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Weather-based prediction models for pests (aphids, thrips and leafhoppers) in cotton crop were developed on various aspects viz., crop age at first appearance of pests and maximum population of pests at Akola (Maharashtra). Historical data from 1997-98 to 2010-11 on population of aphids, thrips and leafhoppers along with weekly weather data (two weeks before sowing) were considered fordevelopment of models and validated for the subsequent three years from 2011-12 to 2013-14. Weatherbased indices were generated, which were used as explanatory variables in development of models. The study reveals that the prediction for crop age at first appearance of pests, crop age at peak population of pests, and maximum population of pests can be issued at least two to three weeks in advance. 

The observed uneven distribution of economic activity across space is influenced by variation in exogenous geographical characteristics and endogenous interactions between agents in goods and factor markets. Until the past decade, the theoretical literature on economic geography had focused on stylized settings that could not easily be taken to the data. This article reviews more recent research that has developed quantitative models of economic geography. These models are rich enough to speak to first-order features of the data, such as many heterogeneous locations and gravity equation relationships for trade and commuting. At the same time, these models are sufficiently tractable to undertake realistic counterfactual exercises to study the effect of changes in amenities, productivity, and public policy interventions such as transport infrastructure investments. We provide an extensive taxonomy of the different building blocks of these quantitative spatial models and discuss their main properties and quantification.

A new theoretical framework for the testing effect —the finding that retrieval practice is usually more effective for learning than are other strategies—is proposed, the empirically supported tenet of which is that separate memories form as a consequence of study and test events. A simplest case quantitative model is derived from that framework for the case of cued recall. With no free parameters, that model predicts both proportion correct in the test condition and the magnitude of the testing effect across 10 experiments conducted in our laboratory, experiments that varied with respect to material type, retention interval, and performance in the restudy condition. The model also provides the first quantitative accounts of (a) the testing effect as a function of performance in the restudy condition, (b) the upper bound magnitude of the testing effect, (c) the effect of correct answer feedback, (d) the testing effect as a function of retention interval for the cases of feedback and no feedback, and (e) the effect of prior learning method on subsequent learning through testing. Candidate accounts of several other core phenomena in the literature, including test-potentiated learning, recognition versus cued recall training effects, cued versus free recall final test effects, and other select transfer effects, are also proposed. Future prospects and relations to other theories are discussed.

Most artists have been influenced by their predecessors or peers in their lives, who brought them inspiration to form their own composing style and direction. This pattern is particularly important for the study of the developing trend of the music field. Based on the da ta which records the influence relationships of musicians over the years, we construct a directed influence network, reflecting the connection among musicians. Then from the perspective of the subnetwork, a synthetic appraisal model is put forward to quantify the influence of artists. In order to indicate its correctness and objectivity, the influence patterns between musicians and their works are further explored by analyzing the attributes of songs. Finally, for the music itself, the properties of the music are analyzed through standardized regression to obtain the characteristics that have the greatest impact on the music influence.

Defect engineering has been explored as a means to improve the performance of graphene-based gas sensors. However, sensor sensitivity often exhibits a non-monotonic relationship with defect density, presenting challenges for device optimization. In this study, we introduce a quantitative physical model aimed at describing and explaining this behavior. Graphene samples were irradiated with deuteron ions to generate a controlled range of defect densities, which were characterized using Raman spectroscopy. Sensitivity to hydrogen showed a Λ-shaped dependence on defect density, with a peak response observed at an intermediate concentration. This behavior is attributed to the competing effects of signal enhancement and structural degradation induced by defects. The proposed model expresses sensitivity as a function of two experimentally derived parameters: an activation coefficient ( C n ) and a degradation coefficient ( γ n ). These parameters provide physical insight into the underlying sensing mechanism.

Over the last decade, resilience has become an indispensable aspect to be considered when managing supply chains given to the recent challenges they were subjected to, and a constituting element of their sustainability. However, despite literature on supply chain resilience is copious, tools for quantifying the resilience of a business are lacking, especially when dealing with the a priori resilience of a system, since several assessments are a posteriori carried out, after a disruption has manifested. In response, an analytic quantitative model is here proposed, whose output is a Global Resilience Index for a company. The model is divided into 3 phases: Supply (8 factors), Production (12 factors) and Distribution (5 factors); these elements were derived from literature and semi-structured interviews with practitioners. The logical functioning of the model is based on weighted averages attributed to each single factor; for defining the weights a survey was sent, in which respondents had to express their opinion with reference to the perceived impact of those factors on resilience. For validating the model, it was implemented in three companies manufacturing the following products: fresh milk, ginseng coffee and vegetable preserves. Despite none of them reached the higher resilience level, results offer interesting insights for let the users understand where the system is weaker. This model is intended to be made available to those who desire to include the resilience assessment to manage operational decisions; moreover, this value could be included in a wider sustainability assessment of a business.

Sustainability in supply chains (SC) is a topic that has been widely discussed recently and, similarly to the quest to develop green SCs, it does not contemplate the integration of economic and social dimensions of sustainable SC management (SSCM). This article proposes a conceptual framework to provide key factors that act as a valuable tool to further develop quantitative models of location, inventory and transportation (LIT) problems in SSCs. The objectives of this paper are to: (i) review the literature by focusing on conceptual frameworks for sustainable SC; (ii) map and construct the conceptual framework blocks by describing each element and dimension; and (iii) validate the conceptual framework. The conceptual framework comprises five main blocks: SC structures, sustainable inputs, quantitative models, inventory policies and sustainability objectives. These five blocks are then formed by different dimensions and elements, which are described in this paper and validated in a mining case study. The results show that no previous studies propose a reference conceptual framework to model a sustainable LIT problem in a CLSC context, which is the main novelty of this paper. Finally, the conceptual framework is validated and applied as a diagnostic tool in two copper mining companies.

Vigorous human activities have strengthened the development and utilization of land, causing huge damage to the earth’s surface, while mining the disturbance pattern of human activities can capture the influence process and spatial interaction between human activities and land use. Therefore, in order to explore the inherent relationship between human activities and land use in mountainous counties, a spatial quantitative model of human activity disturbance intensity and land use intensity was proposed based on GF-6 image, traffic data, and socioeconomic data. The model can quantitatively evaluate the disturbance intensity of human activity and land use intensity from “production-living-ecological space”, and unfold the correlation between human activity disturbance intensity and land use intensity with Pearson correlation coefficient and bivariate spatial autocorrelation method. Our study presents several key findings: (1) the spatial difference of human activity disturbance is significant in Mianzhu City, and it has steady aggregation (Moran’s I index is 0.929), showing a decreasing trend from the southeast to the northwest area; (2) there is a strong positive correlation between the disturbance intensity of human activity and the intensity of land use with Pearson value 0.949; (3) among the eight selected factors, the proportion of construction land area plays a leading role in the disturbance intensity of human activity in Mianzhu City, while the township final account data have the least impact. The study results can provide an important reference for the quantitative identification and evaluation of human disturbances in similar cities and the coordinated development of the human–land relationship.

Creep is one of the primary degradation mechanisms affecting the performance of the 800H alloy under long-term high-temperature stress conditions. Understanding the microstructural evolution during creep and developing a quantitative model to relate these changes to mechanical properties are essential for assessing creep damage and ensuring the safe operation of high-temperature equipment. By conducting a multiscale quantitative characterization of the microstructures in the 800H alloy across different creep stages, we systematically examined the evolution of various microstructural features and their influence on Young’s modulus. A quantitative prediction model of Young’s modulus based on microstructural characteristics was developed, achieving a prediction accuracy exceeding 95% with a mean absolute percentage error of just 1.59% compared to experimental values. This work not only elucidates the intrinsic relationship between microstructural features and macroscopic mechanical properties but also provides a foundation for the in-service creep damage assessment of high-temperature components.