A Systematic Survey on Large Language Models for Static Code Analysis

Static code analysis aids in improving software quality, security, and maintainability by detecting vulnerabilities, errors, and programming issues in source code without executing it. The latest advancements in Artificial Intelligence (AI), especially the development of Large Language Models (LLMs) such as ChatGPT, have enabled transformational opportunities in this domain. Thus, it is essential to explore this hot field of research alongside many directions. This systematic survey focuses on the use of LLMs for static code analysis, detailing their applications, advantages, contexts, limitations, etc. In this study, the research papers that have been published on the topic from well-known literature databases were examined to answer several research questions regarding state-of-the-art use of LLMs for static code analysis. Also, different research gaps and challenges were identified and discussed alongside many directions. The results of this study demonstrate how LLMs can be useful for static code analysis and overcome different constraints. Thus, it opens the doors for developers and researchers to employ LLMs for affordable and effective static code analysis to improve software development process

Levofloxacin Determination in Pharmaceutical Tablets by Sensitive Spectrofluorometric Method with L-Tryptophan as a Fluorescent Probe

Proper dosage, therapeutic effectiveness, patient safety, and quality control throughout manufacture and storage can only be achieved by closely monitoring the concentration of pharmaceutical products. Aprecise and reliable spectrofluorometric approach for  quantitative analysis and detection of levofloxacin (LEVO) in various pharmaceutical products was developed in this work using the fluorescent reagent L-tryptophan. When L-tryptophan, which has its inherent fluorescence signal quenched by LEVO, is mixed with Britton-Robinson buffer solution (pH 9.0), a stable ion-associated complex forms. The fluorescence intensity of L-tryptophan decreased at 365 nm after excitation at 281 nm. The method showed linearity for LEVO concentrations from 0.3 to 18.0 μg/mL, with a minimum detectable value of 0.10 μg/mL. An effective linear relationship (R2 = 0.9985) between the concentration and fluorescence intensity (ΔF) was obtained. This technique has been well-proven to be minimally affected by impurities commonly found in pharmaceutical formulations. The results were validated through comparative analyses with high-performance liquid chromatography. The study revealed that both equivalence levels and analytical quality (as measured by precision and accuracy) are very satisfactory. This study addresses the increasing demand for established and reliable methods in the quality control of pharmaceutical products

Artificial Intelligence-based Digital Pathology Assessment of CD44s Expression in Breast Cancer: Association with Clinicopathological Features and Survival Outcomes

Breast cancer (BC) exhibits considerable molecular and clinical heterogeneity, complicating prognostic evaluation. The cluster of differentiation 44 standard (CD44s) isoform has been proposed as a prognostic marker in various cancers; however, its role in BC remains unclear. This study evaluated CD44s expression in BC tissues and its association with clinicopathological features and survival outcomes using an artificial intelligence (AI)-based digital pathology scoring method. A retrospective analysis of 98 BC tissue samples is conducted, with CD44s cell membrane protein expression assessed through both manual and AI based immunohistochemical (IHC) scoring. Statistical analyses included Pearson’s chi-square test, Kaplan-Meier (log-rank), and Cox regression. CD44s expression was observed in 65.31% of patients. No significant associations are found between CD44s expression and clinicopathological characteristics, including age, tumor size, lymph node metastasis, histological grade, lymphovascular invasion (LVI), or hormone receptor status (all p > 0.05). Survival analysis reveals no significant association between CD44s expression and overall survival (OS, p = 0.1345) or progression-free survival (p = 0.0669). While CD44s expression is prevalent in BC samples, it is not an independent prognostic factor; LVI is the only significant predictor of OS (p = 0.036). Finally, the moderate agreement between AI and manual scoring (Cohen’s Kappa = 0.4337, p < 0.0001) supports the potential of AI-assisted methods for biomarker quantification, warranting further validation in larger cohorts

Influence of Microstructure and Droplet Volume on Atmospheric Pitting Corrosion of 304L Austenitic Stainless Steel

This research investigates the atmospheric pitting corrosion behavior of 304L austenitic stainless steel subjected to MgCl2 droplets, emphasizing the effects of microstructure and droplet volume. X-ray diffraction and scanning electron microscopy (SEM) show that both austenite and ferrite are present, and it is observed that the ferrite bands dissolved more in the direction the steel is rolled. SEM-energy-dispersive X-ray spectroscopy analysis identified mixed oxides and MnS inclusions. The shape of the pits changed depending on the direction of the plate: Layered pits mostly occurred on the longitudinal–transverse side, while striped pits are seen on the longitudinal–short transverse and short transverse sides, indicating variations in the material’s structure. An increase in droplet volume from 0.5 µL to 2.5 µL led to a linear rise in total pit area and a measurable increase in pit depth. These findings show that the direction of the microstructure and the size of the droplets significantly affect how likely pitting is to occur, which is important for designing and using stainless steels in environments with a lot of chloride

Chromosome Instability and Micronucleus Frequency on the Oral Mucosa of HIV-positive Patients

Extranuclear structures known as micronuclei (MN) are composed of whole or fragmented chromosomes that were not incorporated into the nucleus following cell division. The genotoxic impact of HIV infection on oral cavity cancers remains uncertain. This study sought to determine the impact of HIV infection on MN in HIV+ patients’ oral mucosa and its correlation with early cytogenetic alterations in oral carcinogenesis. A total of forty-four non-HIV patients and thirty-eight HIV+ patients were assessed in this study. Smears were collected from the oral cavity and stained with 5% methylene blue. The smears were then examined at a ×100 magnification using a standard microscope. For each participant, 100 buccal cells were counted. Further observations of the viral load (VL), lymphocytes, and granulocytes were made to determine the pattern of MN presence in HIV+ patients. Significant differences were observed between HIV+ patients and healthy controls regarding alcohol consumption (p = 0.004 < 0.05) and smoking (p = 0.041 < 0.05). The relationship between micronucleus and VL is substantial. After calculating the linear regression model, it was discovered that the VL ratio of HIV-positive patients could predict the micronucleus cells (R-Sq = 55%, p < 0.000). In conclusion, HIV VL shows increased genomic instability. These findings are relevant to understanding the mechanisms of cellular damage and developing potential strategies to mitigate carcinogenesis in HIV+ patients

Dolomitization and Hypogenic Dissolution of the Eocene Avanah Formation, Iraqi Kurdistan

This study constrains the mechanism of extensive dolomitization and its impact on reservoir quality of the shallowwater marine ramp carbonates of the Avanah Formation (Eocene), Iraqi Kurdistan. The presence of shoal deposits, which semiisolate a lagoon water body from the open marine, suggests that dolomitization was by seepage reflux of brines. Nevertheless, the absence of eogenetic gypsum/anhydrite in the dolostones succession indicates that the dolomitizing fluids were mesohaline/penesaline brines formed during cycles of relative sea level (RSL) fall. Dolomitization resulted in the formation of abundant intercrystalline and moldic/vuggy pores. Restriction of dolomitization and related reservoir quality improvement to the lower part of the formation is attributed to an overall 3rd order fall in the RSL. Conversely, the lack of dolomitization in the upper part of the formation is attributed to deposition during 3rd order marine transgression, which prevented severe restriction and evaporation of the inner ramp and, consequently, inhibited the development of dolomitizing brines. It is suggested that hypogenic dissolution (karstification) by upward flow of aggressive fluids along faults and fractures during the Zagros Orogeny caused dissolution and considerable porosity and permeability improvement of the dolostones. A greater extent of dolostones dissolution in the flanks, which was accompanied by calcite cementation, compared to the crest, reflects the role of oil emplacement in the retardation of diagenetic reactions

In-depth Analysis on Machine Learning Approaches: Techniques, Applications, and Trends

Machine learning (ML) approaches cover several aspects of daily life tasks, including knowledge representation, data analysis, regression, classification, recognition, clustering, planning, reasoning, text recommendation, and perception. The ML approaches enable applications to learn and adapt with or without being directly programmed from previous data or experience. The ML techniques, coupled with current technologies, provide a range of solutions, starts from vision-based applications to text-generation applications. To this end, this article presents a comprehensive overview of the approaches of ML, including supervised, unsupervised, semi-supervised, reinforcement, and self-learning. This review critically examines the roles performed by these aforementioned approaches in terms of their weaknesses and strengths. Furthermore, within this study, a new comparative analysis is conducted by reviewing existing studies and evaluating ML techniques using metrics including data requirement, accuracy, complexity, interpretability, scalability, applications, and challenges. Thereafter, the implemented ML techniques are classified, and their key findings are examined. The comprehensive review demonstrates that neither standalone nor hybrid ML techniques can completely satisfy all of the evaluated metrics, the necessity of customized solutions based on the requirements of particular applications

Formalizing Public Transit in Mid-Sized Developing Cities: A Review of Strategies for Sustainable and Resilient Mobility

Mid-sized cities in developing cities face increasing demand to modernize their public transit (PT) systems to advance sustainability, equity, and resilience. Many of these cities remain dependent on informal transit modes such as minibuses, privately owned taxis, and shared vans which, despite their flexibility, often lead to operational inefficiencies, safety risks, and limited accessibility. This review examines strategies for transitioning to formal public bus transit (BT) systems through analysis of peer-reviewed literature. The analysis is organized around five core domains that directly reflect the structure of this study: assessment of the current state of PT systems, strategies for transitioning from informal to formal networks, selection of appropriate PT modes for mid-sized cities, planning processes for BT systems, and sustainable and resilient approaches for BT development. Based on these findings, this study proposes a structured decision-support framework in the form of a decision tree to guide context-sensitive formalization efforts. Future studies should prioritize long-term impact evaluation, inclusive transition mechanisms for informal operators, and the integration of smart and sustainable technologies

A Spatio-Temporal Deep Learning Approach for Efficient Deepfake Video Detection

Deepfake videos have grown to be a big concern in the modern digital media landscape as they cause difficulties undermining the legitimacy of channels of information and communication. Humans often find it challenging to tell the difference between a fake and a genuine video due to the increasing realism of facial deepfakes. Identification of these misleading materials is the first step in preventing deepfakes from spreading through social media. This work introduces Spatio-temporal Intelligent Deepfake Detector (STIDD), a deep learning system including enhanced spatial and temporal modeling techniques. By means of a pre-trained EfficientNetV2-B0 model, the proposed framework efficiently extracts spatial characteristics from each frame, subsequently, and Bidirectional Long Short-Term Memory layers help to capture temporal relationships from video sequences. We evaluate STIDD on the FaceForensics++ (FF++) dataset encompassing all five manipulation techniques (DeepFakes, FaceSwap, Face2Face, FaceShifter, and NeuralTextures). The experimental results reveal that STIDD achieved precision, recall, and F1-scores  all higher than 0.99 and a final test accuracy of 99.51% on the combined FF++ test set. The results demonstrate that the integration of sophisticated spatial extraction and strong temporal modeling allows STIDD to achieve high detection performance while maintaining computing efficiency at just 0.39 Giga Floating-Point Operations (GFLOPs) per inference.

Performance Evaluation of Membrane Bioreactor Operational Design in Sewage Treatment Plant using GPS-X Software

This study evaluates the operational performance of a full-scale membrane bioreactor wastewater treatment plant located in Kuala Lumpur using GPS-X 8.0 simulation software. Key performance indicators–including mixed liquor suspended solids (MLSS), transmembrane pressure (TMP), dissolved oxygen, and effluent total suspended solids (TSS)were monitored over 30 days. The simulations were conducted using the advanced mode and calibrated with actual plant data. Results show that although the plant complies with Malaysian effluent discharge standards, it operates well below its design capacity, with MLSS levels significantly lower than recommended. This operational underload contributes to increased energy consumption and reduced treatment efficiency, particularly in terms of TSS and chemical oxygen demand removal