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Genus Echinorhinus is a very poorly known group of deep-water sharks with widespread but patchy distribution in all major oceans. Echinorhinid sharks consist of two valid species, Echinorhinus brucus is reported more commonly in the Indian Ocean region than its congeneric species, E. cookei. Regular data collections in major shark landing sites in Indonesia have documented additional records of both E. brucus and E. cookei from the Indonesian waters, with updated information on their distributions in the Indian Ocean.

Ethnicity, native and/or foreign language knowledge, as well as the learned writing systems potentially influence the development of an individual’s handwriting. The unique education system consisting of National schools, Chinese-medium vernacular schools, Tamil-medium vernacular schools, and Islamic religious schools in Malaysia may have established specific characteristic handwritten allographic features that deserve investigation within the intelligence context. This study was aimed to explore handwritten allographic features of handwriting samples from 120 subjects (30 writers from four different educational backgrounds mentioned above). Characteristic features which could be attributed to the study groups were statistically analyzed and identified. In this study, thirteen allographic features, including letters \"A\", \"B\", \"D\", \"H\", \"p\", \"T\", \"t\", \"w\", \"X\" and \"x\", were found to be discriminative. Such information could serve to indicate the primary education system undergone by a writer; enabling the comparison of different handwriting profiles and allowing characterization of writers to a specific group of people.

The triangular fermatean fuzzy sets integrated by fermatean fuzzy sets and triangular fuzzy variables are presented in this object. This paper presented a triangular fermatean fuzzy sets and operational laws. We define Einstein technique to TFFSs and define the multi-attribute group decision-making based on TOPSIS technique. We define the TFF-AHP-TOPSIS technique for particle swarm optimization. Then, a novel TF-Einstein-based multi-attribute group decision-making model combining the proposed aggregation operators and generalized distance is created. Their TFF-AHP-TOPSIS technique deliberated and a PIS and NIS are offered. Finally, a numerical example is based on TFF-AHP-TOPSIS technique. We advance examination the rationality and advantages of the proposed method through sensitivity analysis and reliability study. Multiple attribute decision-making expression main parts in our ordinary lifetime.

Background 18F‐flortaucipir allows in‐vivo visualization of tau tangles in Alzheimer’s disease (AD) with a standardized definition of tau‐PET positivity. However, semi‐quantification is often used in research and in therapeutic trials and plays an important role in the AT(N) framework. This work aims to determine quantitative positivity thresholds and compare semi‐quantification to visual interpretation. Method N=189 flortaucipir scans from A05 (NCT02016560) study were included (15 young‐controls (YC), 48 elderly cognitively unimpaired (eCU), 85 MCIs, and 41 with AD) (Table 1), wherein the scans were visually interpreted as either T+/T‐. Using a PET‐only tau quantification software, volume‐weighted SUVRs were calculated on Braak I‐II, Braak III‐IV, Braak V‐VI, and temporal‐meta region (TMR) (Figure 3). Three methods to calculate T+ cut‐points were used: (T1) Youden‐index (MCI/AD vs. Aβ‐ eCU), (T2) mean+2SDs of Aβ‐ eCU, and (T3) mean+2SDs of YC SUVRs. Using calculated cut‐points, a subject is hierarchically assigned to a Braak stage if they are T+ at that stage and at all previous stages, otherwise, the subject is Braak‐staging discordant. Result Patients characteristics and visual A+/T+ proportions are summarized in Table 1. T1‐derived thresholds are: 1.26 (Braak I‐II), 1.18 (Braak III‐IV), 1.17 (Braak V‐VI), and 1.29 (TMR) (Table 2). TMR‐based T+ threshold using T2 was 1.30. Overall, the concordance of visual read with TMR‐based quantification was 92%, 93%, and 88.4%, and only 8/189 (4.2%), 6/189 (3.2%), and 0% subjects were Braak staging discordant, using thresholding methods T1, T2, and T3, respectively (Figure 2b). Braak staging was associated with cognitive decline (MMSE and ADAS‐Cog‐11), and with increase in A+/T+ proportions. All Braak V‐VI subjects had a T+ visual read. These subjects were also all Aβ+ MCI/ADs. However, 80% (8/10) of Braak III‐IV, 26.9% (7/26) of Braak I‐II, and 86% (44/51) of TMR+ subjects, using T1‐derived thresholds, had a T+ read, whereas 95.7% (44/46) of TMR+ subjects who were also Aβ+ had a T+ read. Conclusion Using the temporal‐meta region, visual read and SUVR were highly concordant. Visual T+ aligns more with amyloid positivity. Hierarchical Braak staging was associated with cognitive performance and amyloid load and its concordance with visual interpretation was moderate‐to‐high for the limbic and neocortical stages.

In this paper, a new metamaterials-based hypersensitized liquid sensor integrating omega-shaped resonator with microstrip transmission line is proposed. Microwave transmission responses to industrial energy-based liquids are investigated intensively from both numerical and experimental point of view. Simulation results concerning three-dimensional electromagnetic fields have shown that the transmission coefficient of the resonator could be monitored by the magnetic coupling between the transmission line and omega resonator. This sensor structure has been examined by methanol–water and ethanol–water mixtures. Moreover, the designed sensor is demonstrated to be very sensitive for identifying clean and waste transformer oils. A linear response characteristic of shifting the resonance frequency upon the increment of chemical contents/concentrations or changing the oil condition is observed. In addition to the high agreement of transmission coefficients (S21) between simulations and experiments, obvious resonant-frequency shift of transmission spectrum is recognized for typical pure chemical liquids (i.e., PEG 300, isopropyl alcohol, PEG1500, ammonia, and water), giving rise to identify the type and concentration of the chemical liquids. The novelty of the work is to utilize Q factor and minimum value of S21 as sensing agent in the proposed structure, which are seen to be well compatible at different frequencies ranging from 1–20 GHz. This metamaterial integrated transmission line-based sensor is considered to be promising candidate for precise detection of fluidics and for applications in the field of medicine and chemistry.

AI platforms are equipped with advanced ‎algorithms that have the potential to offer a wide range of ‎applications in healthcare services. However, information about the accuracy of AI chatbots against ‎conventional drug-drug interaction tools is limited‎. This study aimed to assess the sensitivity, specificity, and accuracy of ChatGPT-3.5, ChatGPT-4, Bing AI, and Bard in predicting drug-drug interactions. AI-based chatbots (ie, ChatGPT-3.5, ChatGPT-4, Microsoft Bing AI, and Google Bard) were compared for their abilities to detect clinically relevant DDIs for 255 drug pairs. Descriptive statistics, such as specificity, sensitivity, accuracy, negative predictive value (NPV), and positive predictive value (PPV), were calculated for each tool. When a subscription tool was used as a reference, the specificity ranged from a low of 0.372 (ChatGPT-3.5) to a high of 0.769 (Microsoft Bing AI). Also, Microsoft Bing AI had the highest performance with an accuracy score of 0.788, with ChatGPT-3.5 having the lowest accuracy rate of 0.469. There was an overall improvement in performance for all the programs when the reference tool switched to a free DDI source, but still, ChatGPT-3.5 had the lowest specificity (0.392) and accuracy (0.525), and Microsoft Bing AI demonstrated the highest specificity (0.892) and accuracy (0.890). When assessing the consistency of accuracy across two different drug classes, ChatGPT-3.5 and ChatGPT-4 showed the highest ‎variability in accuracy. In addition, ChatGPT-3.5, ChatGPT-4, and Bard exhibited the highest ‎fluctuations in specificity when analyzing two medications belonging to the same drug class. Bing AI had the highest accuracy and specificity, outperforming Google's Bard, ChatGPT-3.5, and ChatGPT-4. The findings highlight the significant potential these AI tools hold in transforming patient care. While the current AI platforms evaluated are not without limitations, their ability to quickly analyze potentially significant interactions with good sensitivity suggests a promising step towards improved patient safety.

Purpose The purpose of this study is to examine the appropriate components and sub-components of Muslim-friendly hospital practices that comply with Islamic medical ethics and Sharia requirements. Design/methodology/approach A systematic literature review procedure based on the preferred reporting items for systematic reviews and meta-analyses publication standards was used to analyse and identify the precise components and sub-components of Muslim-friendly hospital practices from 239 shortlisted papers. Findings The study revealed that Sharia compliance prescription, Islamic infrastructure, Islamic medical practices, compassion and support, competencies of Islamic medical services, conducive Islamic surroundings, reasonable and convenience services and Islamic work culture are important elements that need to be in practice by Muslim-friendly hospital providers. Research limitations/implications This study focuses on studies conducted between January 2010 to August 2022 from the Web of Science, Scopus and Google Scholar databases. This study contributes to the literature through the identification of eight main components and 53 sub-components that were found to be the core and essential determinants in the establishment of the Muslim-friendly hospital practices framework. Practical implications The proposed Muslim-friendly hospital practices framework are envisaged to motivate policymakers in Malaysia and Indonesia as well as other Muslim countries to implement the proposed framework through a greater number of supportive legislative measures as well as practical promotion and education policies to further enhance the market. Originality/value The number of health-care providers contemplating the integration of Islamic or Muslim-friendly practices into their strategic plans and daily operations is continuously increasing. However, the dearth of studies that provide conclusive evidence of the correct determinants of Muslim-friendly hospital practices that ensure strict Islamic medical ethics and Sharia-compliant service delivery standards had created an impediment that needs to be addressed urgently.

Semantic code clone detection plays an essential role in software maintenance and quality assurance, as it helps uncover fragments of code that express the same logic even when their syntax has been altered or deliberately obfuscated. In this study, we propose a framework that combines hybrid representation learning with deep bidirectional LSTM networks. The model is applied to two intermediate forms of Java programs—Baf and Jimple—extracted through the Soot framework, which together provide both syntactic structure and semantic detail. This design allows the method to cope with difficult obfuscation strategies such as polymorphism and metamorphism. In our experiments, the framework showed strong and stable performance. Training accuracy reached about 98%, while validation accuracy stayed above 95%, with good generalization across the different clone categories described in the Twilight-Zone taxonomy. When compared with other recurrent models, the BiLSTM consistently performed better, especially when combined with multiple intermediate representations and attention mechanisms. On the BigCloneBench dataset, the approach matched or exceeded the results of state-of-the-art tools, achieving recall and F1-scores of up to 97% on challenging clone types. These findings confirm the practical applicability of hybrid intermediate representations for semantic clone detection and suggest promising directions for future research using transformer-based models and large-scale deployment.

Background As Indonesia aims for malaria elimination by 2030, provisional malaria epidemiology and risk factors evaluation are important in pursue of this national goal. Therefore, this study aimed to understand the risk factor of malaria in Northern Sumatera. Methods Malaria cases from 2019 to 2020 were obtained from the Indonesian Ministry of Health Electronic Database. Climatic variables were provided by the Center for Meteorology and Geophysics Medan branch office. Multivariable logistic regression was undertaken to understand the risk factors of imported malaria. A zero-inflated Poisson multivariable regression model was used to study the climatic drivers of indigenous malaria. Results A total of 2208 (indigenous: 76.0% [1679] and imported: 17.8% [392]) were reported during the study period. Risk factors of imported malaria were: ages 19–30 (adjusted odds ratio [AOR] = 3.31; 95% confidence interval [CI] 1.67, 2.56), 31–45 (AOR = 5.69; 95% CI 2.65, 12.20), and > 45 years (AOR = 5.11; 95% CI 2.41, 10.84). Military personnel and forest workers and miners were 1,154 times (AOR = 197.03; 95% CI 145.93, 9,131.56) and 44 times (AOR = 44.16; 95% CI 4.08, 477,93) more likely to be imported cases as compared to those working as employees and traders. Indigenous Plasmodium falciparum increased by 12.1% (95% CrI 5.1%, 20.1%) for 1% increase in relative humidity and by 21.0% (95% CrI 9.0%, 36.2%) for 1 °C increase in maximum temperature. Plasmodium vivax decreased by 0.8% (95% CrI 0.2%, 1.3%) and 16.7% (95% CrI 13.7%, 19.9%) for one meter and 1 °C increase of altitude and minimum temperature. Indigenous hotspot was reported by Kota Tanjung Balai city and Asahan regency, respectively. Imported malaria hotspots were reported in Batu Bara, Kota Tebing Tinggi, Serdang Bedagai and Simalungun. Conclusion Both indigenous and imported malaria is limited to a few regencies and cities in Northern Sumatera. The control measures should focus on these risk factors to achieve elimination in Indonesia.