Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of a multi-fibre needle Raman probe for tissue analysis

Raman spectroscopy is a rapid technique for the identification of cancers. Its coupling with a hypodermic needle provides a minimally invasive instrument with the potential to aid real time assessment of suspicious lesions in vivo and guide surgery. A fibre optic Raman needle probe was utilised in this study to evaluate the classification ability of the instrument as a diagnostic tool together with multivariate analysis, through measurements of tissues from different animal species as well as various different porcine tissue types. Cross validation was performed and preliminary classification accuracies were calculated as 100% for the identification of tissue type and 97.5% for the identification of animal species. A lymph node sample was also measured using the needle probe to assess the use of the technique for human tissue and hence its efficiency as a clinical instrument. This needle probe has been demonstrated to have the capabilities to classify tissue samples based on their biochemical components. The Raman needle probe also has the potential to act as a diagnostic and surgical tool to delineate cancerous from non-cancerous cells in real time, thus assisting complete removal of a tumour.
Weighted averaging and stochastic approximation

We explore the relationship between weighted averaging and stochastic approximation algorithms, and study their convergence via a sample-path analysis. We prove that the convergence of a stochastic approximation algorithm is equivalent to the convergence of the weighted average of the associated noise sequence. We also present necessary and sufficient noise conditions for convergence of the average of the output of a stochastic approximation algorithm in the linear case. We show that the averaged stochastic approximation algorithms can tolerate a larger class of noise sequences than the stand-alone stochastic approximation algorithms.
A Prognostic Cloud Scheme for Operational NWP Models

Abstract An explicit cloud prediction model has been developed and incorporated into the Eta Model at the National Centers for Environmental Prediction. In this scheme, only one predictive variable, cloud mixing ratio, is added to the model’s prognostic equations to represent both cloud liquid water and cloud ice. Precipitation is diagnostically calculated from cloud mixing ratio. Extensive tests have been performed. The statistical results show a significant improvement in the model precipitation forecasts. Diagnostic studies suggest that the inclusion of cloud ice is important in transferring water vapor to precipitation and in the enhancement of latent heat release; the latter subsequently affects the vertical motion field significantly.
An Empirical Study on the Factors Affecting Consumer Behavior in the Fast-Food Industry

Abstract This empirical study investigates the factors that affect consumer behavior in the fast-food industry. The research examines the role of various factors, including quality of food, price, convenience, location, and brand image, in determining the behavior of consumers when purchasing fast food. The study uses a quantitative research approach to gather data from a sample of 500 respondents using an online survey. The results suggest that quality of food and price are the most significant factors influencing consumer behavior in the fast-food industry. However, convenience, location, and brand image also play a significant role in determining consumer behavior.
THE INFRARED SPECTRUM OF PROTONATED OVALENE IN SOLID PARA-HYDROGEN AND ITS POSSIBLE CONTRIBUTION TO INTERSTELLAR UNIDENTIFIED INFRARED EMISSION

The mid-infrared emission from galactic objects, including reflection nebulae, planetary nebulae, proto-planetary nebulae, molecular clouds, etc, as well as external galaxies, is dominated by the unidentified infrared (UIR) emission bands. Large protonated polycyclic aromatic hydrocarbons (H+PAHs) were proposed as possible carriers, but no spectrum of an H+PAH has been shown to exactly match the UIR bands. Here, we report the IR spectrum of protonated ovalene (7-C32H15+) measured in a para-hydrogen (p-H2) matrix at 3.2 K, generated by bombarding a mixture of ovalene and p-H2 with electrons during matrix deposition. Spectral assignments were made based on the expected chemistry and on the spectra simulated with the wavenumbers and infrared intensities predicted with the B3PW91/6-311++G(2d,2p) method. The close resemblance of the observed spectral pattern to that of the UIR bands suggests that protonated ovalene may contribute to the UIR emission, particularly from objects that emit Class A spectra, such as the IRIS reflection nebula, NGC 7023.
A Multiwinding Coupling Model for the Forward Input-Series Transformer-Integration Converter

The forward input-series transformer-integration (ISTI) converter is suitable for high-input voltage multiple-output applications. Active input voltage sharing (IVS) of this converter is achieved through coupling of the transformer primary windings (PWs), which can be affected by windings arrangements of the integrated-transformer. To describe coupling influences of various PWs, as well as coupling influences between each PW and the secondary windings adequately, a multiwinding coupling model of the forward integrated-transformer is proposed. Based on this model, active IVS performance of the forward ISTI converter is analyzed in detail, through which design principles of the forward integrated-transformer to improve active IVS performance are concluded. Finally, the forward integrated-transformers with four typical PWs arrangements are design for a 1 kV/96 W laboratory-made forward ISTI converter, and the analysis based on the proposed model is verified by the simulating and experimental results.
Counteracting Data Bias and Class Imbalance—Towards a Useful and Reliable Retinal Disease Recognition System

Multiple studies presented satisfactory performances for the treatment of various ocular diseases. To date, there has been no study that describes a multiclass model, medically accurate, and trained on large diverse dataset. No study has addressed a class imbalance problem in one giant dataset originating from multiple large diverse eye fundus image collections. To ensure a real-life clinical environment and mitigate the problem of biased medical image data, 22 publicly available datasets were merged. To secure medical validity only Diabetic Retinopathy (DR), Age-Related Macular Degeneration (AMD) and Glaucoma (GL) were included. The state-of-the-art models ConvNext, RegNet and ResNet were utilized. In the resulting dataset, there were 86,415 normal, 3787 GL, 632 AMD and 34,379 DR fundus images. ConvNextTiny achieved the best results in terms of recognizing most of the examined eye diseases with the most metrics. The overall accuracy was 80.46 ± 1.48. Specific accuracy values were: 80.01 ± 1.10 for normal eye fundus, 97.20 ± 0.66 for GL, 98.14 ± 0.31 for AMD, 80.66 ± 1.27 for DR. A suitable screening model for the most prevalent retinal diseases in ageing societies was designed. The model was developed on a diverse, combined large dataset which made the obtained results less biased and more generalizable.
Hybrid of Supervised Learning and Optimization Algorithm for Optimal Detection of IoT Distributed Denial of Service Attacks

The high-speed internet has led to the development of Internet of Things (IoT) with a fundamental Three-Layer IoT architecture. However, small amount of un-indicative data captured at the end level of IoT network makes the edge IoT devices susceptible to cyber-security attacks aimed at its transport layer. The Distributed Denial of Service (DDoS) poses significant cyber-security threat to the heterogenous IoT devices which are rendered vulnerable by ineffectiveness of conventional cybersecurity softwares. The literature reveals numerous studies that employed machine learning for the mitigation of IoT DDoS attacks but they lack in terms of an extensive investigation on optimization of machine learning classifiers. Therefore, this study first evaluates the prediction performance of machine learning classification algorithms trained on an authenticated/validated real-time IoT traffic dataset. The results reveal Logistic Regression (LR) as the most effective supervised machine learning classifier for detecting IoT DDoS attacks with a prediction accuracy of 97%. Following this, another investigation on the hybridization of LR with optimization algorithms yields Grasshopper Optimizer Algorithms (GOA) as the most effective optimizer in improving its prediction accuracy to 99%. Hence, the LR hybridized by GOA is developed as the optimal IoT DDoS Attack detection solution. Thus, the study serves to lay the foundation of a data-driven approach for the mitigation of the emerging variants of malicious IoT DDoS attacks such as zero-day attacks.
A Novel Approach for Disaster Victim Detection Under Debris Environments Using Decision Tree Algorithms With Deep Learning Features

Search and Rescue operations for victim identification in an unstructured collapsed building are high-risk and time-consuming. The possibility of saving a victim is high only during the first 48 hours, and then the prospect tends to zero. The faster the response and identification, the sooner the victim can be taken to medical assistance. Combining mobile robots with practical Artificial Intelligence (AI) driven Human Victim Detection (HVD) systems managed by professional teams can considerably reduce this problem. In this paper, we have developed a Transfer Learning-based Deep Learning approach to identify human victims under collapsed building environments by integrating machine learning classification algorithms. A custom-made human victim dataset was created with five class labels: head, hand, leg, upper body, and without the body. First, we extracted the class-wise features of the dataset using fine-tuning-based transfer learning on ResNet-50 deep learning model. The learned features of the model were then extracted, and then a feature selection was performed using J48 to study the impact of feature reduction in classification. Several decision tree algorithms, including decision stump, hoeffiding tree, J48, Linear Model Tree (LMT), Random Forest, Random Tree, Representative (REP) Tree, J48 graft, and other famous algorithms like LibSVM, Logistic regression, Multilayer perceptron, BayesNet, Naive Bayes are then used to perform the classification. The classification accuracy of the abovementioned algorithms is compared to recommend the optimal approach for real-time use. The random tree approach outperformed all other tree-based algorithms with a maximum classification accuracy of 99.53% and a computation time of 0.02 seconds.
An explanation for SARS-CoV-2 rebound after Paxlovid treatment

In a fraction of SARS-CoV-2 infected individuals treated with the oral antiviral Paxlovid, the virus rebounds following treatment. The mechanism driving rebound is not understood. Here, we show that viral dynamic models based on the hypothesis that Paxlovid treatment near the time of symptom onset halts the depletion of target cells, but may not fully eliminate the virus, which can lead to viral rebound. We also show that the occurrence of viral rebound is sensitive to model parameters, and the time treatment is initiated, which may explain why only a fraction of individuals develop viral rebound. Finally, the models are used to test the therapeutic effects of two alternative treatment schemes. These findings also provide a possible explanation for rebounds following other antiviral treatments for SARS-CoV-2.
Effect of direct seeded rice on yield, water productivity and saving of farm energy in reclaimed sodic soil

A field experiment was conducted on conventional rice transplanting (TPR) and direct seeded rice (DSR) in different sets of crop establishment technique with the objective to examine water productivity and save the natural resources. The basmati rice (CSR30) produced maximum grain yield in TPR with and without residue incorporation (3.72 and 3.68t ha, respectively) followed by DSR without residue (3.67 t ha). DSR with sesbania co-culture yielded 3.50 t ha. High yielding Pusa 44 produced maximum grain yield in TPR with residue incorporation (7.66t ha) followed by TPR without residue (7.40t ha). There was no significant difference in yield of puddled and un-puddled transplanted rice. DSR in wheat stubbles resulted minimum yield (4.54t ha). DSR with sesbania brown manuring yielded 5.79 t ha. Water productivity was more in DSR compared to TPR. The maximum water saving (39.4%) was recorded in DSR with sesbania co-culture. Water productivity of rice Pusa 44 was 0.45 kg m when grown in DSR without crop residue while it was 0.43 kg m with sesbania co-culture. The maximum water saving was 32.3% in DSR with sesbania. In case of permanent raised bed, rice transplanting saved 23.3% irrigation water. DSR in different combinations saved irrigation water 30.2%, reduced diesel consumption by 42% in reduced tillage method and by 86% in zero tillage in comparison to transplanted rice. DSR technology reduced labour requirement by 24% in reduced tillage and 30% in zero tillage, whereas power saving in DSR was more than 29%.
A Multi-Objective Cloud Workflow Scheduling Strategy Based on the Modified HEFT Algorithm

Aiming at the issues of poor system resource utilization and the challenge of ensuring the quality of service (QoS) in scientific workflow scheduling in the current cloud computing environment. By combining the existing IaaS cloud resource model with the scientific workflow task model, the article proposed a cloud workflow scheduling model framework. Firstly, proposed multi-objective QoS constraints based on the execution time and costs in scientific workflow scheduling. Then, modified the HEFT algorithm which was a typical scientific workflow scheduling algorithm. Based on the modified HEFT algorithm makes full use of the idle time of resources and maximizes the multi-objective QoS requirements of users. The comparative experiments show that the scheduling strategy of the modified HEFT algorithm can effectively optimize the costs and execution time of scientific workflow scheduling and achieve higher system resource utilization.
A review on the inference of doping TiO2 with metals/non-metals to improve its photocatalytic activities

The band-gap energy of TiO2 nanoparticles has been modified using different methods. The study reviewed the effects of doping and co-doing TiO2 nanotubes with different metals and nonmetals to modify its band gap and improves the photocatalytic activity of the nanoparticles. From our findings reviewing different articles, co-doing TiO2 using dissimilar metal ions like Cerium (Ce) together with nitrogen (N) ions or chromium (Cr) and iron (Fe) ions will decrease particle size, broaden the area of the surface, and as well modifies the particle’s band-gap for visible light to energize an electron causing its movement from the valance band to the conduction band. The studies also revealed that co-doping TiO2 with chromium and iron metal ion demonstrated twice the photocatalytic activity for photodecomposition of gaseous-isopropyl alcohol when compared with single (Cr/Fe) doped TiO2 nanoparticles when exposed to visible light. The reviewed work estimated the optimal amount of Ce for Ce/N co-doped TiO2 at 0.05 grams. Different works of literature reviewed show that doping TiO2 with different transition metals reduces the band-gap for easy absorption of visible light and improvement in the photocatalytic activities of the nanoparticles.
Dimethyl itaconate ameliorates the deficits of goal-directed behavior in Toxoplasma gondii infected mice

Background The neurotrophic parasite Toxoplasma gondii (T. gondii) has been implicated as a risk factor for neurodegenerative diseases. However, there is only limited information concerning its underlying mechanism and therapeutic strategy. Here, we investigated the effects of T. gondii chronic infection on the goal-directed cognitive behavior in mice. Moreover, we evaluated the preventive and therapeutic effect of dimethyl itaconate on the behavior deficits induced by the parasite. Methods The infection model was established by orally infecting the cysts of T. gondii. Dimethyl itaconate was intraperitoneally administered before or after the infection. Y-maze and temporal order memory (TOM) tests were used to evaluate the prefrontal cortex-dependent behavior performance. Golgi staining, transmission electron microscopy, indirect immunofluorescence, western blot, and RNA sequencing were utilized to determine the pathological changes in the prefrontal cortex of mice. Results We showed that T. gondii infection impaired the prefrontal cortex-dependent goal-directed behavior. The infection significantly downregulated the expression of the genes associated with synaptic transmission, plasticity, and cognitive behavior in the prefrontal cortex of mice. On the contrary, the infection robustly upregulated the expression of activation makers of microglia and astrocytes. In addition, the metabolic phenotype of the prefrontal cortex post infection was characterized by the enhancement of glycolysis and fatty acid oxidation, the blockage of the Krebs cycle, and the disorder of aconitate decarboxylase 1 (ACOD1)-itaconate axis. Notably, the administration of dimethyl itaconate significantly prevented and treated the cognitive impairment induced by T. gondii, which was evidenced by the improvement of behavioral deficits, synaptic ultrastructure lesion and neuroinflammation. Conclusion The present study demonstrates that T. gondii infection induces the deficits of the goal-directed behavior, which is associated with neuroinflammation, the impairment of synaptic ultrastructure, and the metabolic shifts in the prefrontal cortex of mice. Moreover, we report that dimethyl itaconate has the potential to prevent and treat the behavior deficits.
Exorista sorbillans (Diptera: Tachinidae) parasitism shortens host larvae growth duration by regulating ecdysone and juvenile hormone titers in Bombyx mori (Lepidoptera: Bombycidae)

Abstract The tachinid fly, Exorista sorbillans, is a notorious ovolarviparous endoparasitoid of the silkworm, Bombyx mori, causing severe damage to silkworm cocoon industry. Silkworm larvae show typically precocious wandering behavior after being parasitized by E. sorbillans; however, the underlying molecular mechanism remains unexplored. Herein, we investigated the changes in the levels of 20-hydroxyecdysone (20E) and juvenile hormone (JH) titer, and they both increased in the hemolymph of parasitized silkworms. Furthermore, we verified the expression patterns of related genes, which showed an upregulation of 20E signaling and biosynthesis genes but a significant downregulation of ecdysone oxidase (EO), a 20E inactivation enzyme, in parasitized silkworms. In addition, related genes of the JH signaling were activated in parasitized silkworms, while related genes of the JH degradation pathway were suppressed, resulting in an increase in JH titer. Notably, the precocious wandering behavior of parasitized silkworms was partly recoverable by silencing the transcriptions of BmCYP302A1 or BmCYP307A1 genes. Our findings suggest that the developmental duration of silkworm post parasitism could be shortened by regulation of 20E and JH titers, which may help silkworm to resist the E. sorbillans infestation. These findings provide a basis for deeper insight into the interplay between silkworms and E. sorbillans and may serve as a reference for the development of a novel approach to control silkworm myiasis.
Preclinical evaluation of Raman spectroscopy for pedicular screw insertion surgical guidance in a porcine spine model

Abstract. Significance Orthopedic surgery is frequently performed but currently lacks consensus and availability of ideal guidance methods, resulting in high variability of outcomes. Misdirected insertion of surgical instruments can lead to weak anchorage and unreliable fixation along with risk to critical structures including the spinal cord. Current methods for surgical guidance using conventional medical imaging are indirect and time-consuming with unclear advantages. Aim The purpose of this study was to investigate the potential of intraoperative in situ near-infrared Raman spectroscopy (RS) combined with machine learning in guiding pedicular screw insertion in the spine. Approach A portable system equipped with a hand-held RS probe was used to make fingerprint measurements on freshly excised porcine vertebrae, identifying six tissue types: bone, spinal cord, fat, cartilage, ligament, and muscle. Supervised machine learning techniques were used to train—and test on independent hold-out data subsets—a six-class model as well as two-class models engineered to distinguish bone from soft tissue. The two-class models were further tested using in vivo spectral fingerprint measurements made during intra-pedicular drilling in a porcine spine model. Results The five-class model achieved >96 % accuracy in distinguish all six tissue classes when applied onto a hold-out testing data subset. The binary classifier detecting bone versus soft tissue (all soft tissue or spinal cord only) yielded 100% accuracy. When applied onto in vivo measurements performed during interpedicular drilling, the soft tissue detection models correctly detected all spinal canal breaches. Conclusions We provide a foundation for RS in the orthopedic surgical guidance field. It shows that RS combined with machine learning is a rapid and accurate modality capable of discriminating tissues that are typically encountered in orthopedic procedures, including pedicle screw placement. Future development of integrated RS probes and surgical instruments promises better guidance options for the orthopedic surgeon and better patient outcomes.
Effects of kangaroo mother care on maternal and paternal health: systematic review and meta-analysis

Abstract Objective To investigate the effect of kangaroo mother care for low-birth-weight and preterm infants on parents’ mental and physical health. Methods The Cochrane Central Register of Controlled Trials, Cochrane Register of Studies Online, PubMed®, Web of Science, Scopus and EMBASE® databases were searched on 16 January 2023 for randomized and quasi-randomized trials on kangaroo mother care. Records identified were screened independently by two reviewers. Pooled relative risks (RRs) are reported for categorical variables, and standardized mean differences (SMDs) or mean differences are reported for continuous variables. Evidence quality was assessed using the GRADE approach. Findings The search identified 30 studies involving 7719 preterm or low-birth-weight infants. There was high-certainty evidence that kangaroo mother care substantially reduced the risk of moderate-to-severe postpartum maternal depressive symptoms compared with no kangaroo mother care (RR: 0.76; 95% confidence interval, CI: 0.59 to 0.96). In addition, there was low-certainty evidence that kangaroo mother care reduced scores for maternal stress (SMD: −0.82; 95% CI: −1.32 to −0.32) and anxiety (SMD: −0.62; 95% CI: −1.01 to −0.23) and increased mother–infant attachment and bonding scores (SMD: 1.19; 95% CI: 0.27 to 2.10). Limited evidence indicated father–infant interactions may be improved, though no marked effect on paternal depression or stress was observed. No trial reported parental physical health outcomes. Conclusion Kangaroo mother care for preterm and low-birth-weight infants was associated with less postpartum maternal depression, stress and anxiety and better mother–infant attachment and bonding. More research is required to evaluate effects on paternal health.