Weak Capability Research Articles

The role of financing small agricultural projects in increasing production and reducing unemployment in Iraq for the period 1985-2015

The agricultural sector in Iraq is characterized by the presence of tens of thousands of small projects, some of which are due to agricultural ownership and tenure laws, and the other part is due to inheritance laws, while there are several thousand small agricultural projects due to agricultural reform laws, as well as the presence of several thousand poultry fields, fish lakes and cow stations, which It contributes to the provision of food at high rates and its contribution to the formation of the gross domestic product and its employment for tens of thousands of workers and depends on the state in the financing process through the agricultural bank mainly. The process of financing small projects is an urgent necessity in order to serve agricultural development due to the weak financial capabilities of the owners of small projects, so you find that when granted loans, they promote production and employment of the unemployed, while in periods of low financing and loans, production decreases due to the suspension of thousands of agricultural, plant and animal projects, leaving behind them. Thousands of unemployed people, so the financing is the dynamo that drives the activity of small agricultural projects.

LittleFaceNet: A Small-Sized Face Recognition Method Based on RetinaFace and AdaFace.

For surveillance video management in university laboratories, issues such as occlusion and low-resolution face capture often arise. Traditional face recognition algorithms are typically static and rely heavily on clear images, resulting in inaccurate recognition for low-resolution, small-sized faces. To address the challenges of occlusion and low-resolution person identification, this paper proposes a new face recognition framework by reconstructing Retinaface-Resnet and combining it with Quality-Adaptive Margin (adaface). Currently, although there are many target detection algorithms, they all require a large amount of data for training. However, datasets for low-resolution face detection are scarce, leading to poor detection performance of the models. This paper aims to solve Retinaface's weak face recognition capability in low-resolution scenarios and its potential inaccuracies in face bounding box localization when faces are at extreme angles or partially occluded. To this end, Spatial Depth-wise Separable Convolutions are introduced. Retinaface-Resnet is designed for face detection and localization, while adaface is employed to address low-resolution face recognition by using feature norm approximation to estimate image quality and applying an adaptive margin function. Additionally, a multi-object tracking algorithm is used to solve the problem of moving occlusion. Experimental results demonstrate significant improvements, achieving an accuracy of 96.12% on the WiderFace dataset and a recognition accuracy of 84.36% in practical laboratory applications.

In Situ Growth of Metal-Organic Layer on Polyoxometalate-etching Cu2O to Boost CO2 Reduction with High Stability.

Low-cost Cu2O with a suitable band gap holds great potential for solar utilization. However severe photocorrosion and weak CO2 capture capability have significantly hindered their application in artificial photosynthesis. Herein, polyoxometalate (POM)-etching and in situ growth of metal-organic framework (MOF) can simultaneously incorporate electron-sponge and HKUST protective layer into Cu2O. The resulting ternary composites Cu2O@POM@HKUST-n (POM = PMo12O40 and PW12O40) with dual hetero-interfaces can efficiently convert CO2 to HCOOH with 5226 µmol g-1 yield, over 5 and 55 times higher than that of Cu2O (1010 µmol g-1) and Cu2O@HKUST (95.02 µmol g-1). In situ XPS and DFT studies reveal that Cu mainly existed in the form of Cu2O and Cu-MOF, while a unique Cux+ (1< x ≤2) surface layer formed upon the Cu2O matrix surrounding POMs for CO2 absorption and activation. Systematic investigations demonstrate that the electron-sponge can efficiently capture electrons from excited Cu2O to promote the generation of a Cux+ surface layer, while the closely surface-coating metal-organic layer can act as protective layer and CO2 adsorbent. This dual function concurrently contributes to promote photocatalysis and prevent Cu2O degradation. Remarkably, the ternary composites exhibit much enhanced photochemical stability and can be used for over 60 h without noticeable activity loss.

Optimization of cold chain logistics distribution path considering traffic condition and replenishment along the way.

Road traffic congestion on the cold chain logistics not only increase the cost and time, but also creates certain negative impact on the national carbon emissions. To fully utilize the traffic resources, this study has classified urban road traffic congestion and defined the various vehicle delivery speeds with dynamic congestion levels. Simultaneously, it has developed the cold chain products replenishment strategy by considering delivery route, multi-depot condition and even vehicle types, aiming to minimize the total cost and carbon emissions, and maximizing the cold chain products freshness. To achieve this, this study build up a multi-objective vehicle routing optimization model and designed a hybrid algorithm combining large-scale neighborhood search and NAGA-II. Through computational analysis, this algorithm effectively overcomes the weak local search capability of NAGA-II and efficiently solves multi-objective problems. Moreover, under the simulated random traffic congestion conditions, this model able to demonstrate relatively stable planning results and address complex road traffic situations. Finally, this study able to analyze the impacts of various replenishment strategies, by considering multiple depots and sensitivity coefficients of cold chain products from delivery objectives. The analysis results also provides valuable insights for actual cold chain logistics distribution industry.

Household Sector Indebtedness and Its Impact on Financial Stability Indicators in Egypt

The research examines with studying the problematic relationship between the indebtedness of the family sector and financial stability by studying the facts provided by economic theory, research and studies, and then studying the case of Egypt by collecting and analyzing the facts and data related to the studied variables, and the problem of the research is to question the impact of the indebtedness of the family sector on financial stability in Egypt, and the hypothesis of the research is that the developments of financial globalization and financial openness, and by collecting and analyzing the facts Economic and related historical data, as well as the adoption of the inductive approach by making hypotheses among the studied relationships, and analyzing the relationship between variables .The research reached a set of conclusions, the most important of which is the weak impact of family sector indebtedness on financial stability due to the limited amount of bank financing for the family sector and the structural imbalance in the relationship between family spending and bank credit, and the weak capabilities of the private sector in accessing and employing finance , and the most prominent recommendations were the need to follow an economic policy to enhance the role of the family sector in economic growth by increasing the credit provided to the family sector to enhance economic growth According to the levels of household debt commensurate with the requirements of sustainable financial stability.

CHALLENGES AND STRATEGIES TO ENHANCE COLLEGE STUDENTS' INNOVATION AND ENTREPRENEURSHIP IN CHINA

In recent years, driven by the wave of innovation and entrepreneurship, universities have increasingly focused on fostering college students' innovative undertakings. However, the development of innovation and entrepreneurship education in China began relatively late and still exhibits numerous shortcomings. College students' awareness of innovation and entrepreneurship remains weak, their innovative and entrepreneurial capabilities are underdeveloped, and the scarcity of qualified teaching resources continues to be a significant barrier to progress in this field. These factors collectively hinder the advancement of innovation and entrepreneurship among college students. This paper employs the interview method, selecting three representative college students from local universities to explore the current state of innovation and entrepreneurship from multiple perspectives, including the students themselves, society, family, and government. Using ATLAS.ti software, the study analyzes internal and external factors and proposes targeted strategies to effectively enhance college students' innovation and entrepreneurial capabilities. KEYWORDS: Current Situation of Innovation and Entrepreneurship, College Students, Influencing Factors, Countermeasures

Video Anomaly Detection with Hyperbolic Graph Embedding and Masked Normalizing Flows

Video anomaly detection plays a vital role in intelligent video monitoring systems. It has found extensive applications in the fields of public safety and social security. Nevertheless, the area of video anomaly detection continues to be a formidable task because of the intricate nature of actual data and the challenge of precisely identifying anomalies. Current anomaly detection approaches suffer from overpowered generalization ability, weak spatio-temporal feature extraction capability, and insufficient global information aggregation ability. Therefore, an unsupervised Hyperbolic Graph-based Normalizing Flows (HGNF) model is proposed in this paper, which is constructed with a Spatio-temporal Encoder (STEncoder) and stacked normalizing flows to reduce the overgeneralization of auto-encoder-based anomaly detection models. STEncoder consists of spatio-temporal attention and inter-frame feature aggregation. In normalizing flows, a Poincaré ball graph extractor is developed to improve the representation ability of the dynamic changes of the input data, and a masked affine coupling block is established to improve the performance of this model in global information aggregation. According to the experimental results obtained on four public datasets, HGNF achieves excellent performance and the best AUC (Area Under the Curve) score of 74.5% on the UBnormal dataset.

A Novel Intelligent Fault Diagnosis Method of Rolling Bearings Based on the ConvNeXt Network with Improved DenseBlock.

Rolling bearings are critical rotating components in machinery and equipment; they are essential for the normal operation of such systems. Consequently, there is a pressing need for a highly efficient, applicable, and reliable method for bearing fault diagnosis. Currently, one-dimensional data-driven fault diagnosis methods, which rely on one-dimensional data, represent a mainstream approach in this field. However, these methods exhibit weak diagnostic capabilities in noisy environments and when confronted with insufficient sample sizes. In order to solve these limitations, a new fault diagnosis method for rolling bearings is proposed, which combines the ConvNeXt network and improved DenseBlock into a parallel network with a feature fusion function. The network can fully extract the global feature and the detail feature of the signal and integrate them, which shows a good diagnostic ability in the face of a strong noise environment. Additionally, the Dy-ReLU function is introduced into the network, which enhances the generalization ability of the network and improves the convergence speed. Comparative experiments show that this method still has strong fault diagnosis capability under the condition of noise pollution and insufficient training samples.

Zero-Dimensional Lead-Free Perovskite Single Crystals for the Sensitive Detection of Visible Light and X-Rays.

The excellent performance and facile fabrication process of perovskite semiconductor materials make them highly promising candidates in the field of optoelectronic devices. Whether for visible light or X-rays, detectors with low detection limits can better identify weak light signals, significantly reducing the intensity of the received light source during device operation. In particular, for X-ray detectors, reducing X-ray dosage can greatly enhance the safety of X-ray imaging technology. This work develops detectors with low detection limit based on zero-dimensional (0D) MA3Bi2I9 single crystals. The anisotropy of MA3Bi2I9 and its influence on the detector's performance were investigated in detail. Detectors perpendicular to the (001) crystal plane have better weak light detection capability and require significantly less light intensity compared to devices parallel to the (001) crystal plane. X-ray detector with a limit of detection (LoD) of 0.68 nGyair s-1 and sensitivity of 5072 μC Gyair -1 cm-2 was obtained. The present study offers comprehensive insights into the operational mechanism of layered perovskites, which can provide valuable guidelines for future designs and fabrications of highly sensitive photo and X-ray detectors.

Structure-Induced Energetic Coordination Compounds as Additives for Laser Initiation Primary Explosives.

Laser ignition of primary explosives presents more reliable alternative to traditional electrical initiation methods. However, the commercial initiator lead azide (LA) requires a high-power density laser to detonate, with the minimum laser initiation energy (Emin) of 2402 mJ. Currently, the laser-ignitable metal complex-based igniters still suffer from weak detonation capabilities and high Emin values. Here, the approach is first proposed to design laser ignition primary explosives within the high energy azide and tetrazole-based energetic coordination compounds (ECCs), [Co(N3)(2-bmttz)(H2O)]2 1 and [Co(N3)(2-bmttz)(MeOH)]2 2 as additives to LA. Material 1e with 4wt.% of 1 in LA, exhibits ultra-low laser initiation threshold (Emin = 1.6 mJ) and ultrafast corresponding time (Tmin = 0.2ms). Specially, compared to LA, the threshold of 1e is as low as 1/1500 of that of LA. Moreover, 30mg 1e successfully detonates RDX with a laser energy of 1.6 mJ. Theoretical calculations and experiment results reveal that 1 exhibits the superior additive effect compared to 2, attributed to its more enhanced ability to generate free radicals and higher photothermal conversion efficiency under laser conditions. This work represents a paradigm shift, with the potential to develop a laser-driven micro-detonator combining powerful detonation capabilities with exceptionally low laser initiation energy.

Study on flexural resilience of composite foam sandwich structures under hygrothermal environment

Under hygrothermal environments, the structural stability and strength of all-fiber composite aircraft are significantly affected during long-term flight use. The wing skin, as a critical structural component, plays a vital role in bearing and transmitting aerodynamic loads. Therefore, it is crucial to investigate the structural compressive stability and strength of the wing skin throughout the aircraft's entire life cycle under these conditions. This study employs a real wing carbon fiber foam sandwich structure to investigate the compressive stability and strength of the wing skin structure of a new energy aircraft under actual flight conditions, specifically during the entire process of the room temperature dry state (RTD) and elevated temperature wet state (ETW). Initially, three-point bending tests were conducted on carbon fiber reinforced polymer (CFRP) laminates, foam cores, and CFRP reinforced foam sandwich structures. The CFRP laminates fully rebounded after bending damage in both the RTD and ETW environments. While CFRP reinforced foam sandwich structures also rebounded fully in the RTD environment, their rebound performance diminished in hygrothermal conditions due to the thermoplastic mobility of the foam cores, resulting in only weak rebound capabilities. In hygrothermal environments, the thermoplastic mobility of the foam core leads to diminished resilience after bending damage, resulting in only weak rebound capabilities. Subsequently, compressive instability tests were conducted on the wing skin foam sandwich structure. The results indicated that the basic test study effectively predicted the structural test outcomes. Structural components in the RTD environment exhibited overall flexural instability under compressive load, with damage morphology resembling a circular curve; the damaged specimens fully rebounded after unloading. Conversely, specimens in the ETW environment displayed localized instability, characterized by a wrinkled damage profile, resulting in only weak rebound capabilities after unloading.

The Interaction and Sustainable Efficiency Between Tourism Systems and the Energy–Economy–Environment System: A Novel Parallel Network Super-Efficiency Slacks-Based Measure Model

Sustainable tourism entails balancing economic growth, environmental protection, and energy utilisation. However, the current interactive dynamics between urban agglomeration tourism systems and the energy–economy–environment (EEE) system, as well as the sustainable efficiency of the tourism–energy–economy–environment (TEEE) system, remain unclear. For the first time, this study employs a super-efficiency network slacks-based measure (SE-NSBM) model to interact with tourism and EEE systems, proposing a novel network structure that includes feedback variables. To validate the proposed model, this study evaluated the efficiency of the TEEE system and its subsystems in the Yangtze River Delta urban agglomeration (YRDUA) from 2016 to 2020. The study revealed significant variations in the efficiency of the TEEE, tourism, and EEE systems among cities in the YRDUA, with a discernible downward trend. The TEEE and tourism systems exhibit relatively weak coping capabilities when faced with significant unforeseen events, highlighting the urgent need to strengthen system resilience. The results also demonstrate that the new model effectively resolves the issue of efficiency overestimation in the TEEE system observed in traditional models, which tend to overestimate actual efficiency by 3%. The novel model and empirical results offer decision-makers new perspectives and practical insights into formulating sustainable tourism policies.

A novel bearing weak fault diagnosis method based on rank constrained low-rank and sparse decomposition

Abstract Addressing the challenge of diagnosing incipient bearing faults amidst significant noise, a novel diagnostic approach is introduced, leveraging a Rank Constrained Low-Rank and Sparse Decomposition (RCLRSD) model tailored for weak fault detection in bearings. Initially, we raised the Autocorrelation Function of the Square Envelope in Frequency Domain as an innovative method for the estimation of fault frequencies. Subsequently, we constructed a two-dimensional observation matrix, which is formulated independently of predefined assumptions. Then, we examine the configuration and distribution patterns of bearing fault signals, uncovering the low-rank nature of fault characteristics within a designated two-dimensional transform domain. Moreover, we found the noise signal to exhibit sparsity and an approximate Gaussian distribution. Based on this, a rank-constrained low-rank sparse decomposition model is established, and rank-constrained low-rank regular constraints for feature information and sparse regular constraints and Gaussian constraints for interference signals are constructed respectively. Ultimately, we employed the Gray Wolf Optimization algorithm to refine the model parameters, and we deduced the model’s solver via the Alternating Direction Method of Multipliers. The proposed RCLRSD model decomposes bearing fault data into three constituents: low-rank, sparse, and Gaussian components, effectively addressing the challenge of extracting weak fault signatures from bearings. The weak feature extraction capability of the RCLRSD model is verified using a multi-interference simulation model and experimental data of bearing failures under strong noise conditions; the generalization of the model is verified by the classification effect of the Support Vector Machine under two bearing failure datasets. Comparison with various algorithms confirms the superiority of the proposed algorithm.

A Novel Dataset and Detection Method for Unmanned Aerial Vehicles Using an Improved YOLOv9 Algorithm.

With the growing popularity of unmanned aerial vehicles (UAVs), their improper use is significantly disrupting society. Individuals and organizations have been continuously researching methods for detecting UAVs. However, most existing detection methods fail to account for the impact of similar flying objects, leading to weak anti-interference capabilities. In other words, when such objects appear in the image, the detector may mistakenly identify them as UAVs. Therefore, this study aims to enhance the anti-interference ability of UAV detectors by constructing an anti-interference dataset comprising 5062 images. In addition to UAVs, this dataset also contains three other types of flying objects that are visually similar to the UAV targets: planes, helicopters, and birds. This dataset can be used in model training to help detectors distinguish UAVs from these nontarget objects and thereby improve their anti-interference capabilities. Furthermore, we propose an anti-interference UAV detection method based on YOLOv9-C in which the dot distance is used as an evaluation index to assign positive and negative samples. This results in an increased number of positive samples, improving detector performance in the case of small targets. The comparison of experimental results shows that the developed method has better anti-interference performance than other algorithms. The detection method and dataset used to test the anti-interference capabilities in this study are expected to assist in the development and validation of related research methods.

Signal Fusion of Multi-Frequency Continuous Wave Ground Penetrating Radar

Abstract Carrier-less pulse radar, with its short pulse duration, low average power, and weak anti-interference capability, poses significant limitations to the application of ground penetrating radar (GPR). Fusing continuous wave signals from multiple frequency components forms the basis for the design and data interpretation of continuous wave radar. We firstly introduce two fusion methods: the application of the Inverse Fast Fourier Transform (IFFT) to multi-frequency signals and direct superposition. And we demonstrate these methods by synthesizing three basic sinusoidal signals and analyse their time-frequency relationship. Furthermore, we perform Fast Fourier Transform (FFT) on the transmit waveform of pulse radar to obtain a set of frequency components. By superimposing three sets of frequency components with different numbers of frequency points, we obtain the fused waveform and compare it with the original Ricker wavelet. The results show that while reducing the number of frequency points may increase the error in the synthesized signal slightly, it remains capable of accurately synthesizing the Ricker wavelet. However, this reduction also decreases the time window of the synthesized signal and may result in temporal overlapping. These findings offer insights into selecting parameters for frequency components in multi-frequency continuous wave radar and lay a foundation for signal fusion in such radar systems.

RSPS-SAM: A Remote Sensing Image Panoptic Segmentation Method Based on SAM

Satellite remote sensing images contain complex and diverse ground object information and the images exhibit spatial multi-scale characteristics, making the panoptic segmentation of satellite remote sensing images a highly challenging task. Due to the lack of large-scale annotated datasets for panoramic segmentation, existing methods still suffer from weak model generalization capabilities. To mitigate this issue, this paper leverages the advantages of the Segment Anything Model (SAM), which can segment any object in remote sensing images without requiring any annotations and proposes a high-resolution remote sensing image panoptic segmentation method called Remote Sensing Panoptic Segmentation SAM (RSPS-SAM). Firstly, to address the problem of global information loss caused by cropping large remote sensing images for training, a Batch Attention Pyramid was designed to extract multi-scale features from remote sensing images and capture long-range contextual information between cropped patches, thereby enhancing the semantic understanding of remote sensing images. Secondly, we constructed a Mask Decoder to address the limitation of SAM requiring manual input prompts and its inability to output category information. This decoder utilized mask-based attention for mask segmentation, enabling automatic prompt generation and category prediction of segmented objects. Finally, the effectiveness of the proposed method was validated on the high-resolution remote sensing image airport scene dataset RSAPS-ASD. The results demonstrate that the proposed method achieves segmentation and recognition of foreground instances and background regions in high-resolution remote sensing images without the need for prompt input, while providing smooth segmentation boundaries with a panoptic segmentation quality (PQ) of 57.2, outperforming current mainstream methods.

Point-MPP: Point Cloud Self-Supervised Learning From Masked Position Prediction.

Masked autoencoding has gained momentum for improving fine-tuning performance in many downstream tasks. However, it tends to focus on low-level reconstruction details, lacking high-level semantics and resulting in weak transfer capability. This article presents a novel jigsaw puzzle solver inspired by the idea that predicting the positions of disordered point cloud patches provides more semantic information, similar to how children learn by solving jigsaw puzzles. Our method adopts the mask-then-predict paradigm, erasing the positions of selected point patches rather than their contents. We first partition input point clouds into irregular patches and randomly erase the positions of some patches. Then, a Transformer-based model is used to learn high-level semantic features and regress the positions of the masked patches. This approach forces the model to focus on learning transfer-robust semantics while paying less attention to low-level details. To tie the predictions within the encoding space, we further introduce a consistency constraint on their latent representations to encourage the encoded features to contain more semantic cues. We demonstrate that a standard Transformer backbone with our pretraining scheme can capture discriminative point cloud semantic information. Furthermore, extensive experiments indicate that our method outperforms the previous best competitor across six popular downstream vision tasks, achieving new state-of-the-art performance. Codes will be available at https://git.openi.org.cn/OpenPointCloud/Point-MPP.

Physics-Informed Neural Network (PINN) model for predicting subgrade settlement induced by shield tunnelling beneath an existing railway subgrade

For the shield tunnelling beneath an existing railway subgrade, factors such as disturbance during shield tunnel excavation can cause settlements of the existing subgrade above. Traditional prediction methods for subgrade settlement may not fully reflect the construction site conditions. Recently, machine learning has been widely applied due to its fast computation speed and accurate results. However, data-driven machine learning is considered a “black-box” algorithm, lacking a mechanical theory framework and having weak generalization capabilities. To address the limitations of machine learning and enhance the efficiency and accuracy of subgrade settlement prediction during shield tunnel construction, this paper proposes a prediction model based on a physics-informed neural network (PINN). Firstly, by integrating the non-uniform displacement mode function of the tunnel, the physical equations are constructed for the shield tunnel excavation process. Subsequently, the physical equations are coupled with the machine learning framework to develop a predictive model for shield tunnelling beneath an existing railway subgrade based on the PINN model. The performance of the proposed PINN model is evaluated through the application of two engineering cases and compared with traditional numerical simulations and data-driven machine learning. The results indicate that the proposed model can not only predict the settlement of the existing railway subgrade under scant and noisy monitoring data but also have accuracy in the inversion of tunnel displacement mode parameters. Finally, parametric analyses are conducted to reveal the influence of critical variables on the prediction performance of the PINN model.

CJE-PCHF: Chinese Joint Entity and Relation Extraction Model Based on Progressive Contrastive Learning and Heterogeneous Feature Fusion

The joint extraction of entities and relations is a critical task in information extraction, and its performance directly affects the performance of downstream tasks. However, existing joint extraction models based on deep learning exhibit weak processing capabilities for the phenomenon of multiple pronunciations of one character and multiple characters of one pronunciation when processing Chinese texts, resulting in a performance loss. To address these issues, this paper introduces part-of-speech (POS) and pinyin features to aid the model in learning semantic features that are more contextually appropriate. We propose a Chinese Joint Entity and Relation Extraction Model based on progressive contrastive learning and heterogeneous feature fusion (CJE-PCHF). During model training, an interactive fusion network based on progressive contrastive learning is employed to learn the dependencies between pinyin, POS, and semantic features. This guides the model in heterogeneous feature fusion, capturing higher-order semantic associations between heterogeneous features. On the commonly used DuIE evaluation dataset for joint extraction, our model achieved a significant improvement, with the F1 score increasing by 5.4% compared to the benchmark model CasRel.

Fabrication of hexagonal platelets of Fe3O4/Fe2O3/C modified with polyacrylamide for excellent electromagnetic wave absorption

In order to improve the weak dielectric loss capability and poor impedance matching of Fe3O4, the hexagonal plates of Fe3O4/Fe2O3/C nanocomposites were rationally constructed in this paper by utilizing electrostatic attraction and cross-linking, using polyacrylamide (PAM) as templating agent and viscosity modifier. The stacked hexagonal plates constructed a conductive network and utilized the structural properties of Fe3O4 to promote the transfer and jump of free electrons, which effectively improved the conduction loss. The heterogeneous interface was effectively constructed due to the presence of Fe2O3 and trace carbon, while the transfer and leap of free electrons promoted the generation of interfacial polarization, resulting in magnetoelectric coupling and effective improvement of polarization loss. The results show that the effective absorption broadband reaches 4.08 GHz when the matching thickness of the composite is 2 mm. A minimum reflection loss of −43.79 dB is realized at a matching thickness of 1 mm. The PAM modification proved to be an effective means to modulate the morphology and composition of the materials.