Complex Source Research Articles

Beyond Cohesion and Coupling: Integrating Control Flow in Software Modularization Process for Better Code Comprehensibility

As software systems evolve to meet the changing needs of users, understanding the source code becomes a critical step in the process. Clustering techniques, also known as modularization techniques, offer a solution to breaking down complex source code into smaller, more manageable parts. This facilitates improved analysis and understanding of the software’s structure. However, the effectiveness of clustering algorithms in code understanding heavily relies on the chosen criteria. While existing methods typically consider cohesion, coupling, and balance between clusters, we argue that these criteria alone may not fully satisfy one of the primary objectives of clustering, which is to enhance understanding. This is because spaghetti-like structures can be created even when these criteria are satisfied. To address this issue, we introduce two new criteria incorporating program control flow to regulate cluster dependencies. By controlling the uniformity of input and output directions, as well as the distribution of inputs and outputs, clustering algorithms can generate clusters that are more developer-friendly and easier to comprehend. We provide intuitive explanations and real-world projects to demonstrate the effectiveness of our approach, and also incorporate feedback from academics and expert programmers. This paper reveals that integrating these new criteria into existing clustering algorithms enables developers to gain deeper insights into the structure of software systems. This, in turn, leads to better design decisions and improved developer understanding of the source code.

Knowledge-based In Silico Fragmentation and Annotation of Mass Spectra for Natural Products with MassKG

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) is a potent analytical technique utilized for identifying natural products from complex sources. However, due to the structural diversity, annotating LC-MS/MS data of natural products efficiently remains challenging, hindering the discovery process of novel active structures. Here, we introduce MassKG, an algorithm that combines a knowledge-based fragmentation strategy and a deep learning-based molecule generation model to aid in rapid dereplication and the discovery of novel NP structures. Specifically, MassKG has compiled 407,720 known NP structures and, based on this, generated 266,353 new structures using chemical language models for the discovery of potential novel compounds. Furthermore, MassKG demonstrates exceptional performance in spectra annotation compared to state-of-the-art algorithms. To enhance usability, MassKG has been implemented as a web server for annotating tandem mass spectral data (MS/MS, MS2) with a user-friendly interface, automatic reporting, and fragment tree visualization. Lastly, the interpretive capability of MassKG is comprehensively validated through composition analysis and MS annotation of Panax notoginseng, Ginkgo Biloba, Codonopsis pilosula, and Astragalus membranaceus. MassKG is now accessible at https://xomics.com.cn/masskg.

Linking Molecular Mechanisms to their Evolutionary Consequences: a primer.

A major obstacle to predictive understanding of evolution stems from the complexity of biological systems, which prevents detailed characterization of key evolutionary properties. Here, we highlight some of the major sources of complexity that arise when relating molecular mechanisms to their evolutionary consequences and ask whether accounting for every mechanistic detail is important to accurately predict evolutionary outcomes. To do this, we developed a mechanistic model of a bacterial promoter regulated by two proteins, allowing us to connect any promoter genotype to six phenotypes that capture the dynamics of gene expression following an environmental switch. Accounting for the mechanisms that govern how this system works enabled us to provide an in-depth picture of how regulated bacterial promoters might evolve. More importantly, we used the model to explore which factors that contribute to the complexity of this system are essential for understanding its evolution, and which can be simplified without information loss. We found that several key evolutionary properties - the distribution of phenotypic and fitness effects of mutations, the evolutionary trajectories during selection for regulation - can be accurately captured without accounting for all, or even most, parameters of the system. Our findings point to the need for a mechanistic approach to studying evolution, as it enables tackling biological complexity and in doing so improves the ability to predict evolutionary outcomes.

Heterogeneity Analysis of Regional Greenhouse Gas Driving Effects: An Empirical Study from Southeast Asian Countries

Southeast Asia is suffering from extreme weather, and its carbon emissions are increasing rapidly. For efficient emission reduction, clarifying the complex sources behind is necessary. This study takes a new perspective of incorporating multiple GHGs (greenhouse gases) into the same analysis framework, employing the STIRPAT model to dissect the contributions of various socio-economic factors to the emissions of CO2, CH4, and F-gases based on panel data. The analysis reveals that the driving coefficients of total population and urbanization rate are several to 10 times higher than those of other factors and can reach up to 2.98 and 4.715 and are the most significant drivers of GHG emissions in the region. Quadratic per capita GDP shows a significant positive driving effect, indicating that most Southeast Asian countries are unlikely to reach the Kuznets point in current development trajectories. The driving coefficients of F-gases in industrialized countries are significantly higher than those of other GHGs, indicating that their growth rate of F-gases will outpace that of CO2 and CH4. In countries with a similar industrial structure, the driving coefficient of CO2 from the secondary industry is up to 0.183 and down to 0.057, shows the influence of specific sector composition in the secondary industry on emissions. These findings provide critical insights for Southeast Asian policymakers aiming to develop effective climate policies.

Forage ecology of Neotropical fish in Brazilian biomes using stable isotopes

Neotropical regions are responsible for harboring most of the global diversity of freshwater fish, providing essential ecosystem services for society. Human activities (e.g., land use changes) jeopardize aquatic ecosystems as well as species, communities, etc., impairing ecosystem services. We investigate the impact of human disturbance on the foraging ecology of Neotropical freshwater fishes across five Brazilian biomes by stable isotope analysis. We analyzed carbon (δ13C) and nitrogen (δ15N) isotopic compositions of Neotropical fishes, sourced from the SIA-BRA dataset. Fishes were categorized into herbivores, omnivores, and carnivores. We tested the correlation between human disturbances, indicated by the human disturbance index (hdi), and changes in fish diets. We expected that the assimilation of C4‑carbon from exotic forage would increase with higher disturbance levels, while δ15N levels would similarly rise due to nitrogen input from anthropogenic sources. We found increases in fish δ13C with human disturbance increases, suggesting greater assimilation of C4 carbon in places where native vegetation was replaced by C4 sources, confirmed by isotopic mixing models. In contrast, δ15N values did not show a significant relationship with human disturbance, probably due to the complex interactions and multiple sources of nitrogen in disturbed environments. Our finds suggest that stable isotope analysis provides a powerful tool for monitoring the effects of landscape changes on aquatic food webs. Particularly, the δ13C values of detritivorous fish that feed on C4 plant detritus could serve as bioindicators of environmental degradation. However, a specific isotopic characterization of each site would be valuable for more accurate niche information.

Volatolomics in Fritillarias and Their Identification by Orientation Controlled cMOF Thin Film Chemiresistors

Comprehensive SummaryAs one of the famous traditional Chinese herbal medicines, Fritillariae Cirrhosae Bulbus (FCB) is widely used in the prevention and treatment of respiratory diseases and has the best curative effect among the known fritillarias medicines. Due to the variety, complex sources, similar appearance and shape, it is difficult to distinguish FCB with high curative effect (h‐FCB) from other common fritillarias (c‐FCB) in the market. In this paper, a very simple chemiresistor is used to identify FCB from three commonly used fritillarias drugs. The sensors are fabricated by anisotropic electrically conductive metal‐organic framework (cMOF) thin film Cu3(HHTP)2 (Cu‐HHTP[001] and Cu‐HHTP[100]) as active materials owing to their ability to detect specific groups of volatile organic compounds (volatolomics) as the functional motifs of chemiresistor. As a result, the sensors show orientation‐dependence identification ability to FCB. Cu‐HHTP[001]‐ based sensor shows the highest response (344.17%) to 0.5 g h‐FCB powder volatiles among its three other c‐FCB which is much higher than Cu‐HHTP[100] (135.50%). Ultimately, Cu‐HHTP[001] can realize the identification of FCB with an accuracy of 97.2% in a simple and real‐time manner.

Anti-inflammatory Fatty Acid Derivatives From Mangrove-derived Actinomycetes Streptomyces sp.

Mangrove-derived actinomycetes are prolific chemical sources of complex and novel natural products, providing an excellent chance for new drug discovery. The chemical investigation of the mangrove-derived Streptomyces sundarbansensis 06037, led to the discovery of two previously undescribed enone fatty acids (1-2), one new phenylpropionate derivate (3), along with the isolation of the ten known components (4-13). Those chemical structures of isolates were elucidated on the basis of the analysis of diverse spectroscopic data. Initial anti-inflammatory tests of 1-3 in lipopolysaccharide (LPS)-activated RAW 264.7 murine macrophage cells revealed that compound 1 possess significant inhibitory effect on the production of Nitro oxidase (NO), with the IC50 value around 15.33±1.32 μM, together with the suppression of NF-κB phosphorylation and reducing the release of oxygen species (ROS) in RAW 264.7 macrophages, those results indicated that compound 1 may exert its anti-inflammatory activity through a reduction in ROS level and the suppression of NF-κB activation pathway.

Research on the Current Situation and Sustainable Development Measures for Urban–Rural Water Supply Integration in Yunnan Province, China

Urban–Rural Water supply integration is one of the effective ways to address rural drinking water safety issues. With the rapid economic development and urbanization, the gap between urban and rural water supply has become increasingly evident, and rural areas face severe challenges such as insufficient water sources and substandard water quality. Yunnan Province, due to its unique topography and natural environment, encounters numerous difficulties in promoting the construction and development of urban–rural water supply integration, and there is a severe lack of research specifically focused on sustainable development measures for urban–rural water supply integration in Yunnan Province. In light of this, this paper first reviews the current research status on urban–rural water supply integration both domestically and internationally, pointing out that existing studies mainly focus on optimizing urban water supply systems, addressing rural drinking water safety issues, and exploring water supply management models. It then analyzes the challenges faced by Yunnan Province in advancing urban–rural water supply integration, including limitations in engineering construction, inadequate operation and maintenance, complex and variable water source conditions, and insufficient public awareness of water conservation. In response to these issues, a series of sustainable development measures are proposed, including revitalizing existing assets, enhancing construction planning and quality, promoting water pricing reforms, upgrading the technical system for drinking water safety assurance, and advancing the information technology construction of urban–rural water supply integration, with the aim of providing references and insights for the sustainable development of urban–rural water supply integration in Yunnan Province and other regions.

Exploring Translation Strategies of Thesis Abstract Written by Non-English Department Students in The University of Mataram

This study investigates the translation problems, strategies, and the most frequently used strategy in translating Indonesian thesis abstracts into English. The subjects of this research comprised eight thesis abstracts written by non-English department students at the University of Mataram during the 2023 academic year. Utilizing Baker’s theory on translation problems and strategies, which encompasses eleven aspects, this study employed a mixed-methods approach, combining quantitative and qualitative analyses. The findings reveal a total of 78 translation problems across the eight Indonesian thesis abstracts. Among Baker's identified problems, only eight were observed, including: culture-specific concepts (31 instances), source language concepts not lexicalized in the target language (4 instances), semantically complex source language words (14 instances), a lack of specific terms in the target language (1 instance), differences in expressive meaning (8 instances), differences in form (2 instances), differences in frequency and purpose of specific forms (6 instances), and the use of loan words in the source text (12 instances). To address these challenges, seven out of eight translation strategies from Baker’s theory were employed in the English thesis abstracts. The most utilized strategy was translation using loan words (35 instances, 45%), followed by translation using a more general word (12 instances, 16%), translation using a neutral word (10 instances, 13%), translation by omission (10 instances, 13%), translation by paraphrase using a related word (4 instances, 5%), and the least employed strategies were translation using cultural substitution (3 instances, 4%) and paraphrase using unrelated words (3 instances, 4%). Of the 78 translation problems identified, 77 were resolved using the seven strategies, while one problem remained unsolved due to an unacceptable translation. This thesis aims to serve as a valuable reference for translators, English department students, and researchers in the field of translation. The findings indicate a significant need for enhanced support and resources in the translation process for non-English department students at the University of Mataram.

The Multi‐Segment Complexity of the 2024 MW ${M}_{W}$ 7.5 Noto Peninsula Earthquake Governs Tsunami Generation

AbstractThe 1 January 2024, moment magnitude 7.5 Noto Peninsula earthquake ruptured in complex ways, challenging analysis of its tsunami generation. We present tsunami models informed by a 6‐subevent centroid moment tensor (CMT) model obtained through Bayesian inversion of teleseismic and strong motion data. We identify two distinct bilateral rupture episodes. Initial, onshore rupture toward the southwest is followed by delayed re‐nucleation at the hypocenter, likely aided by fault weakening, causing significant seafloor uplift to the northeast. We construct a complex multi‐fault uplift model, validated against geodetic observations, that aligns with known fault system geometries and is critical in modeling the observed tsunami. The simulations can explain tsunami wave amplitude, timing, and polarity of the leading wave, which are crucial for tsunami early warning. Upon comparison with alternative source models and analysis of 2000 multi‐CMT ensemble solutions, we highlight the importance of incorporating complex source effects for realistic tsunami simulations.

Multiscale monitoring and analysis of complex rupture and source mechanisms of mining-related seismicity on fault networks

Mining-related seismicity poses significant challenges in underground coal mining due to its complex rupture mechanisms and associated hazards. To bridge gaps in understanding these intricate processes, this study employed a multi-local seismic monitoring network, integrating both in-mine and local instruments at overlapping length scales. We specifically focused on a damaging local magnitude (ML) 2.6 event and its aftershocks that occurred on 10 September 2022 in the vicinity of the 3308 working face of the Yangcheng coal mine in Shandong Province, China. Moment tensor (MT) inversion revealed a complex cascading rupture mechanism: an initial moment magnitude (Mw) 2.2 normal fault slip along the DF60 fault in an ESE–WNW direction, transitioning to a Mw 3.0 event as the FD24 and DF60 faults unclamped. The scale-independent self-similarity and stress heterogeneity of mining-related seismicity were investigated through source parameter calculations, providing valuable insights into the driving mechanism of these seismic sequences. The in-mine network, constrained by its low dynamic changes, captured only the nucleation phase of the DF60 fault. Furthermore, standard decomposition of the MT solution from the seismic network proved inadequate for accurately identifying the complex nature of the rupture. To enhance safety and risk management in mining environments, we examined the implications of source reactivation within the cluster area post-stress-adjustment. This comprehensive multiscale analysis offers crucial insights into the complex rupture mechanisms and hazards associated with mining-related seismicity. The results underscore the importance of continuous multi-local network monitoring and advanced analytical techniques for improved disaster assessment and risk mitigation in mining operations.

Substitute Yeast Extract While Maintaining Performance: Showcase Amorpha-4,11-Diene Production.

Yeast extract (YE) is a complex nutritional source associated with high performance on microbial production processes. However, its inherent compositional variability challenges its scalability. While prior efforts have focused on growth-associated products, the dynamics of growth-uncoupled production, which leads to higher production rates and conversion yields, still need to be explored. This production scenario is common in large-scale applications. This study presents a systematic approach to replace YE for the production of the terpene amorpha-4,11-diene in Escherichia coli. Sequential processing was successfully applied to identify glutamic acid, alanine, leucine, valine, isoleucine and glycine as the key amino acids (AAs) under slow-growth conditions. Thoroughly applying biomass retention as part of sequential processing increased production capacity by 45% using these AAs instead of YE. Further studies, including flux balance analyses, targeted pyruvate as the common AA precursor. The optimized fed-batch process feeding pyruvate with 0.09 gPyr h-1 enhanced amorpha-4,11-diene production by 37%, although adding only 1% carbon via pyruvate. Flux balance analysis revealed the criteria for optimum pyruvate feeding, for example, to prevent succinate secretion and maintain the NADH/NAD+ balance. These findings illustrate the interplay between media composition and metabolic activity and provide a successful guideline for identifying lean, best-performing media for industrial applications.

A Multifrequency View of the Radio Phoenix in the A85 Cluster

Radio phoenices are complex and filamentary diffuse radio sources found in both merging and relaxed clusters. The formation of these sources has been proposed to be due to adiabatic compression of old active galactic nucleus plasma in shock waves. Most of the previous spectral studies of these sources have been limited to integrated spectral indices, which were found to be very steep and show a curved spectrum. Here, we have performed a multifrequency investigation of the radio phoenix in the A85 cluster. Owing to the sensitive high-resolution observations, we found some of the finer filamentary structures that had been previously undetected. We produced resolved spectral index maps of the radio phoenix between 323, 700, and 1280 MHz. The orientation of the filaments, as well as the gradient across the spectral index maps, suggest the possible direction of the shock motion from northeast to southwest. The integrated spectrum of the radio phoenix was found to be very steep and curved toward high frequencies. Furthermore, the spectral index of the filaments was found to be less steep compared to the nonfilamentary regions, implying greater energy injection in the filaments. The observed features in the radio phoenix in the A85 cluster seem to be in support of an adiabatic shock compression mechanism.

Surfactant-assisted electrochemical sensor for phytocannabinoids

The Cannabis sativa plant is known to be a complex source of phytocannabinoids with diverse applications across medicinal, recreational, and industrial domains. However, the proliferation of cannabidiol (CBD)-based or low- Δ9-tetrahydrocannabinol (Δ9-THC) content products has posed challenges for law enforcement agencies due to the complexity of distinguishing lawful from illicit usage. As legislation around cannabis evolves, precise and reliable analytical methods are urgently needed. Traditional techniques, while accurate, are time-consuming and resource intensive. Portable field tests lack specificity and often need follow-up confirmatory tests. Herein, an innovative electrochemical sensor for accurate and efficient identification of phytocannabinoids is presented. The study focuses on the detection of tetrahydrocannabinolic acid (THCA), Δ9-THC, cannabidiolic acid (CBDA), CBD, cannabichromene (CBC) and cannabinol (CBN), employing unmodified carbon screen-printed electrodes (SPEs) as the platform. Additionally, the integration of a cationic surfactant, hexadecyltrimethylammonium bromide (CTAB), is investigated to enhance the sensitivity. This electrochemical sensor demonstrates successful qualitative and semi-quantitative identification of the main phytocannabinoids in seizures. Ultimately, our sensor benefits harm and supply reduction agencies by facilitating quality control and addressing the complexities of varied cannabis products.

Evaluating the Potential of Marine Algae as Sustainable Ingredients in Poultry Feed

Algae are a complex source of crucial nutrients, including proteins, minerals, vitamins, and carbohydrates. The use of marine algae, specifically Sargassum species, Gracilaria species, and Spirulina species, as feed ingredients in poultry feed was examined in the current research. This study’s aims were to carry out lipid profiling, proximate analysis, and heavy metal toxicity testing, as well as to evaluate the presence of hazardous hydrocarbons in the algae and investigate the effect of the fatty acid profile of Sargassum species on broilers. The proximate analysis was conducted on the algae types containing considerable quantities of fats and proteins and a sizable quantity of omega-3 fatty acids. This research assessed the productive effectiveness of broilers with 2.5%, 6%, and 10% algal additions in their diet. Fatty acid profiling of Sargassum species was performed at 1% and 2% levels in the bursa, fat pads, heart, liver, spleen, thymus, thighs, and breast. The findings showed no significant differences in the development performance or feed utilization efficiency of broilers with 2.5%, 6%, and 10% algae supplementation in their diet. The results of fatty acid profiling showed changes in the heart, liver, and spleen at 1% and 2% supplementations levels. However, this research concludes that if algae partially replaces the imported feed proteins used in animal feed, then these can achieve maximum beneficial effects on poultry health and performance. Algae are sustainable, easy to produce, and cost-effective sources of nutrients and contain numerous bioactive compounds, which will contribute to sustainability and food safety.

Advancements in railway noise simulation: assessment of tools for virtual validation

In pursuit of more efficient and cost-effective measurement campaigns, the railway industry is embracing the prospect of virtual validation based on simulations. The primary obstacle lies in the need for noise simulation tools capable of accurately replicating complex sources, installation effects, noise propagation, ground reflection and, for some cases, the diffraction in the different train surfaces. This paper presents insights derived from European project Shift2Rail S2R-CFM-CCA-01-2019 Energy and Noise & Vibration WP6, concentrating on the validation and enhancement of two exterior noise simulation tools, tailored for railway applications. The paper covers four reference cases, simulating exterior noise at standstill with diverse noise sources and varying source locations and noise emission (i.e. electrical noise, cooling, etc.). A correlation with previously conducted measurements at typical receiver locations (7.5 meters from the track centerline) is carried out. The measurements were conducted in a "controlled" configuration, isolating as much as possible the analyzed noise source and gathering all the needed information about the ground and train geometry. The evaluation between measurements and simulations is done against a global and a 1/3rd octave band criteria with successful results. Finally, key features essential for achieving more accurate simulations are listed.

SMART: Search Maps and Routes/Trails of Both Subtasks and Sources to Support Complex Task‐based Search

ABSTRACTTowards the goal of constructing an agent that can assist users in completing complex tasks and searching for information from multiple sources. We propose a complex task‐supporting application, SMART (Search Maps and Routes/Trails), which combines complex task modeling through users' web activities and expands search support to include maps and routes/trails of both subtasks and sources within complex tasks through case‐based reasoning. We also summarize the results of a user study conducted to evaluate the usability of the SMART prototype system. The experimental results show that SMART could help searchers make search strategies, select information sources effectively, increase confidence during task performance, and improve search results. This highlights the value of both types of maps and routes/trails of subtasks and sources in complex task‐supporting.

Monitoring and warning for ammonia nitrogen pollution of urban river based on neural network algorithms.

Ammonia nitrogen (AN) pollution frequently occurs in urban rivers with the continuous acceleration of industrialization. Monitoring AN pollution levels and tracing its complex sources often require large-scale testing, which are time-consuming and costly. Due to the lack of reliable data samples, there were few studies investigating the feasibility of water quality prediction of AN concentration with a high fluctuation and non-stationary change through data-driven models. In this study, four deep-learning models based on neural network algorithms including artificial neural network (ANN), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) were employed to predict AN concentration through some easily monitored indicators such as pH, dissolved oxygen, and conductivity, in a real AN-polluted river. The results showed that the GRU model achieved optimal prediction performance with a mean absolute error (MAE) of 0.349 and coefficient of determination (R2) of 0.792. Furthermore, it was found that data preprocessing by the VMD technique improved the prediction accuracy of the GRU model, resulting in an R2 value of 0.822. The prediction model effectively detected and warned against abnormal AN pollution (> 2mg/L), with a Recall rate of 93.6% and Precision rate of 72.4%. This data-driven method enables reliable monitoring of AN concentration with high-frequency fluctuations and has potential applications for urban river pollution management.

Identification of complex system radar emitter source based on one-dimensional convolutional neural network

Abstract With the continuous innovation of radar technology, the signal modulation of radar emitter sources has become more complex, which makes their identification face more challenges. We propose a method based on a one-dimensional convolutional neural network for the identification of complex system radar emitter sources and conduct an experiment on eight radar emitter sources with similar parameters and complex inter-pulse modulation. The identification accuracy reaches 88.24%.

Analysing fluid flow and heat transfer comparatively in flow passage systems: Evaluating thermal impacts and geometric configurations

This research thoroughly examines heat transfer and fluid flow in a passage flow system, highlighting the difficulties posed by different geometric arrangements and temperature conditions that may affect the system's performance. The main aim is to evaluate the impacts of different geometric characteristics on the velocity, pressure, and temperature profiles inside the flow route. These factors encompass cavity dimensions, tube diameter, and input conditions. An inclusive comparison of three different geometric designs at controlled temperatures is conducted using computational fluid dynamics (CFD) simulations. The findings indicate that the optimal geometric parameters improve thermal performance, with certain arrangements displaying improvements in heat transfer rates up to 30 %. In this regard, the higher cavity dimensions and suitable input velocities are exposed as advantages. The first sample exhibited a higher velocity of 0.024 m s-1 due to its simpler geometry and favorable heating conditions, while the third sample demonstrated a higher temperature of 465 K due to its complex cavity shape and multiple heating sources. This study suggests that enhancing efficiency in heat management applications necessitates a strategic design approach for passage flow systems, which must account for flow characteristics and geometric specifications. This research would provide insightful information for designers and engineers looking at enhancing fluid flow systems across a range of industrial applications.