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Predicting software reuse using machine learning techniques—A case study on open-source Java software systems
Software reuse is an essential practice to increase efficiency and reduce costs in software production. Software reuse practices range from reusing artifacts, libraries, components, packages, and APIs. Identifying suitable software for reuse requires pinpointing potential candidates. However, there are no objective methods in place to measure software reuse. This makes it challenging to identify highly reusable software. Software reuse research mainly addresses two hurdles: 1) identifying reusable candidates effectively and efficiently, and 2) selecting high-quality software components that improve maintainability and extensibility. This paper proposes automating software reuse prediction by leveraging machine learning (ML) algorithms, enabling future research and practitioners to better identify highly reusable software. Our approach uses cross-project code clone detection to establish the ground truth for software reuse, identifying code clones across popular GitHub projects as indicators of potential reuse candidates. Software metrics were extracted from Maven artifacts and used to train classification and regression models to predict and estimate software reuse. The average F1-score of the ML classification models is 77.19%. The best-performing model, Ridge Regression, achieved an F1-score of 79.17%. Additionally, this research aims to assist developers by identifying key metrics that significantly impact software reuse. Our findings suggest that the file-level PUA (Public Undocumented API) metric is the most important factor influencing software reuse. We also present suitable value ranges for the top five important metrics that developers can follow to create highly reusable software. Furthermore, we developed a tool that utilizes the trained models to predict the reuse potential of existing GitHub projects and rank Maven artifacts by their domain.
Molecular mechanisms of lung injury from ultra high and conventional dose rate pulsed radiation based on 4D DIA proteomics study
TAS2R5 screening reveals biased agonism that fails to evoke internalization and downregulation resulting in attenuated desensitization
The bitter taste receptor type 5 (TAS2R5) is expressed on multiple cell types and appears to be a suitable target for novel agonist treatments across multiple therapeutic areas. Like most G protein coupled receptors (GPCRs), TAS2R5 undergoes functional desensitization with prolonged agonist exposure which could limit effectiveness. The net loss of cellular receptors (termed downregulation) is a prominent mechanism of long-term desensitization; we screened 13 agonists for downregulation of receptor protein in TAS2R5-transfected HEK-293T and airway smooth muscle cells in culture, searching for pathway selectivity favoring G protein coupling over downregulation. The benchmark agonist 1,10-phenanthroline (denoted T5-1) evoked as much as 75% downregulation of TAS2R5 protein expression with 18-24 hrs of agonist exposure, while an analogue of T5-1 (denoted T5-12) caused a 2-3 fold increase in expression. Functionally, T5-1 and T5-12 were found to be full agonists when measuring [Ca2+]i or ERK1/2 stimulation. The T5-12 phenotype was found to be due to agonist-induced stabilization of the receptor confining it to the cell membrane with subsequent failure to undergo internalization and receptor degradation. This occurred despite normal (referenced to T5-1) GRK-mediated receptor phosphorylation and β-arrestin recruitment by T5-12. Consistent with the lack of downregulation, T5-12 evoked much less functional desensitization of the [Ca2+]i (43% vs 78%) and ERK1/2 (64% vs > 95%) responses compared to T5-1, respectively. We conclude that TAS2R5 pathway signaling is malleable to a more favorable therapeutic profile by agonist-receptor interactions that preserve primary signaling and minimizes desensitization.
Efficient solid-phase extraction of oligo-DNA from complex media using a nitrocellulose membrane modified with carbon nanotubes and aminated reduced graphene oxide
Influence of road environmental factors on traffic accidents involving vulnerable road users through negative binomial models
Ensuring pedestrian safety is crucial for establishing fair and sustainable transportation systems. However, certain demographics face disproportionately higher risks, necessitating age-appropriate policy and design strategies. This study provides a comprehensive analysis of the relationships between objectively measured road infrastructure attributes and pedestrian accident frequencies involving vulnerable groups in Hunan Province, China. By leveraging detailed historical crash records linked to spatially-explicit infrastructure data, the research team employed advanced count regression modeling techniques, including negative binomial (NB) and zero truncated tail negative binomial (ZTNB) specifications, to systematically evaluate the safety impacts of roadway functional classification, intersection design, traffic controls, alignment geometry, pedestrian segregation, land use context, and traffic volumes. The results revealed that the ZTNB approach, which accounted for the excess zero observations inherent to the crash data, provided statistically superior model fit compared to the standard NB formulation. The ZTNB estimation results offered robust empirical evidence regarding key infrastructure risk factors, highlighting that while higher-order roadways exhibited lower pedestrian accident likelihoods, elements such as multi-leg intersections, lack of traffic controls, curved alignments, and absence of segregated facilities correlated with elevated hazards. Older adults and children are particularly susceptible to accidents on major highways and are more prone to traffic incidents on regular roads as opposed to specialized areas like tunnels and intersections. Importantly, the analysis revealed varying safety impacts among different user groups, underscoring the significance of considering the unique requirements and vulnerabilities of diverse pedestrian populations in transportation planning and design. Overall, the findings offer robust empirical evidence to guide development of tailored interventions that consider the unique capacities and exposures of different pedestrian populations. The age-segmented analyses also contribute transportation equity insights for achieving Vision Zero goals through inclusive infrastructure design.
Author Correction: Exploring the nutritional composition and quality parameters of natural honey from diverse melliferous flora
Bonobos know when you’re in the know ― and when you’re not
Cancer prevalence and its determinants in Hungary: Analyzing data from the 2009, 2014, and 2019 European Health Interview Surveys
Background and aim Hungary has the fifth highest cancer incidence rate in the European Union, with an age-standardized rate (ASR) of 336.7 per 100,000 according to GLOBOCAN 2022. Additionally, Hungary holds the highest cancer mortality rate in the EU, with an ASR of 148.1 per 100,000. This study aimed to investigate the sociodemographic, lifestyle, and chronic disease-related factors affecting cancer prevalence in the Hungarian population. Materials and methods Data from the 2009, 2014, and 2019 installments of the European Health Interview Survey conducted in Hungary were pooled, resulting in a representative sample of 16,480 individuals. Weighted multiple logistic regression models were used to analyze the data, with goodness of fit assessed using the Hosmer-Lemeshow test. The best-fitting models were further evaluated using ROC analysis to calculate the Area Under the Curve (AUC) to assess discriminative ability. Results Urban residency was associated with higher cancer odds in 2014 (OR 1.85 [CI: 1.08–3.16]) and the pooled data (OR 1.44 [CI: 1.08–1.9]). Employed individuals had lower odds of cancer (2014: OR 0.34 [CI: 0.16–0.74]; pooled: OR 0.64 [CI: 0.45–0.92]). Among comorbid conditions, peptic ulcer (2009: OR 1.74 [CI: 1.13–2.69]; 2019: OR 3.2 [CI: 1.58–6.47]; pooled: OR 1.83 [CI: 1.31–2.54]) and chronic liver disease (2009: OR 3.52 [CI: 1.73–7.17]; pooled: OR 2.5 [CI: 1.4–4.47]) were significantly associated with higher cancer odds. Reporting bad health was linked to increased cancer risk (2009: OR 2.92 [CI: 1.87–4.58]; 2014: OR 5.52 [CI: 3.23–9.45]; 2019: OR 2.23 [CI: 1.26–3.95]). Conclusion Comorbid conditions such as peptic ulcer and chronic liver disease significantly increase cancer risk in Hungary. Urban residents require targeted preventive measures, and unemployment should be addressed. Early detection through appropriate screening and effective management of comorbid conditions are essential to prevent escalation and reduce overall cancer prevalence.
Intratumoral administration of mRNA COVID-19 vaccine delays melanoma growth in mice
The impact of reintroduced Eurasian beaver (Castor fiber) dams on the upstream movement of brown trout (Salmo trutta) in upland areas of Great Britain
The return of Eurasian beaver (Castor fiber) to large areas of Europe represents a conservation success with the current population estimated to be around 1.2 million individuals. Their reintroduction to many areas, including Great Britain, has in some cases been controversial. Despite numerous documented benefits to biodiversity, concerns relate to localised flooding, adverse impacts on land use and engineered structures (e.g. culvert blockage), disease transfer, and the influence of beaver habitat modifications on fisheries, particularly in relation to salmonids. This study investigated the impacts of a series of four beaver dams on the upstream movement of brown trout during the spawning period (October—December) at a field site in Scotland. The study site comprised two streams entering a common loch, one modified by a series of four beaver dams, the other remaining unaltered during the Study Period. Trout were captured using electric fishing, fyke nets and rod and line and were tagged with Passive Integrated Transponders (PIT) before release. PIT telemetry antennas were installed below and above each dam to establish successful passage of trout during the monitoring period that included trout spawning movements in 2015 (high flows) and 2016 (low flow). There was a distinct difference in passage success between years, with high flows (using prior rainfall as a proxy measure) and larger fish size being important positive predictors of upstream passage success. A combination of environmental (prior rainfall and water temperature) and biotic (fish size) factors influenced passage success with high flows being a significant covariate at all four dams in two models used to define trout passage dynamics (Weibull and exponential base models), providing the best explanatory variable for fish passage at two of the four dams. Survival analysis and associated modelling indicated that migratory delay was inversely related to previous passage success, whilst motivation was also a determinant of success, with greatest passage in highly motivated trout. Our findings indicate that given the right environmental and biotic factors, brown trout are adept at passing beaver dams, although under certain conditions, beaver dams can impede the movement of brown trout and the magnitude of impact is influenced by these factors. In particular, the barrier effects of beaver dams are exacerbated under low flow conditions, and this may become a greater challenge in the future due to shifting climatic conditions if periods of warmer and drier weather persist and coincide with peak migratory movements of fish.
Polynomial modelling of high-quality yet incomplete rare earth element data sets and a holistic assessment of REE anomalies
Abstract Rare earth elements (REEs) are powerful proxies used in many (bio-)geochemical studies. Interpretation of REE data relies on normalised REE patterns and anomaly quantification, and requires complete data. Therefore, older, high-quality REE data determined by neutron activation or isotope dilution methods are often ignored, as they did not provide complete data. Similarly, modern analytical data can lack certain REEs due to quantification limits, interferences or usage of REE spikes. However, such data may be the only information available since sample material was consumed, sample locations became inaccessible, or samples represent past states of a dynamic natural system. Therefore, the ability to impute such high-quality data is of value for many geoscientific sub-disciplines. We use a polynomial modelling approach to impute missing REE data, verify the method’s applicability with a large data set (>13,000 samples; PetDB), and complement three originally incomplete REE data sets. Good fitting results (SD <6%) are supported by Monte Carlo simulations for assessing the model uncertainties (± 12%). Additionally, we provide a procedure to quantify REE anomalies, including uncertainties, which were usually not determined in the past but are essential for scientific comparison of REE anomaly data between different data sets. All Python scripts are provided.
Prediction of pavement water film depth and estimation of critical rainfall conditions for refined road safety management: A simulation study
The development of a smart expressway ensuring all-weather safe access represents the future trajectory of transportation infrastructure. A key task in this advancement is the precise prediction of water film depth (WFD) on road surfaces. Conventional WFD prediction models often assume constant grade and cross slope, an oversimplification that may affect predictive accuracy. In this study, typical highway alignments were meticulously modeled in three dimensions (3D) using Building Information Modeling (BIM) technology, and WFD simulations were conducted using a coupled discrete phase model and Eulerian wall film model (DE-WFD model). Simulation results revealed that the DE-WFD model consistently predicts higher WFD compared to the RRL and PAVDRN models. In contrast, its predictions are approximately 0.12 mm (40%) lower than those of the Gallaway model when rainfall intensity is below 7.8 mm/h. At higher rainfall intensities, DE-WFD predictions closely align with the Gallaway model. Field tests conducted with a feeler gauge of 0.01 mm resolution confirmed the accuracy of these predictions, showing a maximum deviation of just 7% between predicted and measured values. Additionally, the study assessed the sensitivity of the DE-WFD model to variations in grade and cross slope along the road length. Results indicated that on road surfaces employing dispersed drainage, WFD is approximately 6% higher at sag vertical curves and lower at crest vertical curves compared to constant slope segments. Moreover, WFD increases by over 35% at superelevation transitions. To quantify the impact of rainfall on road safety, a critical WFD parameter was developed. This parameter defines the maximum WFD under specific rainfall conditions that reduces the pavement-tire tangential friction coefficient to a level corresponding to the standard stopping sight distance. Using the DE-WFD model, simulations of hourly rainfall intensity and duration identified conditions under which WFD reaches this critical value for various roadway geometries. These findings provide valuable references for the precision management of highway operational safety. This suggests that traffic safety authorities should implement warning and intervention measures when critical rainfall conditions are exceeded to ensure driving safety.
Impact of dietary Biocide clay on growth, physiological status, and histological indicators of the liver and digestive tract in Nile tilapia (Oreochromis niloticus)
Abstract This study evaluated the effects of Biocide, containing silicon tetrahedrons and organic acids, on growth performance, feed utilization, immune response, and oxidative status in Nile tilapia (Oreochromis niloticus). A total of 300 Nile tilapia fingerlings (initial weight: 3.55 ± 0.01 g) were distributed across 15 tanks and fed diets containing 0.0 (control), 0.25, 0.5, 1, and 2 g kg⁻¹ Biocide for 90 days, with three replicate tanks per treatment. Biocide is enriched with organic acids (fumaric acid and citric acid) and amino acids (glutamine, tyrosine, methionine, serine, and threonine). Fish fed Biocide-supplemented diets demonstrated significantly improved growth performance, with the highest weight gain, feed conversion ratio, and protein efficiency ratio observed in the 1 g kg⁻¹ group. Survival rates did not differ significantly among treatments. Whole-body crude protein content peaked in the 1 g kg⁻¹ group, while moisture, lipid, and ash contents remained unchanged. Hematological parameters, including red blood cell count, hemoglobin concentration, and packed cell volume, improved significantly. Serum lipid profiles showed reduced cholesterol, triglycerides, low-density lipoprotein, and very-low-density lipoprotein levels, alongside increased high-density lipoprotein levels, particularly in the 1 g kg⁻¹ group. Antioxidant enzyme activities (catalase, glutathione peroxidase) and total antioxidant capacity were significantly elevated in the liver and intestine, while malondialdehyde levels decreased. Digestive enzyme activities (amylase, lipase, and protease) were markedly enhanced. Histopathological analysis revealed improved liver, stomach, and intestinal morphology, including increased mucous secretion and enhanced intestinal villi structure, in fish fed 1 g kg⁻¹ Biocide. In conclusion, Biocide supplementation, particularly at 1 g kg⁻¹, significantly improved growth performance, feed utilization, immune function, and antioxidant capacity in Nile tilapia. Notably, the findings highlight Biocide’s primary mode of action on gut health, underscoring its potential as a dietary additive for improving aquaculture productivity.
Guest-host liquid crystal polarizer for electrically switchable polarization in CCTV systems
Non-singular terminal super-twitsing control of servo systems with backlash
Towards predicting posttraumatic stress symptom severity using portable EEG-derived biomarkers
Record-setting trove of buried beads speaks to power of ancient women
The impact of tractor drawbar height on performance and optimization using response surface methodology
Analysis of the roof damage range in close-proximity gently inclined coal seams mining and the feasibility of upward mining
Abstract In view of the feasibility of upward mining under the influence of repetitive mining for the close-proximity gently inclined coal seams, combined with the engineering geology of the coal seams in the south area of Xin’an Coal Mine, a formula for the depth of rock mass failure above the working face roof was proposed to investigate the continuity and integrity of coal seams 2−3 after the mining of the underlying coal seams. The characteristics of the overlying rock collapsed and the deformation law of the rock stratum sinking were analyzed through the similar experiments of physical simulation, to prove whether or not it is technically feasible to mine upward for the coal seams. Numerical simulation software is used to simulate the spatial distribution of mining stress field and stress transfer law of rock layer in the process of coal seams mining. The study shows that coal 2−3 is located within the lower coal seam fissure zone. The rock layer at the bottom of the working face has a certain bearing capacity, and can still maintain good continuity under the influence of repetitive mining. The stress concentration area of coal 3 up-slope mining develops continuously to the upper left rock body, and the peak of stress concentration is getting closer and closer to the coal wall, and the stress of coal 2−3 bottom plate and coal 2−3 top plate does not fall back significantly after the peak of stress occurs. The degree of rock fall and damage after mining is small, meeting the conditions required for upward mining. The results of the study provide a reference for the analysis of overburden structure and feasibility assessment under similar coal seams upward mining conditions.