MSOAO can promote the agent to learn approximately optimal policies.
MAV-Link-Based Control and Coordination of a Multi-Drone Cluster for Intelligence, Surveillance and Reconnaissance Tasks
Authors: Toma Andrea , Cecchinato Niccolò , Ferrin Giovanni , Scagnetto Ivan , Drioli Carlo , Foresti Gian Luca
Published: August 2024

Research on drone systems is expanding rapidly and finds application in many fields. These systems provide powerful and precise means in several domains from surveillance to agriculture. If we look inside a drone system, we will find several parts: the aerial vehicle(s), the onboard sensors, a computing device, and the autopilot, a communication network, a ground control station, control software, and a security module, encrypting commands and messages. In a multi-drone application, the system becomes more complex and coordination along with synchronization of the Unmanned Aerial Vehicles (UAVs) forms the strategic parts of the whole system. This manuscript focuses on an Intelligence, Surveillance and Reconnaissance (ISR) application and investigates a complete HW/SW architecture for a number of deployments where the secure control architecture is based on the MAVLink protocol for automatic control of the UAVs fully enabled by audio and video acquisition and processing. Indeed, one of the key features and novelties introduced by this proposal is the combined use of audio and video inputs, in order to provide a more precise and robust detection of targets and the related reaction of the system. The proposed system setup, user control scheme, ciphering and synchronization methods, and the related experiments are explored for laying the basis for future ideas and developments.
Advancements in UAV Path Planning: A Deep Reinforcement Learning Approach with Soft Actor-Critic for Enhanced Navigation
Authors: Guo Jingrui , Zhou Guanzhong , Huang Hailong , Huang Chao
Published: August 2024

This paper tackles the intricate challenge of autonomous navigation for Unmanned Aerial Vehicles (UAVs) through dynamically changing environments. We focus on a sophisticated Deep Reinforcement Learning (DRL) approach using the Soft Actor-Critic (SAC) algorithm, optimized for UAV path planning within a continuous action space. This methodology leverages environmental image data to enhance the precision of flight maneuvers and effective obstacle avoidance. Our approach, validated through extensive simulations in Gazebo and field tests, demonstrates the algorithm’s efficacy in enabling UAVs to adeptly navigate obstacles using depth maps. The study further explores the robustness of the SAC algorithm by comparing it with traditional DRL methods, emphasizing its superior performance in real-world applications. This research contributes significantly to advancing UAV technology, particularly in autonomous motion planning, by integrating cutting-edge machine learning techniques. The video link is: https://www.youtube.com/watch?v=Nd_aMzejNXY .
UAV Swarm Confrontation with Adaptive Attacking Strategy
Authors: Hong Chen , Yu Nan , He Ning , Chen Aidong , Xiao Mingming , Xue Jian , Liu Xiaofeng
Published: August 2024

With the rapid development of unmanned aerial vehicles (UAVs) technologies, a substantial increase on the employ of UAV swarms in a wide range of civilian and military tasks has been witnessed. Advanced confrontation control approach can greatly improve UAVs’ capabilities and effectively free pilots from dangerous, boring, and burdensome confrontation missions. How to efficiently control UAV swarms in the air-to-air confrontation is still a hard problem. In this paper, considering the influence of the defending angle of UAV, we propose a general attacking cost function and an adaptive attacking strategy (AAS) to improve the capability of UAV swarm against another UAV swarm in an airborne battlefield. A multi-agent based UAV swarm air-to-air confrontation model is established, where red UAV swarm versus blue UAV swarm were simulated in a visual 3D and discrete-event environment. Extensive simulations are performed to verify the performance of AAS, the results show that AAS outperforms other traditional strategies by a large margin. In particular, UAV swarm adopting AAS can obtain a very high winning percentage even though the size of the swarm is only half of its opposing swarm that uses random or low velocity strategy. Meanwhile, AAS is quite robust to cope with different UAV swarm sizes. To improve the usability and practicability of AAS, we also propose a lightweight strategy called empirical adaptive attacking strategy (EAAS). The simulation results indicate that EAAS is easy to use and can retain the similar effects to AAS especially for large scale UAV swarms. Our work will illuminate new insights into the area of UAV swarm versus UAV swarm.
The Physics and Mathematics of Ziplining
Authors: Ivchenko Vladimir
Published: August 2024

We study the kinematic and dynamic characteristics of body motion along an inclined hanging cable in the approximation of its masslessness and inextensibility. It is shown that in this case, the motion of the suspended body occurs along an inclined ellipse, the foci of which are the cable attachment points. Herewith, at any given moment, the net tension force of the cable is perpendicular to the instantaneous velocity of the body. Besides, the tension force is a dynamic characteristic of the cable and increases as the body descends along with the increase in the speed. There is also a sharp increase in the tension at the end of the trajectory, which is explained by a decrease in its radius of curvature.
Changing Profile of India’s Economic Diplomacy: Nehru to Modi
Authors: DE PRABIR
Published: August 2024

Economic diplomacy builds bridges among countries and is an integral part of India’s foreign policy. India’s economic diplomacy in the form of development partnership is primarily demand-driven and guided by the priorities of host countries. Economic diplomacy is defined as an array of activities through which international economic relations are pursued between countries. In an ideal condition, the act of economic diplomacy refers to a set of economic activities being utilized between two or more partner countries through free trade agreements (FTAs), trade and investment promotion, training and capacity building, facilitation of connectivity projects, among others. India is at the forefront of economic diplomacy. India has had a rich trend in economic diplomacy since 1947 starting with Prime Minister Nehru. In this paper, we analyze the trends in India’s journey in economic diplomacy, discuss some of the challenges and present the way forward. India’s main challenges are primarily divided into three categories: (i) resources and initiatives, (ii) external competition; and (iii) integration plans. India has come a long way in establishing a stable economic diplomatic source for the world, the foundation of which was laid down by PM Nehru and PM Modi has taken it to a higher level.
Acerca de la alegría y la guerra: feminismo negro/interseccionalidad
Authors: Luna Zakiya , Brown Melissa C. , Johnson Maria S. , Pirtle y Whitney N.L.
Published: August 2024

ResumenLa teorización feminista negra se desarrolló fuera del mundo académico formal para satisfacer las necesidades de las mujeres negras, pero no terminó ahí. Esta reseña ofrece acceso a algunas “guerras” y debates actuales acerca de las políticas del conocimiento sobre teorías, conceptos y praxis feministas negras que se han profundizado dentro de la sociología y se extienden cada vez más a paneles de conferencias en vivo, debates en línea y legislaturas. Las características compartidas dentro del feminismo negro incluyen una atención persistente y crítica a la producción de conocimiento, el poder y el cambio social de las mujeres negras, pero hay mucho más que eso. Basándose en la sociología y otras disciplinas, esta reseña del feminismo negro/interseccionalidad cubre familias de feminismos negros, tendencias de citación disciplinaria, consideraciones metodológicas y tensiones en torno a la encarnación en las demandas sobre el feminismo negro y la interseccionalidad. En las conclusiones proponemos rumbos para destrabar conflictos, desestabilizar guerras y avanzar hacia la alegría y la liberación mientras la lucha continúa.An English translation is available online at
https://doi.org/10.1146/annurev-soc-090123-032434
On Joy and War: Black Feminism/Intersectionality
Authors: Luna Zakiya , Brown Melissa C. , Johnson Maria S. , Pirtle Whitney N.L.
Published: August 2024

Black feminist theorizing developed outside the formal academy to meet the needs of Black women but did not end there. This review offers entrée to some current “wars” and debates on politics of knowledge about Black feminist theories, concepts, and praxis that have deepened within sociology and increasingly extend into live conference panels, online debates, and legislatures. Shared characteristics within Black feminism include persistent and critical attention to Black women's knowledge production, power, and social change—but there is much more. Drawing on sociology and other disciplines, this review of Black feminism/intersectionality covers families of Black feminisms, disciplinary citation trends, methodological considerations, and tensions around embodiment in claims to Black feminism and intersectionality. In the conclusions, we propose directions to untether conflicts, unsettle wars, and move toward joy and liberation as the struggle continues.A Spanish translation is available online at
https://doi.org/10.1146/annurev-soc-030424-062839