Computational Advances Research Articles

Analyzing Programming Language Trends Across Industries: Adoption Patterns and Future Directions

This study examines the adoption of programming languages across industries such as finance, healthcare, game development, data science, and embedded systems. It analyzes factors like performance, developer productivity, and ecosystem support influencing language choice. The research shows that while Java, C++, and Python remain dominant due to their maturity, versatility, and widespread usage, newer languages like Rust, Go, and Kotlin are gaining popularity in specific fields that require improved safety, scalability, and developer-centric features. The paper also explores the challenges of balancing modern language adoption with legacy systems, including compatibility, resource allocation, and organizational inertia. Additionally, it investigates the role of community support, tooling, and frameworks in driving language adoption. The study predicts future trends driven by advancements in AI, cloud computing, and cybersecurity, highlighting how these technological shifts shape language preferences. Furthermore, it delves into the influence of programming paradigms, emerging technologies, and organizational priorities in shaping industry-specific language trends. The research underscores the need for a strategic approach to language adoption, balancing innovation with the practical challenges posed by legacy systems and workforce adaptability. As industries evolve, they must navigate the trade-offs between adopting innovative languages and maintaining legacy systems, which remain critical for many operations. This research provides valuable insights into how programming languages are evolving to meet the demands of a rapidly changing technological landscape, emphasizing the importance of security, efficiency, and developer productivity in shaping the future of software development.

Exceptional Absorption in a Deep‐Subwavelength Plasmonic Film

AbstractIn the realm of photonic integrated circuit design, on‐chip absorbers are imperative for preventing signal degradation caused by unintended stray photons, which induce signal crosstalk and operational errors. An ideal on‐chip optical absorber is expected to have a superior absorption capability across a wide range of frequencies while maintaining a minimal footprint. However, widely employed optical thin‐film absorbers suffer from limited absorption due to inherent refractive index mismatches and short light‐matter interaction lengths. Here a ≈45‐nm thin metalayer is demonstrated that exhibits uniformly high absorption over a broad wavelength range from 350 nm to 4.5 µm in a wide range of incident angles up to over 60 degrees. The metalayer's performance notably exceeds the absorption predicted by standard plane‐wave optics theories, potentially attributing to the Anderson localization effect. Integration of such a deep‐subwavelength metalayer absorber into photonic circuits will facilitate the creation of highly efficient photonic chips, propelling the advancement of optical communication and computing.

Advanced Custom Interconnects: A Paradigm Shift in High-Performance Computing Data Transfer Efficiency

Recent advancements in high-performance computing (HPC) systems have highlighted the critical role of custom interconnects in achieving optimal system performance and efficiency. This article comprehensively analyzes modern custom interconnect architectures, focusing on their capacity to enhance data transfer speeds while maintaining power efficiency. The article examines the integration mechanisms for heterogeneous computing elements, including CPUs, GPUs, and specialized accelerators, and evaluates their impact on system-wide performance. The article explores advanced routing algorithms and coherence protocols that enable seamless communication across multiple processing elements while ensuring data consistency. The findings demonstrate that custom interconnects significantly improve system throughput and reduce latency in large-scale HPC deployments. Furthermore, the article analyzes the implications of these advancements for cloud computing, artificial intelligence, and big data analytics applications. This article contributes to the understanding of next-generation computing infrastructure by highlighting the crucial relationship between interconnect design and overall system performance while addressing the escalating demands of modern computational workloads.

Targeting Lin28: Insights into Biology and Advances with AI-Driven Drug Development

Lin28, a conserved RNA-binding protein, promotes cancer stem cell features, epithelial-to-mesenchymal transition, and treatment resistance. Lin28 regulates mRNA translation, RNA stability, and transcription, which improve tumor plasticity and treatment resistance, in addition to suppressing let-7 microRNA production. Lin28 is an interesting cancer target due to its many functions; however, its structural complexity makes treatment development difficult. Lin28's proven and new non-canonical activities in cancer biology are examined in this work, focusing on cancer stem cell maintenance, metastasis, and treatment resistance. We highlight computational drug discovery advances targeting Lin28 utilizing virtual screening and machine learning. Generative artificial intelligence has made it easier to develop inhibitors for challenging targets like Lin28. This work integrates current knowledge and technology to demonstrate Lin28's therapeutic potential and outline future techniques to overcome RNA-binding protein targeting challenges.

Future Directions for Quantitative Systems Pharmacology.

In this chapter, we envision the future of Quantitative Systems Pharmacology (QSP) which integrates closely with emerging data and technologies including advanced analytics, novel experimental technologies, and diverse and larger datasets. Machine learning (ML) and Artificial Intelligence (AI) will increasingly help QSP modelers to find, prepare, integrate, and exploit larger and diverse datasets, as well as build, parameterize, and simulate models. We picture QSP models being applied during all stages of drug discovery and development: During the discovery stages, QSP models predict the early human experience of in silico compounds created by generative AI. In preclinical development, QSP will integrate with non-animal "new approach methodologies" and reverse-translated datasets to improve understanding of and translation to the human patient. During clinical development, integration with complementary modeling approaches and multimodal patient data will create multidimensional digital twins and virtual populations for clinical trial simulations that guide clinical development and point to opportunities for precision medicine. QSP can evolve into this future by (1) pursuing high-impact applications enabled by novel experimental and quantitative technologies and data types; (2) integrating closely with analytical and computational advancements; and (3) increasing efficiencies through automation, standardization, and model reuse. In this vision, the QSP expert will play a critical role in designing strategies, evaluating data, staging and executing analyses, verifying, interpreting, and communicating findings, and ensuring the ethical, safe, and rational application of novel data types, technologies, and advanced analytics including AI/ML.

Considering Multiecosystem Trade-Offs Is Critical When Leveraging Systematic Conservation Planning for Restoration.

Conservationists are increasingly leveraging systematic conservation planning (SCP) to inform restoration actions that enhance biodiversity. However, restoration frequently drives ecological transformations at local scales, potentially resulting in trade-offs among wildlife species and communities. The Conservation Interactions Principle (CIP), coined more than 15 years ago, cautions SCP practitioners regarding the importance of jointly and fully evaluating conservation outcomes across the landscape over long timeframes. However, SCP efforts that guide landscape restoration have inadequately addressed the CIP by failing to tabulate the full value of the current ecological state. The increased application of SCP to inform restoration, reliance on increasingly small areas to sustain at-risk species and ecological communities, ineffective considerations for the changing climate, and increasing numbers of at-risk species, are collectively intensifying the need to consider unintended consequences when prioritizing sites for restoration. Improper incorporation of the CIP in SCP may result in inefficient use of conservation resources through opportunity costs and/or conservation actions that counteract one another. We suggest SCP practitioners can avoid these consequences through a more detailed accounting of the current ecological benefits to better address the CIP when conducting restoration planning. Specifically, forming interdisciplinary teams with expertise in the current and desired ecosystem states at candidate conservation sites; improving data availability; modeling and computational advancements; and applying structured decision-making approaches can all improve the integration of the CIP in SCP efforts. Improved trade-off assessment, spanning multiple ecosystems or states, can facilitate efficient, proactive, and coordinated SCP applications across space and time. In doing so, SCP can effectively guide the siting of restoration actions capable of promoting the full suite of biodiversity in a region.

Role of Artificial Intelligence in VLSI Design: A Review

: Artificial intelligence (AI) related technologies are being employed more and more in a range of industries to increase automation and improve productivity. The increasing volumes of data and advancements in high-performance computing have led to a sharp increase in the application of these methods in recent years. AI technology has been widely applied in the field of hardware design, notably in the design of digital and analogue integrated circuits (ICs), to address challenges such as rising networked devices, aggressive time-to-market, and everincreasing design complexity. However, very little attention has been paid to the issues and problems related to the design of integrated circuits. The authors of this article review the stateof- the-art in AI for circuit design and optimization. AI offers knowledge-based technologies that give challenges a foundation and structure. A technology known as AI makes it possible for machines to mimic human behavior. Data in all formats, including unstructured, semistructured, and structured, can be processed by AI. It is crucial to incorporate all of the features and levels of the many CAD programmes into a single, cohesive environment for creation, as was mentioned in the section that came before this one. Consequently, the application of AI automation helps to enhance the effectiveness and efficiency of CAD's performance.

Characteristics of Phase Transitions in Dry Aligning Active Matter

Abstract Active matter is a non-equilibrium condensed system consisting of self-propelled particles capable of converting stored or ambient energy into collective motion. Typical active matter systems include cytoskeleton biopolymers, swimming bacteria, artificial swimmers, and animal herds. In contrast to wet active matter, dry active matter is an active system characterized by the absence of significant hydrodynamic interactions and conserved momentum. In dry active matter, the role of surrounding fluids is providing viscous friction at low Reynolds numbers and can be neglected at high Reynolds numbers. This review offers a comprehensive overview of recent experimental, computational, and theoretical advances in understanding phase transitions and critical phenomena in dry aligning active matter, including polar particles, self-propelled rods, active nematics, and their chiral counterparts. Various ways of determining phase transition points as well as non-equilibrium phenomena, such as collective motion, cluster formation, and creation and annihilation of topological defects are reviewed.

Enhancing AI Through Statistical Methods for Improved Decision-Making

This study examines the crucial relationship between Artificial Intelligence (AI) and statistics, which has grown in importance due to the information boom and computational advances. Modern data-intensive applications have led these domains to merge, despite their parallel history. This document explores AI's fundamentals, focusing on machine learning and deep learning, and how statistical methods help AI perform tasks like decision-making and pattern identification. We integrate AI with robust statistical modelling and prediction to improve AI transparency and effectiveness. This multidisciplinary approach emphasizes theoretical advances, practical applications, ethical considerations, and future problems at the interface of AI and statistics. We encourage AI and statistics communities to collaborate to promote innovation and responsible AI development and deployment. This collection of publications is a comprehensive resource for researchers and practitioners using AI and statistics to better decision-making and predictive analytics

Dual-role ion dynamics in ferroionic CuInP2S6: revealing the transition from ferroelectric to ionic switching mechanisms

Due to its “ferroionic” nature, CuInP2S6 combines switchable ferroelectric polarization with highly mobile Cu ions, allowing for multiple resistance states. Its conductive mechanism involves ferroelectric switching, ion migration, and corresponding intercoupling, which are highly sensitive to external electric field. Distinguishing the dominant contribution of either ferroelectric switching or ion migration to dynamic conductivity remains a challenge and the conductive mechanism is not clear yet. Here, based on polarization switching analyses and first-principles calculations, this work demonstrates that the Cu ion migration pathways enable the formation of a quadruple-well state, determining the conductive mechanism. Accordingly, it favors the manipulation of Cu ion transport in the intralayer and interlayer in a controlled manner, and makes a transition from ferroelectric-dominated to ion-migration-dominated conductivity, by tailoring the electric fields. This work deepens the understanding of ion migration dynamics and conductive switching in ferroionic systems, which is critical for the advancement of memristor-based neuromorphic computing.

Computational and experimental advances in liver-on-a-chip technology for cancer research: a systematic review

Computational and experimental advances in liver-on-a-chip technology for cancer research: a systematic review

Evaluating DL Model Scaling Trade-Offs During Inference via an Empirical Benchmark Analysis

With generative Artificial Intelligence (AI) capturing public attention, the appetite of the technology sector for larger and more complex Deep Learning (DL) models is continuously growing. Traditionally, the focus in DL model development has been on scaling the neural network’s foundational structure to increase computational complexity and enhance the representational expressiveness of the model. However, with recent advancements in edge computing and 5G networks, DL models are now aggressively being deployed and utilized across the cloud–edge–IoT continuum for the realization of in situ intelligent IoT services. This paradigm shift introduces a growing need for AI practitioners, as a focus on inference costs, including latency, computational overhead, and energy efficiency, is long overdue. This work presents a benchmarking framework designed to assess DL model scaling across three key performance axes during model inference: classification accuracy, computational overhead, and latency. The framework’s utility is demonstrated through an empirical study involving various model structures and variants, as well as publicly available datasets for three popular DL use cases covering natural language understanding, object detection, and regression analysis.

Optimization and Safety Research on Autonomous Vehicles Based on Blockchain, Quantum Computing, and Artificial Intelligence

The field of autonomous vehicles is undergoing rapid development, with research focused on enhancing safety, efficiency, and intelligence to drive the full realization and transformation of intelligent transportation systems. This paper explores the potential applications and challenges of blockchain technology, quantum computing, and artificial intelligence in the realm of autonomous vehicles. The study finds that integrating blockchain with autonomous vehicles significantly improves data encryption security through quantum hash algorithms. However, the advancement of quantum computers still faces challenges in technological maturity, stability, and cost. In the operating mechanisms of autonomous vehicles, quantum reinforcement learning demonstrates unique advantages in path optimization, while quantum annealing and quantum optimization algorithms improve decision-making efficiency and precision. This research highlights the innovative opportunities brought by blockchain, quantum computing, and artificial intelligence to the field of autonomous vehicles, while also addressing the challenges of technology integration, providing valuable insights for the development of intelligent transportation systems.

Role of Computer-Aided Design and Development in Modern Pharmaceuticals

The integration of molecular biology and computational technologies has transformed modern drug discovery and development processes. Advanced computational methodologies, particularly artificial intelligence (AI) and machine learning (ML), have reshaped traditional drug development approaches through unprecedented access to ligand property data, target binding interactions, three-dimensional protein structures, and virtual libraries containing billions of drug-like molecules. AI and deep learning (DL) implementations have enhanced multiple stages of drug discovery, from target identification to lead optimization. These computational advances, backed by improved hardware capabilities and sophisticated algorithms, now enable targeting of previously "undruggable" proteins. This review presents modern computational approaches in pharmaceutical development, including strategies for challenging protein targets through covalent regulation, allosteric inhibition, protein-protein interaction modulation, and targeted protein degradation. AI-driven methods have accelerated drug discovery pipelines, reduced development costs, and improved clinical trial success rates. The transition from traditional broad-spectrum approaches to precision medicine, supported by computational tools, has enabled personalized therapeutic strategies. Current limitations in computer-aided drug design persist, yet the combination of computational predictions with experimental validation continues to advance therapeutic development. Recent developments in quantum computing and advanced neural networks promise to further enhance drug discovery efficiency and success rates in the coming decades.

Breaking Uncertainty Barriers: Approximate Bayesian Computation Advances in Rainfall–Runoff Modeling

Breaking Uncertainty Barriers: Approximate Bayesian Computation Advances in Rainfall–Runoff Modeling

A Container Escape Detection Method Based on a Dependency Graph

With the rapid advancement in edge computing, container technology has gained widespread adoption. This is due to its lightweight isolation mechanisms, high portability, and fast deployment capabilities. Despite these advantages, container technology also introduces significant security risks. One of the most critical is container escape. However, current detection research is incomplete. Many methods lack comprehensive detection coverage or fail to fully reconstruct the attack process. To address these gaps, this paper proposes a container escape detection method based on a dependency graph. The method uses various nodes and edges to describe diverse system behaviors. This approach enables the detection of a broader range of attack types. It also effectively captures the contextual relationships between system events, facilitating attack traceability and reconstruction. We design a method to identify container processes on the dependency graph through label generation and propagation. Based on this, container escape detection is implemented using file access control within the graph. Experimental results demonstrate the effectiveness of the proposed method in detecting container escapes.

A comparative study of the effect of knowledge sharing via Adobe Connect and Google Meet on EFL teachers’ creativity

Information exchange takes place in every community and the advancement of computers and the use of the Internet has made it simpler for people of all communities to communicate information. Teachers also may share personal experiences, information, and communication with their peers in the profession. They can also improve their expertise and skills. As such, the purpose of the present study was to compare the effect of knowledge sharing via Adobe Connect and Google Meet on English as Foreign Language (EFL) teachers’ creativity. To do so, 60 EFL teachers from 15 high schools were asked to fill the creativity questionnaire. Then, to find the role of the social platforms on EFL teachers’ creativity in their teaching, a week after the treatment, the questionnaire as the posttest was given to the EFL teachers. Next, a semi-structured interview with 20 volunteers sought the Iranian EFL teachers' attitude toward the role of social platforms in their creativity. Overall, the finding indicated that knowledge sharing through Google Meet has more significant effect on EFL teachers’ creativity compared to Adobe Connect. In addition, it was found that the teachers agreed with the positive effect of social platforms on their creativity. The findings have implications for pedagogy as well as further research.

Analytical Performance of a Novel Nanopore Sequencing for SARS-CoV-2 Genomic Surveillance.

The genomic analysis of SARS-CoV-2 has served as a crucial tool for generating invaluable data that fulfils both epidemiological and clinical necessities. Long-read sequencing technology (e.g., ONT) has been widely used, providing a real-time and faster response when necessitated. A novel nanopore-based long-read sequencing platform named QNome nanopore has been successfully used for bacterial genome sequencing and assembly; however, its performance in the SARS-CoV-2 genomic surveillance is still lacking. Synthetic SARS-CoV-2 controls and 120 nasopharyngeal swab (NPS) samples that tested positive by real-time polymerase chain reaction were sequenced on both QNome and MGI platforms in parallel. The analytical performance of QNome was compared to the short-read sequencing on MGI. For the synthetic SARS-CoV-2 controls, despite the increased error rates observed in QNome nanopore sequencing reads, accurate consensus-level sequence determination was achieved with an average mapping quality score of approximately 60 (i.e., a mapping accuracy of 99.9999%). For the NPS samples, the average genomic coverage was 89.35% on the QNome nanopore platform compared with 90.39% for MGI. In addition, fewer consensus genomes from QNome were determined to be good by Nextclade compare with MGI (p < 0.05). A total of 9004 mutations were identified using QNome sequencing, with substitutions being the most prevalent, in contrast, 10 997 mutations were detected on MGI (p < 0.05). Furthermore, 23 large deletions (i.e., deletions≥ 10 bp) were identified by QNome while 19/23 were supported by evidence from short-read sequencing. Phylogenetic analysis revealed that the Pango lineage of consensus genomes for SARS-CoV-2 sequenced by QNome concorded 83.04% with MGI. QNome nanopore sequencing, though challenged by read quality and accuracy compared to MGI, is overcoming these issues through bioinformatics and computational advances. The advantage of structure variation (SV) detection capabilities and real-time data analysis renders it a promising alternative nanopore platform for the surveillance of the SARS-CoV-2.

Recent Advancements in 2D Material-Based Memristor Technology Toward Neuromorphic Computing.

Two-dimensional (2D) layered materials have recently gained significant attention and have been extensively studied for their potential applications in neuromorphic computing, where they are used to mimic the functions of the human brain. Their unique properties, including atomic-level thickness, exceptional mechanical stability, and tunable optical and electrical characteristics, make them highly versatile for a wide range of applications. In this review, we offer a comprehensive analysis of 2D material-based memristors. Furthermore, we examine the ability of 2D material-based memristors to successfully mimic the human brain by referencing their neuromorphic applications.

Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing data

Predicting the extent of massive wildfires once ignited is essential to reduce the subsequent socioeconomic losses and environmental damage, but challenging because of the complexity of fire behavior. Existing physics-based models are limited in predicting large or long-duration wildfire events. Here, we develop a deep-learning-based predictive model, Fire-Image-DenseNet (FIDN), that uses spatial features derived from both near real-time and reanalysis data on the environmental and meteorological drivers of wildfire. We trained and tested this model using more than 300 individual wildfires that occurred between 2012 and 2019 in the western US. In contrast to existing models, the performance of FIDN does not degrade with fire size or duration. Furthermore, it predicts final burnt area accurately even in very heterogeneous landscapes in terms of fuel density and flammability. The FIDN model showed higher accuracy, with a mean squared error (MSE) about 82% and 67% lower than those of the predictive models based on cellular automata (CA) and the minimum travel time (MTT) approaches, respectively. Its structural similarity index measure (SSIM) averages 97%, outperforming the CA and FlamMap MTT models by 6% and 2%, respectively. Additionally, FIDN is approximately three orders of magnitude faster than both CA and MTT models. The enhanced computational efficiency and accuracy advancements offer vital insights for strategic planning and resource allocation for firefighting operations.