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Ignimbrites have been widely used as building materials in many historical and touristic structures in the Kayseri region of Türkiye. Their diverse colours and textures make them a popular choice for modern construction as well. However, ignimbrites are particularly vulnerable to atmospheric conditions, such as freeze-thaw cycles, due to their high porosity, which is a result of their formation process. When water enters the pores of the ignimbrites, it can freeze during cold weather. As the water freezes and expands, it generates internal stress within the stone, causing micro-cracks to develop. Over time, repeated freeze-thaw (F-T) cycles lead to the growth of these micro-cracks into larger cracks, compromising the structural integrity of the ignimbrites and eventually making them unsuitable for use as building materials. The determination of the long-term F-T performance of ignimbrites can be established after long F-T experimental processes. Determining the long-term F-T performance of ignimbrites typically requires extensive experimental testing over prolonged freeze-thaw cycles. To streamline this process, developing accurate predictive equations becomes crucial. In this study, such equations were formulated using classical regression analyses and artificial neural networks (ANN) based on data obtained from these experiments, allowing for the prediction of the F-T performance of ignimbrites and other similar building stones without the need for lengthy testing. In this study, uniaxial compressive strength, ultrasonic propagation velocity, apparent porosity and mass loss of ignimbrites after long-term F-T were determined. Following the F-T cycles, the disintegration rate was evaluated using decay function approaches, while uniaxial compressive strength (UCS) values were predicted with minimal input parameters through both regression and ANN analyses. The ANN and regression models created for this purpose were first started with a single input value and then developed with two and three combinations. The predictive performance of the models was assessed by comparing them to regression models using the coefficient of determination (R2) as the evaluation criterion. As a result of the study, higher R2 values (0.87) were obtained in models built with artificial neural network. The results of the study indicate that ANN usage can produce results close to experimental outcomes in predicting the long-term F-T performance of ignimbrite samples. [ABSTRACT FROM AUTHOR]

Most competitive location models assume that as the distance increases, the patronage of a facility declines at the same rate regardless of the facility attractiveness. We observed that the rate at which patronage declines is slower for more attractive facilities. Customers are willing to drive long distances to patronize an attractive facility. Less attractive facilities hardly attract customers from long distances. We propose to modify the effect of attractiveness on the appeal of the facility to customers. Many methods for estimating the market share captured by a facility can be modified to incorporate such property. We implemented the new modification on the gravity model and tested it on a real data set of shopping malls in Orange County, California. The approach was statistically validated and is computationally straightforward to implement with existing software such as R. Further, new facility location decisions are expected to be more accurate due to the improved market share estimates by the proposed model. [ABSTRACT FROM AUTHOR]

This paper stands for the almost sure practical stability of nonlinear stochastic differential delay equations (SDDEs) with a general decay rate. We establish some sufficient conditions based upon the construction of appropriate Lyapunov functionals. Furthermore, we provide some numerical examples to validate the effectiveness of the abstract results of this paper.

The users' click prediction holds significant commercial value in online purchase platforms. Currently, some session-based interest extraction models overlook the impact of temporal interval factors on the distribution pattern of behaviors within a session. To address the above issue, this paper designs a module named session-based time-aware gated recurrent units (ST-GRU) to integrate the temporal interval factors into session sequence. The ST-GRU module utilizes a memory decay function, enabling the simulation process to identify the users' memory changes within each session. Subsequently, to account for the interdependence between different sessions, this paper introduces bi-directional gated recurrent units (Bi-GRU) with an attention mechanism to learn interaction relations of user interests. Additionally, for high-dimensional potential features, this paper integrates above modules and proposes a method called time interval aware deep session interest network (TIAE-DSIN) model. Correspondingly, comparative experiments are carried out on three public datasets, and the obtained results indicate that the AUC of TIAE-DSIN model is superior to other session interest extraction models. [ABSTRACT FROM AUTHOR]

Extensive evidence shows that repeat victimization is common and widespread, but studies on the prevalence of repeat victimization in Asia are limited. This study examines the extent and patterns of repeat and near-repeat burglary victimization in Taiwan using both 2015 Taiwan Area Victimization Survey data and police recorded burglary data. Results indicated that: (1) burglaries against the same household in Taiwan are highly concentrated (with the top 10% most burgled households making up around 30% of reported victimizations), more so than is often found in many Western countries; (2) the risk of (repeat) burglary is not consistently spread over space and time, particularly within the 100-m range of an initial burglary incident; and (3) the levels of near repeat burglaries identified in this study are notably lower than was observed in prior studies both in China and in many western countries. The findings highlight the value of developing prevention strategies specifically targeting repeat burglary victimization. [ABSTRACT FROM AUTHOR]

The freeze–thaw (FT) process plays a dominant role as far as the slope stability in the cold regions is concerned. The upper mountainous regions of the Himalayas are prone to recurrent FT. Himalayan rock is also used as building materials for the construction in cold hilly areas. Therefore, it becomes necessary to understand the behaviour of the Himalayan rock with respect to its deterioration prior to the construction of any engineering project. Rock samples of the Himalayan schist collected from Solang Valley in Himachal Pradesh, India were subjected to multiple FT conditioning in the laboratory. The microscopic damages and the degradation in physico-mechanical properties such as effective porosity, P-wave velocity and tensile and compressive strengths were observed on the rock specimens conditioned to 0th, 10th, 20th and 30th cycle of FT. The micro-photographs from the scanning electron microscope test reveal the widening of cracks due to FT. A significant gain in effective porosity and water absorption of about 33% and a reduction of about 11.5% in P-wave velocity, 28% in compressive strength and 40% in tensile strength was observed for rocks conditioned with 30 FT cycles. These results were further used in mathematical modelling to arrive at various coefficients, damage factor and the disintegration rate of Himalayan schist under the influence of FT. [ABSTRACT FROM AUTHOR]

Flexural strength is an important mechanical parameter for natural stones used as surface claddings. This study was carried out to investigate the decrease in flexural strength under the influence of weathering by freeze-thaw cycles. Five types of building stones which are granite, marble, micritic limestone, sparitic limestone and travertine were exposed to 14, 28, 56, 84, 120 and 200 freeze-thaw cycles, flexural strength tests were conducted as three and four points flexural strength tests. Results indicated that even if no visible disintegration on samples was monitored, noticeable reductions on ultrasonic P-wave velocity, the Leeb hardness, and flexural strength results were observed after freeze-thaw cycles. Correlation between test results for fresh and weathered samples were presented. In addition, anisotropic properties of sample groups were calculated from ultrasonic P-wave velocity values measured in 3 directions and their increases due to freeze-thaw cycles were determined. The long-term durability of stone groups against freeze-thaw cycles was revealed by half-lives calculated from decay function parameters. A decrease in flexural strength of up to 30 % has been revealed under the influence of weathering. • 300 slab samples were subjected to 200 freeze-thaw cycles and flexural strength reduction due to weathering was revealed. • Anisotropic properties were monitored by ultrasonic wave measurements in three perpendicular directions. • Leeb surface hardness were also used to monitor weathering. • Flexural strength losses were revealed, and decay patterns were proposed. [ABSTRACT FROM AUTHOR]

This paper stands for the almost sure practical stability of nonlinear stochastic differential delay equations (SDDEs) with a general decay rate. We establish some sufficient conditions based upon the construction of appropriate Lyapunov functionals. Furthermore, we provide some numerical examples to validate the effectiveness of the abstract results of this paper. [ABSTRACT FROM AUTHOR]

This paper is concerned with the almost sure partial practical stability of stochastic differential equations with general decay rate. We establish some sufficient conditions based upon the construction of appropriate Lyapunov functions. Finally, we provide a numerical example to demonstrate the efficiency of the obtained results. [ABSTRACT FROM AUTHOR]

The hub maximal covering location problem aims to find the best locations for hubs so as to maximize the total flows covered by predetermined number of hubs. Generally, this problem is defined in the framework of binary coverage. However, there are many real-life cases in which the binary coverage assumption may yield unexpected decisions. Thus, the partial coverage is considered by stipulating that the coverage of an origin-destination pair is determined by a non-increasing decay function. Moreover, as this problem contains strategic decisions in long range, the precise information about the parameters such as travel times may not be obtained in advance. Therefore, we present uncertain hub maximal covering location models with partial coverage in which the travel times are depicted as uncertain variables. Specifically, the partial coverage parameter is introduced in uncertain environment and the expected value of partial coverage parameter is further derived and simplified with specific decay functions. Expected value model and chance constrained programming model are respectively proposed and transformed to their deterministic equivalent forms. Finally, a greedy variable neighborhood search heuristic is presented and the efficiency of the proposed models is evaluated through computational experiments. [ABSTRACT FROM AUTHOR]

The collaborative filtering (CF) technique has been widely utilized in recommendation systems due to the precise prediction of users' interests. Most prior CF methods adapted overall ratings to make predictions by collecting preference information from other users. However, in real applications, people's preferences usually vary with time; the traditional CF could not properly reveal the change in users' interests. In this paper, we propose a novel CF-based recommendation, dynamic decay collaborative filtering (DDCF), which captures the preference variations of users and includes the concept of dynamic time decay. We extend the idea of human brain memory to specify the level of a user's interests (i.e., instantaneous, short-term, or long-term). According to different interest levels, DDCF dynamically tunes the decay function based on users' behaviors. The experimental results show that DDCF with the integration of the dynamic decay concept performs better than traditional CF. In addition, we conduct experiments on real-world datasets to demonstrate the practicability of the proposed DDCF. [ABSTRACT FROM AUTHOR]

Forecasts of agents who are actively involved in the setting of prices and wages are less readily available than those of professional analysts but may be more relevant for understanding inflation dynamics. Here we compare inflation expectations anchoring between analysts, businesses and trade unions for one country for which comparable forecasts are available for almost two decades: South Africa. Forecasts are modelled as monotonically diverging from an estimated long-run anchor point, or 'implicit anchor', towards actual inflation as the forecast horizon shortens. We find that the estimated inflation anchors of analysts lie within the 3–6 percent inflation target range of the central bank. However, those for businesses and trade unions, which our evidence suggests may be the most relevant for driving the inflation process, have remained above the top end of the official target range. Our results point to challenges for central banks seeking to gain credibility with agents whose decisions directly influence inflation. [ABSTRACT FROM AUTHOR]

Traditional continuous hydrological models have a large number of free parameters whose values need to be determined through calibration, and thus their applicability is limited to gauged basins. For prediction in ungauged catchments, hydrologists generally follow regionalization methods to develop region-specific calibration-free continuous models. An alternative attempt was made recently to develop a calibration-free model by proposing an empirically derived universal 'decay function' that enables definition of instantaneous dryness index as a function of antecedent rainfall and solar energy. The model was earlier tested in the USA, and its performance was found to be comparable to that shown by regionalization-based models. Here, we test the instantaneous dryness index-based calibration-free model considering data from 108 Indian catchments. The medians of coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE) and Kling-Gupta efficiency (KGE) values for the study catchments, respectively, are 0.50, 0.38 and 0.40. Furthermore, the model's performance significantly improved upon Box-Cox transformation (RBC 2, NSEBC and KGEBC, respectively, are 0.70, 0.52 and 0.57), suggesting that the model predicts discharge quite well except during flood periods. Overall, our results suggest the model can be used as an alternative platform for predicting discharge in ungauged catchments in the USA and peninsular India, if not in every part of the world. [ABSTRACT FROM AUTHOR]

The frost sensitivity of five French limestones (Massangis (MA), Lens (LS), Savonnières (SA), Saint-Maximin (MX) and Migné (MI)), with different physical and mechanical properties, was studied. The total porosity ranges from 10% to 35% and the uniaxial compressive strength ranges between 10 MPa and 60 MPa. The freeze-thaw tests were applied on the samples saturated in natural condition. During freeze-thaw cycles we recorded the evolution of temperature and volumetric strain of a specimen for each stone and after the freeze-thaw cycles, the weathering evolution in stone samples was monitored by measurement of different physical and mechanical parameters (porosity, P-wave velocity, fracture toughness, compressive strength and elastic static modulus). Two behaviors were observed: a volumetric expansion during freezing phase accompanied by a very important damage from the first cycle until the failure of specimen for MI and MX stones, and a volumetric contraction accompanied with a light damage for the high number of freeze-thaw cycle in the cases of MA, LS and SA. Following the freeze–thaw tests, the durability of stones was evaluated using a decay function model. The decay constant values determined from the evolution of P-wave velocity were correlated with different fresh stone properties. Contrary to generally accepted ideas, the correlation coefficients between the decay constant and total porosity or degree of saturation are very low. Porosity in natural condition seems to have the strongest influence on the decay constant. However, its negative impact can be offset by a bigger part of trapped porosity. The results indicate that it is possible to predict the frost damage of the stones, with a better level of confidence, from the ratio of the volume fraction of water to that of air rather than from only the total porosity or degree of saturation. [ABSTRACT FROM AUTHOR]

Attention-based models have been widely used in aspect-level sentiment analysis owing to their contextual semantic-alignment capabilities. However, these models lack a mechanism that can incorporate syntactic tendencies. Thus, attention-based neural networks are less accurate when dealing with complex syntactic relationships and long-span syntactically dependent utterances. To solve this problem, a neural network model combining a conditional random field and a graph convolutional network is proposed. The model uses a conditional random field chain to extract the opinion span of an aspect-specific word, integrates the contextual information within the opinion span to the global nodes through a multilayer graph convolutional network using the improved position decay function, and predicts the aspect-specific sentiment polarity labels by computing the vector expressions of the global nodes. This study addresses the problem of fluctuations in model accuracy when multiple aspect words exist in an utterance by introducing global nodes into a graph convolutional network. The proposed model performed well in the experiments on all four datasets tested. [Display omitted] • The model combines the advantages of CRF and GCN. • The model uses CRF chains to extract opinion span of a particular aspect word. • The improved position decay function regulates importance of context words. • The global node integrates opinion span information corresponding to the aspect word. • The experimental results demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Influence prediction is the key segment of online negative word-of-mouth processing for the enterprise. This paper forms the feature space by combing characteristics of information publisher and information content features, and describes the varied decay tendency of the influence of negative word-of-mouth by the influence decay function. Then, the paper proposes a IMM-RTDF model based on regression tree and decay function, which is aimed at predicting the influence of negative word-of-mouth. Experimental results show that considering information content features contributes to improve the prediction accuracy, what's more, using different decay functions can describe the decay tendency for heterogeneous negative word-of-mouth better and improve model performance as well. The strategy for negative word-of-mouth processing based on this method provides a good decision support for enterprise on customer relationship management and resources allocation. [ABSTRACT FROM AUTHOR]

In this paper we investigate the almost sure practical stability for a class of stochastic functional evolution equations. We establish some sufficient conditions based on the construction of appropriate Lyapunov functional. The abstract results are then applied to some illustrative examples. [ABSTRACT FROM AUTHOR]

This paper is concerned with the synchronization problem for a class of delayed chaotic neural networks with discontinuous activations. First a lemma which concerns stability in general decay rate is constructed. Based on this lemma, the general decay synchronization stability criteria of discontinuous neural networks are derived via a designed controller. The general decay synchronization is obtained by introducing a decay function and it contains exponential synchronization and polynomial synchronization as its two special cases. Finally, two examples are given to verify the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]