Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Approach to Identifying a Complete Photonic Band Gap in Two-dimensional Photonic Crystals with Omnidirectional Light Propagation

Omnidirectional light propagation in two-dimensional (2D) photonic crystals (PCs) is investigated. An e-cient approach to identifying a complete photonic band gap (PBG) in 2D PCs has been developed. The in-plane band structure of 2D photonic crystals is calculated by an adaptive flnite element method. With adopting the suitable boundary conditions, the eigenvalues can be easily and rapidly calculated no matter how complex the geometric structures are. By symmetry, the omnidirectional photon density of states (PDOS) can be calculated based on the in-plane dispersion relation within the irreducible Brillouin zone. The PDOS corresponding to both the radiation and evanescent waves can be obtained accurately and e-ciently. We demonstrate that the \complete band gaps" showed by some previous work due to considering only the radiation modes will be closed by including the contributions of the evanescent modes. These results are of general importance and relevant to the spontaneous emission by an atom, or to dipole radiation in two-dimensional periodic structures.
An Intuitive and Noniterative Design Methodology for CLLC Chargers Employing Simplified Operation Modes Model

This article mainly focuses on the simplified operation modes (SOM) model and resonant parameter design for the CLLC charger. Based on the mathematical and detailed operation waveform assumptions, the voltage gain model expressions and the operation mode boundaries are calculated directly, providing the high efficiency and high reliability of the CLLC converter. The proposed SOM model is more accurate in depicting the voltage gain compared with the conventional fundamental harmonic approximation model. Moreover, the SOM model is more intuitive and has less computational complexity than the complicated and unsolvable time-domain model. As for the parameter design process, the inductance ratio $k$ and characteristic impedance $Z_{0}$ are selected instead of specific inductances and capacitances. Relying on the SOM model, a step-by-step parameter design methodology is studied, which avoids repetitive iterations and streamlines the procedure. The voltage gain range, efficiency, soft-switching operation, mode boundaries, and system stability are considered comprehensively and realized in this process. The simulations and experiments validate that the proposed SOM model is accurate, and the design methodology is straightforward through a 1-kW CLLC charger prototype with 97% peak efficiency.
Testing the assumptions of rationality, continuity and symmetry when applying discrete choice experiments in health care

In the absence of revealed preference behaviour, economists use experimental techniques to estimate welfare changes. Such an approach relies on a number of assumptions concerning individual behaviour. This paper uses a discrete choice experiment to test three of these assumptions – rationality, continuity and symmetry. The experiment was carried out with users of a rheumatology clinic in the Grampian area of Scotland and was concerned with preferences for a specialist-nurse-led clinic. Two tests of ‘rationality’ were included in the experiment. Tests were carried out to see if respondents always chose on the basis of their preferred staffing, suggesting discontinuities in the utility function. The axiom of symmetry was tested using a split sample design, with respondents divided into two groups. Each group received a different questionnaire that varied with respect to the order of the choices. Over 30% of respondents provided at least one ‘irrational’ response. Such respondents did not differ significantly in their characteristics from ‘rational’ responses, suggesting that utility estimates would not be biased if this group were excluded from the analysis. Seventeen per cent of respondents showed signs of having non-compensatory utility functions. Evidence was found to support the axiom of symmetry. Future work should explore the axioms of rationality and continuity in more detail.
Optimal L8 Model reduction using genetic algorithms

Abstract In this paper, we are concerned with the solution of optimal L8 model‐reduction problems using genetic algorithms (GAs). More precisely, we present an approach to facilitate using GAs to effectively search optimal reduced‐order models for high‐order linear time‐invariant systems such that the L8 ‐norm of approximation error is minimized. The proposed approach has the following distinct features: (i) the parameter space for GA search is bounded; (ii) the finite and infinite zero structures, as well as the stability of the original system is retained in the reduced‐order models; and (ii) the GA solution accuracy can be greatly enhanced. The first two features are achieved through representing a reduced‐order model in a transfer function parameterized in terms of Schur and anti‐Schur polynomials, whereas the third feature is gained by using schemes of region contraction and local improvement.
Detection of Alzheimer’s disease from temporal lobe grey matter slices using 3D CNN

ABSTRACT Magnetic Resonance Imaging helps in detecting brain atrophy caused by Alzheimer’s disease (AD). Morphological changes start to occur in the temporal lobe of the brain in persons affected by AD. A 3D Convolutional Neural Network is proposed that focuses on the temporal lobe of the brain for AD classification from normal controls. By investigating the temporal region of the grey matter in the brain instead of the entire brain region high performance is obtained with an accuracy of 88.06%, a sensitivity of 94.03%, and a specificity of 82.09% in classifying the MRI volumes of Alzheimer’s Disease Neuroimaging Initiative (ADNI-2) dataset. When Apolipoprotein ε4 allele information and mini-mental state examination scores are used with the MRI volumes, an accuracy of 89.55%, a sensitivity of 82.09% and a specificity of 97.01% for ADNI-2 MRI volumes are achieved. When tested on ADNI-3 MRI volumes the proposed method resulted in an accuracy of 90.14%, a sensitivity of 79.17% and a specificity of 92.37%.
An Improved Dense CNN Architecture for Deepfake Image Detection

Recent advancements in computer vision processing need potent tools to create realistic deepfakes. A generative adversarial network (GAN) can fake the captured media streams, such as images, audio, and video, and make them visually fit other environments. So, the dissemination of fake media streams creates havoc in social communities and can destroy the reputation of a person or a community. Moreover, it manipulates public sentiments and opinions toward the person or community. Recent studies have suggested using the convolutional neural network (CNN) as an effective tool to detect deepfakes in the network. But, most techniques cannot capture the inter-frame dissimilarities of the collected media streams. Motivated by this, this paper presents a novel and improved deep-CNN (D-CNN) architecture for deepfake detection with reasonable accuracy and high generalizability. Images from multiple sources are captured to train the model, improving overall generalizability capabilities. The images are re-scaled and fed to the D-CNN model. A binary-cross entropy and Adam optimizer are utilized to improve the learning rate of the D-CNN model. We have considered seven different datasets from the reconstruction challenge with 5000 deepfake images and 10000 real images. The proposed model yields an accuracy of 98.33% in AttGAN, [Facial Attribute Editing by Only Changing What You Want (AttGAN)] 99.33% in GDWCT,[Group-wise deep whitening-and-coloring transformation (GDWCT)] 95.33% in StyleGAN, 94.67% in StyleGAN2, and 99.17% in StarGAN [A GAN capable of learning mappings among multiple domains (StarGAN)] real and deepfake images, that indicates its viability in experimental setups.
Regulation of the regulators: Transcription factors controlling biosynthesis of plant secondary metabolites during biotic stresses and their regulation by miRNAs

Biotic stresses threaten to destabilize global food security and cause major losses to crop yield worldwide. In response to pest and pathogen attacks, plants trigger many adaptive cellular, morphological, physiological, and metabolic changes. One of the crucial stress-induced adaptive responses is the synthesis and accumulation of plant secondary metabolites (PSMs). PSMs mitigate the adverse effects of stress by maintaining the normal physiological and metabolic functioning of the plants, thereby providing stress tolerance. This differential production of PSMs is tightly orchestrated by master regulatory elements, Transcription factors (TFs) express differentially or undergo transcriptional and translational modifications during stress conditions and influence the production of PSMs. Amongst others, microRNAs, a class of small, non-coding RNA molecules that regulate gene expression post-transcriptionally, also play a vital role in controlling the expression of many such TFs. The present review summarizes the role of stress-inducible TFs in synthesizing and accumulating secondary metabolites and also highlights how miRNAs fine-tune the differential expression of various stress-responsive transcription factors during biotic stress.
Insights into dendritic cell maturation during infection with application of advanced imaging techniques

Dendritic cells (DCs) are crucial for the initiation and regulation of adaptive immune responses. When encountering immune stimulus such as bacterial and viral infection, parasite invasion and dead cell debris, DCs capture antigens, mature, acquire immunostimulatory activity and transmit the immune information to naïve T cells. Then activated cytotoxic CD8+ T cells directly kill the infected cells, while CD4+ T helper cells release cytokines to aid the activity of other immune cells, and help B cells produce antibodies. Thus, detailed insights into the DC maturation process are necessary for us to understand the working principle of immune system, and develop new medical treatments for infection, cancer and autoimmune disease. This review summarizes the DC maturation process, including environment sensing and antigen sampling by resting DCs, antigen processing and presentation on the cell surface, DC migration, DC-T cell interaction and T cell activation. Application of advanced imaging modalities allows visualization of subcellular and molecular processes in a super-high resolution. The spatiotemporal tracking of DCs position and migration reveals dynamics of DC behavior during infection, shedding novel lights on DC biology.
YOLO-FR: A YOLOv5 Infrared Small Target Detection Algorithm Based on Feature Reassembly Sampling Method

The loss of infrared dim-small target features in the network sampling process is a major factor affecting its detection accuracy. In order to reduce this loss, this paper proposes YOLO-FR, a YOLOv5 infrared dim-small target detection model, based on feature reassembly sampling, which refers to scaling the feature map size without increasing or decreasing the current amount of feature information. In this algorithm, an STD Block is designed to reduce the loss of features during down-sampling by saving spatial information to the channel dimension, and the CARAFE operator, which increases the feature map size without changing the feature mapping mean, is adopted to ensure that features are not distorted by relational scaling. In addition, in order to make full use of the detailed features extracted by the backbone network, the neck network is improved in this study so that the feature extracted after one down-sampling of the backbone network is fused with the top-level semantic information by the neck network to obtain the target detection head with a small receptive field. The experimental results show that the YOLO-FR model proposed in this paper achieved 97.4% on mAP50, which is a 7.4% improvement compared to the original network, and it also outperformed J-MSF and YOLO-SASE.
Preoperative optimization of cardiac valve patients’ expectations: Study protocol of the randomized controlled ValvEx-trial

Introduction Complete recovery after surgery depends on psychological factors such as preoperative information, expectations, and surgery-associated anxiety. Prior studies have shown that even a short preoperative psychological intervention aiming at optimized expectations (EXPECT) can improve postoperative outcomes in coronary artery bypass patients. However, this intervention may benefit only a small subgroup of heart surgery patients since implementing preoperative psychological interventions into the daily clinical routine is difficult due to the additional time and appointments. It is unclear whether the EXPECT intervention can be shortened and whether heart valve patients would also benefit from interventions that optimize patients’ expectations. The multicenter ValvEx trial aims (i) to adapt an effective preoperative psychological intervention (EXPECT) to make it brief enough to be easily integrated into the preoperative routine of heart valve patients and (ii) to examine if the adapted preoperative psychological intervention improves the subjectively perceived illness-related disability (PDI) up to 3 months after surgery. Materials and analysis In two German university hospitals, N = 88 heart valve patients who undergo heart surgery are randomized into two groups [standard of care (SOC) vs. standard of care plus interventional expectation manipulation (SOC and EXPECT)] after baseline assessment. Patients in the EXPECT group additionally to standard of care participate in the preoperative psychological intervention (30–40 min), focusing on optimizing expectations and have two booster-telephone calls (4 and 8 weeks after the surgery, approx. 15 min). Both groups have assessments again on the evening before the surgery, 4 to 6 days, and 3 months after the surgery. Discussion The trial demonstrates excellent feasibility in the clinical routine and a high interest by the patients. Ethics and dissemination The Ethics Committees of the Department of Medicine of the Philipps University of Marburg and the Department of Medicine of the University of Giessen approved the study protocol. Study results will be published in peer-reviewed journals and presented at congresses. Clinical trial registration ClinicalTrials.gov, identifier NCT04502121.
Therapeutic potential of naringin in improving the survival rate of skin flap: A review

Naringin is the main component of Drynaria. Modern pharmacological studies have shown that naringin has a wide range of pharmacological activities, including antioxidant, anti-inflammatory, anti-apoptotic, anti-ulcer, and anti-osteoporosis effects. Its therapeutic effects have been observed in various clinical models, such as atherosclerosis, cardiovascular diseases, diabetes, neurodegenerative diseases, and rheumatic diseases. This review investigates the pharmacological effects of naringin and the associated mechanisms in improving flap survival. This review will also provide a reference for future rational application of naringin, especially in research to improve flap survival.
Respiratory support strategies in the prevention and treatment of bronchopulmonary dysplasia

Neonates who are born preterm frequently have inadequate lung development to support independent breathing and will need respiratory support. The underdeveloped lung is also particularly susceptible to lung injury, especially during the first weeks of life. Consequently, respiratory support strategies in the early stages of premature lung disease focus on minimizing alveolar damage. As infants grow and lung disease progresses, it becomes necessary to shift respiratory support to a strategy targeting the often severe pulmonary heterogeneity and obstructive respiratory physiology. With appropriate management, time, and growth, even those children with the most extreme prematurity and severe lung disease can be expected to wean from respiratory support.
A Ku-Band 20.4% Tuning Range VCO With a Tunable Harmonic-Resonant Transformer in 55 nm CMOS

This letter presents a digital-process-based and wide-tuning-range voltage-controlled oscillator (VCO) with a tunable harmonic-resonant transformer. By adding fixed proportional common-mode (CM) and differential-mode (DM) capacitors banks, the first-to-second- and first-to-third-harmonic resonances are achieved across the entire frequency range lowering the average phase noise. A tunable transformer is also utilized to further widen the tuning range. The VCO is fabricated in a 55 nm complementary metal-oxide-semiconductor (CMOS) process without thick top metal, occupying an area of <inline-formula> <tex-math notation="LaTeX">$0.59~\mathrm {mm^{2}}$ </tex-math></inline-formula>. It achieves a tuning range of 2.94 GHz (20.4%) with a center frequency of 14.4 GHz. The measured phase noise at 1-MHz offset is −108.62 dBc/Hz. The power consumption of the oscillator core is 15 mW with a supply of 1.2 V, demonstrating a figure of merit (FOM) and FOM<inline-formula> <tex-math notation="LaTeX">$\mathrm {_{T}}$ </tex-math></inline-formula> of −180.10 and −186.22 dBc/Hz, respectively.
A posteriori error estimates of mixed DG finite element methods for linear parabolic equations

In this article, we analyse a posteriori error estimates of mixed finite element discretizations for linear parabolic equations. The space discretization is done using the order λ ≥ 1 Raviart–Thomas mixed finite elements, whereas the time discretization is based on discontinuous Galerkin (DG) methods (r ≥ 1). Using the duality argument, we derive a posteriori l ∞(L 2) error estimates for the scalar function, assuming that only the underlying mesh is static.
Use of GIS technology and cellular automata for modeling multiple socio-economic scenarios of regional spatial development and inter-regional cooperation

ABSTRACT The advancement of effective spatial planning to support sustainable development and inter-regional cooperation has become an issue of serious concern for regional authorities. Spatial planning research helps to identify economic clusters and analyze their changing spatial patterns, which is important for understanding regional economic space dynamics and potential inter-regional cooperation. To support decision-makers in the development of efficient plans of spatial development encompassing the identification of the best-suited territories, a combined Geographic Information System (GIS) based approach to interpret qualitatively expressed multiple socio-economic scenarios in quantitative map-based terms of graded suitability, and a formalized approach to the socio-economic evaluation of the territory is offered. Based on GIS technology coupled with integrated cellular automata decision analysis techniques, the study provides a method that performs socio-economic assessment of the study area according to the generated scenarios of regional spatial and socio-economic development. The proposed method is applied to Primorsky and Khabarovsk Krais, located in the Russian Far East. Socio-economic scenarios of spatial development initiated by investors and regional authorities were assessed and evaluated. The generated socio-economic scenarios illustrate how the unified set of spatial and socio-economic variables can be linked and used to gain insights into inter-regional socio-economic and spatial development. The application results demonstrate the advantage of the proposed method in identifying the best-suited unit areas for targeted regional development.
Safety neighbournoods for the invariants of the matrix similarity

We give safety neighbourhoods for the necessary conditions in the change of the Jordan canonical form of a matrix under small perturbations. We also obtain the minimum distance from an n × n complex matrix which has less than k nonconstant invariant factors (2≤ k≤ n) to the set of matrices which have more or equal to k. When k= 2, we get in particular the distance from a nonderogatory matrix to the set of derogatory matrices.
Bracket Bond Failures: Incidence and Association with Different Risk Factors—A Retrospective Study

Background: Bracket bonding failure is one of the relevant problems in fixed orthodontics therapy, which affects the total treatment and quality of treatment results. The purpose of this retrospective study was to evaluate the frequency of bracket bond failure and find out risk factors. Methods: A total of 101 patients with an age range of 11–56 years were included in this retrospective study and treated for a mean period of 30.2 months. Inclusion criteria were: males and females with permanent dentition and completed orthodontic treatment in both fully bonded dental arches. Risk factors were calculated using binary logistic regression analysis. Results: The overall bracket failure rate was 14.65%. The bracket failure rate was significantly higher in the younger patients’ group (p = 0.003). In most cases, patients experienced bracket failures in the first month of the treatment. Most of the bracket bond failures occurred on the left lower first molar (29.1%) and were twice as common in the lower dental arch (66.98%). Patients with increased overbite had an increased likelihood of bracket loss (p = 0.042). Class II malocclusion increased the relative risk of bracket failure, while Class III decreased the rate of bracket failure, but the difference was not statistically significant (p = 0.093). Conclusions: The bracket bond failure rate was higher in younger patients than in older patients. Brackets placed on mandibular molars and premolars had the highest failure rate. Class II was associated with an increased bracket failure rate. Increased overbite statistically significantly increases bracket failure rate.
Computational and experimental studies of salvianolic acid A targets 3C protease to inhibit enterovirus 71 infection

Hand, foot, and mouth disease (HFMD) is a common childhood infectious disease caused by enterovirus (EV) infection. EV71 is one of the major pathogens causing hand, foot, and mouth disease and is more likely to cause exacerbation and death than other enteroviruses. Although a monovalent vaccine for EV71 has been developed, there are no clinically available anti-EV71 specific drugs. Here, we performed virtual screening and biological experiments based on the traditional Chinese medicine monomer library. We identified a traditional Chinese medicine monomer, Salvianolic acid A (SA), a polyphenolic compound isolated from Salvia miltiorrhiza. Salvianolic acid A inhibits EV71 virus infection in a concentration-dependent manner, and its antiviral activity is higher than that of other reported natural polyphenols and has a high biosafety. Furthermore, molecular dynamics simulations showed that salvianolic acid A can anchor to E71, a member of the enzyme catalytic triad, and cause H40 to move away from the catalytic center. Meanwhile, molecular mechanics generalized born surface area (MMGBSA) and steered molecular dynamics (SMD) results showed that the P1 group of SA was most easily unbound to the S1 pocket of 3Cpro, which provided theoretical support to further improve the affinity of salvianolic acid A with 3Cpro. These findings suggest that salvianolic acid A is a novel EV71 3Cpro inhibitor with excellent antiviral activity and is a promising candidate for clinical studies. Graphical Abstract