This article mainly designed and implemented an aviation maintenance management system based on java technology. In the system, it was introduced how the website completes aviation aircraft maintenance information man...
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java Island, located in Indonesia, is the country's main island, with a population of 150 million, more than half the population of the country. There are at least four big cities located on the island that have s...
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java Island, located in Indonesia, is the country's main island, with a population of 150 million, more than half the population of the country. There are at least four big cities located on the island that have seen fast development in the last 30 years. The land subsidence (LS) issue caused by groundwater overexploitation, compaction, and geological setting, has been known on the island for more than 20 years. However, past studies have mostly focused on one particular important area, while the big picture of LS on the whole island is often overlooked. This study utilized Sentinel-1 Synthetic Aperture Radar (SAR) data from 2017 to 2023, analyzed using Small Baseline Subset (SBAS) interferometry, to map LS across java Island. We used DEMNAS to eliminate the topographic signal. We found ten regions with a noticeable LS rate, affecting nearly 60 million people who reside in the LS zones, namely, Serang, Greater Jakarta, Cianjur, Bandung, Cirebon, Brebes and Tegal, Pekalongan, Greater Semarang, Surabaya, and Sidoardjo. The highest rates and the large coverage of LS were observed in Greater Jakarta (up to 150 mm/year), Bandung (200 mm/year), Semarang (160 mm/year), and Pekalongan (up to 110 mm/year). LS was also detected in smaller areas or districts, such as Serang, Cianjur, Cirebon, Brebes, Tegal, Surabaya, and Sidoarjo, with rates ranging from 60 to 140 mm/year. The two areas of Cianjur and Brebes, which have never been mentioned in previous studies, show LS rates of about 80 mm/year and 70 mm/year, respectively. The LS rate in all areas was shown to be linear over time, except in Pekalongan, which shows rate deflation after 2021. We also found that most affected regions are urban and industrial zones, indicating a strong correlation with anthropogenic activities. LS leads to widespread socioeconomic and environmental impacts, including damage to infrastructure, increased flooding, and reduced groundwater capacity.
Representative workloads and principled methodologies are the foundation of performance analysis, which in turn provides the empirical grounding for much of the innovation in systems research. However, benchmarks are ...
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The Lembang Fault, located north of Bandung in West java, Indonesia, is an active fault that can pose a significant earthquake hazard. The Fault extends 29 km in an east-west direction and is capable of generating ear...
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The Lembang Fault, located north of Bandung in West java, Indonesia, is an active fault that can pose a significant earthquake hazard. The Fault extends 29 km in an east-west direction and is capable of generating earthquakes of magnitude 6.5-7.0 based on surface geological observations and previous paleoseismological studies. In earthquake mitigation, it is crucial to accurately describe the geometry of potential earthquake sources. Therefore, a subsurface model supported by high-resolution data is necessary to adequately characterize the geometry of the Lembang fault. Love wave ambient seismic noise tomography was used in this study to create a seismic velocity model based on data from 74 recording stations. The model accurately characterizes the high velocity contrast and low shear wave velocity anomalies associated with the Lembang Fault Zone. Pronounced velocity anomalies are observed, suggesting that they are related to the fault plane, which is confirmed by seismic activity in the region. In addition, the evidence has been found for another possible fault. Lembang fault has two fault planes: One is a vertical fault and the other is a south-dipping thrust fault. This fault is a cause for concern as it has the potential to generate earthquake with significant consequences.
Application programs are a possible source of attacks to databases. SQL injection is a well-known attack that exploits the lack of user input sanitization by applications. Following secure code practices to avoid vuln...
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Application programs are a possible source of attacks to databases. SQL injection is a well-known attack that exploits the lack of user input sanitization by applications. Following secure code practices to avoid vulnerabilities is the best way to prevent attacks. However, developers usually make mistakes either due to a lack of knowledge (i.e., a beginner developer) or due to bad code practices like copy-paste, which duplicates bugs and vulnerabilities in the code. Detecting such vulnerabilities manually is expensive and time-consuming, especially for very large code bases. Fixing vulnerabilities is also expensive as it requires manual interventions. It is thus clear that in order to systematically find and fix vulnerabilities we need automatic tools. In this article, we address such a need. We propose the Database Client Applications Fixer (DCAFixer) tool, which automatically detects and repairs three types of common vulnerabilities in SQL application programs, namely unsanitized user inputs, insecure credentials handling, and unencrypted connections. DCAFixer operates in three phases: fault localization, patch generation and selection, and patch validation.
The Indonesian government set a policy to use renewable energy, including bioethanol, that is still in the market trial stage. Sorghum is a plant that has the potential to be quite promising as a raw material for maki...
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The Indonesian government set a policy to use renewable energy, including bioethanol, that is still in the market trial stage. Sorghum is a plant that has the potential to be quite promising as a raw material for making bioethanol. This study focused on optimizing the supply chain for small-scale integrated sorghum-based bioethanol industry. A linear programming model was used to formulate the optimal result. Key aspects included farmer- owned sorghum plantations supplying three bioethanol plants with a capacity of 4 klpd (kiloliters per day) each, transportation, and distribution of bioethanol to three fuel depots in East java, Indonesia. Results showed that over 10 years, the total supply chain cost was 932 billion IDR, while revenues from bioethanol and byproducts reached 1.272 billion IDR, resulting in a net income of 340 billion IDR. The calculation results indicated that the use of sweet sorghum for bioethanol and by-products is feasible to develop.
This research was conducted in the mountainous area of Dieng, Central java. The study area is among the highest-ranked landslide-prone areas on java due to steep slopes and intensively weathered Tertiary volcanic rock...
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This research was conducted in the mountainous area of Dieng, Central java. The study area is among the highest-ranked landslide-prone areas on java due to steep slopes and intensively weathered Tertiary volcanic rocks, which dominate the area. The annual rainfall in the Dieng region is very high, over 3000 mm/year, which represents a primary trigger for landslides. This present contribution aims at assessing landslide susceptibility through a combination of multi-temporal remote-sensing and machine learning such as Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN). The multi-temporal remote sensing approach was utilized to inventory landslide occurrences over the period from 2014 to 2024 using PlanetScope and Google Earth Platform. Those images and platform enabled us to map landslide occurrences comprehensively and accurately, in a relatively efficient manner, thereby reducing the extensive and costly fieldwork. Machine learning was applied as a solution to the accuracy issues inherent in semi-quantitative and probabilistic statistical methods for landslide prediction. The assessment of landslide susceptibility revealed that all three models achieved very high accuracy and could be applied to both the study area and other regions. However, accuracy assessment with various indicators showed that ANN produced the best results, followed by RF and SVM. Thus, the findings of this study can be adopted by national or local authorities in disaster mitigation as part of disaster risk reduction instruments. This is highly relevant to support the Sustainable Development Goals (SDGs) number 11: Sustainable Cities and Communities, which aims to make cities and human settlements inclusive, safe, resilient, and sustainable, including disaster risk reduction.
Automated program repair (APR) plays a vital role in enhancing software quality and reducing developer maintenance efforts. Neural Machine Translation (NMT)-based methods demonstrate notable potential by learning tran...
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Automated program repair (APR) plays a vital role in enhancing software quality and reducing developer maintenance efforts. Neural Machine Translation (NMT)-based methods demonstrate notable potential by learning translation patterns from bug-fix code pairs. However, traditional approaches are constrained by limited model capacity and training data scale, leading to performance bottlenecks in generalizing to unseen defect patterns. In this paper, we propose CodeTransFix, a novel APR approach that synergistically combines neural machine translation (NMT) methods with code-specific large language models of code (LLMCs) such as CodeBERT. The CodeTransFix approach innovatively learns contextual embeddings of bug-related code through CodeBERT and integrates these representations as supplementary inputs to the Transformer model, enabling context-aware patch generation. The repair performance is evaluated on the widely used Defects4j v1.2 benchmark. Our experimental results showed that CodeTransFix achieved a 54.1% performance improvement compared to the best NMT-based baseline model and a 23.3% performance improvement compared to the best LLMCs for fixing bugs. In addition, CodeTransFix outperformed existing APR methods in the Defects4j v2.0 generalization test.
Webshells are widely used by attackers to maintain access during the post-exploitation phase. As security defenses improve, traditional file-based Webshells are increasingly detectable. To evade detection, attackers a...
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Webshells are widely used by attackers to maintain access during the post-exploitation phase. As security defenses improve, traditional file-based Webshells are increasingly detectable. To evade detection, attackers are shifting toward fileless Webshells, which reside entirely in memory and present significant challenges to conventional security tools. However, research on fileless Webshell detection remains limited. To address this gap, we analyzed various fileless Webshell samples, summarized their behavioral patterns, and constructed a corresponding threat model. Based on this, we propose a novel detection approach named GAShellBreaker, which leverages grayscale image transformation and deep learning. GAShellBreaker first establishes a dual-layer in-memory monitoring mechanism to capture suspicious classes within the java Virtual Machine (JVM) and export them as bytecode files. It then extracts opcode sequences from these files, transforms them into grayscale images, and employs a ResNet50-based classifier for detection. Due to the limited availability of fileless samples, we trained and evaluated the model on a larger dataset of 1351 file-based scripts (383 Webshells and 968 benign samples), and used 56 fileless Webshells for validation. Experimental results show that GAShellBreaker achieves 99.10% accuracy on file-based Webshells and 89.29% accuracy on fileless Webshells, outperforming existing algorithms. Moreover, it maintains low computational overhead (6.7%), confirming its practical feasibility.
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