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Abstract Purpose To determine the association of gut microbiome diversity and sight-threatening diabetic retinopathy (STDR) amongst patients with pre-existing diabetes. Methods A cross-sectional study was performed, wherein 54 participants selected in total were placed into cases cohort if diagnosed with STDR and those without STDR but had a diagnosis of diabetes mellitus of at least 10-year duration were taken as controls. Statistical analysis comparing the gut microbial alpha diversity between cases and control groups as well as patients differentiated based on previously hypothesized Bacteroidetes/Firmicutes(B/F) ratio with an optimal cut-off 1.05 to identify patients with STDR were performed. Results Comparing gut microbial alpha diversity did not show any difference between cases and control groups. However, statistically significant difference was noted amongst patients with B/F ratio ≥1.05 when compared to B/F ratio 1.05:728.03 ± 227.37; p-0.016]; Chao1index [Cut-off 1.05:728.13 ± 227.58; p-0.016]; Simpson index [Cut-off 1.05:0.997 ± 0.001; p-0.006]; Shannon index [Cut-off 1.05:6.10 ± 0.43; p-0.003]. Sub-group analysis showed that cases with B/F ratio ≥ 1.05, divided into proliferative diabetic retinopathy (PDR) and clinically significant macular edema (CSME), showed decreased diversity compared to controls (B/F ratio

Objective: This review aims to explore the role of physical activity as an integral palliative care strategy in managing deep vein thrombosis (DVT), providing a comprehensive analysis and practical insights for clinicians. Methods: A thorough review of relevant literature was conducted, encompassing studies, clinical trials, and expert opinions that examine the relationship between physical activity and palliative care from the perspective of DVT. The search included databases such as PubMed, PEDro, and Scopus, with an emphasis on articles published within the last decade. Results: The review highlights the multifaceted benefits of incorporating physical activity into the palliative care approach for individuals with DVT. Physical activity has been effective in enhancing overall well-being, alleviating symptoms, and contributing to the holistic management of DVT-related complications. In addition, the literature underscores the importance of personalized exercise regimens tailored to the patient’s condition, ensuring safety and effectiveness. Conclusion: This comprehensive review underscores the significance of physical activity as a pivotal element in palliative care for individuals with DVT. Integrating personalized exercise regimens into the management strategy offers a holistic approach that addresses the physical and psychosocial aspects of DVT. As clinicians navigate the complexities of DVT palliative care, a thorough and timely incorporation of physical activity can significantly contribute to enhancing the overall quality of life for affected individuals.

Abstract Efficient transportation systems are essential for the development of smart cities. Autonomous vehicles and Intelligent Transportation Systems (ITS) are crucial components of such systems, contributing to safe, reliable, and sustainable transportation. They can reduce traffic congestion, improve traffic flow, and enhance road safety, thereby making urban transportation more efficient and environmentally friendly. We present an innovative combination of photonic radar technology and Support Vector Machine classification, aimed at improving multi-target detection in complex traffic scenarios. Central to our approach is the Frequency-Modulated Continuous-Wave photonic radar, augmented with spatial multiplexing, enabling the identification of multiple targets in various environmental conditions, including challenging weather. Notably, our system achieves an impressive range resolution of 7 cm, even under adverse weather conditions, utilizing an operating bandwidth of 4 GHz. This feature is particularly crucial for precise detection and classification in dynamic traffic environments. The radar system's low power requirement and compact design enhance its suitability for deployment in autonomous vehicles. Through comprehensive numerical simulations, our system demonstrated its capability to accurately detect targets at varying distances and movement states, achieving classification accuracies of 75% for stationary and 33% for moving targets. This research substantially contributes to ITS by offering a sophisticated solution for obstacle detection and classification, thereby improving the safety and efficiency of autonomous vehicles navigating through urban environments.

Abstract Auxetic materials have a negative Poisson’s ratio and are of significant interest in applications that include impact mitigation, membrane separations and biomedical engineering. While there are numerous examples of structured materials that exhibit auxetic behavior, the examples of engineered auxetic structures is largely limited to periodic lattice structures that are limited to directional or anisotropic auxetic response. Structures that exhibit a three-dimensionally isotropic auxetic response have been, unfortunately, slow to evolve. Here we introduce an inverse design algorithm based on global node optimization to design three-dimensional auxetic metamaterial structures from disordered networks. After specifying the target Poisson’s ratio for a structure, an inverse design algorithm is used to adjust the positions of all nodes in a disordered network structure until the desired mechanical response is achieved. The proposed algorithm allows independent control of shear and bulk moduli, while preserving the density and connectivity of the networks. When the angle bending stiffness in the network is kept low, it is possible to realize optimized structures with a Poisson’s ratios as low as −0.6. During the optimization, the bulk modulus of these networks decreases by almost two orders of magnitude, but the shear modulus remains largely unaltered. The materials designed in this manner are fabricated by dual-material 3D-printing, and are found to exhibit the mechanical responses that were originally encoded in the computational design engine. The approach proposed here provides a materials-by-design platform that could be extended for engineering of optical, acoustic, and electrical properties, beyond the design of auxetic metamaterials.

Abstract Background Alpha synuclein (α-synuclein) is coded by SNCA gene and found in a helical form with phospholipids or in an unfolded arrangement in the cytosol and belongs to the synuclein family other than beta synuclein and gamma synuclein. It is a short protein made of 140 amino acids with three domains: an N-terminal lipid binding helix, a non-amyloid-ß component (NAC), and an acidic tail at the C-terminus. α-Synuclein is present in aggregated and fibrillar form in Lewy bodies and its association has been related to multiple system atrophy (MSA), Parkinson’s disease (PD), and Dementia with Lewy bodies (DLB). Our objective is to investigate and prioritise the possible nsSNPs in the α-synuclein protein that have been potentially connected to human neurodegenerative diseases. Results We used the series of computational tools to predict the mutation's harmful effect on three-dimensional structure of α-synuclein based on consensus approach. Our findings pointed to a significant computational blueprint for discovering nsSNPs connected to neurodegenerative illnesses from a large SNP data set while also minimising the expenses of experimentally showing harmful nsSNPs. Conclusions The prioritised G25S (rs1433622151), V66E (rs1261243630), and V77D (rs745815563) mutations can be employed in additional experimental studies to assess the α-synuclein protein mutation in relation to neurodegenerative illnesses and develop a therapeutics against them. Graphical Abstract

Abstract Under grid voltage sags, over current protection and exploiting the maximum capacity of the inverter are the two main goals of grid-connected PV inverters. To facilitate low-voltage ride-through (LVRT), it is imperative to ensure that inverter currents are sinusoidal and remain within permissible limits throughout the inverter operation. An improved LVRT control strategy for a two-stage three-phase grid-connected PV system is presented here to address these challenges. To provide over current limitation as well as to ensure maximum exploitation of the inverter capacity, a control strategy is proposed, and performance the strategy is evaluated based on the three generation scenarios on a 2-kW grid connected PV system. An active power curtailment (APC) loop is activated only in high power generation scenario to limit the current’s amplitude below the inverter’s rated current. The superior performance of the proposed strategy is established by comparison with two recent LVRT control strategies. The proposed method not only injects necessary active and reactive power but also minimizes overcurrent with increased exploitation of the inverter’s capacity under unbalanced grid voltage sag.

Thyroid Hormones (TH) are essential for the healthy functioning of the female reproductive system because they regulate ovarian, uterine, and placental tissue metabolism and development. Therefore, hypo-and hyperthyroidism may result in infertility in women. Previous studies have been conducted on women with thyroid dysfunction, including prospective and retrospective studies, in-vitro and in-vivo tests for hypo-and hyperthyroidism using ovarian, uterine, and placental cell culture, and experimental animal models. In order to better understand the physiology of the reproductive system and to develop more effective therapy methods for reproductive dysfunctions that result from thyroid dysfunctions, these studies sought to shed light on the mechanisms by which TH affect reproduction. This comprehensive narrative review investigates the diagnostic and therapeutic implications of TH in female infertility. By scrutinising existing literature, the study aims to elucidate the intricate relationship between thyroid function and reproductive health in women. Such insights are crucial for enhancing diagnostic accuracy and formulating effective therapeutic interventions to address thyroid-related factors influencing female infertility.

We present a spintronic device based hardware implementation of UNet for segmentation tasks. Our approach involves designing hardware for convolution, deconvolution, rectified activation function (ReLU), and max pooling layers of the UNet architecture. We designed the convolution and deconvolution layers of the network using the synaptic behavior of the domain wall MTJ. We also construct the ReLU and max pooling functions of the network utilizing the spin hall driven orthogonal current injected MTJ. To incorporate the diverse physics of spin-transport, magnetization dynamics, and CMOS elements in our UNet design, we employ a hybrid simulation setup that couples micromagnetic simulation, non-equilibrium Green’s function, and SPICE simulation along with network implementation. We evaluate our UNet design on the CamVid dataset and achieve segmentation accuracies of 83.71% on test data, on par with the software implementation with 821 mJ of energy consumption for on-chip training over 150 epochs. We further demonstrate nearly one order of magnitude (10×) improvement in the energy requirement of the network using unstable ferromagnet (Δ = 4.58) over the stable ferromagnet (Δ = 45) based ReLU and max pooling functions while maintaining similar accuracy. The hybrid architecture comprising domain wall MTJ and unstable FM-based MTJ leads to an on-chip energy consumption of 85.79 mJ during training, with a testing energy cost of 1.55 µJ.

There is a growing need for a more streamlined and automated method of refining biofuels, as there are currently no universally applicable process inspection instruments on the market. All process variables in bio-oil upgrading operations are maintained according to the offline specifications of the products and intermediates. Failure of the process and loss of resources can result from batch-wise monitoring not having real-time product standards. Consequently, in order to cut down on waste and lessen the chances of process failure, a quick and accurate tool for specifying intermediates and products is required. To resolve this issue, we created a model using response surface methodology and an artificial neural network that can increase the bio-oil yield involving parameters, i.e., biomass particle size (mm), temperature (°C), and residence time (min). The maximum bio-oil production (47.0883%) was achieved at 3 mm particle size, 523°C temperature, and 20 min residence time. All results are “better” for root mean squared error (∼1), and the highest coefficient of regression for bio-oil production is in the range of 0.97–0.99. Temperature is the most significant factor in bio-oil yield, followed by particle size and residence time. Based on physicochemical properties, bio-oil has the maximum kinematic viscosity (11.3 Cst) and water content (18.7%). Making bio-oil precious compounds allows it to be used as boiler feedstock and steam generation fuel.

Obesity has erupted as an epidemic around the world. It has set itself as a fast wave among other prevailing specific clusters of non-communicable diseases. The current study reviews and presents an updated meaningful review of the vast research work performed at schools located in different cities of India. A systematic search was conducted in PubMed, Scopus, Google Scholar and PEDro. Studies representing data on obesity and overweight among children in Indian cities were included in the review. A total of 21 articles with 71,466 participants were included in the review for analysis. Obesity developed in childhood and adolescence is greatly associated with heart disease, stroke and cancer (breast and ovarian in women and prostate in men) in the late stage of life. In India, despite being a country with a faster rate of population becoming overweight and obese in urban areas, in contrast, rural areas are still struggling with malnutrition.

Abstract SARS-CoV-2, the causative agent of COVID-19, uses the host endolysosomal system for entry, replication, and egress. Previous studies have shown that the SARS-CoV-2 virulence factor ORF3a interacts with the lysosomal tethering factor HOPS complex and blocks HOPS-mediated late endosome and autophagosome fusion with lysosomes. Here, we report that SARS-CoV-2 infection leads to hyperactivation of the late endosomal and lysosomal small GTP-binding protein Rab7, which is dependent on ORF3a expression. We also observed Rab7 hyperactivation in naturally occurring ORF3a variants encoded by distinct SARS-CoV-2 variants. We found that ORF3a, in complex with Vps39, sequesters the Rab7 GAP TBC1D5 and displaces Rab7 from this complex. Thus, ORF3a disrupts the GTP hydrolysis cycle of Rab7, which is beneficial for viral production, whereas the Rab7 GDP-locked mutant strongly reduces viral replication. Hyperactivation of Rab7 in ORF3a-expressing cells impaired CI-M6PR retrieval from late endosomes to the trans-Golgi network, disrupting the biosynthetic transport of newly synthesized hydrolases to lysosomes. Furthermore, the tethering of the Rab7- and Arl8b-positive compartments was strikingly reduced upon ORF3a expression. As SARS-CoV-2 egress requires Arl8b, these findings suggest that ORF3a-mediated hyperactivation of Rab7 serves a multitude of functions, including blocking endolysosome formation, interrupting the transport of lysosomal hydrolases, and promoting viral egress.

Abstract The exponential growth of the power of modern digital computers is based upon the miniaturization of vast nanoscale arrays of electronic switches, but this will be eventually constrained by fabrication limits and power dissipation. Chemical processes have the potential to scale beyond these limits by performing computations through chemical reactions, yet the lack of well-defined programmability limits their scalability and performance. Here, we present a hybrid digitally programmable chemical array as a probabilistic computational machine that uses chemical oscillators using Belousov-Zhabotinsky reaction partitioned in interconnected cells as a computational substrate. This hybrid architecture performs efficient computation by distributing information between chemical and digital domains together with inbuilt error correction logic. The efficiency is gained by combining digital logic with probabilistic chemical logic based on nearest neighbour interactions and hysteresis effects. We demonstrated the computational capabilities of our hybrid processor by implementing one- and two-dimensional Chemical Cellular Automata demonstrating emergent dynamics of life-like entities called Chemits. Additionally, we demonstrate hybrid probabilistic logic as a viable logic for solving combinatorial optimization problems.

Background In the post-operative period during the NICU stay, neonates must undergo various painful procedures. Non-pharmacological methods may be beneficial in managing the harmful effects of procedural pain on the development of neonates in their early life. Objectives To investigate the effect of non-pharmacological methods on post-operative procedural pain in neonates admitted to neonatal intensive care units. Methods A search in electronic databases was done to identify randomized clinical trials published from 2010 to 2020 that encompassed neonates undergoing painful procedures in the NICU and followed PRISMA guidelines. Studies with non-human subjects, neonates with unstable vital signs, non-clinical studies, and incomplete methodology were excluded. PubMed, Cochrane, and Physiotherapy Evidence Database (PEDro) were evaluated respectively using Medical Subject Headings (MeSH) and Health Sciences Descriptors (DeCS). Results Two reviewers examined articles independently and found 11 articles that met the study's inclusion criteria, with a total of 955 neonates with non-pharmacological methods of pain management in neonates. Non-pharmacological methods, such as massage therapy, oral sucrose, kangaroo mother care, and facilitated tucking showed significant reduction in pain scores among neonates who underwent painful procedures in NICU. Outcomes showed variability in effectiveness, emphasizing the need for tailored approaches. Conclusions The findings indicated that non-pharmacological methods can effectively manage pain in neonates admitted to the NICU. Pain management improves the clinical condition of neonates and promotes parents-neonate bonding, with consequent reduction in length of stay in the hospital.

Abstract Chest radiographs are examined in typical clinical settings by competent physicians for tuberculosis diagnosis. However, this procedure is time consuming and subjective. Due to the growing usage of machine learning techniques in applied sciences, researchers have begun applying comparable concepts to medical diagnostics, such as tuberculosis screening. In the period of extremely deep neural nets which comprised of hundreds of convolution layers for feature extraction, we create a shallow-CNN for screening of TB condition from Chest X-rays so that the model is able to offer appropriate interpretation for right diagnosis. The suggested model consists of four convolution-maxpooling layers with various hyperparameters that were optimized for optimal performance using a Bayesian optimization technique. The model was reported with a peak classification accuracy, F1-score, sensitivity and specificity of 0.95. In addition, the receiver operating characteristic (ROC) curve for the proposed shallow-CNN showed a peak area under the curve value of 0.976. Moreover, we have employed class activation maps (CAM) and Local Interpretable Model-agnostic Explanations (LIME), explainer systems for assessing the transparency and explainability of the model in comparison to a state-of-the-art pre-trained neural net such as the DenseNet.

Practice variability may represent an opportunity to improve care by identifying the differences in outcomes associated with differences in practice. To characterize differences in depression treatment outcomes among individual providers in outpatient psychiatry practices and primary care practices, we examined a longitudinal cohort derived from outpatient electronic health records from two academic medical centers and six community hospitals in Eastern Massachusetts. This cohort included antidepressant-treated individuals with an ICD-9/10 diagnosis of major depressive disorder, and deidentified health care providers treating at least 10 such patients per year between 2008 and 2022. We examined the association between individual provider prescribing characteristics and proportions of treated patients who do not follow up after initial antidepressant prescription or who achieve a stable ongoing prescription. In binomial regression models, among 104 psychiatrists, greater heterogeneity in antidepressant prescribing and lesser proportion of serotonin reuptake inhibitors (SSRIs)1 prescribed were associated with greater rates of achieving stability (for heterogeneity, adjusted odds ratio AOR, 1.55 [95 % CI, 1.22 – 2.06]; for proportion of SSRIs, AOR, 0.01 [95 % CI, 0.00–0.59]). Among 369 primary care physicians, greater volume of depression encounters per year, but not prescribing heterogeneity, was associated with greater rates of achieving stability (for encounters, AOR, 2.15 [95 % CI, 1.61 – 2.89]; for heterogeneity, AOR, 0.99 [95 % CI, 0.85 – 1.15]). Primary care and psychiatry predictors are not the same and therefore suggest potentially distinct strategies to improve clinical outcomes in each setting. Trial Registration: N/A

Immersive learning, or experiential learning, involves placing teachers in realistic scenarios where they can practice their skills and receive feedback in a safe, controlled environment. This study conducts systematic research to investigate the impact of immersive learning on teacher effectiveness. The research included 16 articles from electronic databases including IEEE, ERIC, and Google Scholar that met the inclusion criteria based on PRISMA guidelines. Evaluation of the selected articles was based on the CASP checklist. The findings suggest that immersive learning has a positive impact on teacher effectiveness, improving teachers’ content knowledge, pedagogical skills, and confidence in their abilities to handle real-life situations in the classroom. The study highlights the need for further research in this area, particularly regarding the long-term impact of immersive learning on teacher effectiveness. The use of immersive learning in teacher education can enhance the quality of teacher preparation programs and provide professional development opportunities for practicing teachers.

Background: Juvenile myelomonocytic leukemia (JMML), previously known as juvenile chronic myeloid leukaemia, is a rare, unique, and aggressive myeloproliferative neoplasm of early childhood. Making a diagnosis of JMML is challenging because of the overlapping clinical and haematological features with other myeloproliferative neoplasms (MPN). However, some unique features like monocytosis, the absence of BCR-ABL translocation, and the presence of specific mutations (PTPN-11, K-RAS, N-RAS, CBL, or NF1) clinch the correct diagnosis. Methods: A prospective analysis of six JMML patients with variable clinical features treated with injection azacytidine as frontline therapy during the study period of 2 years. Results: The median age was 4.5 years with male:female ratio 2:4. Pallor and splenomegaly were the most common presenting signs. Four patients (66.67%) achieved complete remission (CR), two patients (33.33%) had partial remission (PR), and one patient (16.67%) had progressive disease (PD). The overall survival rate was 66.67% (four out of six), and the mortality rate was 33.33%. Conclusion: Azacitidine is an effective treatment option as upfront therapy for JMML, especially in resource poor developing countries.

Abstract The accurate prediction of air pollutants, particularly Particulate Matter (PM), is critical to support effective and persuasive air quality management. Numerous variables influence the prediction of PM, and it's crucial to combine the most relevant input variables to ensure the most dependable predictions. This study aims to address this issue by utilizing correlation coefficients to select the most pertinent input and output variables for an air pollution model. In this work, PM2.5 concentration is estimated by employing concentrations of sulfur dioxide, nitrogen dioxide, and PM10 found in the air through the application of Artificial Neural Networks (ANNs). The proposed approach involves the comparison of three ANN models: one trained with the Levenberg–Marquardt algorithm (LM-ANN), another with the Bayesian Regularization algorithm (BR-ANN), and a third with the Scaled Conjugate Gradient algorithm (SCG-ANN). The findings revealed that the LM-ANN model outperforms the other two models and even surpasses the Multiple Linear Regression method. The LM-ANN model yields a higher R2 value of 0.8164 and a lower RMSE value of 9.5223.

Objective. The study aimed to assess the awareness, perception, and practices regarding oral health among school-going adolescents in Ahmedabad City, Gujarat, India. Materials and methods. A total of 600 school-attending adolescents with a mean age of 14.2 ± 1.19 years were enrolled. Ten schools (five public and five private schools) were selected randomly. They were interviewed by using a face-to-face questionnaire which comprised 13 items. Out of 13 questions four questions were related to awareness, four questions were related to attitude, and five questions were related to the practice of oral health. The statistical association between the two genders, age groups, and between public and private schools was determined using the Chi-square test. Results. Most of the participants were aware of the number of permanent teeth present; however they were unaware of the number of primary teeth, and the effect of sugar and tobacco consumption on oral health. Male and private school-going students had better knowledge compared to females and government school students... A total of 73% of the participants brushed their teeth twice daily. Tooth pain was the most common reason (49.7%) for visiting a dentist. 44.7% of them visit the dentist only when needed while 23.5% visit the dentist every 6 months for regular check-ups. Conclusion. The findings of the study conclude that oral health-related awareness, perception, and practices of adolescent students were not satisfactory. Hence, there is a need for regular oral health education for adolescents, as well as their parents and school teachers, which could impart a better and long-lasting understanding of oral health awareness, which in turn will reflect better oral health-related practices.

The performance evaluation of a Photovoltaic (PV) system heavily relies on accurately estimating the parameters based on its current—voltage relationships. However, due to the PV model’s inherent complexity, obtaining these parameters with precision and efficiency is a challenging task. In this study, a new variant known as teaching learning-based optimization with unique exemplar generation schemes (TLBO-UEGS) is proposed to address PV module parameter estimation problems with robustness and effectiveness. To enhance the performance of TLBO-UEGS, a modified initialization scheme that leverages the strengths of chaotic maps and dynamic oppositional based learning is introduced. This scheme ensures the generation of an initial population with improved solution quality. Furthermore, both the modified teacher phase and modified learner phase are integrated within the TLBO-UEGS optimization framework. This integration allows for different learning strategies to be employed based on the fitness values of each learner, effectively updating their search trajectories. Within the modified teacher phase, two unique exemplar generation schemes are designed to facilitate more effective guidance for learners in the first half of the population while maintaining population diversity. Meanwhile, the modified learner phase emulates a realistic knowledge acquisition process by enabling learners in the second half of the population to engage in collaborative learning with multiple peer learners or retain valuable knowledge from previous learning processes. Extensive simulations demonstrate that TLBO-UEGS achieves superior results, with the minimum root mean square error (RMSE) values of 3.5644 × 10−04 ± 0.0014, 1.3237 × 10−04 ± 0.0043, and 6.6016 × 10−06 ± 0.00011 obtained for Photowatt-PWP201, Leibold Solar (LSM 20), and Leybold Solar (STE 4/100) PV modules, respectively.

The imbalance between parking availability and demand has led to a rise in traffic challenges in many cities. The adoption of technologies like the Internet of Things and deep learning algorithms has been extensively explored to build automated smart parking systems in urban environments. Non-human-mediated, scalable smart parking systems that are built on decentralized blockchain systems will further enhance transparency and trust in this domain. The presented work, PARKTag, is an integration of a blockchain-based system and computer vision models to detect on-field free parking slots, efficiently navigate vehicles to those slots, and automate the computation of parking fees. This innovative approach aims to enhance the efficiency, scalability, and convenience of parking management by leveraging and integrating advanced technologies for real-time slot detection, navigation, and secure, transparent fee calculation with blockchain smart contracts. PARKTag was evaluated through implementation and emulation in selected areas of the MIT Art Design Technology University campus, with a customized built-in dataset of over 2000 images collected on-field in different conditions. The fine-tuned parking slot detection model leverages pre-trained algorithms and achieves significant performance metrics with a validation accuracy of 92.9% in free slot detection. With the Solidity smart contract deployed on the Ethereum test network, PARKTag achieved a significant throughput of 10 user requests per second in peak traffic hours. PARKTag is implemented as a mobile application and deployed in the mobile application store. Its beta version has undergone user validation for feedback and acceptance, marking a significant step toward the development of the final product.

BACKGROUND: Anecdotally, the concept of death consistently evokes profound unease. This study explores the intricate relationship between individuals’ attitudes toward death and the associated anxiety, introducing a two-step model that posits dispositional mindfulness and tranquil ego as mediators. MATERIALS AND METHODS: We used a correlational design in this study and assessed 209 Indian adults (111 males and 98 females) who recovered from corona symptoms on self-report measures of attitudes toward death, death anxiety, dispositional mindfulness, and tranquil ego. Bivariate correlational analyses and path analysis were used to analyze the data. RESULTS: Findings revealed that attitudes toward death involving fear, avoidance, approach, and escape acceptance of death correlated positively with death anxiety. Dispositional mindfulness and tranquil ego correlated negatively with death anxiety. Path analyses with percentile bootstrapping supported our hypothesis and showed that dispositional mindfulness and tranquil ego sequentially mediated the relationship. CONCLUSION: These findings indicate that various aspects of attitudes toward death differentially predict death anxiety. Moreover, the relationship between death attitudes and death anxiety is potentially mediated by dispositional mindfulness and a tranquil ego. The findings were discussed in light of existing literature.

Animals often grow and develop in unpredictable environments where factors like food availability, temperature, and oxygen levels can fluctuate dramatically. To ensure proper sexual maturation into adulthood, juvenile animals need to adapt their growth and developmental rates to these fluctuating environmental conditions. Failure to do so can result in impaired maturation and incorrect body size. Here we describe a mechanism by which Drosophila larvae adapt their development in low oxygen (hypoxia). During normal development, larvae grow and increase in mass until they reach critical weight (CW), after which point a neuroendocrine circuit triggers the production of the steroid hormone ecdysone from the prothoracic gland (PG), which promotes maturation to the pupal stage. However, when raised in hypoxia (5% oxygen), larvae slow their growth and delay their maturation to the pupal stage. We find that, although hypoxia delays the attainment of CW, the maturation delay occurs mainly because of hypoxia acting late in development to suppress ecdysone production. This suppression operates through a distinct mechanism from nutrient deprivation, occurs independently of HIF-1 alpha and does not involve dilp8 or modulation of Ptth, the main neuropeptide that initiates ecdysone production in the PG. Instead, we find that hypoxia lowers the expression of the EGF ligand, spitz, and that the delay in maturation occurs due to reduced EGFR/ERK signaling in the PG. Our study sheds light on how animals can adjust their development rate in response to changing oxygen levels in their environment. Given that hypoxia is a feature of both normal physiology and many diseases, our findings have important implications for understanding how low oxygen levels may impact animal development in both normal and pathological situations.

The tri-layer magnetic tunnel junction (MTJ) has surfaced as a building block for engineering next-generation integrated circuits while combining the attributes of non-volatility and meager energy consumption. Nevertheless, the perceptible switching energy (≈20–50 fJ/bit) and sub-optimal tunnelmagnetoresistance (TMR) (≈200%–300%) have acted as major hindrances, concealing its potential to supersede the capabilities of static and dynamic random access memories. In this work, we introduce a novel device that features a minimalistic non-uniform heterostructure/superlattice instead of the oxide layer in a conventional MTJ and analyze it in the premise of the self-consistent coupling of the Non-Equilibrium-Green’s Function (NEGF) and the Landau-Liftshitz-Gilbert-Slonczewski (LLGS) equation. We ascertain that the coupling of the electrodes to the proposed heterostructure renders a highly spin-selective broadband transmittance, thereby enabling a towering TMR (%) of 3.7 × 104% along with a significant reduction in the spin transfer torque (STT) switching energy (≈1.96 fJ). Furthermore, the sizable slonczewski term (Is‖) originating from the heterostructure facilitates a swift STT-switching within the scale of a few hundred picoseconds (≈400 ps).

Annealing of laser powder bed fusion (LPBF) processed Fe-49Co-2 V (Hiperco) samples at 865 °C for 4 h leads to a substantial improvement in its soft magnetic properties. While the as built LPBF samples exhibited relatively higher coercivities (Hc) ranging from 25.8 – 26.5 Oe, the annealed LPBF samples showed significantly lower coercivities (Hc) of 6.9 – 10.8 Oe. These lower Hc values can not only be attributed to the 15–20 times larger grain sizes, but also the higher degree of B2 ordering in the annealed condition. The enhanced degree of B2 ordering also increases the saturation magnetization (Ms), from 213 to 228 emu/g, in samples processed with a laser fluence (energy/density) of 3.4 J/mm2. These results reveal the underlying mechanisms leading to an enhancement of soft magnetic properties in LPBF processed Hiperco via annealing-induced microstructural control.

Silica nanoparticles (SiO2 NPs) were synthesized from the sugarcane bagasse (SB), pinewood stem (PS), walnut shell (WS), and rice husk (RH) using ambient burning, hydro-ball-milling, filtration, and post-annealing at 650 °C. The broadened XRD peak pattern (2θ = 22°) indicates the amorphous nature of all four samples, further confirmed by the circles of the SAED pattern. Microscopic images (snap-shots, SEM, TEM) revealed the agglomeration of SiO2 NPs with an average grain size of 40–80 nm. All samples' bandgap (Eg) was found in the 5.69–5.78 eV range determined from the Tauc's plot. The symmetric stretching of O–Si–O at 440 cm−1, Si–O at 880 cm−1, and asymmetric stretching of Si–O–Si peaks at 1062 cm−1 indicate the formation of polymorphic SiO2 NPs. Electronic and chemical states of SiO2 NPs were investigated by X-ray photoelectron spectroscopy (XPS), and the chemical bonding of SiOx and Si–O–Si occurred at 99.5 eV and 528.6 eV, respectively. Silicon (Si) concentration was observed to be ∼14.35 %, ∼18.88 %, ∼21.25 %, and ∼9.5 %, estimated from core-level XPS analysis. The pinewood stem extracted SiO2 NPs were observed to have a regular spherical shape with a size of 40 nm. The different atomic concentration of Si in all samples establishes their utilization in medical and industrial applications.

There is an urgent need to explore climate-resilient alternative agriculture production systems that focus on resilience, resource efficiency, and disease management. Hydroponics, a soilless cultivation system, gaining interest as it reduces the dependency on agricultural land, and pesticides, and can be implemented in areas with poor soil quality, thus mitigating the negative effects of extreme weather events. Potato is an essential dietary staple crop grown throughout the world and is a major source of food security in underdeveloped countries. However, due to the climatic changes, it is predicted that a significant loss in the suitability of land for potato production would occur, thus leading to potato yield loss. Recently, many case studies have emerged to highlight the advancement of agricultural hydroponic systems that provide a promising solution to the massive production of potato mini tuber at high efficiency. This review paper evaluates popular hydroponic methods and demonstrates how hydroponic has emerged as the go-to, long-term, sustainable answer to the perennial problem of insufficient access to high-quality potato seed stock. The paper discusses the research and innovation possibilities (such as artificial intelligence, nanoparticles, and plant growth-promoting rhizobacteria) that potentially increase tuber production per plant under optimal hydroponic growth circumstances. These approaches are examined considering new scientific discoveries and practical applications. Furthermore, it emphasizes that by enduring significant reforms in soilless food production systems (particularly for potatoes), the food supply of a rapidly growing population can be addressed. Since hydroponics systems are productive and easily automated without soil and optimal environmental conditions, future hydroponics farming is promising. In conclusion, the hydroponics system provides better yield and crop productivity by saving water, energy, and space. Henceforth, it can be the alternate choice for modern sustainable agriculture.

We present a detailed investigation of various routes to optimize the power consumption of the spintronic-based devices for implementing rectified linear activation (ReLU) and max-pooling functions. We examine the influence of various spin Hall effect layers, and their input resistances on the power consumption of the ReLU-max pooling functions, we also access the impact of the thermal stability factor of the free-ferromagnet layer on the power consumption and accuracy of the device. The design for ReLU-max pooling relies on the continuous rotation of magnetization, which is accomplished by applying orthogonal spin current to the free-FM layer. We also demonstrate the non-trivial power-resistance relation, where the power consumption decreases with an increase in SHE resistance. We utilize the hybrid spintronic-CMOS simulation platform that combines Keldysh non-equilibrium Green’s function (NEGF) with Landau-Lifshitz-Gilbert-Slonzewski (LLGS) equations and the HSPICE circuit simulator to evaluate our network. Our design takes 0.343 μW of power for ReLU emulation and 17.86 μW of power for ReLU-max pooling network implementation at a thermal stability factor of 4.58, all while maintaining reliable results. We validate the efficiency of our design by implementing a convolutional neural network that classifies the handwritten-MNIST and fashion-MNIST datasets. This implementation illustrates that the classification accuracies achieved are on par with those attained using the ideal software ReLU-max pooling functions, with an energy consumption of 167.31 pJ per sample.

Clinical guidelines have become an integral part of clinical care. We assessed professional society-based clinical guidelines from 2012 to 2022 to elucidate the trends in numbers of documents, recommendations, and classes of recommendations. Our results found that 40% of the guidelines do not follow all recommendations made by the Institute of Medicine for trustworthy documents. There has been a significant increase in documents in cardiology, gastroenterology, and hematology/oncology. In addition, of more than 20,000 recommendations, there was significant variability in recommendations made by different professional societies within a specialty. In documents from 11 of the 14 professional societies, more than 50% of the recommendations are supported with the lowest levels of evidence. In cardiology, in addition to the guideline documents, 140 nonguideline documents provide 1812 recommendations using the guideline verbiage, and 74% of the recommendations are supported by the lowest level of evidence. These data have important implications for health care because guidelines and guideline-like documents can be used for health policy issues such as assessment of quality of care, medical liability, education, and payment.

Introduction Gynecomastia is benign proliferation of male breast tissue that can be idiopathic or secondary to hormonal imbalance. Consumption of steroids plays a major role in the development of gynecomastia. The increased consumption of anabolic androgenic steroid (AAS) in youngsters to boost the physical strength and improve appearance is associated with increased prevalence of gynecomastia. True estimation of AAS-associated gynecomastia is difficult to calculate and prone to underestimation because of low social acceptance. Accurate estimation is required to assess future healthcare, for prevention and to give appropriate treatment.

Per- and polyfluoroalkyl substances (PFAS) are a group of fluorinated synthetic chemicals that are highly recalcitrant, toxic, and bio-accumulative and have been detected in biosolids worldwide, posing potential risks to humans and the environment. Recent studies suggest that the organic C-F bond in PFAS can be destructed and potentially mineralised into inorganic fluorides during thermal treatment. This study focuses on thermodynamic equilibrium investigations and the fate of fluorine compounds post-PFAS destruction during biosolid thermal treatment. The results indicate that gas-phase fluorine compounds are mainly hydrogen fluoride (HF) and alkali fluorides, whereas solid-phase fluorine compounds include alkaline earth fluorides and their spinels. High moisture and oxygen content in the volatiles increased the concentration of HF in the gas phase. However, adding minerals reduced the emission of HF in the gas phase significantly and enhanced the capture of fluorine as CaF2 spinel in the solid phase. This study also investigates the effect of feedstock composition on the fate of fluorine. High ash content and low volatile matter in the feedstock reduced HF gas emissions and increased fluorine capture in the solid product. The findings of this work are useful in designing thermal systems with optimised operating conditions for minimising the release of fluorinated species during the thermal treatment of PFAS-containing biosolids.