hossein rahmani

Stance detection is the process of identifying and classifying an author's point of view or stance towards a specific target in a given text. Most of previous studies on stance detection neglect the contextual information hidden in the input data and as a result lead to less accurate results. In this paper, we propose a novel method called ConSPro, which uses decoder-only transformers to consider contextual input data in the process of stance detection. First, ConSPro applies zero-shot prompting of decoder only transformers to extract the context of target in the input data. Second, in addition to target and input text, ConSPro uses the extracted context as the third type of parameter for the ensemble method. We evaluate ConSPro on SemEval2016 and the empirical results indicate that ConSPro outperforms the non-contextual approaches methods, on average 9% with respect to f-measure. The findings of this study show the strong capabilities of zero-shot prompting for extracting the informative contextual information with significantly less effort comparing to previous methods on context extraction.

It is challenging to definitively determine whether another categorization of judicial proceedings into "acceptable" and "unacceptable" exists. However, the following points can be made: Firstly, the answer is negative, as there is no precedence for such a categorization in the legal literature authored by prominent professors and legal experts in our country. In principle, judicial proceedings should be acceptable and admirable, leaving no room for the recognition of unacceptable or objectionable proceedings. Secondly, the answer is affirmative. This is because, when discussing judicial proceedings deemed acceptable and desirable by the framers of the Constitution and the proponents of the judicial process, it is apparent that there exists a form of proceeding that does not align with the intentions of the lawgiver and is not desirable for the public seeking judicial recourse. Doubts arise because, when examining and scrutinizing the notion of "unacceptable judicial proceedings," resistance is encountered from both judicial authorities and legal scholars. The opposition from the former is seemingly natural, as they view the judicial process in the courts as legitimate and do not tolerate anything to the contrary. From the perspective of the latter, there is a concern that by recognizing unacceptable judicial proceedings, actions within this framework may appear justified, even though such proceedings lack the characteristics, privileges, and status of acceptable judicial proceedings.

Due to high toxicity, durability in natural conditions, and bioaccumulation in the food chain, potentially toxic elements are considered serious pollutants.

toxic elements (Al, As, Cr, Cd, Co, Cu, Ni, Pb, Zn, V, and Mn) in sediment samples from some coastal rivers flowing into the southern Caspian Sea (Tajan, Babolroud, and Shirood) were assessed. Single (Cf , EF, Igeo, Hq, PLI, and QoC) and integrated contamination indices (m-PEC-q, m-PEL-q, MERMQ, NPI, and CSI) were used to assess the ecotoxicological risk of the metals.

At all sites, the level of Cd was less than the detection limit (<5 mg.kg-1), indicating no significant source of pollution containing Cd. The mean concentration order of the metals in the rivers varied, suggesting that their contaminant sources significantly differed. The metal content of the Tajan River was substantially lower than that of the other rivers. EF values of Cu, Ni, and As showed partial enrichment, probably indicating their anthropogenic origin. According to the single indices of CF, Igeo, PLI, and Hq, the Babolrood and Shirood Rivers, sediment was significantly contaminated by As, Ni, and Zn. Based on NPI values, the Shirood River was extremely polluted by As. Integrated ecotoxicological risk indices of CSI, m-ERM-Q, and m-PEL-q suggest that metals pose medium to low levels of environmental toxicity in the Babolrood and Shirood Rivers.

This research demonstrated the necessity of using management and pollution control strategies such as improving wastewater treatment, promoting sustainable agriculture, and regulating industrial discharges.

Document similarity matching is essential for efficient text retrieval, plagiarism detection, and content analysis. Existing studies in this field can be categorized into three approaches: statistical analysis, deep learning, and hybrid approaches. However, to the best of our knowledge, none have incorporated the importance of named entities into their methodologies. In this paper, we propose DOSTE, a method that first extracts name entities and then utilizes them to enhance document similarity matching through statistical and graph-based analysis. Empirical results indicate that DOSTE achieves better results by emphasizing named entities, resulting in an average improvement of 9% in the average recall metric compared to baseline methods. Also, DOSTE unlike LLM-based approaches, does not require extensive GPU resources. Additionally, non-empirical interpretations of the results indicate that DOSTE is particularly effective in identifying similarity in short documents and complex document comparisons.

In recent years, new technologies have brought new innovations into the financial and commercial world, giving fraudsters many ways to commit fraud and cost companies big time. We can build systems that detect fraudulent patterns and prevent future incidents using advanced technologies. Machine learning algorithms are being used more for fraud detection in financial data. But the common challenge is the imbalance of the dataset which hinders traditional machine learning methods. Finding the best approach towards these imbalance datasets is the problem many of the researchers are facing when trying to use machine learning methods. In this paper, we propose the method called FinFD-GCN that use Graph Convolutional Networks (GCNs) for fraud detection in credit card transaction datasets. FinFD-GCN represents transactions as graph in which each node represents a transaction and each edge represents similarity between transactions. By using this graph representation FinFD-GCN can capture complex relationships and anomalies that may have been overlooked by traditional methods or were even impossible to detect with conventional approaches, thus enhancing the accuracy and robustness of fraud detection in financial data. We use common evaluation metrics and confusion matrices to evaluate the proposed method. FinFD-GCN achieves significant improvements in recall and AUC compared to traditional methods such as logistic regression, support vector machines, and random forests, making it a robust solution for credit card fraud detection. By using the GCN model for fraud detection in this credit card dataset we outperformed base models 5% and 10%, with respect to F1 and AUC, respectively.

Multi-label text classification is a critical challenge in natural language processing, where the goal is to assign multiple labels to a given document. Recent advances have primarily focused on deep learning approaches, yet many fail to adequately capture the intricate relationships between documents and labels. In this paper, we propose a novel method called MultiCGCN, in which we leverage Graph Convolutional Networks (GCNs) for multi-label text classification by modeling text as a heterogeneous graph. This unified graph incorporates document similarities, label relationships, and document-label associations, enabling the model to effectively capture both document and label dependencies. We transform the multi-label classification problem into a link prediction task, using Term Frequency–Inverse Document Frequency (TF-IDF) for document similarity and applying GCNs to predict label assignments. Our empirical evaluations demonstrate that MultiCGCN achieves a significant performance boost, improving F1 score by 10% over traditional baseline models. This approach opens new avenues for enhancing the accuracy of multi-label classification in various domains.

This study used a dynamic model to predict the fishing status of the Persian Sturgeon (Acipenser persicus) along the coast of the Golestan province in the Caspian Sea. The findings indicated that based on the initial population in 2017 equal to 2500 samples of fish if harvested at the current rate, the stocks of this valuable sturgeon fish will be destroyed in the next 50 years. However, when considering the harvest rate of 0.2 per year (nearly half of the current catch), the amount of fish harvested will increase so that this stock will completely regain its ability to regenerate itself.

The Internet has become an integral societal component, with its accessibility being imperative. However, malicious actors strive to disrupt internet services and exploit service providers. Countering such challenges necessitates robust methods for identifying network attacks. Yet, prevailing approaches often grapple with compromised precision and limited interpretability. In this paper, we introduce a pioneering solution named ENIXMA, which harnesses a fusion of machine learning classifiers to enhance attack identification. We validate ENIXMA using the CICDDoS2019 dataset. Our approach achieves a remarkable 90% increase in attack detection precision on the balanced CICDDoS2019 dataset, signifying a substantial advancement compared to antecedent methodologies that registered a mere 3% precision gain. We employ diverse preprocessing and normalization techniques, including z-score, to refine the data. To surmount interpretability challenges, ENIXMA employs SHAP, LIME, and decision tree methods to pinpoint pivotal features in attack detection. Additionally, we scrutinize pivotal scenarios within the decision tree. Notably, ENIXMA not only attains elevated precision and interpretability but also showcases expedited performance in contrast to prior techniques.

The presence- absent and frequency of any species in a riverian habitat is hnfluenced by numerous factors, and the particular domains prefer the changes of each habitat factor. The purpose of this study was to investigate the habitat suitability index of Alburnoides eichwaldii in the Lavij River in Mazandaran province. A sampling of fish was carried out at 22 stations in Junuary 2017 by electrofishing, frome the top of the river to the Chamestan city (Kashpel forest park) in downstream of the river. At each station, 11 environmental factors were measured. The results showed that, station 21 is the most suitable station for A. eichwaldii and at this station, parameters such as altitude (until 150 m), width (6-9 m), depth (19-25 cm), substrate (3-7 cm) and water flow velocity (0.6-1 m/s) played the highest role in the high habitat suitability index. According to the obtained chart of the CANOCO software, temperature and ammonium the importance and impact of these variables on the frequency of species, especially A. eichwaldii in the Lavij River. The results showed that areas like station 21, which have been less destroyed in terms of human activities, had the highest level of suitability.

Diet composition and feeding strategies of two native and non-native trout species i.e. the brown trout(Salmo trutta) and rainbow trout (Oncorhynchusmykiss)were investigated from autumn 2018 until summer 2019 in the uppermost of Emamzadeh Ali Lake (Haraz River, North of Iran). A total of 79 individuals including 59 brown trouts and 20 rainbow trouts were caught and studied. Digestive contents of feeding indices including CV (coefficient of vacuity), RLG (relative length of gut) and IF (intensity of feeding) revealed that both trout species were voracious carnivores, and showed a desirable nutritional status at all sampling seasons. Rainbow trout had a wider niche breadth than the brown trout in autumn, winter, and summer, except for spring. Feeding niche of the both trouts was highly overlapped in autumn and winter i.e. feedingon the same common resources. Percentage of overlap of both trouts was the most in autumn and winter and similar with the results of Morisita′s overlap index.

Fraud in financial data is a significant concern for both businesses and individuals. Credit card transactions involve numerous features, some of which may lack relevance for classifiers and could lead to overfitting. A pivotal step in the fraud detection process is feature selection, which profoundly impacts model accuracy and execution time. In this paper, we introduce an ensemble-based, explainable feature selection framework founded on SHAP and LIME algorithms, called "X-SHAoLIM". We applied our framework to diverse combinations of the best models from previous studies, conducting both quantitative and qualitative comparisons with other feature selection methods. The quantitative evaluation of the "X-SHAoLIM" framework across various model combinations revealed consistent accuracy improvements on average, including increases in Precision (+5.6), Recall (+1.5), F1-Score (+3.5), and AUC-PR (+6.75). Beyond enhanced accuracy, our proposed framework, leveraging explainable algorithms like SHAP and LIME, provides a deeper understanding of features' importance in model predictions, delivering effective explanations to system users.

Among freshwater invertebrates, leeches are one of the main groups of the world's most widely distributed water resources communities, found in almost all types of freshwater habitats. The aim of this study was to investigate the effect of different blood treatments on the reproduction parameters of Caspian leeches, has been done with three repetitions in the year 2019.

In each replication, 10 progenitors were bred over a 5 month period, and once they were fed with blood treatments. At the end of the period, the number of cocoons and larvae were counted and the length, weight and diameter characteristics of the cocoons were measured.

The results showed that in the growth parameters, including the final weight of the generators, the percentage of body weight gain and the specific growth coefficient in chicken (3.06، 461.5، 0.086) and horse (5.22، 857.2، 0.113) treatments were the lowest and highest, respectively (p<0.05). The results of the parameters of reproduction including weight, diameter and length of cocoons in different treatments showed that in chicken (0.205، 0.6، 0.6) and horse (0.785،1.16، 1.5) treatments were the lowest and highest values, respectively (p<0.05). The amount of protein that can be absorbed has also been shown to be almost standard in cow and horse blood.

Based on the results of this experiment and according to the studied parameters, horse and cow blood had the best performance and sheep and chicken treatments are not suitable for the reproduction of Caspian leeches.

The present study was designed and conducted to investigate the effects of varying levels of choline chloride on the microbial flora of the rainbow trout, Oncorhynchus mykiss fry. A total of 750 fish were fed with diets containing 0 (control), 250 (Cho250), 500 (Cho500), 1000 (Cho1000), and 2000 (Cho2000) mg of choline chloride per kg of feed for 90 days. The required feed was obtained from the 21 Bayza Company. The amount of microbial flora and final weight was significantly higher in the Cho1000 and Cho2000 than in the control group. The addition of choline to the diet effectively improved the microbial flora. The most suitable amount of choline chloride was between 1000 and 2000 mg/kg in the diet of rainbow trout.

The search for effective treatments for complex diseases, while minimizing toxicity and side effects, has become crucial. However, identifying synergistic combinations of drugs is often a time-consuming and expensive process, relying on trial and error due to the vast search space involved. Addressing this issue, we present a deep learning framework in this study. Our framework utilizes a diverse set of features, including chemical structure, biomedical literature embedding, and biological network interaction data, to predict potential synergistic combinations. Additionally, we employ autoencoders and principal component analysis (PCA) for dimension reduction in sparse data. Through 10-fold cross-validation, we achieved an impressive 98 percent area under the curve (AUC), surpassing the performance of seven previous state-of-the-art approaches by an average of 8%.

Parallel robots are widely used in many industrial and medical applications. Reconfigurable parallel robots could be defined as a group of parallel robots that can have different geometries, thus obtaining different degrees of freedom derived from the basic structure. These robots have some disadvantages like having erratic workspace and singular points in the workspace. These limitations should be studied for proper usage of parallel manipulators. This paper presents the kinematics and workspace analysis of a 3DOF parallel reconfigurable robot. This robot has two different configurations. The first configuration is a Tricept robot (3UPS-PU) and the second is a fully Spherical robot (3UPS-S). The kinematic equations are derived based on the geometry of the system and then Jacobian matrices are determined via velocity loop closure analysis. The kinematic model is verified by the results obtained from robot simulation in ADAMS software. Then, the workspace of the robot is determined by considering the kinematic constraints.

Graph representation of data can better define relationships among data components and thus provide better and richer analysis. So far, movies have been represented in graphs many times using different features for clustering, genre prediction, and even for use in recommender systems. In constructing movie graphs, little attention has been paid to their textual features such as subtitles, while they contain the entire content of the movie and there is a lot of hidden information in them. So, in this paper, we propose a method called MoGaL to construct movie graph using LDA on subtitles. In this method, each node is a movie and each edge represents the novel relationship discovered by MoGaL among two associated movies. First, we extracted the important topics of the movies using LDA on their subtitles. Then, we visualized the relationship between the movies in a graph, using the cosine similarity. Finally, we evaluated the proposed method with respect to measures genre homophily and genre entropy. MoGaL succeeded to outperforms the baseline method significantly in these measures. Accordingly, our empirical results indicate that movie subtitles could be considered a rich source of informative information for various movie analysis tasks.

To investigate fish biodiversity indexes of Roodbabol River and their relationships with environmental factors, sampling was conducted at 20 stations. At each sampling station factors including river depth, river width, water velocity, substrate rubble diameter, temperature, dissolved oxygen, electrical conductivity and altitude were measured. 6 freshwater fish species has been recorded namely Barbus (Luciobarbus capito), Hiircan Crested Loach (Paracobitis hircanica), Iranian Goby (Ponticola iranicus), Tabarestan Spirlin (Alburnoides tabarestanensis), Grass Carp (Ctenopharyngoden idella), Crucian Carp (Carassius carassius), and Rainbow Trout (Oncorhynchus mykiss). Shanon diversity and Simpson dominance indexes were the highest at station 6 (H′= 1.068 and λ = 0.6071) and 7 (H′= 1.212 and λ = 0.6653) and the lowest at station 9 (H′= 0.103, λ = 0.041). Stations 7 and 13 were the richest stations for species richness index (0.9705 and 0.8736, respectively) while stations 9 and 11 showed the lowest species richness (0.2597 and 0.2153, respectively). The most and least values of Pielou′s evenness index were observed at stations 11 and 20 (0.9816 and 0.3435, respectively). Water temperature with fish abundance, Margalof, Shannon diversity and Simpson dominance indexes, river depth with fish species number, and river width with Pielou′s index had positive and significant correlation. In addition, a significant and negative correlation was discovered between electrical conductivity with Margalof index and between river width with fish abundance. PCA indicated that factors like depth, velocity, rubble diameter and river width primarily and other factors of altitude, salinity and shadow level secondly affect some fish species abundance in particular stations.

Non-native species are one of the important threats to biodiversity. Some non-native fish species pose environmental impacts and are known as invasive species. Rivers of the southern Caspian Sea are home to a vast variety of native or commercial fish species. Non-native species invasiveness risk assessment tools are used to identify the species with high invasive potential risks and are used as a decision–making tool. The main aim of this study is to calibrate and validate the Aquatic Species Invasiveness Screening Kit (AS-ISK) for non-native fish species of rivers in the southern Caspian Sea basin.

AS-ISK was used to assess non-native (NN) fish that may pose a high potential risk of becoming invasive in rivers of the South Caspian Sea. Fifteen NN fish species were assessed, 14 of which are extant and 1 is horizon species in the risk assessment (RA) area. The statistical methods were used to compare the scores obtained by each assessor. The threshold score was calculated using receiver operating characteristic curve analysis. Also, the Köppen-Geiger climate system was used to match the climate between the native range of NN fish species and the risk assessment area.

Risk assessment of NN fish species led to the identification of 73 % of the species as potential invasive species in the RA area. Carassius gibelio and Carassius auratus achieved the highest risk score and Anguilla anguilla obtained the lowest risk score among all NN fish species. There was not a statistically significant difference between assessor-specific mean risk scores. Threshold scores were 9.5 and 11.5 for AS-ISK and Basic Risk Assessment (BRA), respectively. The area under curve was 100%, which shows an excellent output for the efficiency of the AS-ISK in the RA area. Eleven out of the 15 NN fish species were identified as potential invasive species and the rest as non-invasive NN species in the RA area. Confidence factors were different from 73% to 64%. All NN fish species were categorized in three lists i.e., black, grey and white in regard to their risk ranks in order to manage prioritization.

The AS-ISK efficiently identified NN fish species that pose a high risk of being/becoming invasive in rivers in the south Caspian Sea to prioritize them based on their risk rank and to support conservation managers, decision-makers and policymakers in the aquaculture development plans.

Accepted in the background without argument or catching the attention of the addressee, presuppositions are linguistic structures discussed in semantics and applied pragmatics. Based on Yule’s popular divisions of suppositions - existential, factive, lexical, structural, non-factive and counterfactual, this research aims to answer the following research questions: What are the most and least frequently used presuppositions? Which presuppositions are used by the powerful and powerless families and how is this justified? How are men and women different in their use of presuppositions How are presuppositions and plot line related? The finding showed that the structural and non-factive presuppositions are the most and least frequently used presuppositions, respectively. The powerful family members use more structural presuppositions in order to ask, find and complete their information. The powerless family members use more lexical presuppositions in order to help characterization of themselves and others and help the development of the plot. Although men’s use of presuppositions is higher than women’s, this difference in factive presuppositions is more obvious due to the men’s tendency to be more direct. Furthermore, the plot line and the use of presuppositions are inversely related.

Body size is a fundamental characteristic of organisms and is expected to decrease in response to environmental stresses such as heat stress, and its estimation is an important step in addressing many ecological questions in aquatic environments. The aim of this study was to investigate the effect of environmental factors on body size and diversity of macrobenthos communities in Gomishan wetland and Miankaleh Bay. For this purpose, macrobenthos was carried out by leaf packages in summer 2017. The results showed that the average temperature, salinity and electrical conductivity in Gomishan lagoon were higher than Miankale Bay, but no difference was observed in terms of dissolved oxygen and pH. The average dry weight and total length of the Zygoptera showed no significant difference, Shannon's species diversity and evenness indices showed a significant difference in two ecosystems. The results of covariance analysis between dissolved oxygen, salinity, body weight and abundance on leaf pack decomposition showed that only water salinity has an inverse relationship with the decomposition rate. The results of factorial rotation matrix showed that temperature, longitude and latitude and k-day had a positive effect and pH, salinity and oxygen had a negative effect on the abundance of macrobenthos. The results showed that the changes in environmental parameters such as temperature had an effect on the metabolism and weight of the biomass of different species, so that the average weight of many different species in the warmer water of Gomishan Lagoon was lower than that of Miankaleh Bay.

Brain damages and aphasia resulting from them are very common and their early detection is vital. Description of aphasic patients’ language and speech abilities can lead to the disorder diagnosis, its evaluation, and screening. By making use of a descriptive-analytic method, the present research intended to determine the language disorders caused by brain damages and show how the structural differences of languages affect aphasia symptoms among the patients at the speech therapy centers of Tehran. The control group included 10 safe and sound people, who were thoroughly the same as the case group composed of 7 patients in terms of age, education, and language. The tests included an oral and a written test. The studied people had to describe some pictures in the oral test and were supposed to unscramble some sentences in the written test. The findings indicated that the control group used agent-theme orders for describing the pictures without regarding animacy, while animacy was an effective factor on the order of noun phrases in the sentences and led to pre-posing of the themes in some cases. Additionally, it seemed that in patients with brain damages, animacy concept was an effective factor on the order of noun phrases in the sentences.

One of the most neglected issues in urban areas is the use of residents' social capital capacities. The importance of social capital in creating a spirit of participation among the inhabitants of urban neighborhoods is undeniable; Considering the importance of the issue of social capital in urban areas, this study was conducted to analyze the relationship between adherence to the culture of citizenship and social capital in urban areas from the perspective of citizens of Isfahan. The research method is survey and the statistical population of people 18 years and older in the city of Isfahan, whose number according to the latest statistics is about 1500000 thousand people and 600 of these people using the Cochran's formula is considered as a sample size. .Findings indicate that between the total level of adherence to the culture of citizenship (0.33) and all its dimensions, ie social responsibility (0.34), tendency to follow the law (0.34), social participation (0.40) And generalism (0.28) has a positive, direct and significant relationship with the amount of social capital in urban areas. The results obtained from the structural equation model (Smart PLS) also indicate that adherence to the culture of citizenship has a moderate effect on social capital in urban areas (0.53) and the variable of adherence to the culture of citizenship in total (0.28) of variance Explains the social capital variable in urban areas.

Face, an image a person adopts for himself/herself in social interactions, can be lost, maintained or boosted and is of two aspects: (positive: will to be self-operative and Negative (will to be approved of by other). In any instance of interaction, face, which is tightly related to power, comes into play. Power has been traditionally set in the males’ hands and this made it easy for them to threaten the face of their inferiors, though this has been sometimes contested. Comparing the FTAs Mahmud Shaygan and his wife do in “Monji dar Sobhe Namnak” (Savior in a Damp Morning) based on Brown and Levinson’s (1987) model, the present article shows how the female character is different from the gender-cliché’s we have in mind. The findings show that the woman threatened her husband’s face more through disagreement, criticism and confrontation. In other words, the more the male character commits acts that threatens the face of his wife, the more the woman does FTAs to threaten the face of her husband.

In this study of the some parameters of population dynamics of spirlin (Alburnoides eichwaldii) was performed in reservoir of Shahid Rajaii dam in four seasons. 226 specimens were caught by gill nets, were in 5 age groups 0+ to 4+ years that, 2-year-olds were prevailing in both sexes. The sex ratio of males to females were 1:3.17, that, indicates the predominance of the female sex (p<0.05, χ2=60.84). Specimens ranged from 62 to 131 mm in length and 2.59 to 30.46 gr in weight. In overall, the length and weight of the females was higher than males (p<0.05). The growth pattern was calculated as positive allometric according to the slope of the regression line between length and weight and based on the Pauly growth equation. The Von Bertalanffy growth equations for males and females were calculated respectively   and   that the infinity length in females was more than males. The condition factor almost was equal in both sexes (p>0.05). The average absolute and relative fecundity were 2690±1048 and 193±98 respectively. The average of female egg diameter was 0.86 ±0.21 mm in this study. Examination of the nutritional index showed the tendency of this species to eat carnivores. After 15 years of the formation of the lake, the view of many dynamic parameters of Alburnoides eichwaldii in reservoir better than river.

To disagree, the addressee – in his/her relative turn (T2) – needs to rebut the speaker turn’s (T1) statement. Thus, disagreement initiates with T2. This article aims at investigating T2 disagreements in God’s messengers’ talks to disbelievers. The data, driven from Qur’ān and composed of 150 cases, were collected and categorized based on Muntigl and Turnbull’s taxonomy of disagreement, namely, Irrelevancy Claim (IC), Challenges (CH), Contradiction (CT), Counterclaim (CC) and Act Combinations (ACs), to (1) determine the most frequent T2 type disagreement and the dominant Act Combination Type disagreement form and (2) specify the relative disagreement types used by people against God’s and His Prophets’ claims and vice versa and (3) justify their differences. The findings indicate that Challenges are the most frequent T2-type-disagreement and the Combination of Contradiction and Counter Claim is the most frequent AC and that people use challenges more while God and His prophets use more ACs. Comparatively, it was found that people’s disagreements take the forms of Challenges and Counter Claims while God and His Prophets use more of Contradiction and Act combination. People were afraid of using Contradictions as they dared not to directly threaten the face of God’s messengers. Instead, they showed more inclination towards challenges through which they cast doubts on the messengers’ claims.