Accurate Targeting Research Articles

Maritime Electro-Optical Image Object Matching Based on Improved YOLOv9

The offshore environment is complex during automatic target annotation at sea, and the difference between the focal lengths of visible and infrared sensors is large, thereby causing difficulties in matching multitarget electro-optical images at sea. This study proposes a target-matching method for visible and infrared images at sea based on decision-level topological relations. First, YOLOv9 is used to detect targets. To obtain markedly accurate target positions to establish accurate topological relations, the YOLOv9 model is improved for its poor accuracy for small targets, high computational complexity, and difficulty in deployment. To improve the detection accuracy of small targets, an additional small target detection head is added to detect shallow feature maps. From the perspective of reducing network size and achieving lightweight deployment, the Conv module in the model is replaced with DWConv, and the RepNCSPELAN4 module in the backbone network is replaced with the C3Ghost module. The replacements significantly reduce the number of parameters and computation volume of the model while retaining the feature extraction capability of the backbone network. Experimental results of the photovoltaic dataset show that the proposed method improves detection accuracy by 8%, while the computation and number of parameters of the model are reduced by 5.7% and 44.1%, respectively. Lastly, topological relationships are established for the target results, and targets in visible and infrared images are matched based on topological similarity.

Stimulation and imaging of neural cells via photonic nanojets

Various neuromodulation techniques have been developed to modulate the peak activity of neurons, thereby regulating brain function and alleviating neurological disorders. Additionally, neuronal stimulation and imaging have significantly contributed to the understanding and treatment of these diseases. Here, we propose utilizing photonic nanojets for optical stimulation and imaging of neural cells. The application of resin microspheres as microlenses enhances fluorescence imaging of neural lysosomes, mitochondria, and actin filaments by generating photonic nanojets. Moreover, optical tweezers can precisely manipulate the microlenses to locate specific targets within the cell for real-time stimulation and imaging. The focusing capabilities of these microlenses enable subcellular-level spatial precision in stimulation, allowing highly accurate targeting of neural cells while minimizing off-target effects. Furthermore, fluorescent signals during neural cell stimulation can be detected in real-time using these microlenses. The proposed method facilitates investigation into intercellular signal transmission among neural cells, providing new insights into the underlying mechanisms of neuronal cell activities at a subcellular level.

Lightweight Network of Multi-Stage Strawberry Detection Based on Improved YOLOv7-Tiny

The color features of strawberries at different growth stages vary slightly and occluded during growth. To address these challenges, this study proposes a lightweight multi-stage detection method based on You Only Look Once version 7-tiny (YOLOv7-tiny) for strawberries in complex environments. First, the size of the model is reduced by replacing the ordinary convolution of the neck network used for deep feature extraction and fusion with lightweight Ghost convolution. Then, by introducing the Coordinate Attention (CA) module, the model’s focus on the target detection area is enhanced, thereby improving the detection accuracy of strawberries. The Wise Intersection over Union (WIoU) loss function is integrated to accelerate model convergence and enhance the recognition accuracy of occluded targets. The advanced Adaptive nesterov momentum algorithm (Adan) is utilized for gradient descent, processing averaged sample data. Additionally, considering the small size of strawberry targets, a detection head specifically for small targets is added, performing detection on a 160 × 160 × 64 feature map, which significantly improves the detection performance for small strawberries. Experimental results demonstrate that the improved network model achieves an mAP@0.5 of 88.2% for multi-stage strawberry detection, which is 2.44% higher than the original YOLOv7-tiny algorithm. Meanwhile, GFLOPs and Params are reduced by 1.54% and 12.10%, respectively. In practical detection and inference, the improved model outperforms current mainstream target detection models, enabling a quicker and more accurate identification of strawberries at different growth stages, thus providing technical support for intelligent strawberry picking.

EFFECTIVENESS OF IMPLEMENTATION AND EVALUATION OF THE IMPACT OF THE SUSTAINABLE FOOD YARD (P2L) PROGRAM ON WOMEN FARMERS (KWT) GROUPS IN TUMANGGAL VILLAGE, PENGADEGAN DISTRICT, PURBALINGGA REGENCY

This study evaluates the effectiveness and impact of the implementation of the Sustainable Food Yard (P2L) program on the Women Farmers Group (KWT) in Tumanggal Village, Pengadegan District, Purbalingga Regency. The main purpose of the study is to determine the effectiveness of the implementation of the P2L program and evaluate the impact resulting from the program. The study uses a Mixed Methods approach with qualitative analysis to assess effectiveness and quantitative analysis to evaluate impacts. The research sample was conducted through the purposive sampling method with 45 respondents. The results of the study show that the implementation of the P2L program in Tumanggal Village is in accordance with the P2L technical instructions (Juknis) and reaches 100% on all effectiveness indicators, such as target accuracy, program socialization, goal achievement, and program monitoring. This program also has a very positive impact in various aspects including economic, social, cultural, environmental, health, and psychology. The P2L program is not only successful in its implementation but also provides extensive sustainable benefits for program participants.

The Effectiveness of Religious Programs in Strengthening the Disciplinary Character of Students at Al-Islah Integrated MTs in Gorontalo City

The effectiveness of a Religious Program is an achievement or result obtained from a program to obtain the best results and in accordance with the goals to be achieved. This research aims to: 1) Analyze the forms of religious programs in strengthening the disciplinary character of students at Al-Ishlah Integrated MTs (Madrasah Tsanawiyah) in Gorontalo City. 2) Analyzing the impact of religious programs in strengthening the disciplined character of students at al-Ishlah Integrated MTs in Gorontalo City. 3) Analyzing the effectiveness of implementing religious programs in strengthening the disciplined character of students at al-Ishlah Integrated MTs in Gorontalo City. The type of research used is qualitative research, with a phenomenological approach and using data collection methods of observation, interviews, documentation and research questionnaires. The research results show: 1) Forms of religious programs that are effective in strengthening the disciplined character of students, namely routine religious programs, periodic religious programs and annual religious programs. 2) The impact of religious programs in strengthening disciplined character, namely students' time discipline, worship discipline, scientific discipline, dress discipline, behavioral discipline and social discipline. 3) The effectiveness of religious programs in strengthening disciplinary character has been running smoothly, seen from several aspects, namely, firstly, the accuracy of the right targets for students and the entire community in the madrasah environment. Second, socialization of religious programs is often carried out through seminars, training, accepting new students and through the learning process in the classroom. Third, the program objectives are achieved, namely, students have straight and correct aqidah, perform correct worship, have commendable morals and are independent and careful with time. Fourth, monitoring of religious programs by madrasas involves collaboration with teachers and parents of students.

Machine learning for gap‐filling in greenhouse gas emissions databases

AbstractGreenhouse gas (GHG) emissions datasets are often incomplete due to inconsistent reporting and poor transparency. Filling the gaps in these datasets allows for more accurate targeting of strategies aiming to accelerate the reduction of GHG emissions. This study evaluates the potential of machine learning methods to automate the completion of GHG datasets. We use three datasets of increasing complexity with 18 different gap‐filling methods and provide a guide to which methods are useful in which circumstances. If few dataset features are available, or the gap consists only of a missing time step in a record, then simple interpolation is often the most accurate method and complex models should be avoided. However, if more features are available and the gap involves non‐reporting emitters, then machine learning methods can be more accurate than simple extrapolation. Furthermore, the secondary output of feature importance from complex models allows for data collection prioritization to accelerate the improvement of datasets. Graph‐based methods are particularly scalable due to the ease of updating predictions given new data and incorporating multimodal data sources. This study can serve as a guide to the community upon which to base ever more integrated frameworks for automated detailed GHG emissions estimations, and implementation guidance is available at https://hackmd.io/@luke‐scot/ML‐for‐GHG‐database‐completion and https://doi.org/10.5281/zenodo.10463104. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.

Multi-Scale Marine Object Detection in Side-Scan Sonar Images Based on BES-YOLO.

Aiming at the problem of low accuracy of multi-scale seafloor target detection in side-scan sonar images with high noise and complex background texture, a model for multi-scale target detection using the BES-YOLO network is proposed. First, an efficient multi-scale attention (EMA) mechanism is used in the backbone of the YOLOv8 network, and a bi-directional feature pyramid network (Bifpn) is introduced to merge the information of different scales, finally, a Shape_IoU loss function is introduced to continuously optimize the model and improve its accuracy. Before training, the dataset is preprocessed using 2D discrete wavelet decomposition and reconstruction to enhance the robustness of the network. The experimental results show that 92.4% of the mean average accuracy at IoU of 0.5 (mAP@0.5) and 67.7% of the mean average accuracy at IoU of 0.5 to 0.95 (mAP@0.5:0.95) are achieved using the BES-YOLO network, which is an increase of 5.3% and 4.4% compared to the YOLOv8n model. The research results can effectively improve the detection accuracy and efficiency of multi-scale targets in side-scan sonar images, which can be applied to AUVs and other underwater platforms to implement intelligent detection of undersea targets.

Feature-Enhanced Attention and Dual-GELAN Net (FEADG-Net) for UAV Infrared Small Object Detection in Traffic Surveillance

With the rapid development of UAV and infrared imaging technology, the cost of UAV infrared imaging technology has decreased steadily. Small target detection technology in aerial infrared images has great potential for applications in many fields, especially in the field of traffic surveillance. Because of the low contrast and relatively limited feature information in infrared images compared to visible images, the difficulty involved in small road target detection in infrared aerial images has increased. To solve this problem, this study proposes a feature-enhanced attention and dual-GELAN net (FEADG-net) model. In this network model, the reliability and effectiveness of small target feature extraction is enhanced by a backbone network combined with low-frequency enhancement and a swin transformer. The multi-scale features of the target are fused using a dual-GELAN neck structure, and a detection head with the parameters of the auto-adjusted InnerIoU is constructed to improve the detection accuracy for small infrared targets. The viability of the method was proved using the HIT-UAV dataset and IRTS-AG dataset. According to a comparative experiment, the mAP50 of FEADG-net reached more than 90 percent, which was higher than that of any previous method and it met the real-time requirements. Finally, an ablation experiment was conducted to demonstrate that all three of the modules proposed in the method contributed to the improvement in the detection accuracy. This study not only designs a new algorithm for small road object detection in infrared remote sensing images from UAVs but also provides new ideas for small target detection in remote sensing images for other fields.

Ratiometric Fluorescent Probe for Super-Resolution Imaging of Lysosome HClO in Ferroptosis Cells.

Ferroptosis is an iron-dependent programmed cell death that is characterized by the dysregulation of lipid reactive oxygen species (ROS) production, causing abnormal changes in hypochlorous acid (HClO) levels in lysosomes. Super-resolution imaging can observe the fine structure of the lysosome at the nanometer level; therefore, it can be used to detect lysosome HClO levels during ferroptosis at the suborganelle level. Herein, we utilize a ratiometric fluorescent probe, SRF-HClO, for super-resolution imaging of lysosome HClO. Structured-illumination microscopy (SIM) improves the accuracy of lysosome targeting and enables the probe SRF-HClO to be successfully applied to rapidly monitor the up-regulated lysosome HClO at the nanoscale during inflammation and ferroptosis. Importantly, the probe SRF-HClO can also detect HClO changes in inflammatory and ferroptosis mice and evaluate the inhibitory effect of ferroptosis on mice tumors.

Analysis of Partnership Patterns Between Gaharu Plant Farmers and PT. Cahaya Gaharu Langkat

This research aims to determine the partnership pattern between gaharu plant farmers and PT. Cahaya Gaharu Langkat and to determine the effectiveness of the partnership program. The type of research used is descriptive and qualitative. Determining the research location was carried out deliberately, namely selecting subjects based on certain characteristics or characteristics that were previously known. The data collected consisted of primary data, namely direct interviews with respondents, and secondary data from relevant agencies related to this research. The results of this research are 1). The partnership pattern that occurs between PT. Cahaya Gaharu Langkat with gaharu plant farmers is a partnership pattern for Agribusiness Operational Cooperation. The development of gaharu plant farmers land is fully managed by farmers with the assistance of the company. 2). The level of effectiveness of the partnership pattern between gaharu plant farmers and PT. Cahaya Gaharu Langkat is 79.25 which is in the effective category. With the program target accuracy indicator, the score was 91%, the program socialization indicator was 78%, the program objective was 67%, the program monitoring indicator was 81%.

Effectiveness of Financing in Supporting MSME Development at PT Pegadaian UPS Kwanyar

Introduction: This research aims to analyze the effectiveness of financing in supporting MSME development at PT Pegadaian UPS Kwanyar. Research Methods: This research uses a descriptive case study approach to describe the efficiency and challenges of KUR Syariah at PT Pegadaian UPS Kwanyar. Data was collected through direct observation and interviews with the company and the MSME players. The data was then analyzed qualitatively using descriptive analysis. Results: The results reveal that the KUR Syariah program at PT Pegadaian UPS Kwanyar effectively supports MSME capital by implementing strict procedures such as timeliness, accuracy of amount, accuracy of target, and accuracy of credit. MSMEs that use KUR Syariah report increased income and productivity, although they still face challenges such as product limitations, low user interest, a complicated application process, and a lack of direct promotion. Conclusion: Improvements in the application process and promotion are needed to increase the utilization of KUR Syariah by MSMEs in Bangkalan City.

Manajemen Filantropi

Islamic philanthropy management plays an important role in managing zakat, infaq, alms and waqf funds in a structured and accountable manner. The aim is to achieve social justice and equal distribution of welfare, help the poor, support social development, and strengthen the Islamic brotherhood. The basic principles of Islamic philanthropy management include sincerity, justice, target accuracy, efficiency and accountability. In its implementation, professional philanthropic institutions are established, effective fund collection systems, use of information technology, development of human resources, and development of innovative philanthropy programs. With effective management of Islamic philanthropy, it is hoped that it can create a just, prosperous and noble society.

The Effectiveness of Fisherman Community Empowerment Programs Through Fisheries Actor Business Cards

This study aims to determine the effectiveness of the Fisherman Community Empowerment Program through Fisheries Actor Business Cards in Landolia Village, Ranteangin District, North Kolaka Regency. This type of research was conducted with qualitative research methods, data collected from primary data and secondary data. Primary data sourced from interviews with informants totaling 11 people and from observations. As for secondary data by collecting data through literature studies and other supporting literature. The data analysis technique used is qualitative data analysis using data collection, data reduction, data presentation, and drawing conclusions put forward by Miles and Hubberman. Based on the results of the study indicate that the effectiveness of the Fisher Community Empowerment Program through the Fisheries Actor Business Card (KUSUKA) Landolia Village, Ranteangin District, North Kolaka Regency through 4 indicators to measure the effectiveness of the program, namely: 1) the accuracy of the program targets, the effectiveness of the Fisherman Community Empowerment Program through the Kusuka Card in the village. Landolia, Ranteangin District, North Kolaka Regency has not been effective because the target accuracy indicator has not been effective because the target achievement is less than all program targets, 2) Program socialization on program socialization indicators in the fishermen community empowerment program through Kusuka is still not effective because of the low interest of the community to participate in socialization. 3) program objectives, with the Kusuka program for fishermen groups in Landolia Village, it is said to be quite effective because it really helps the community in meeting their daily needs. 4) Program Monitoring Based on the explanation above, the authors conducted further searches and saw data on the members of the fishing group who received the assistance provided through the Kusuka program, which is still not considered effective because not all members of the fishermen use the assistance programs properly.

Staleness aware semi-asynchronous federated learning

As the attempts to distribute deep learning using personal data have increased, the importance of federated learning (FL) has also increased. Attempts have been made to overcome the core challenges of federated learning (i.e., statistical and system heterogeneity) using synchronous or asynchronous protocols. However, stragglers reduce training efficiency in terms of latency and accuracy in each protocol, respectively. To solve straggler issues, a semi-asynchronous protocol that combines the two protocols can be applied to FL; however, effectively handling the staleness of the local model is a difficult problem. We proposed SASAFL to solve the training inefficiency caused by staleness in semi-asynchronous FL. SASAFL enables stable training by considering the quality of the global model to synchronise the servers and clients. In addition, it achieves high accuracy and low latency by adjusting the number of participating clients in response to changes in global loss and immediately processing clients that did not to participate in the previous round. An evaluation was conducted under various conditions to verify the effectiveness of the SASAFL. SASAFL achieved 19.69%p higher accuracy than the baseline, 2.32 times higher round-to-accuracy and 2.24 times higher latency-to-accuracy. Additionally, SASAFL always achieved target accuracy that the baseline can't reach.

Deep Argo Observations of Antarctic Bottom Water in the Deep Fracture Zones of the Southwest Indian Ridge

AbstractThe Madagascar Basin is the primary pathway for Antarctic Bottom Water to ventilate the entire western Indian Ocean as part of the Global Overturning Circulation. The only way for this water mass to reach this basin is by crossing the Southwest Indian Ridge through its deep fracture zones. However, due to the scarcity of observations, the Antarctic Bottom Water presence has only been well‐established in the Atlantis II fracture zone. In May 2023, the Deep Madagascar Basin Experiment deployed three Deep SOLO Argo floats in the exit of the fracture zones that were more likely to transport Antarctic Bottom Water: Atlantis II, Novara, and Melville. These floats have been collecting temperature and salinity profiles every 3–5 days with high vertical resolution in the deep ocean. In the present paper, we use the first 7 months of float data to characterize the Antarctic Bottom Water in the deep fracture zone area, revisiting a half‐century puzzle about the Melville contribution. We also collected shipboard‐based profiles to calibrate float salinity and show it is within the Deep Argo program target accuracy. We find Antarctic Bottom Water in both Melville and Novara fracture zones, not only in Atlantis II. This is the first time the Novara contribution has been revealed. The floats also uncover their distinct properties, which may result from the different mixing histories.

Construction and application of a core competitiveness evaluation index system for public health personnel training based on the CIPP model

BACKGROUND: With the change of human disease spectrum and the increase of public emergencies, the public’s demand for health services and health is gradually increasing and higher expectations are placed on public health personnel. OBJECTIVE: Research needs to establish a comprehensive system of evaluation indices for accurate assessment of the core competencies of public health personnel, to enhance their core competitiveness, and introduce novel approaches to evaluate talent development in the field of public health. METHODS: The study is based on the CIPP (Context Input Process Product) model and uses literature analysis, semi-structured interviews, and Delphi methods to construct an evaluation index system for the core competitiveness of public health talent cultivation. The entropy method is used to determine the weight of the model evaluation index. Finally, the improved Artistic Be Colony algorithm (ABC) is used to optimize the BP network, and apply it to evaluate the core competitiveness of public health talent cultivation. RESULTS: The improved BP network achieved the target accuracy within 11 iterations, with the optimal value observed after 16 iterations, producing an MSE (Mean Square Error) value of 10–13. The evaluation of the index system reported a 97% accuracy, and upon application to the university’s public health training programme, nearly 50% of students and teachers achieved core competitiveness quality scores above 90. CONCLUSIONS: The aforementioned method suggests that it can proficiently assess the fundamental competitiveness of training for public health personnel and offer guidance for future development in the domain of public health.

One-Step Synthesis and Oriented Immobilization of Strep-Tag II Fused PDGFRβ for Screening Intracellular Domain-Targeted Ligands.

Accurate orientations and stable conformations of membrane receptor immobilization are particularly imperative for accurate drug screening and ligand-protein affinity analysis. However, there remain challenges associated with (1) traditional recombination, purification, and immobilization of membrane receptors, which are time-consuming and labor-intensive; (2) the orientations on the stationary phase are not easily controlled. Herein, a novel one-step synthesis and oriented-immobilization membrane-receptor affinity chromatography (oSOMAC) method was developed to realize high-throughput and accurate drug screening targeting specific domains of membrane receptors. We employed Strep-tag II as a noncovalent immobilization tag fused into platelet-derived growth factor receptor β (PDGFRβ) through CFPS, and meanwhile, the Strep-Tactin-modified monolithic columns are prepared in batches. The advantages of oSOMAC are as follows: (1) targeted membrane receptors can be expressed independent of living cell within 1-2 h; (2) orientation of membrane receptors can be flexibly controlled and active sites can expose accurately; and (3) targeted membrane receptors can be synthesized, purified, and orientation-immobilized on monolithic columns in one step. Accordingly, three potential PDGFRβ intracellular domain targeted ligands: tanshinone IIA (Tan IIA), hydroxytanshinone IIA, and dehydrotanshinone IIA were successfully screened out from Salvia miltiorrhiza extract through oSOMAC. Pharmacological experiments and molecular docking further demonstrated that Tan IIA could attenuate hepatic stellate cells activation by targeting the protein kinase domain of PDGFRβ with a KD value of 9.7 μM. Ultimately, the novel oSOMAC method provides an original insight for accurate drug screening and interaction analysis which can be applied in other membrane receptors.

WBSP: Addressing stragglers in distributed machine learning with worker-busy synchronous parallel

Parameter server is widely used in distributed machine learning to accelerate training. However, the increasing heterogeneity of workers’ computing capabilities leads to the issue of stragglers, making parameter synchronization challenging. To address this issue, we propose a solution called Worker-Busy Synchronous Parallel (WBSP). This approach eliminates the waiting time of fast workers during the synchronization process and decouples the gradient upload and model download of fast workers into asymmetric parts. By doing so, it allows fast workers to complete multiple steps of local training and upload more gradients to the server, improving computational resource utilization. Additionally, the global model is only updated when the slowest worker uploads the gradients, ensuring the consistency of global models that are pulled down by all workers and the convergence of the global model. Building upon WBSP, we propose an optimized version to further reduce the communication overhead. It enables parallel execution of communication and computation tasks on workers to shorten the global synchronization interval, thereby improving training speed. We conduct theoretical analyses for the proposed mechanisms. Extensive experiments verify that our mechanism can reduce the required time to achieve the target accuracy by up to 60% compared with the fastest method and increase the proportion of computation time from 55%–72% in existing methods to 91%.

Ultrasound and x-ray imageable poloxamer-based hydrogel for loco-regional therapy delivery in the liver.

Intratumoral injections have the potential for enhanced cancer treatment efficacy while reducing costs and systemic exposure. However, intratumoral drug injections can result in substantial off-target leakage and are invisible under standard imaging modalities like ultrasound (US) and x-ray. A thermosensitive poloxamer-based gel for drug delivery was developed that is visible using x-ray imaging (computed tomography (CT), cone beam CT, fluoroscopy), as well as using US by means of integrating perfluorobutane-filled microbubbles (MBs). MBs content was optimized using tissue mimicking phantoms and ex vivo bovine livers. Gel formulations less than 1% MBs provided gel depositions that were clearly identifiable on US and distinguishable from tissue background and with minimal acoustic artifacts. The cross-sectional areas of gel depositions obtained with US and CT imaging were similar in studies using ex vivo bovine liver and postmortem in situ swine liver. The gel formulation enhanced multimodal image-guided navigation, enabling fusion of ultrasound and x-ray/CT imaging, which may enhance targeting, definition of spatial delivery, and overlap of tumor and gel. Although speculative, such a paradigm for intratumoral drug delivery might streamline clinical workflows, reduce radiation exposure by reliance on US, and boost the precision and accuracy of drug delivery targeting during procedures. Imageable gels may also provide enhanced temporal and spatial control of intratumoral conformal drug delivery.

Puncture Accuracy of Robot-Assisted CT-Based Punctures in Interventional Radiology: An Ex Vivo Study.

The purpose of this study was to assess the performance of an optically tracked robot for computed-tomography (CT)-guided needle placements in a phantom study. In total, 240 needle punctures were carried out with the help of an optically tracked robotic device (Micromate) based on CT image datasets at three different slice thicknesses (1, 3, and 5 mm). Conically shaped targets inside a gelatin-filled plexiglass phantom were punctured. The target positioning error between the planned and actual needle trajectory was assessed by measuring the lateral positioning error (ND) between the target and the puncture needle and the Euclidean distance (ED) between the needle tip and target in control CTs. The mean ND and ED for the thinnest CT slice thickness were 1.34 mm (SD ± 0.82) and 2.1 mm (SD ± 0.75), respectively. There was no significant impact of target depth on targeting accuracy for ND (p = 0.094) or ED (p = 0.187). The mean duration for the planning of one trajectory and for needle positioning were 42 s (SD ± 4) and 64 s (SD ± 7), respectively. In this ex vivo study, the robotic targeting device yielded satisfactory accuracy results at CT slice thicknesses of 1 and 3 mm. This technology may be particularly useful in interventions where the accurate placement of needle-like instruments is required.