The smallest eating the largest: the oldest mammalian feeding traces on dinosaur bone from the Late Jurassic of the Junggar Basin (northwestern China).

Reconstructing trophic interactions in ancient ecosystems is an important and fascinating branch of palaeontological research. Here we describe small bioerosional traces that are preserved on sauropod bone from the early Late Jurassic Qigu Formation (Oxfordian) of Liuhuanggou gorge in the southern Junggar Basin (Xinjiang Province, northwestern China). The most likely producers of these traces are tiny Mesozoic mammals as evinced by the small size of the traces as well as by their paired and opposed arrangement. The feeding traces are only superficially preserved on the bone surface and most likely were inflicted unintentionally during feeding. The occurrence of the bite marks along small ridges and the "gnawed" appearance of the bone surface points to selective feeding on the remaining soft tissues of the dinosaur carcass. The traces represent the oldest direct evidence for mammalian feeding behaviour in the fossil record. Additionally, these traces expand the known range of the early mammalian feeding repertoire significantly and shed light on the palaeobiology and palaeoecology of early mammals, a field that has remained evasive for a long time.

A bipolar fuzzy decision-making system for assessing high-risk coexisting tuberculosis disease in pregnant women.

Tuberculosis (TB) diagnosis poses a formidable challenge in global healthcare, particularly impacting older individuals and pregnant women. Diagnosing TB disease during pregnancy and in comorbid patients is more challenging due to overlapping symptoms with normal pregnancy conditions and existing treatments for other diseases, necessitating careful assessment to differentiate TB symptoms from those of other underlying conditions. To address this issue, this study designs a novel bipolar fuzzy decision-support system by integrating the concept of complex proportional assessment (COPRAS) and a technique for order preference by similarity to the ideal solution (TOPSIS) approaches using bipolar heptagonal fuzzy numbers. The approach is utilized to assess the high-risk of TB coinfection disease in pregnant women. The bipolar fuzzy set provides positive and negative membership degrees of an element, which divulge a balanced perspective by both the presence and absence of the disease. Additionally, a defuzzification algorithm is proposed for bipolar heptagonal fuzzy numbers, converting bipolar heptagonal fuzzy into a bipolar crisp score (CBHpFBCS). The bipolar fuzzy entropy measure is utilized to weight the criteria. The findings highlight that TB+HIV (G3) coinfection is more severe in pregnant women compared to other TB comorbidities. Finally, sensitivity and comparative analyses are executed across diverse criteria weight scenarios and with existing fuzzy multi-criteria decision-making (MCDM) methods to validate the robustness of the proposed method and its outcomes.

A bipolar intuitionistic fuzzy decision-making model for selection of effective diagnosis method of tuberculosis.

OBJECTIVES: Tuberculosis (TB) is a contagious illness caused by Mycobacterium tuberculosis. The initial symptoms of TB are similar to other respiratory illnesses, posing diagnostic challenges. Therefore, the primary goal of this study is to design a novel decision-support system under a bipolar intuitionistic fuzzy environment to examine an effective TB diagnosing method. METHODS: To achieve the aim, a novel fuzzy decision support system is derived by integrating PROMETHEE and ARAS techniques. This technique evaluates TB diagnostic methods under the bipolar intuitionistic fuzzy context. Moreover, the defuzzification algorithm is proposed to convert the bipolar intuitionistic fuzzy score into crisp score. RESULTS: The proposed method found that the sputum test (T3) is the most accurate in diagnosing TB. Additionally, comparative and sensitivity analyses are derived to show the proposed method's efficiency. CONCLUSION: The proposed bipolar intuitionistic fuzzy sets, combined with the PROMETHEE-ARAS techniques, proved to be a valuable tool for assessing effective TB diagnosing methods.

Fuzzy entropy DEMATEL inference system for accurate and efficient cardiovascular disease diagnosis.

The global population is at risk from both communicable and non-communicable deadly diseases, including cardiovascular disease. Early detection and prevention of cardiovascular disease require an accurate self-detection model. Therefore, this study introduces a novel fuzzy entropy DEMATEL inference system for accurate self-detection of cardiovascular disease. It combines fuzzy DEMATEL, entropy, and Mamdani fuzzy inference, utilizing innovative strategies like attribute reduction, entropy-based clustering, influential factor selection, and rule reduction. The system achieves high accuracy (98.69%) and sensitivity (98.62%), outperforming existing methods. Validation includes satisfactory factor analysis, performance measures and statistical analysis, demonstrating its effectiveness in addressing complexity and prioritizing factors.

Bipolar intuitionistic fuzzy graph based decision-making model to identify flood vulnerable region.

Bipolar intuitionistic fuzzy graphs (BIFG) are an extension of fuzzy graphs that can effectively capture uncertain or imprecise information in various applications. In graph theory, the covering, matching, and domination problems are benchmark concepts applied to various domains. These concepts may not be defined precisely using a crisp graph when the vertices and edges are more uncertain. Therefore, this study defines the covering, matching and domination concepts in bipolar intuitionistic fuzzy graphs (BIFG) using effective edges with certain important results. To define these concepts when the effective edges are absent, some novel approaches are discussed. To illustrate the domination concepts, the applications in disaster management and location selection problems are discussed. Further, a BIFG-based decision-making model is designed to identify the flood-vulnerable zones in Chennai, where the city's most and least vulnerable zones are identified. From the proposed model, Kodambakkam ([Formula: see text]) is the most susceptible zone in Chennai. Finally, a comparative analysis is done with the existing techniques to show the efficiency of the model.

A new specimen of Solnhofia parsonsi from the Upper Jurassic (Kimmeridgian) Plattenkalk deposits of Painten (Bavaria, Germany) and comments on the relationship between limb taphonomy and habitat ecology in fossil turtles.

The limestones of the Solnhofen area in southern Germany are one of the most important fossil Lagerstätten from the entire Mesozoic era, especially famous for the exquisitely preserved vertebrates. The turtles from the Solnhofen Limestone have been always of special interest because they include some of the best-preserved specimens from the Mesozoic. Here, we describe a new turtle specimen from the Torleite Formation (Kimmeridgian) of Painten and refer it to the thalassochelydian turtle Solnhofia parsonsi based on the presence of a unique combination of characters. The far majority of morphological differences from previously published specimens can be explained by ontogeny as the new specimen represents a larger, more ossified, and presumably older individual. Additionally, the specimen from Painten is the first described specimen of S. parsonsi preserving the largely complete and articulated limbs, the preservation of which indicates that the taxon did not possess stiffened paddles present in more pelagic marine turtles and is consistent with a previously inferred nearshore marine lifestyle. Contrary to previous inferences, we argue that taphonomic preservation of digits in articulated fossil turtles from laminated deposits cannot be used alone to infer marine or freshwater habitat. Finally, the new specimen from Painten is only the second, for which detailed information on its stratigraphic position and locality of origin are known.