Background: There is a strong interest in tick-borne diseases worldwide due to their negative impact on both human and animal health. Epidemiological studies of tick-borne diseases depend on reliable data on tick population dynamics and activity patterns. Such data are essentially based on tick sampling in the field. This study aimed to evaluate the impact of cloth type on the efficiency of field sampling by the flagging technique. Methods: The impact of cloth type on the efficiency of field sampling by the flagging technique was investigated by comparing tick sampling yields of two different fabrics, the Munich type (MUC) vs. the Oxford type (OX), based on 30 pairs of transect lines. Data analysis included classical statistics and computer modelling. Results: The MUC flag yielded nearly five times more larval ticks than the OX flag, whereas the differences in yields for nymphs and adult ticks were not statistically significant based on classical statistics. Conclusion: The flag made of MUC type fabric, thanks to its tight and relatively flat texture, facilitates detection and collection of ticks from its surface. The OX flag, due to its loose texture, is unsuitable for the quantitative sampling of larval Ixodes ricinus.

The current study was aimed at exploring Differential Item Functioning (DIF) items in Iranian TEFL MA Entrance Exam employing two beneficial and valuable statistical methods: Logistic Regression (LR) and Mantel-Haenszel (MH). Besides, the founded DIF items were gone through a content analysis in order to explore the potential linguistic resources of such biases. To this end, the answer sheets of 2217 female and 735 male examinees in 2015 were analyzed to find items containing DIF. The findings of LR technique determined eight items as DIF containing items. Half of the items were advantageous to the men and the other half of the items favoured women. MH procedure explored eleven items as DIF flagging items. Out of these items, six items favoured male test takers and five items showed tendency toward female test takers. No particular linguistic source for such deviated behaviour of items was proposed through the content analysis of the DIF items.

This research paper presented a comprehensive analysis of the literature on winter sports tourism, tracing its development over a span of 56 years. It was reviewed with bibliometric techniques with VOSviewer software. The extensive bibliometric analysis revealed emerging trends, notable research contributions, and key participants in the field. Prominent countries in this niche are Australia and Canada, while the Universities of Innsbruck and Waterloo, journals such as Sustainability Journal (Switzerland), and authors such as Falk and Muller, Peters, Scottwere have shown significant focus in this area. Therefore, the study highlights the increasing importance of this niche tourism sector, reflecting a surge of interest in recent years as indicated by the rising number of relevant research papers. The review also underscored how vital bibliographic studies are to comprehending the landscape of winter sports tourism. The findings provided trends, patterns, and a roadmap for prospective researchers, flagging the necessity of keeping pace with the evolving realities of winter sports tourism and establishing a valuable foundation for understanding and examining current trends and future developments in winter sports tourism. That will undoubtedly prove useful for both researchers and decision-makers in the field of winter sports tourism.

The Global Positioning System (GPS) is integral to the safety and efficiency of modern railway networks; however, its susceptibility to jamming and spoofing interference poses a significant threat to operational integrity. Conventional detection systems often rely on fully supervised models requiring extensive labeled data or specialized, costly hardware, limiting their scalability. This paper addresses this gap by proposing and evaluating an Enhanced Semi-Supervised K-Means (ESS-KMeans) algorithm designed to operate effectively with minimal labeled data. We compare its performance against a standard unsupervised K-Means algorithm using a challenging, synthetically generated dataset based on GPS signal characteristics such as latitude/longitude variation, altitude deviation, and Automatic Gain Control (AGC) levels. The proposed ESS-KMeans leverages a small labeled subset for robust centroid initialization and mutual information-based feature weighting, while also uniquely identifying and flagging ambiguous, low-confidence samples. Experimental results demonstrate that ESS-KMeans achieves perfect (1.000) accuracy on confidently classified samples, a significant improvement over standard K-Means (0.960), and improves cluster quality by over 45% (Silhouette Score). By delivering superior accuracy and providing a mechanism for uncertainty quantification with minimal supervision, this semi-supervised approach presents a scalable, cost-effective, and reliable solution for enhancing the resilience of railway systems against GPS interference.

Differential item functioning (DIF) is considered to be one of the tools for the examination of test fairness. This method is capable of finding the factors affecting the subjects’ performance and prevent the occurrence of bias in the test. A plethora of methods for detecting Differential Item Functioning (DIF) has been suggested during the last couple of decades. The multiplicity of methods for diagnosing DIF might be a confusing issue for applied researchers and might lead to complications in the comparability of the findings of various DIF studies which have utilized different DIF detection techniques. This study aimed to investigate the comparability of results from three widely used DIF detection techniques: the Rasch model, Logistic Regression, and Mantel-Haenszel (MH). The data comes from an administration of the University of Tehran English Proficiency Test (UTEPT) which is a high-stakes test administered annually to PhD candidates. DIF analysis through the three techniques indicated that the three methods did not have significant differences in their performance. The Mantel-Hansel model flagged two items having DIF just similar to the findings of logistic regression model. Likewise, the items that were detected as strong-DIF items in Rasch model were the same as items detected by the two aforementioned models. Therefore, it might be concluded that use of different DIF detection techniques does not necessarily lead to flagging different items.

Under Islam, such punishments as retaliation in kind, cutting off of hand, imprisonment or flagging are provided for such crimes as murder, theft and breach of trust; however, such punishments do not justify humiliation of the perpetrator. That is, the punishment for each criminal act is provided for under sharia and Islamic law and the provided punishment shall be imposed exactly while making sure that the punished person shall by no means be subject to disrespect and humiliation in the course of punishment. This claim is based on the doctrine of respect for the human nature, which is originally rooted in the doctrine of dignity and the doctrine of non-association of the body and dignity of the criminal as well as the doctrine of respect for the Muslims. The proof of such claim requires a study of the evidence supporting the respect for the criminals as a doctrine of Islamic jurisprudence and law, which requires explanation and scrutiny. Given of the Qur’anic principles as well as the opinions of Muslim jurists and scholars, there are sufficient grounds in support of the argument in favor of the respect for the criminal as a law of Islamic jurisprudence, ethics and law. This paper tried to provide proof of the human’s enjoyment of the right to dignity and also to discuss the philosophy of punishment with respect to the doctrine of the respect for the criminal as a doctrine of law.

Background: Domestic violence is a global public health concern as stated by World Health Organization. We aimed to conduct a textual analysis of tweets associated with domestic violence through keyword identification, word trends and word collocations. The data was obtained from Twitter, focusing on publicly available tweets written in English. The objectives are to find out if the identified keywords, word trends and word collocations can help differentiate between domestic violence-related tweets and non-domestic violence-related tweets, as well as, to analyze the textual characteristics of domestic violence-related tweets and non-domestic violence-related tweets. Methods: Overall, 11, 041 tweets were collected using a few keywords over a period of 15 days from 22 March 2021 to 5 April 2021. A text analysis approach was used to discover the most frequent keywords used, the word trends of those keywords and the word collocations of the keywords in differentiating between domestic violence-related or non-domestic violence-related tweets. Results: Domestic violence-related tweets and non-domestic violence-related tweets had differentiating characteristics, despite sharing several main keywords. In particular, keywords like “, domestic”, , “, violence”,and “, suicide”, featured prominently in domestic-violence related tweets but not in non-domestic violence-related tweets. Significant differences could also be seen in the frequency of keywords and the word trends in the collection of the tweets. Conclusion: These findings are significant in helping to automate the flagging of domestic-violence related tweets and alert the authorities so that they can take proactive steps such as assisting the victims in getting medical, police and legal help as needed.