Miracle-dz/UniFAR-FTUs huggingface dataset:
Shale Core Fracture Extraction Method Based On Edge Detection and Hierarchical Semantic Fusion Network

Shale gas is an emerging clean, stable and efficient energy. Counts of Shale core fracture distribution density, length and growth direction are necessary to establish a basic knowledge of total organic carbon content in the region and assess shale gas reservoirs. As fractures exhibit different orientations and scales with distribution density, it is difficult for professionals to label all fractures in core scanning electron microscopy images. This paper proposes a semantic segmentation network, HEF-Net (Hierarchical Extraction and Fusion Networks), to remedy these shortcomings by automatically extracting microfractures from scanning images of shale cores. It contains two branches, edge detection and convolutional structure, for detecting the complete contours of pores and fractures and filtering out mineral and background interference to extract the main content of large-scale fractures, which are subsequently fused into one by a feature fusion module for complete and accurate extraction. The comparison validates that our method leads the way in fracture boundary clarity and overall detection rate, achieving 84.26% for IoU and 90.57% & 91.05% for Dice and Pix_acc, respectively, on a shale sample dataset from the Fuling, Chongqing. Useful for digital core reconstruction studies and regional resource abundance assessments.
Recognition of Citrus on Trees in Different Growth Periods

Aiming at the problem of citrus identification in different growth periods, this paper proposes a detection method improved manifold ranking based on graph to realize the status monitoring and yield evaluation of green citrus in growth periods; to realize target recognition of mature yellow citrus and help automatic picking. Firstly, aiming at the problem of uneven image brightness, this paper uses a method combining fuzzy set theory and local contrast to enhance the image. Then, to solve the problem of inaccurate detection caused by the traditional manifold ranking algorithm relying on the prior edge, a method combining the relative total variation and local complexity is used to extract the foreground to remove the boundary foreground hyperpixel blocks. After manifold ranking, the saliency map is obtained. Subsequently, the saliency map is merged with the foreground and then the final saliency map is obtained by manifold ranking again. In addition, experiments have found that there is a problem of edge information loss during citrus segmentation. Therefore, this paper proposes a segmentation optimization method that combines Otsu method and hyperpixel segmentation map. The experimental results show that the algorithm can effectively recognize green and yellow citrus regions, and the recognition accuracy of yellow citrus is slightly higher than that of green citrus, in which the segmentation accuracy of green and yellow citrus is 94.87% and 97.08% respectively.
Le Varan du Nil (Varanus niloticus), indicateur de la pollution des zones humides d’Afrique Sub-Saharienne

En Afrique, la contamination des zones humides par les metaux et les pesticides va se poursuivre durablement. Pour autant, le statut toxicologique de ces milieux reste trop peu documente. Le but du present travail est d’estimer la valeur du varan du Nil (Varanus niloticus) en tant qu’espece sentinelle pour la contamination des zones humides continentales d’Afrique sub-Saharienne. Plomb, cadmium, et pesticides organochlores et organophosphores ont ete quantifies, par spectrometrie d’absorption atomique et chromatographie gazeuse (respectivement), dans plusieurs tissus provenant de 71 specimens issus de quatre sites (au Mali et au Niger) juges a priori inegalement contamines. Bien que des differences claires apparaissent, la contamination environnementale s’avere moderee sur les quatre sites, et ne semble pas constituer un risque notable pour les varans ni pour les humains qui s’en nourrissent occasionnellement. Toutefois la variabilite interindividuelle est importante. Les organotropismes des polluants detectes sont coherents avec ceux prealablement decrits. Si l’on n’a pu mettre en evidence de difference liee au sexe en ce qui concerne les pesticides, les femelles presentaient des charges en metaux superieures. La relation entre d’autres facteurs (taille, proportion de graisse) et les concentrations tissulaires a egalement ete consideree. Les varans sont susceptibles de reveler des differences subtiles de contamination environnementale entre sites, et la resolution spatiale de l’outil semble tres fine. La possibilite pratique d’utiliser cet indicateur se trouve donc validee. Un travail experimental sur des varans captifs a par ailleurs ete mene pour approfondir l’etude
School takeovers, leadership change, and personnel policy *

Failing, high-poverty public schools notoriously struggle to attract and retain good teachers. This paper studies a setting where successful businesses or educational charities take over the management of low-performing schools, while funding remains public. Exploiting the stag-gered expansion of English Sponsor-led academies since the early 2000s, we show that the new business-oriented management strongly innovates its personnel policy, leading to profound changes in the teaching body. The probability that the Sponsor appoints a new headteacher doubles upon the takeover, with the new headteacher being, on average, younger, better paid, and more likely to come from outstanding schools. The takeover also induces teacher sorting, with older, lower-achieving teachers leaving the school, and new teachers joining the Sponsor-led school from outstanding schools. Lastly, Sponsors substantially restructure teachers’ re-warding scheme and abandon a pay scale entirely based on seniority and education, leading to a 10 percent increase in pay dispersion across equally experienced/educated teachers.
Underwater Biological Target Detection Algorithm Based on MSD-YOLOv5

In oceanography, target detection technologies play a key role in various applications, such as assisting underwater robots in detecting the seabed, identifying ships and people at sea, and helping aquaculture farmers monitor the types and distribution of aquatic organisms, adjusting culture densities to ensure their growth and development. However, challenges like small aquatic target size, overlap and occlusion issues, poor underwater lighting conditions, and high ambient noise and interference can result in reduced detection accuracy. Moreover, real-time detection is essential in aquaculture. To address these issues, this paper proposes the MSD-YOLOv5 model, which integrates Space-to-Depth into the Backbone and Neck of YOLOv5 and replaces the Head of YOLOv5 with a separated Head to enhance model accuracy while meeting real-time detection requirements. Experimental results demonstrate that the MSDYOLOv5 model improves mAP@0.5 and mAP@0.5:0.95 by 2.7 percentage points compared to the YOLOv5 model, and achieves a detection speed of 196 FPS, meeting the requirements for real-time detection of underwater organisms.
Analysis of Sparse Reconstruction Effect on Compressive Imaging Space Object

Sparse reconstruction is the key factor of Compressive Sensing (CS) theory that can overcome the Nyquist sampling limitation and sense large amount of data in low measurement. Based on the admirable sparse representation of space object scene, CS is promising in the compressive imaging of space observation. The main process of CS is to sparsely reconstruct the original signal from the low-dimensional measurements, where the reconstruction quality is the principal consideration to guarantee the application of CS in this field. This paper investigates the sparse reconstruction effect on space object images by different algorithms such as convex optimization and greedy. The numerical simulations are performed in increasing compression ratios with respect to different datasets. The reconstruction effect is evaluated by image quality indexes in different cases. The statistical results show that the sparse reconstruction of space object can keep the image quality to a large extent. The conclusion verifies the feasibility for compressive imaging application in space observation.
Optimal Design of UAVs Leader-Following Consensus Formation Control Based on Riccati Equation

Formation control is one of the key technologies for UAV swarm operation. With the growth of the formation scale, the computation and communication among the UAVs based on the centralized control will increase exponentially. The leader-following strategy, based on the distributed consensus formation control theory, may effectively alleviate this problem, and improve the reliability and safety of UAV formation flight. Since the values of the control gains directly affect the flight quality of the UAV formation, the determination of reasonable control gains is a key problem in the consensus formation control. To solve this problem, based on the Linear Quadratic Regulator (LQR) control theory and the Riccati equation, the feedback control gains are optimized to generate the optimal trajectory commands for the UAVs, including the velocity magnitude, the flight path angle, and the heading angle commands. It is shown that the optimized consensus controller realizes the rapid formation flight control of the UAVs, with smooth control commands.
Joint Mode Selection and Task Assignment in Multi-Mode Sensor Network

Sensors with multiple functions and working modes constitute the nodes of the multi-mode sensor network. In this work, the mathematical model of task assignment problem in multi-mode sensor network is established. The model consists of task priorities, capability matching relationships, costs of tasks, resources of sensors and mode constraints. A two-stage scheme composed of joint mode selection and task assignment is proposed and evaluated. First, the cooperative working modes of sensors are determined with quantum-behaved particle swarm optimization (QPSO). Next, task assignment is performed with modified Hungarian algorithm under market-based assumption. Our proposed two-stage approach exhibits higher flexibility and achieves better tasks completion ratio compared with the benchmark of greedy policy, while the time complexity is much less than the conventional assignment scheme with PSO algorithm. According to the Monte Carlo simulation, the assignment of 20 sensors and 1000 tasks is completed in 0.225 seconds with the proposed strategy, while the completion percentage of tasks is 7% higher than those achieved with the greedy algorithm.
Deciphering The Emerging Role of Programmed Cell Death in Diabetic Wound Healing

Diabetic wounds are characterized by delayed and incomplete healing. As one of the most common complications of diabetes, diabetic wounds can be fatal in some cases. Programmed cell death (PCD) is an active and ordered cell death mode determined by genes, including apoptosis, autophagy, pyroptosis, necroptosis, ferroptosis, and cuproptosis. It is currently believed that PCD plays a crucial role in diabetic wound healing. Diabetic hyperglycemic environments can lead to abnormal PCD in various cells during healing processes, thereby affecting the activity and function of cells and interfering with diabetic wound healing. Therefore, this review focuses on the new roles and mechanisms of PCD in diabetic wound healing. Moreover, the challenges and perspectives related to PCD in diabetic wound healing are presented, which will bring new insights to improve diabetic wound healing.
Design of a Novel Low Cost Induction Assisted Interior Permanent Magnet Synchronous Motor for Electrified Vehicle Application

This paper introduces the concept of an Induction Assisted Interior Permanent Magnet Synchronous Motor (IAIPMSM) as a novel low cost solution for Electrified Vehicle (EV) application which reduces the magnet volume and thereby the overall cost of the machine. In the proposed configuration of this machine an induction motor is incorporated in the same machine frame of an Interior Permanent Magnet Synchronous Motor (IPMSM) by reducing the permanent magnet volume of the standard Toyota Prius 2004 IPMSM model. For the design of the proposed machine, the Toyota Prius 2004 IPMSM is taken as a baseline and the basic stator frame size, physical airgap length and the shaft diameter has been kept same. The stator of the designed machine has two windings wound with different number of poles to ensure magnetic decoupling between them. The current rating of both the windings is taken as half of the current rating of the baseline IPMSM stator winding. All the design details of the proposed machine and the Finite Element Method (FEM) based simulation results are presented and finally a comparison of different performance parameters and cost with the baseline machine is reported.
Enabling Resiliency Through Outage Management and Data-Driven Real-Time Aggregated DERs

The integration of distributed energy resources (DERs) has revolutionized the power distribution grids, transitioning them into a decentralized mode. While DERs bring numerous advantages, they also introduce the complexity and uncertainties to the distribution system operation, necessitating adaptable operational strategies to enhance system resilience. This article presents a pioneering approach that exploits the load and DERs in outage managements to enhance the distribution grid restoration. The proposed network equivalent model aggregates DERs and loads, preserving controllability information for effective real-time system outage management and restoration. A data-driven load model that combines an artificial neural network-based short-term load forecasting model and a delay exponential cold load pickup (CLPU) model is developed to compute the reliable safety margin for restoration and pick up the maximum number of loads during the restoration. Furthermore, an optimal restoration strategy is presented, encompassing restored network topology, controllable utility loads, and DERs set-point determination.
Joint Relay Selection and Power Allocation Based on Deep Neural Network in the Cooperative Relay-Eavesdropper Channel

The traditional cooperative communication algorithms show inherent limitations facing massive data processing and ultra-high-speed communication requirements in the future communication scenarios. A joint relay selection and power allocation optimization scheme based on deep neural network is introduced modeling the relay selection and power allocation problems as multi-classification and regression problems respectively. Taking all the channel state information (CSI) as the input feature, a multi-layer neural network is designed to complete relay selection and power allocation tasks jointly. The simulation results show that the joint relay selection and power allocation scheme based on deep neural network (DNN) not only achieves the secrecy rate close to the semi-definite relaxation (SDR) -based method, but also greatly reduces the complexity of signal processing to promisingly realize real-time secure communication. The SDR-based algorithm takes 123510s on 3000 test examples while DNN-based scheme only takes 3.6s.
Holocene sea level changes in Kelang and Kuantan, Peninsular Malaysia

A proper methodological approach of investigating Holocene sea level changes is a prerequisite in order that the sea-level index points can be useful and significant. This requires the correct identification of the indicative meaning. The indicative meaning of the sea level indicator is defined as the altitudinal relationship of the local environment in which it accumulated to the contemporaneous reference tide level. This study identifies the index points using the litho-, bio-, and chrono-stratigraphic approach. The sea level indicator is derived from the regressive contact of the intercalated peat and marine clastic sequence, while the indicative meaning is estimated based upon the relationship with the contemporary samples. The study was carried out at two contrasting coastal locations, the fossil sites from Meru and Mardi in Kelang in the west and Penur (north and south transects) in Kuantan in the east, while the contemporary sites are from various ecological environments from both areas. Microfossil analysis of pollen and diatoms indicates that the former are more applicable, and defined the changing microfossil assemblages within the regressive contact of the sea level indicator samples. Seven sea level index points are identified. The finding agrees to the general assumptions of high mid-Holocene sea level history in peninsular Malaysia. A relative sea level difference between the west and east coast is indicated, but the significance, if any, is dealt with caution. The explanations of presumed differential crustal movement or sheer age/altitude variability of the index points are suggested.
Optimal Impedance Research for Industrial Robot Based on Improved Hybrid-PSO

In order to improve the adaptability of impedance control to external forces, an optimal impedance control parameter identification method based on improved hybrid particle swarm optimization algorithm was proposed. Based on the closed-loop equation of the impedance control system of the manipulator, the optimal impedance control parameter expected damping matrix B and expected stiffness K is sought by the particle swarm optimization algorithm after the dynamics modeling of the robot. At the same time, a bionic rollback algorithm is proposed, which takes the behavior of natural bird blocks as inspiration, and reduces the possibility of the particle swarm optimization process entering the local optimal state, and the diversity of the population is increased and the convergence speed is accelerated. In this strategy, both the force on the end and the position of the end are used as the objective function to optimize the operation. Finally, the reinforcement learning platform Mujoco is used to simulate the Shenzhen Elephant P3 robot. The results show that compared with the traditional impedance control, this method can effectively reduce the force control error and position error.
Joint Sum Rate and Error Probability Optimization of Power Line Communication in Finite Blocklength Regime

PLC provides an effective solution for mass data transmission in the Power IoT. However, the application of power line communication in the Power IoT still faces the tradeoff problem of differential performance indicators. Our objective is to maximize the sum rate of all users while minimize the maximum decoding error probability by jiontly optimizing the precoding vector and decoding error probability. We first use the weighted sum method to transform the problem into a single objective form.Then, we propose a SCA-based method to solve the problem. The results reveal that compared with the benchmark method, the proposed method provides a significant SNR gain in PLC channel.The results show that the sum rate is increased by 51.7% compared with ZF method and 148.9% compared with MRT method when SNR is 40dB, and the maximum error probability is reduced to 11.6% of MRT method when SNR is 10dB.
Quantized Control for Highly Nonlinear Stochastic Systems with Multiple Delays under DoS Attacks

In this paper, the quantized control problem is discussed for a class of highly nonlinear stochastic systems with multiple delays under the DoS attacks. The coefficients are allowed to be highly nonlinear, and the control input is subject to the quantization effects and the DoS attacks. The aim is to deal with the stabilization problem for unstable highly nonlinear stochastic systems with multiple delays. The $p-\text{th}$ moment exponential stability and almost surely exponential stability are discussed in light of the Lyapunov theory. Finally, an illustrative example is given to verify the validity of the theoretical results.
Prescribed-Time Stabilization of Nonholonomic Chained Systems with Asymmetric Output Constraints

In this paper, the problem of prescribed-time stabilization is studied for a class of uncertain nonholonomic systems (NSs) in chained form with with asymmetric output constraints. First, a tan-type Barrier Lyapunov Function (BLF) is exploited to handle the obstacle caused by the asymmetric output constraints. Then, by suitably introducing the time-varying function into the virtual (actual) controllers, a non-scaling design scheme for state feedback is developed to forces the states of the closed-loop system (CLS) to zero in any prescribed finite time without disobeying the constraints. Finally, the effectiveness of the proposed scheme is confirmed by simulation results.
The ultrastructural nature of human oocytes’ cytoplasmatic abnormalities and the role of cytoskeleton dysfunction

Background Egg quality is a limiting factor of female fertility and assisted reproductive technology (ART) success. Oocytes recovered from hyperstimulated ovaries often display morphological anomalies suspected to compromise their fertilization and developmental potential. Knowledge of (ab)normal oocyte’s intracellular organization is vital to establish reliable criteria for morphological evaluation of oocytes intended for in vitro fertilization (IVF). Methods Transmission electron microscopy (TEM) was used to investigate the fine morphology of 22 dysmorphic IVF eggs exhibiting different types of cytoplasmic irregularities, namely (1) refractile bodies, (2) centrally-located cytoplasmic granularity (CLCG), (3) smooth endoplasmic reticulum (SER) disc, and (4) vacuoles. The cytoskeleton targeting compounds were employed to address the causative mechanism behind the anomalous cytoplasmic architecture observed in abnormal egg samples. A total of 133 immature oocytes were exposed to chemical inhibitors/control conditions, and their morphology was examined by fluorescent and electron microscopy. Results TEM exposed the structural basis of the common oocyte aberrations and revealed that the underlying cause of two of the studied morphotypes was excessive organelle clustering. Inhibition experiments showed that disruption of actin, not microtubules, allows inordinate aggregation of subcellular structures resembling the ultrastructural pattern seen in morphologically abnormal eggs retrieved in IVF cycles. These results imply that actin serves as a regulator of organelle distribution during human oocyte maturation. Conclusions The ultrastructural analogy between dysmorphic eggs and oocytes, in which actin network integrity was perturbed, suggests that malfunction of the actin cytoskeleton might be implicated in generating common cytoplasmic aberrations. Knowledge of human oocytes’ inner workings and the origin of morphological abnormalities is a step forward to more objective egg quality assessment in clinical practice.