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Information and communication technology (ICT) components, especially actuators in automated irrigation systems, are essential for managing precise irrigation and optimal soil moisture, enhancing orchard growth and yield. However, actuator malfunctions can lead to inefficient irrigation, resulting in water imbalances that impact crop health and reduce productivity. The objective of this study was to develop a signal processing technique to detect potential malfunctions based on the power consumption level and operating status of actuators for an automated orchard irrigation system. A demonstration orchard with four apple trees was set up in a 3 m × 3 m soil test bench inside a greenhouse, divided into two sections to enable independent irrigation schedules and management. The irrigation system consisted of a single pump and two solenoid valves controlled by a Python-programmed microcontroller. The microcontroller managed the pump cycling 'On' and 'Off' states every 60 s and solenoid valves while storing and transmitting sensor data to a smartphone application for remote monitoring. Commercial current sensors measured actuator power consumption, enabling the identification of normal and abnormal operations by applying threshold values to distinguish activation and deactivation states. Analysis of power consumption, control commands, and operating states effectively detected actuator operations, confirming reliability in identifying pump and solenoid valve failures. For the second solenoid valve in channel 2, with 333 actual instances of normal operation and 60 actual instances of abnormal operation, the model accurately detected 316 normal and 58 abnormal instances. The proposed method achieved a mean average precision of 99.9% for detecting abnormal control operation of the pump and solenoid valve of channel 1 and a precision of 99.7% for the solenoid valve of channel 2. The proposed approach effectively detects actuator malfunctions, demonstrating the potential to enhance irrigation management and crop productivity. Future research will integrate advanced machine learning with signal processing to improve fault detection accuracy and evaluate the scalability and adaptability of the system for larger orchards and diverse agricultural applications. [ABSTRACT FROM AUTHOR]

LiDAR sensors have great potential for enabling crop recognition (e.g., plant height, canopy area, plant spacing, and intra-row spacing measurements) and the recognition of agricultural working environments (e.g., field boundaries, ridges, and obstacles) using agricultural field machinery. The objective of this study was to review the use of LiDAR sensors in the agricultural field for the recognition of crops and agricultural working environments. This study also highlights LiDAR sensor testing procedures, focusing on critical parameters, industry standards, and accuracy benchmarks; it evaluates the specifications of various commercially available LiDAR sensors with applications for plant feature characterization and highlights the importance of mounting LiDAR technology on agricultural machinery for effective recognition of crops and working environments. Different studies have shown promising results of crop feature characterization using an airborne LiDAR, such as coefficient of determination (R2) and root-mean-square error (RMSE) values of 0.97 and 0.05 m for wheat, 0.88 and 5.2 cm for sugar beet, and 0.50 and 12 cm for potato plant height estimation, respectively. A relative error of 11.83% was observed between sensor and manual measurements, with the highest distribution correlation at 0.675 and an average relative error of 5.14% during soybean canopy estimation using LiDAR. An object detection accuracy of 100% was found for plant identification using three LiDAR scanning methods: center of the cluster, lowest point, and stem–ground intersection. LiDAR was also shown to effectively detect ridges, field boundaries, and obstacles, which is necessary for precision agriculture and autonomous agricultural machinery navigation. Future directions for LiDAR applications in agriculture emphasize the need for continuous advancements in sensor technology, along with the integration of complementary systems and algorithms, such as machine learning, to improve performance and accuracy in agricultural field applications. A strategic framework for implementing LiDAR technology in agriculture includes recommendations for precise testing, solutions for current limitations, and guidance on integrating LiDAR with other technologies to enhance digital agriculture. [ABSTRACT FROM AUTHOR]

Drip irrigation pressure is considered a key parameter for controlling and designing the drip irrigation system in sandy soils. Understanding soil water content (SWC) movements under varying pressures can enhance water use efficiency and support sustainable irrigation strategies for crops in arid regions. The objectives of this study were to investigate the effects of irrigation pressure on the spatial, vertical, and temporal variability of SWC in sandy loam soil using surface drip irrigation. Experiments were carried out in a soil bin located in a greenhouse. SWC sensors were placed at depths 10, 20, 30, 40, and 50 cm to monitor SWC variability under low, medium, and high drip irrigation pressures (25, 50, and 75 kPa) at a constant emitter flow rate of 3 L/h. A pressure controller was used to regulate drip irrigation pressure, while microcontrollers communicated with SWC sensors, collected experimental data, and automatically recorded the outputs. At low irrigation pressure, water content began to increase at 0.53 h and saturated at 3.5 h, with both values being significantly lower at medium and high pressures. The results indicated that lower pressures led to significant variability in water movement at shallow depths (10 to 30 cm), becoming uniform at deeper layers but requiring longer irrigation times. Competitively higher pressures showed uniform water distribution and retention statistically throughout the soil profiles with shorter irrigation times. The variation in water distribution resulting in non-uniform coverage across the irrigated area demonstrates how pressure changes affect the flow rate of the emitter. The results provide information maps with soil water data that can be adjusted with irrigation pressure to maximize water use efficiency in sandy loam soils, aiding farmers in better irrigation scheduling for different crops using surface drip irrigation techniques in arid environments. [ABSTRACT FROM AUTHOR]

BACKGROUND Monozygotic (MZ) twins are believed to arise from the fission of a single fertilized embryo at different stages. Monochorionic MZ twins, who share one chorion, originate from the splitting of the inner cell mass (ICM) within a single blastocyst. In the classic model for dichorionic MZ twins, the embryo splits before compaction, developing into two blastocysts. However, there are a growing number of ART cases where a single blastocyst transfer results in dichorionic MZ twins, indicating that embryo splitting may occur even after blastocyst formation. OBJECTIVE AND RATIONALE For monochorionic MZ twins, we conducted a comprehensive analysis of the cellular mechanisms involved in ICM splitting, drawing from both ART cases and animal experiments. In addition, we critically re-examine the classic early splitting model for dichorionic MZ twins. We explore cellular mechanisms leading to two separated blastocysts in ART, potentially causing dichorionic MZ twins. SEARCH METHODS Relevant studies including research articles, reviews, and conference papers were searched in the PubMed database. Cases of MZ twins from IVF clinics were found by using combinations of terms including 'monozygotic twins' with 'IVF case report', 'ART', 'single embryo transfer', or 'dichorionic'. The papers retrieved were categorized based on the implicated mechanisms or as those with unexplained mechanisms. Animal experiments relating to MZ twins were found using 'mouse embryo monozygotic twins', 'mouse 8-shaped hatching', 'zebrafish janus mutant', and 'nine-banded armadillo embryo', along with literature collected through day-to-day reading. The search was limited to articles in English, with no restrictions on publication date or species. OUTCOMES For monochorionic MZ twins, ART cases and mouse experiments demonstrate evidence that a looser ICM in blastocysts has an increased chance of ICM separation. Physical forces facilitated by blastocoel formation or 8-shaped hatching are exerted on the ICM, resulting in monochorionic MZ twins. For dichorionic MZ twins, the classic model resembles artificial cloning of mouse embryos in vitro , requiring strictly controlled splitting forces, re-joining prevention, and proper aggregation, which allows the formation of two separate human blastocysts under physiological circumstances. In contrast, ART procedures involving the transfer of a single blastocysts after atypical hatching or vitrified-warmed cycles might lead to blastocyst separation. Differences in morphology, molecular mechanisms, and timing across various animal model systems for MZ twinning can impede this research field. As discussed in future directions, recent developments of innovative in vitro models of human embryos may offer promising avenues for providing fundamental novel insights into the cellular mechanisms of MZ twinning during human embryogenesis. WIDER IMPLICATIONS Twin pregnancies pose high risks to both the fetuses and the mother. While single embryo transfer is commonly employed to prevent dizygotic twin pregnancies in ART, it cannot prevent the occurrence of MZ twins. Drawing from our understanding of the cellular mechanisms underlying monochorionic and dichorionic MZ twinning, along with insights into the genetic mechanisms, could enable improved prediction, prevention, and even intervention strategies during ART procedures. REGISTRAITON NUMBER N/A. [ABSTRACT FROM AUTHOR]

A method for Underdetermined Blind Source Separation is proposed using third‐order cumulants and tensor compression. To effectively suppress symmetrical distributed noise, the third‐order cumulant is considered. Additionally, the complexity of high‐dimensional tensors can be reduced through high order singular value decomposition (HOSVD) for compression purposes. The method begins by calculating the third‐order cumulant tensor for whitening signals at different time delays, and then stacks several cumulants into a fourth‐order tensor. The HOSVD decomposition is applied to the fourth‐order tensor, compressing the high‐dimensional tensor into a low‐dimensional core tensor. Next, the core tensor is further decomposed using the canonical polyadic decomposition, and the resulting factor matrices are fused to obtain an estimation of the mixed matrix. Finally, leveraging the signal independence, a matrix diagonalisation method is employed to recover the source signals. Theoretical analysis and simulation results demonstrate that the proposed method effectively suppresses the influence of Gaussian noise, reduces computational complexity, and saves computational time. Moreover, compared with five representative approaches, the proposed method achieves superior separation results. Specifically, for the 3 × 4 mixed model with a signal‐to‐noise ratio of 20 dB, the average relative error of speech signal and radio signal are −11.02 and −6.8 dB respectively. [ABSTRACT FROM AUTHOR]

Demand and market value for pennywort largely depend on the quality of the leaves, which can be affected by various ambient environment or fertigation variables during cultivation. Although early detection of defects in pennywort leaves would enable growers to take quick action, conventional manual detection is laborious and time consuming as well as subjective. Therefore, the objective of this study was to develop an automatic leaf defect detection algorithm for pennywort plants grown under controlled environment conditions, using machine vision and deep learning techniques. Leaf images were captured from pennywort plants grown in an ebb-and-flow hydroponic system under fluorescent light conditions in a controlled plant factory environment. Physically or biologically damaged leaves (e.g., curled, creased, discolored, misshapen, or brown spotted) were classified as defective leaves. Images were annotated using an online tool, and Mask R-CNN models were implemented with the integrated attention mechanisms, convolutional block attention module (CBAM) and coordinate attention (CA) and compared for improved image feature extraction. Transfer learning was employed to train the model with a smaller dataset, effectively reducing processing time. The improved models demonstrated significant advancements in accuracy and precision, with the CA-augmented model achieving the highest metrics, including a mean average precision (mAP) of 0.931 and an accuracy of 0.937. These enhancements enabled more precise localization and classification of leaf defects, outperforming the baseline Mask R-CNN model in complex visual recognition tasks. The final model was robust, effectively distinguishing defective leaves in challenging scenarios, making it highly suitable for applications in precision agriculture. Future research can build on this modeling framework, exploring additional variables to identify specific leaf abnormalities at earlier growth stages, which is crucial for production quality assurance. [ABSTRACT FROM AUTHOR]

In order to reduce the probability of leakage and improve detection accuracy, multiple radar equipment is required to perform multiple detections on the same airspace. Due to the overlap of airspace, there are multiple local tracks reported by various radar equipment that belong to the same target. The local tracks reported by each radar require track fusion to form a system track, and track association is a prerequisite for track fusion. This paper proposes a multi-target and multi-sensor track association analysis method, which can distinguish whether local tracks come from the same target and associate local tracks with the same target, providing conditions for track fusion to form system tracks. This method divides track association into two steps: Coarse association and fine association, which greatly improves the accuracy of track association. [ABSTRACT FROM AUTHOR]

Ballistic missile attack-defense confrontation is a typical system-of-systems confrontation process. Ballistic missile early warning operations have the characteristics of high time-sensitivity, high complexity and high dynamic. It is impossible to deal with complex battlefield situations at the moment of decision. It is necessary to develop a detection plan in advance for possible battlefield situations, and intelligently recommend the optimal detection plan for the commander to make decisions based on the battlefield situation. The ballistic missile early warning detection plan is designed from three aspects: the design of search screen parameters, the determination of search data rate, and the allocation strategy of search resources in different airspace; Establish the evaluation indicator system and evaluation model of the ballistic missile early warning detection plan, evaluate and optimize multiple detection plans, and automatically recommend them to the commander according to the advantages and disadvantages of the operational effectiveness, connect the auxiliary decision-making chain of "design evaluation recommendation decision", providing support for the intelligent decision-making of the ballistic missile early warning operation. [ABSTRACT FROM AUTHOR]

Blind Source Separation (BSS) of complex signals composed of radar, communication and jamming signals is the first step in an integrated electronic system, which requires higher accuracy of separation. However, the traditional Single‐Channel Blind Source Separation (SCBSS) method has low separation accuracy and poor robustness. Aiming at this problem, this paper proposes a SCBSS method based on spatial‐temporal fusion deep learning model. This is a deep neural network model, which realizes spatial‐temporal of mixed signals by integrating Convolutional Neural Network (CNN) and Bidirectional Long Short‐Term Memory Network (BiLSTM). Convolutional Neural Network is used to extract spatial features from input sequences, and BiLSTM is used to mine timing rules of signals. A batch normalisation layer and a dropout layer are added to improve stability and prevent overfitting. The experiments show that the average similarity coefficient of the separated signals is above 0.99 and the Signal‐Distortion Ratio (SDR) is up to 27 dB without noise. When the Signal‐Noise Ratio is 0–20 dB and Jamming‐Signal Ratio is 15 dB, the SDR is 5–30 dB higher than the traditional methods and the single network structure deep learning methods. [ABSTRACT FROM AUTHOR]