Analysis on congestion mechanism of CAVs around traffic accident zones.

Unexpected traffic accidents cause traffic congestion and aggravate the unsafe situation on the roadways. Reducing the impact of such congestion by introducing Connected and Autonomous Vehicles (CAVs) into the traditional traffic flow is possible. It requires estimating the incident's duration and analyzing the incident's impact area to determine the appropriate strategy. To guide the driver in making efficient and accurate judgments and avoiding secondary traffic congestion, the Cooperative Adaptive Cruise Control (CACC) model with dynamic safety distance and the Intelligent Driver Model (IDM) based on the safety potential field theory are introduced to build the evolution model of accidental traffic congestion under diversion interference and non-interference. The Huatao Interchange section of the Inner Ring Highway in the Banan District of Chongqing, China, was selected as the test section for simulating mixed traffic flow under different CAVs permeability (Pc). The relationship between the evacuation time, evacuation traffic volume, and the accident impact degree index (including the farthest queue length and accident duration) under the diversion intervention scenario was analyzed, respectively. The results of the study indicate that the higher the penetration of CAVs, the more significant the improvement in traffic flow occupancy, flow, and speed. Diversion interventions reduce congestion, about 50 % of the duration without interventions, when Pc ≤ 80 %. The traffic volume of diversion interference is non-linearly positively correlated with the maximum queue length, and the earlier the interference time, the stronger the positive correlation. The negative correlation between the interference time and queue length is weak at low evacuation traffic volume. With the increase in evacuation traffic volume, the influence of evacuation time on queue length becomes stronger. The maximum queue length value interval under different conditions is [348 m, 3140 m], and the shortest evacuation time is [1649 s, 2834 s]. The traffic flow data obtained from the simulation are imported into the episodic traffic congestion evolution model. The congestion evaluation indexes are calculated under non-interference and interference measures and compared with the simulation results. The maximum relative error is within 5.38 %. The results can be of great significance in relieving congestion caused by traffic accidents and promptly restoring road capacity.

Common food preservatives impose distinct selective pressures on Salmonella Typhimurium planktonic and biofilm populations.

Food preservatives are crucial in controlling microbial growth in processed foods to maintain food safety. Bacterial biofilms pose a threat in the food chain by facilitating persistence on a range of surfaces and food products. Cells in a biofilm are often highly tolerant of antimicrobials and can evolve in response to antimicrobial exposure. Little is known about the efficacy of preservatives against biofilms and their potential impact on the evolution of antimicrobial resistance. In this study we investigated how Salmonella enterica serovar Typhimurium responded to subinhibitory concentrations of four food preservatives (sodium chloride, potassium chloride, sodium nitrite or sodium lactate) when grown planktonically and in biofilms. We found that each preservative exerted a unique selective pressure on S. Typhimurium populations. There was a trade-off between biofilm formation and growth in the presence of three of the four preservatives, where prolonged preservative exposure resulted in reduced biofilm biomass and matrix production over time. All three preservatives selected for mutations in global stress response regulators rpoS and crp. There was no evidence for any selection of cross-resistance to antibiotics after preservative exposure. In conclusion, we showed that preservatives affect biofilm formation and bacterial growth in a compound specific manner. We showed trade-offs between biofilm formation and preservative tolerance, but no antibiotic cross-tolerance. This indicates that bacterial adaptation to continuous preservative exposure, is unlikely to affect food safety or contribute to antibiotic resistance.

Study on Plastic Constitutive Relation and Ductile Fracture Criterion of AM60B Magnesium Alloy.

It is currently a challenge to accurately predict the deformation and fracture behavior of metal parts in automobile crashes. Many studies have shown that the deformation and fracture behavior of materials are significantly affected by the stress state during automobile crashes with complex stress state characteristics. In order to further promote the application of die-cast magnesium alloys in automobiles, it is particularly important to study the material deformation and fracture behavior of die-cast magnesium alloys. In this paper, the mechanical properties of the AM60B die-cast magnesium alloy sheet under four stress states (shear, tension, R10 notch tension, and cupping) were designed and tested. Based on the von Mises isotropic constitutive model and Swift weighted Hockett-Sherby hardening model, the plastic constitutive model of die-cast magnesium alloy was established. Based on the plastic model and the fracture model (JC, MMC, and DIEM) considering the influence of three stress states, the deformation and fracture behavior of the AM60B die-cast magnesium alloy front-end members in three-point bending were predicted by experiments and finite element simulation. The experimental results show that the deformation mode and loading-displacement curve trend of the AM60B die-cast magnesium alloy front members are the same, the crack initiation point and crack initiation time are the same, and the crack shape is similar. The results show that the complex stress state constitutive model parameters and the DIEM fracture model obtained in this paper can accurately predict the deformation and fracture failure behavior of the AM60B die-cast magnesium alloy sheet.

Competitive and synergic evolution of the water-food-ecology system: A case study of the Beijing-Tianjin-Hebei region, China.

A vital approach to attaining sustainable development lies in the in-depth examination of both competition and synergy between these subsystems from a water-food-ecology (WFE) system perspective, while previous or existing studies have limitations in to quantitative characterize and evaluation the cooperative and competitive relationships between different systems. In this study, an evaluation indicator system is constructed from the two dimensions of resources and efficiency, and the WFE synergic development capacity (WFE-SDC) is proposed by integrating the order degree of the coupled system, enables a multidimensional and comprehensive quantitative assessment of the sustainable development of the WFE system. Then a synergic evolution model is constructed to explore the competitive and synergic evolution of the WFE system in the Beijing-Tianjin-Hebei region. The following key insights were obtained: (1) The WFE-SDC (range of 0-1) shows a fluctuating increase, indicating a shift from mild dysfunctional recession to intermediate synergic development (0.24 to 0.72). (2) Principal factors impeding WFE-SDC encompass diversion water, ecology water consumption, grain demand, reclaimed water consumption, and outbound water, both come from resource dimension, with a combined impediment degree of over 46 %, and the improvement of efficiency dimension may improve the WFE-SDC. (3) The water subsystem acts as a driving force for synergic development, fostering cooperation within the food and ecology subsystems, although they mainly operate in a competitive state. (4) Within the WFE system, Beijing, Tianjin, and Hebei exhibited mutual cooperation and significantly contributed to one another's development. Beijing has played a pivotal role in the progress of both Tianjin and Hebei. This study offers valuable insights for the formulation of policies and the application of technical approaches for the sustainable development of the WFE system in relevant regions.

Concrete incorporating supplementary cementitious materials: Temporal evolution of compressive strength and environmental life cycle assessment.

The use of Supplementary Cementitious Materials (SCMs) or industrial wastes as a partial replacement for cement in the production of concrete is an urgent need in the construction industry due to cement's growing environmental challenges and rising cost. In respect of this, we conducted research work on proportioning binary concrete mixes. Fly ash (FA) replaced 10 %, 20 %, and 30 % of the cement, while silica fume (SF) replaced 5 %, 10 %, and 15 % of the cement. A control concrete mix was also developed with 100 % cement and no SCM. The results showed no increase in compressive strength for FA concrete compared to control at the early age of 3-28 days, but a maximum increase in compressive strength of 4 % was discovered at a later age of 56 days for concrete with 20 % FA. For 5 % SF concrete, a considerable strength increase of 15 % was seen at the early age of 3 days. Like with FA concrete, 2 % improvement in strength was recorded at the later age of 56 days for 10 % SF concrete. This study further focused on the concrete's temporal evolution of compressive strength by developing a strength evolution model (SEM) using nonlinear regression analysis at a 95 % confidence level. Pearson correlation coefficient was used to determine the correlation between the model values and the experimental results. For comparison, the fib Model Code 2010 was applied to the experimental data, and a good agreement was observed among the proposed model, the fib Model values, and the experimental results. The proposed model can be expanded to address further regression-related problems. Finally, environmental life cycle assessment revealed that utilizing 10 %, 20 %, and 30 % of FA lowered Global Warming Potential (GWP) by 9 %, 19 %, and 29 %, respectively. Likewise, using 5 %, 10 %, and 15 % of SF reduced the GWP by 5 %, 9 %, and 14 %.

The Dynamic Evolution Model of the Chemical and Carbon Isotopic Composition of C1-3 during the Hydrocarbon Generation Process.

A new approach is presented in this paper for the dynamic modeling of the chemical and isotopic evolution of C1-3 during the hydrocarbon generation process. Based on systematic data obtained from published papers for the pyrolysis of various hydrocarbon sources (type I kerogen/source rock, type II kerogen/source rock, type III kerogen/source rock, crude oil, and asphalt, etc.), the empirical evolution framework of the chemical and isotopic composition of C1-3 during the hydrocarbon generation process was built. Although the empirical framework was built only by fitting a large amount of pyrolysis data, the chemical and isotopic composition of C1-3 derived from the pyrolysis experiments all follow evolution laws, convincing us that it is applicable to the thermal evolution process of various hydrocarbon sources. Based on the simplified formula of the isotopic composition of mixed natural gas at different maturities (δ13Cmixed), δ13Cmixed = X×niA×δ13CiA+Y×niB×δ13CiBX×niA+Y×niB, it can be derived that the cumulative isotopic composition of alkane generated in a certain maturity interval can be expressed by the integral of the product of the instantaneous isotopic composition and instantaneous yield at a certain maturity point, and then divided by the cumulative yield of alkane generated in the corresponding maturity interval. Thus, the cumulative isotopic composition (A(X)), cumulative yield (B(X)), instantaneous isotope (C(X)), and instantaneous yield (D(x)) in the dynamic model, comply with the following formula during the maturity interval of (X0~X). A(X) = ∫X0XCX×DXdxB(X), where A(X) and B(X) can be obtained by the fitting of pyrolysis data, and D(x) can also be obtained from the derivation of B(X). The dynamic model was applied on the pyrolysis data of Pingliang Shale to illustrate the quantitative evolution of the cumulative yield, instantaneous yield, cumulative isotope, and instantaneous isotope of C1-3 with increasing maturity. The dynamic model can quantify the yield of methane, ethane, and propane, as well as δ13C1, δ13C2, and δ13C3, respectively, during the hydrocarbon generation process. This model is of great significance for evaluating the natural gas resources of hydrocarbon source rock of different maturities and for identifying the origin and evolutionary process of hydrocarbons by chemical and isotopic data. Moreover, this model provides an approach to study the dynamic evolution of the isotope series of C1-3 (including reversed isotopic series), which is promising for revealing the mechanism responsible for isotopic reversal when combined with post-generation studies.

Evolutionary predictions for a parasite metapopulation: Modelling salmon louse resistance to pest controls in aquaculture.

Pests often evolve resistance to pest controls used in agriculture and aquaculture. The rate of pest adaptation is influenced by the type of control, the selective pressure it imposes, and the gene flow between farms. By understanding how these factors influence evolution at the metapopulation level, pest management strategies that prevent resistance from evolving can be developed. We developed a model for the metapopulation and evolutionary dynamics of the salmon louse (Lepeophtheirus salmonis), which is a major parasite affecting salmon aquaculture. Different management scenarios were simulated across a network of salmon farms covering half of Norway, and their effects on louse epidemiology and evolution were investigated. We compared louse controls that differed in how they were deployed through time (discrete vs. continuous), how they impacted the louse life cycle, and in their overall efficacy. We adjusted the strength of selection imposed by treatments, the dominance effect of the resistant allele, and the geographic location at which resistance originated. Continuously acting strategies (e.g., louse-resistant salmon) were generally more effective than discrete strategies at controlling lice, especially when they increased louse mortality during early developmental stages. However, effective strategies also risked imposing frequent and/or strong selection on lice, thus driving rapid adaptation. Resistant alleles were more likely to be lost through genetic drift when they were recessive, had a low-fitness advantage, or originated in low-farm-density areas. The north-flowing current along the Norwegian coastline dispersed resistant genes from south to north, and limited gene flow in the opposite direction. We demonstrate how evolutionary models can produce quantitative predictions over large spatial and temporal scales and for a range of pest control scenarios. Quantitative outputs can be translated into practical management decisions applied at a regional level to minimise the risk of resistance developing.

The 3 31 Nucleotide Minihelix tRNA Evolution Theorem and the Origin of Life.

There are no theorems (proven theories) in the biological sciences. We propose that the 3 31 nt minihelix tRNA evolution theorem be universally accepted as one. The 3 31 nt minihelix theorem completely describes the evolution of type I and type II tRNAs from ordered precursors (RNA repeats and inverted repeats). Despite the diversification of tRNAome sequences, statistical tests overwhelmingly support the theorem. Furthermore, the theorem relates the dominant pathway for the origin of life on Earth, specifically, how tRNAomes and the genetic code may have coevolved. Alternate models for tRNA evolution (i.e., 2 minihelix, convergent and accretion models) are falsified. In the context of the pre-life world, tRNA was a molecule that, via mutation, could modify anticodon sequences and teach itself to code. Based on the tRNA sequence, we relate the clearest history to date of the chemical evolution of life. From analysis of tRNA evolution, ribozyme-mediated RNA ligation was a primary driving force in the evolution of complexity during the pre-life-to-life transition. TRNA formed the core for the evolution of living systems on Earth.

A novel major accidents evolution model and its application in Chinese industrial accident.

The accident mortality rate of major accidents (MAs) show that China is still in the bottleneck period of accident prevention and control. To further promote the MAs prevention and control, this paper presents a novel major accidents evolution model from the theoretical perspective of information processing (IP). Firstly, based on the safety science paradigm of accident prevention and the emergency management paradigm of accident control, a safety information processing (SIP) process is proposed. Secondly, established the SIP model for different stages of accident prevention and control, which involves danger information processing (DIP), potential hazard information processing (PHIP), risk information processing (RIP), and emergency information processing (EIP). Thirdly, revealed the SIP of various management subject and the failure principle of accident prevention and control, that is, MAs occur under the premise of continuous failures of DIP, PHIP, RIP, and EIP under the social-technical system. Finally, the DPRE-IP model is proposed from the whole evolution path of "danger-potential hazard-risk-accident". To demonstrate the viability of the model, this model is applied to the "6·13" Wenling major explosion accident. The results show that the proposed DPRE-IP model can provide new ideas for the formulation of accident prevention and control measures and accident analysis.

Damage Model of Steel Fiber-Reinforced Coal Gangue Concrete under Freeze-Thaw Cycles Based on Weibull Distribution.

In order to improve the utilization rate of coal gangue and expand the application range of coal gangue concrete (CGC), a certain proportion of steel fiber was added to the concrete, and the freeze-thaw cycles (FTCs) and flexural tests were used to explore the effects of different mass replacement rates of coal gangue (0%, 25%, 50%, 75%, and 100%) and different proportions of the volumetric blending of the steel fiber (0%, 0.8%, 1.0%, and 1.2%) on the frost resistance of steel fiber-reinforced CGC (SCGC). The governing laws of mass loss rate, relative dynamic elastic modulus and load-midspan deflection curve were obtained on the base of the analysis of testing results. The damage mechanisms of the SCGC under the FTCs were analyzed using the results of scanning electron microscopy (SEM). Based on the Lemaitre's strain equivalence principle and Krajcinovic's vector damage theory, a damage evolution model of the SCGC under the FTCs was established by introducing the damage variable of the SCGC satisfying Weibull distribution. The results show an increasing mass loss rate of the SCGC and a decreasing relative dynamic elastic modulus with an increasing mass replacement rate of coal gangue. The proper content of the steel fiber can reduce the mass loss rate of concrete by 10~40% and the relative loss rate of dynamic elastic modulus of concrete by 2~8%, thus significantly improving the ductility and toughness of the concrete. The established damage evolution model is well validated by the experimental results, which further help to improve the modelling accuracy. This study provides key experimental data and a theoretical basis for a wider range of proper utilization of coal gangue in cold regions.

Network Evolution Model-based prediction of tumor mutation burden from radiomic-clinical features in endometrial cancers.

BACKGROUND: Endometrial Cancer (EC) is one of the most prevalent malignancies that affect the female population globally. In the context of immunotherapy, Tumor Mutation Burden (TMB) in the DNA polymerase epsilon (POLE) subtype of this cancer holds promise as a viable therapeutic target. METHODS: We devised a method known as NEM-TIE to forecast the TMB status of patients with endometrial cancer. This approach utilized a combination of the Network Evolution Model, Transfer Information Entropy, Clique Percolation (CP) methodology, and Support Vector Machine (SVM) classification. To construct the Network Evolution Model, we employed an adjacency matrix that utilized transfer information entropy to assess the information gain between nodes of radiomic-clinical features. Subsequently, using the CP algorithm, we unearthed potentially pivotal modules in the Network Evolution Model. Finally, the SVM classifier extracted essential features from the module set. RESULTS: Upon analyzing the importance of modules, we discovered that the dependence count energy in tumor volumes-of-interest holds immense significance in distinguishing TMB statuses among patients with endometrial cancer. Using the 13 radiomic-clinical features extracted via NEM-TIE, we demonstrated that the area under the receiver operating characteristic curve (AUROC) in the test set is 0.98 (95% confidence interval: 0.95-1.00), surpassing the performance of existing techniques such as the mRMR and Laplacian methods. CONCLUSIONS: Our study proposed the NEM-TIE method as a means to identify the TMB status of patients with endometrial cancer. The integration of radiomic-clinical data utilizing the NEM-TIE method may offer a novel technology for supplementary diagnosis.

Circular mixed sets.

In this article, we introduce the new mathematical concept of circular mixed sets of words over an arbitrary finite alphabet. These circular mixed sets may not be codes in the classical sense and hence allow a higher amount of information to be encoded. After describing their basic properties, we generalize a recent graph theoretical approach for circularity and apply it to distinguish codes from sets (i.e. non-codes). Moreover, several methods are given to construct circular mixed sets. Finally, this approach allows us to propose a new evolution model of the present genetic code that could have evolved from a dinucleotide world to a trinucleotide world via circular mixed sets of dinucleotides and trinucleotides.

The origin of genetic and metabolic systems: Evolutionary structuralinsights.

DNA is derived from reverse transcription and its origin is related to reverse transcriptase, DNA polymerase and integrase. The gene structure originated from the evolution of the first RNA polymerase. Thus, an explanation of the origin of the genetic system must also explain the evolution of these enzymes. This paper proposes a polymer structure model, termed the stable complex evolution model, which explains the evolution of enzymes and functional molecules. Enzymes evolved their functions by forming locally tightly packed complexes with specific substrates. A metabolic reaction can therefore be considered to be the result of adaptive evolution in this way when a certain essential molecule is lacking in a cell. The evolution of the primitive genetic and metabolic systems was thus coordinated and synchronized. According to the stable complex model, almost all functional molecules establish binding affinity and specific recognition through complementary interactions, and functional molecules therefore have the nature of being auto-reactive. This is thermodynamically favorable and leads to functional duplication and self-organization. Therefore, it can be speculated that biological systems have a certain tendency to maintain functional stability or are influenced by an inherent selective power. The evolution of dormant bacteria may support this hypothesis, and inherent selectivity can be unified with natural selection at the molecular level.

Influence of Fly Ash Content on the Durability of Mortar Specimens under Dry/Wet Sulfate Attack.

To investigate the durability of cementitious materials under complex environmental conditions in Xinjiang, this study conducted durability tests on mortar specimens with different fly ash contents under dry/wet sulfate attack conditions, with standard curing and steam curing at 70 °C. The appearance loss and flexural and compressive strength variations in the specimens were analyzed, and an evolution model of the mortar strength under a dry/wet sulfate attack was established. Moreover, XRD and SEM techniques were used to characterize the erosion products and microstructure, and to explore the erosion resistance mechanism of fly ash cementitious materials. The results showed that, after 160 cycles of erosion, the flexural strength of the specimens decreased with the increase in the fly ash content. In the context of steam-cured mortar specimens, throughout the entire erosion period, specimens with a fly ash content of 45% exhibited the highest relative compressive strength. The established strength evolution model had a minimum determination coefficient of 0.879, indicating a good agreement between the model and experimental results. Microscopic research showed that fly ash would undergo a pozzolanic reaction under the action of sulfate and calcium hydroxide, which was beneficial to the improvement of the erosion resistance. As the fly ash content increased, the erosion products of the specimens gradually became dominated by gypsum.

Evolution Model and Simulation Study of the Public Risk Perception of COVID-19.

The evolution of the public perception of the risk in public health emergencies is closely related to risk response behavior. There are few systematic explanations and empirical studies on how the individual receiving the risk information affects the change in the individual risk perception through internal mechanisms in the context of COVID-19. Based on the understanding of the existing research, this paper constructs the evolution model of the public risk perception level based on the limited memory theory and a simulation analysis is performed. The results are as follows: memory rate, association rate, information reception and information stimulation in a single period of time have significant indigenous effects on the risk perception; when the amount of information received and the information stimulus remain unchanged, the public's risk perception follows a monotonic upward trend, but there is an upper limit function, and the upper limit is determined by the memory rate and association rate, and the influence of the association rate is higher than that of the memory rate; When the amount of information received and the information stimulus changes, the public's risk perception will also change, and there is a lag effect, which is determined by the memory rate. The impact of the acceptance of the information on the risk perception is greater than that of the information stimulus.

Tailings dams: Assessing the long-term erosional stability of valley fill designs.

Tailings is a generic term for waste material from the extraction and processing of minerals and frequently contain mineral and chemical residues. They are usually highly erodible and transportable via fluvial processes. Tailings are commonly stored in 'tailings dams' and such dams are a feature of many mine sites. As they impound water and sediment, tailings dams can be at risk from both catastrophic and gradual failure, especially if unmanaged. A fundamental question for their management is, can tailings dams ever be walk-away structures? Catastrophic failure occurs when there is a large scale rapid structural failure of the dam wall suddenly releasing large volumes of water and sediment. However, over time, there will the increased risk of gradual failure by the slow infilling of the dam and the erosion of the dam wall. Failure can occur where water overtops the dam wall and then incises through the wall due to a loss of freeboard in the dam, a situation which is more likely in legacy tailings dams where they have been filled, vegetated and abandoned. Here, firstly, a computer based landscape evolution model (CAESAR-Lisflood) is employed to assess a hypothetical tailings dam failure by erosion. Secondly, using an idealised example, it is demonstrated that given average climate conditions a dam can be sufficiently robust to last centuries. Thirdly, and longer term it is demonstrated that the tailings can be contained if (a) maintenance is conducted to increase the dam wall height over time or (b) a more robust dam wall is constructed to manage extreme events. However, erosion and infill will continue to reduce the integrity of any structure over time. Therefore, it is highly likely that tailings dams will require continued monitoring and maintenance. The method outlined provides a new tool for assessing any tailings facility for its erosional stability.

An Updated View of the Trypanosoma cruzi Life Cycle: Intervention Points for an Effective Treatment.

Chagas disease (CD) is a parasitic, systemic, chronic, and often fatal illness caused by infection with the protozoan Trypanosoma cruzi. The World Health Organization classifies CD as the most prevalent of poverty-promoting neglected tropical diseases, the most important parasitic one, and the third most infectious disease in Latin America. Currently, CD is a global public health issue that affects 6-8 million people. However, the current approved treatments are limited to two nitroheterocyclic drugs developed more than 50 years ago. Many efforts have been made in recent decades to find new therapies, but our limited understanding of the infection process, pathology development, and long-term nature of this disease has made it impossible to develop new drugs, effective treatment, or vaccines. This Review aims to provide a comprehensive update on our understanding of the current life cycle, new morphological forms, and genetic diversity of T. cruzi, as well as identify intervention points in the life cycle where new drugs and treatments could achieve a parasitic cure.

Collaborative evolution of regional green innovation system under the influence of high-speed rail based on Belousov-Zhabotinsky reaction.

As a green transportation mode and carrying factors with high knowledge-intensive characteristics, high-speed rail (HSR) has contributed to the improvement of regional green innovation. However, the relationship between HSR and regional green innovation is still limited. Based on the Belousov-Zhabotinsky (B-Z) reaction model, this study establishes a three-dimensional logistic dynamic evolution model for factor flow, knowledge spillover and green innovation performance. Through linear stability analysis, the threshold conditions for the evolution of the regional green innovation system under the influence of HSR are explored, and the impact of HSR policy on the regional green innovation system under four different initial states is examined. The results reveal four major points: (1) Factor flow is the order parameter and shows a significant collaborative relationship with green innovation performance. (2) The opening of HSR can effectively promote the evolution of the regional green innovation system, and the powerful stimulus of HSR contributes to shortening its evolutionary cycle. (3) The initial state of the regional green innovation system plays a crucial role in the green innovation performance. (4) In the process of collaborative evolution of regional green innovation system, factor flow and knowledge spillover serve as the premise and foundation, respectively, to jointly promote green innovation performance enhancement. Findings not only provide references for decision-makers to implement green innovation strategy and boost the green innovation performance, but also extend the theoretical system of HSR effect and collaborative evolution.

Variation of Critical Water Pressure for Hydraulic Fracturing in Cement Mortar under Sulfate Attack.

Hydraulic fracturing may be induced easily in a cement-based structure in a sulfate-rich environment, which threatens engineering safety. In order to investigate the evolution of critical water pressure, a series of hydraulic fracturing tests and splitting tensile strength tests on the cement mortar under different sulfate-exposure periods are performed. The critical water pressure of the cement mortar under sulfate attack experiences an initial increase stage and a subsequent decrease stage. A stress intensity factor is modified by two proposed damage variables which are crack length and fracture stress. Then, the relationship between the critical water pressure and the tensile strength is established. Moreover, an evolution model of the critical water pressure is proposed, which reveals that the matrix tensile strength and porosity of cement mortar strongly affect the critical water pressure evolution. Additionally, an empirical formula is suggested to describe the critical water pressure evolution of the cement mortar under sulfate attack, and its validity is verified by experimental results.

LEMABE: a novel framework to improve analogy-based software cost estimation using learnable evolution model.

One of the most important and critical factors in software projects is the proper cost estimation. This activity, which has to be done prior to the beginning of a project in the initial stage, always encounters several challenges and problems. However, due to the high significance and impact of the proper cost estimation, several approaches and methods have been proposed regarding how to perform cost estimation, in which the analogy-based approach is one of the most popular ones. In recent years, many attempts have been made to employ suitable techniques and methods in this approach in order to improve estimation accuracy. However, achieving improved estimation accuracy in these techniques is still an appropriate research topic. To improve software development cost estimation, the current study has investigated the effect of the LEM algorithm on optimization of features weighting and proposed a new method as well. In this research, the effectiveness of this algorithm has been examined on two datasets, Desharnais and Maxwell. Then, MMRE, PRED (0.25), and MdMRE criteria have been used to evaluate and compare the proposed method against other evolutionary algorithms. Employing the proposed method showed considerable improvement in estimating software cost estimation.