Accurate Targeting Research Articles

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.

Augmented Reality in Elementary Education: System Architecture for Implementing an Interactive and Immersive E-Learning Application

Early childhood language acquisition is crucial, but complex scripts such as Bengali present challenges. BIPLOB, an Android-based augmented reality (AR) application, seeks to revolutionize language learning by introducing interactive 3D models and meticulously recorded audio pronunciations. Targeting both engagement and accuracy, BIPLOB addresses issues encountered by traditional methods. Its optimized workflow offers a replicable framework for future AR-based language learning applications, especially for complex scripts. BIPLOB paves the way for immersive and effective early language acquisition through the transformative power of AR technology.

Ischemic stroke infarct segmentation model based on depthwise separable convolution for multimodal magnetic resonance imaging

Magnetic resonance imaging (MRI) plays a crucial role in the diagnosis of ischemic stroke. Accurate segmentation of the infarct is of great significance for selecting intervention treatment methods and evaluating the prognosis of patients. To address the issue of poor segmentation accuracy of existing methods for multiscale stroke lesions, a novel encoder-decoder architecture network based on depthwise separable convolution is proposed. Firstly, this network replaces the convolutional layer modules of the U-Net with redesigned depthwise separable convolution modules. Secondly, an modified Atrous spatial pyramid pooling (MASPP) is introduced to enlarge the receptive field and enhance the extraction of multiscale features. Thirdly, an attention gate (AG) structure is incorporated at the skip connections of the network to further enhance the segmentation accuracy of multiscale targets. Finally, Experimental evaluations are conducted using the ischemic stroke lesion segmentation 2022 challenge (ISLES2022) dataset. The proposed algorithm in this paper achieves Dice similarity coefficient (DSC), Hausdorff distance (HD), sensitivity (SEN), and precision (PRE) scores of 0.816 5, 3.668 1, 0.889 2, and 0.894 6, respectively, outperforming other mainstream segmentation algorithms. The experimental results demonstrate that the method in this paper effectively improves the segmentation of infarct lesions, and is expected to provide a reliable support for clinical diagnosis and treatment.

Target assignment for multiple stages of weapons systems using a deep Q-learning network and a modified artificial bee colony method

Multistage Weapon Target Assignment (MWTA) in defense systems is a critical process that focuses on minimizing deviations and inaccuracies during target assignment across stages like initialization, tracking, and counterattack. The research problem lies in efficiently assigning targets to weapon systems while accounting for distractions and tracking accuracy constraints. Existing approaches often face challenges related to computational complexity, real-time engagement limitations, and scalability issues, leading to inaccuracies in various stages within the system's perceived radius. This paper proposes a novel State-based Modified Artificial Bee Colony Method (SMABCM) that integrates Q-learning to generate adaptive target assignment decisions. SMABCM aims to maximize assignment accuracy by prioritizing low deviation rewards, enabling precise one-to-one target tracking and reducing the population of the artificial bee colony. The Q-learning network dynamically defines generation and reduction states based on deviations to minimize distracting targets and enhance tracking precision. Experimental results demonstrate SMABCM's superior performance over existing approaches in target precision comparison, hit probability, increase in distracting target detection, reduced deviation factor, and computation time. As the speed increases, the proposed SMABCM improves target accuracy by 9.54 %, hit probability by 9.17 %, distracting target detection by 12.35 %, while reducing deviation by 7.39 % and computation time by 10.11 %. This approach addresses complexities in multistage weapon systems through dynamic strategies, contributing to proactive defense against evolving security threats.

Efektivitas Tata Kelola Penanganan Anak Stunting oleh Dokter dan Tenaga Kesehatan di Kota Yogyakarta

The governance management of stunting children in Yogyakarta is carried out through the Stunting Case Audit (AKS) program. This program was implemented as an effort to meet the zero stunting target of Yogyakarta City through early intervention to reduce and prevent the appearance of similar cases from the audited areas. To find out that the AKS program has succeeded in achieving its goals, it is necessary to conduct a study to examine the effectiveness of the AKS program. This research is using a qualitative approach by comparing the suitability of program implementation with indicators of effectiveness. The results of this research explain that from the four effectiveness indicators used, only one indicator met the implementation of AKS. Indicators of timeliness have been met, while indicators of target accuracy, goal attainment and the emergence of expected results have not been met the expectation.

A lightweight fire detection algorithm for small targets based on YOLOv5s

In response to the current challenges fire detection algorithms encounter, including low detection accuracy and limited recognition rates for small fire targets in complex environments, we present a lightweight fire detection algorithm based on an improved YOLOv5s. The introduction of the CoT (Contextual Transformer) structure into the backbone neural network, along with the creation of the novel CSP1_CoT (Cross stage partial 1_contextual transformer) module, has effectively reduced the model’s parameter count while simultaneously enhancing the feature extraction and fusion capabilities of the backbone network; The network’s Neck architecture has been extended by introducing a dedicated detection layer tailored for small targets and incorporating the SE (Squeeze-and-Excitation) attention mechanism. This augmentation, while minimizing parameter proliferation, has significantly bolstered the interaction of multi-feature information, resulting in an enhanced small target detection capability; The substitution of the original loss function with the Focal-EIoU (Focal-Efficient IoU) loss function has yielded a further improvement in the model’s convergence speed and precision; The experimental results indicate that the modified model achieves an mAP@.5 of 96% and an accuracy of 94.8%, marking improvements of 8.8% and 8.9%, respectively, over the original model. Furthermore, the model’s parameter count has been reduced by 1.1%, resulting in a compact model size of only 14.6MB. Additionally, the detection speed has reached 85 FPS (Frames Per Second), thus satisfying real-time detection requirements. This enhancement in precision and accuracy, while simultaneously meeting real-time and lightweight constraints, effectively caters to the demands of fire detection.

Theranostic nanoparticles ZIF-8@ICG for pH/NIR-responsive drug-release and NIR-guided chemo-phototherapy against non-small-cell lung cancer

This study leverages nanotechnology by encapsulating indocyanine green (ICG) and paclitaxel (Tax) using zeolitic imidazolate frameworks-8 (ZIF-8) as a scaffold. This study aims to investigate the chemo-photothermal therapeutic potential of ZIF-8@ICG@Tax nanoparticles (NPs) in the treatment of non-small cell lung cancer (NSCLC). An “all-in-one” theranostic ZIF-8@ICG@Tax NPs was conducted by self-assembly based on electrostatic interaction. First, the photothermal effect, stability, pH responsiveness, drug release, and blood compatibility of ZIF-8@ICG@Tax were evaluated through in vitro testing. Furthermore, the hepatic and renal toxicity of ZIF-8@ICG@Tax were assessed through in vivo testing. Additionally, the anticancer effects of these nanoparticles were investigated both in vitro and in vivo. Uniform and stable chemo-photothermal ZIF-8@ICG@Tax NPs had been successfully synthesized and had outstanding drug releasing capacities. Moreover, ZIF-8@ICG@Tax NPs showed remarkable responsiveness dependent both on pH in the tumor microenvironment and NIR irradiation, allowing for targeted drug delivery and controlled drug release. NIR irradiation can enhance the tumor cell response to ZIF-8@ICG@Tax uptake, thereby promoting the anti-tumor growth in vitro and in vivo. ZIF-8@ICG@Tax and NIR irradiation have demonstrated remarkable synergistic anti-tumor growth properties compared to their individual components. This novel theranostic chemo-photothermal NPs hold great potential as a viable treatment option for NSCLC.Graphical A novel nano-theranostic platform was developed by encapsulating indocyanine green and paclitaxel within a zeolitie imidazolate framework-8 (ZIF-8). This chemo-phototherapic agent demonstrated accurate tumor targeting and effective suppression effects on both tumor growth in non-small cell lung cancer.

Multiple object characterization of recyclable domestic waste using binocular stereo vision

Recycling and recovery of domestic waste pose significant challenges in terms of accurate object positioning and ranging, areas where conventional methods, heavily reliant on monocular image processing, often fail due to limited distance data and reduced precision. This study introduces an innovative strategy for the localization and ranging of recyclable waste objects, leveraging the NanoDet-Plus model within a binocular stereo vision context. A dataset, the Binocular Recyclable Domestic Waste Dataset (BRDWD), was constructed, with transfer learning executed via the pretrained MULTI-TRASH monocular dataset. Procedures encompassed stereo camera calibration and rectification to address geometric distortions, while the integration of the Semi-Global Block Matching (SGBM) algorithm supplied disparity details for triangulation-based distance computations. Enhancements to the NanoDet-Plus model led to improved target accuracy, evidenced by a mean average precision at a threshold of 0.5 (mAP@0.5) reaching 91.88% and a detection speed of 15 frames per second (FPS). Assessments verified a ranging error below 5% within the 0.5 to 1.2-meter span, aligning well with the requirements of typical recyclable waste sorting and recycling situations. Additionally, a human-machine interface was developed utilizing Pyside6, enabling image uploads, real-time result visualization, and interactive process control, thereby furnishing pivotal advancements for the automation and intellectualization of waste categorization and recycling.

Miniaturized electromagnetic tracking enables efficient ultrasound-navigated needle insertions

Ultrasound (US) has gained popularity as a guidance modality for percutaneous needle insertions because it is widely available and non-ionizing. However, coordinating scanning and needle insertion still requires significant experience. Current assistance solutions utilize optical or electromagnetic tracking (EMT) technology directly integrated into the US device or probe. This results in specialized devices or introduces additional hardware, limiting the ergonomics of both the scanning and insertion process. We developed the first ultrasound (US) navigation solution designed to be used as a non-permanent accessory for existing US devices while maintaining the ergonomics during the scanning process. A miniaturized EMT source is reversibly attached to the US probe, temporarily creating a combined modality that provides real-time anatomical imaging and instrument tracking at the same time. Studies performed with 11 clinical operators show that the proposed navigation solution can guide needle insertions with a targeting accuracy of about 5 mm, which is comparable to existing approaches and unaffected by repeated attachment and detachment of the miniaturized tracking solution. The assistance proved particularly helpful for non-expert users and needle insertions performed outside of the US plane. The small size and reversible attachability of the proposed navigation solution promises streamlined integration into the clinical workflow and widespread access to US navigated punctures.

An independent, multi-country head-to-head accuracy comparison of automated chest x-ray algorithms for the triage of pulmonary tuberculosis.

Computer-aided detection (CAD) algorithms for automated chest X-ray (CXR) reading have been endorsed by the World Health Organization for tuberculosis (TB) triage, but independent, multi-country assessment and comparison of current products are needed to guide implementation. We conducted a head-to-head evaluation of five CAD algorithms for TB triage across seven countries. We included CXRs from adults who presented to outpatient facilities with at least two weeks of cough in India, Madagascar, the Philippines, South Africa, Tanzania, Uganda, and Vietnam. The participants completed a standard evaluation for pulmonary TB, including sputum collection for Xpert MTB/RIF Ultra and culture. Against a microbiological reference standard, we calculated and compared the accuracy overall, by country and key groups for five CAD algorithms: CAD4TB (Delft Imaging), INSIGHT CXR (Lunit), DrAid (Vinbrain), Genki (Deeptek), and qXR (qure.AI). We determined the area under the ROC curve (AUC) and if any CAD product could achieve the minimum target accuracy for a TB triage test (≥90% sensitivity and ≥70% specificity). We then applied country- and population-specific thresholds and recalculated accuracy to assess any improvement in performance. Of 3,927 individuals included, the median age was 41 years (IQR 29-54), 12.9% were people living with HIV (PLWH), 8.2% living with diabetes, and 21.2% had a prior history of TB. The overall AUC ranged from 0.774-0.819, and specificity ranged from 64.8-73.8% at 90% sensitivity. CAD4TB had the highest overall accuracy (73.8% specific, 95% CI 72.2-75.4, at 90% sensitivity), although qXR and INSIGHT CXR also achieved the target 70% specificity. There was heterogeneity in accuracy by country, and females and PLWH had lower sensitivity while males and people with a history of TB had lower specificity. The performance remained stable regardless of diabetes status. When country- and population-specific thresholds were applied, at least one CAD product could achieve or approach the target accuracy for each country and sub-group, except for PLWH and those with a history of TB. Multiple CAD algorithms can achieve or exceed the minimum target accuracy for a TB triage test, with improvement when using setting- or population-specific thresholds. Further efforts are needed to integrate CAD into routine TB case detection programs in high-burden communities.

RN-YOLO: A Small Target Detection Model for Aerial Remote-Sensing Images

Accurately detecting targets in remote-sensing images is crucial for the military, urban planning, and resource exploration. There are some challenges in extracting detailed features from remote-sensing images, such as complex backgrounds, large-scale variations, and numerous small targets. This paper proposes a remote-sensing target detection model called RN-YOLO (YOLO with RepGhost and NAM), which integrates RepGhost and a normalization-based attention module (NAM) based on YOLOv8. Firstly, NAM is added to the feature extraction network to enhance the capture capabilities for small targets by recalibrating receptive fields and strengthening information flow. Secondly, an efficient RepGhost_C2f structure is employed in the feature fusion network to replace the C2f module, effectively reducing the parameters. Lastly, the WIoU (Wise Intersection over Union) loss function is adopted to mitigate issues such as significant variations in target sizes and difficulty locating small targets, effectively improving the localization accuracy of small targets. The experimental results demonstrate that compared to the YOLOv8s model, the RN-YOLO model reduces the parameter count by 13.9%. Moreover, on the DOTAv1.5, TGRS-HRRSD, and RSOD datasets, the detection accuracy (mAP@.5:.95) of the RN-YOLO model improves by 3.6%, 1.2%, and 2%, respectively, compared to the YOLOv8s model, showcasing its outstanding performance and enhanced capability in detecting small targets.

An Enhanced Feature Extraction Framework for Cross-Modal Image–Text Retrieval

In general, remote sensing images depict intricate scenes. In cross-modal retrieval tasks involving remote sensing images, the accompanying text includes numerus information with an emphasis on mainly large objects due to higher attention, and the features from small targets are often omitted naturally. While the conventional vision transformer (ViT) method adeptly captures information regarding large global targets, its capability to extract features of small targets is limited. This limitation stems from the constrained receptive field in ViT’s self-attention layer, which hinders the extraction of information pertaining to small targets due to interference from large targets. To address this concern, this study introduces a patch classification framework based on feature similarity, which establishes distinct receptive fields in the feature space to mitigate interference from large targets on small ones, thereby enhancing the ability of traditional ViT to extract features from small targets. We conducted evaluation experiments on two popular datasets—the Remote Sensing Image–Text Match Dataset (RSITMD) and the Remote Sensing Image Captioning Dataset (RSICD)—resulting in mR indices of 35.6% and 19.47%, respectively. The proposed approach contributes to improving the detection accuracy of small targets and can be applied to more complex image–text retrieval tasks involving multi-scale ground objects.

Evaluasi Efektivitas Pemanfaatan Aplikasi E-Musrenbang Berbasis Geospasial dalam Pembangunan di Kota Bontang

The aim of this research is to evaluate how well the e-Musrenbang application performs in the North Bontang Regency area in terms of development planning. The importance of implementing e-Government and Electronic-Based Government Systems in development planning is the driving force for this research. e-Musrenbang, an application with a geographical focus, is a method used to help communities get involved in development. With a research focus on North and South Bontang Regencies, this research uses qualitative methodology. The research objects are represented by Guntung Village and Tanjung Laut Indah Village. Using Budiani's theory, this research focuses on the characteristics of goal setting, socialization, target accuracy, and monitoring. Utilizing secondary data, interviewing relevant informants, and observation are the methods used for data collection. The research findings show how well the e-Musrenbang application is able to increase community involvement in planning and development in North Bontang District and South Bontang District. In this case, the public can convey their ambitions more easily online thanks to this program, which also helps the government in regulating and monitoring these aspirations. In addition, the application of geospatial technology makes development planning easier and more precise and focused. However, there are several shortcomings in this research, such as research topics that are limited to two different sub-districts and data collection techniques that only use qualitative techniques. Therefore, to have a more thorough understanding of the usability of this application, it is recommended that future research integrate qualitative and quantitative methodologies.