Mohammad Reza

Identifying COVID-19 patients’ clinical phenotypes based on characteristics and comorbidities, as well as their differences, helps in terms of clinical care and potential crises. Our goal is to use Latent Class Analysis (LCA) to identify COVID-19 patient profiles based on demographics, symptoms, and comorbidities, and evaluate their correlation with ICU admission, hospitalization duration, and mortality in a cross-sectional study. We included hospitalized patients with positive SARS-CoV-2 tests in two referral hospitals in the south of Iran between January 2020 and July 2021. Data from 7,968 patients were evaluated in two time-specific intervals, known as “stratum,” based on the point at which the COVID-19 fourth peak and vaccinations were nearly initiated. After applying LCA, three classes identified the most at-risk individuals, including elderly women with hypertension, diabetes, and heart disease, and elderly men with heart and lung conditions, compared to two classes of healthier, younger individuals. Hospital stays were longer in the first stratum compared with the second (P-value = 0.043). ICU admission during the first stratum was significantly higher than in the second stratum (P < 0.001) and lower in classes one and two in the first stratum compared to class three (reference class). Mortality rates were not significantly different between strata (P = 0.054). Hospital stays and mortality rates significantly differed between the classes in each first and second stratum (P < 0.001). Using LCA to investigate COVID-19 patients has provided valuable insights into different patterns and outcomes of the disease. Patient variables, including age, gender, and comorbidities, were associated with greater mortality and ICU admission, which can assist in resource allocation and vaccination planning.

Abstract Flash floods represent a significant threat, triggering severe natural disasters and leading to extensive damage to properties and

Improving the quality of extracts derived from medicinal plants is a critical concern due to their extensive use across various industries. The Arvaneh plant (Hymenocrater platystegius Rech. F), a species native to the Khorasan province in Iran and belonging to the mint family, was the focus of this study. We optimized the extraction of Arvaneh plant using both microwave (MW) and pulsed electric field (PEF) techniques. The MW method was tested at different processing times (90, 180, and 270 s) and power levels (180, 540, and 900 W), while the PEF method was evaluated with varying electric field intensities (0.25, 3.25, and 6.25 kV/cm) and pulse numbers (10, 45, and 80) using a central composite design (CCD). The results revealed that extraction efficiency, total phenolic content, and total flavonoid content were significantly higher with the PEF method compared to the MW method (p < 0.05). Moreover, the PEF technique showed superior performance in preserving the antioxidant properties of the extract, as assessed by DPPH and FRAP methods. GC/MS analysis confirmed the presence of 27 bioactive compounds in the Arvaneh extract obtained through PEF-assisted extraction. In conclusion, the PEF method proved to be highly efficient for extracting bioactive compounds from the Arvaneh plant.

Abstract In this paper, the photonic crystal fiber sensor based on the geometry of hyperbolic black holes is proposed.

Abstract Despite the scientific relevance of circular RNAs (circRNAs), the study of these RNAs in non-model organisms, especially in sheep, is still in its infancy.

For decades, fossil fuels have been the backbone of reliable energy systems, offering unmatched energy density and flexibility. However, as the world shifts toward renewable energy, overcoming the limitations of intermittent power sources requires a bold reimagining of energy storage and integration. Power-to-X (PtX) technologies, which convert excess renewable electricity into storable energy carriers, offer a promising solution for long-term energy storage and sector coupling. Recent advancements in machine learning (ML) have revolutionized PtX systems by enhancing efficiency, scalability, and sustainability. This review provides a detailed analysis of how ML techniques, such as deep reinforcement learning, data-driven optimization, and predictive diagnostics, are driving innovation in Power-to-Gas (PtG), Power-to-Liquid (PtL), and Power-to-Heat (PtH) systems. For example, deep reinforcement learning has improved real-time decision-making in PtG systems, reducing operational costs and improving grid stability. Additionally, predictive diagnostics powered by ML have increased system reliability by identifying early failures in critical components such as proton exchange membrane fuel cells (PEMFCs). Despite these advancements, challenges such as data quality, real-time processing, and scalability remain, presenting future research opportunities. These advancements are critical to decarbonizing hard-to-electrify sectors, such as heavy industry, transportation, and aviation, aligning with global sustainability goals.

In this study, we examined the relatively unexplored realm of face perception, investigating the activities within human brain face-selective regions during the observation of faces at both subordinate and superordinate levels. We recorded intracranial EEG signals from the ventral temporal cortex in neurosurgical patients implanted with subdural electrodes during viewing of face subcategories (human, mammal, bird, and marine faces) as well as various non-face control stimuli. The results revealed a noteworthy correlation in response patterns across all face-selective areas in the ventral temporal cortex, not only within the same face category but also extending to different face categories. Intriguingly, we observed a systematic decrease in response correlation coupled with an increased response onset time from human face to mammalian face, bird face and marine faces. Our result aligns with the notion that distinctions at the basic level category (e.g., human face versus non-human face) emerges earlier than those at the superordinate level (e.g., animate versus inanimate). This indicates response gradient in the representation of facial images within human face-sensitive regions, transitioning progressively from human faces to non-face stimuli. Our findings provide insights into spatiotemporal dynamic of face representations which varies spatially and at different timescales depending on the face subcategory represented.

Abstract: This work considers the problem of sampling from a probability distribution known up to a normalization constant while satisfying a set

Abstract Dealing with spinal cord injuries presents problematic due to multiple secondary mechanisms.

Abstract The increase in demand for natural rubber has led to the search for alternative sources.