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Dengue is a distinctive and fatal infectious disease that spreads through female mosquitoes called Aedes aegypti. It is a notable concern for developing countries due to its low diagnosis rate. Dengue has the most astounding mortality level as compared to other diseases due to tremendous platelet depletion. Hence, it can be categorized as a life-threatening fever as compared to the same class of fevers. Additionally, it has been shown that dengue fever shares many of the same symptoms as other flu-based fevers. On the other hand, the research community is closely monitoring the popular research fields related to IoT, fog, and cloud computing for the diagnosis and prediction of diseases. IoT, fog, and cloud-based technologies are used for constructing a number of health care systems. Accordingly, in this study, a DengueFog monitoring system was created based on fog computing for prediction and detection of dengue sickness. Additionally, the proposed DengueFog system includes a weighted random forest (WRF) classifier to monitor and predict the dengue infection. The proposed system’s efficacy was evaluated using data on dengue infection. This dataset was gathered between 2016 and 2018 from several hospitals in the Delhi-NCR region. The accuracy, F-value, recall, precision, error rate, and specificity metrics were used to assess the simulation results of the suggested monitoring system. It was demonstrated that the proposed DengueFog monitoring system with WRF outperforms the traditional classifiers.

In recent years, data security has been a challenging endeavor, especially when the data is being transmitted every second. Internet of Things (IoT) involves continuously sending data over public networks, making the data vulnerable to various security threats. Therefore, ensuring the secure end-to-end communication of IoT data is critical. Cryptography and steganography have proven effective in providing secure connectivity for IoT devices. However, challenges in existing approaches include scalability, computational complexity, implementation, key management, trade-offs, evolving threats, and hyperparameter tuning. Therefore, in this paper, we propose EGCrypto, an efficient and secure model for IoT networks. EGCrypto utilizes a low-complexity elliptic Galois cryptography approach along with matrix XOR steganography to enhance security. To optimize its performance, we employ the zoning evolution of control attributes and adaptive mutation based self-adaptive differential evolution with fitness and diversity ranking. These techniques are utilized to fine-tune the hyperparameters of EGCrypto, enhancing its effectiveness and efficiency. The confidential IoT data is encrypted using low-complexity elliptic Galois cryptography. Following encryption, the encrypted data is embedded or hidden into cover blocks of an image, which are selected using the optimization algorithm. This ensures secure data communication in IoT architectures, as the encrypted data is transferred safely and can be easily recovered and decrypted at the receiving end. The experimental results demonstrate that EGCrypto outperforms competitive models with improvements of 1.8473% in peak signal to noise ratio (PSNR), 1.5490% in strutural similarity index metric (SSIM), 1.7682% in normalized root mean square error (NRMSE), 1.3829% in carrier capacity, and 1.9372% in embedding efficiency.

Information on vaccination rates and factors associated with adherence in persons with HIV (PWH) is limited. We report vaccine adherence in 653 adult PWH attending an urban Infectious Disease Clinic from January 2015 to December 2021. Vaccines evaluated included influenza, pneumococcal, tetanus, hepatitis A virus (HAV) and hepatitis B virus (HBV), human papillomavirus (HPV), and zoster vaccines. Vaccine reminders were triggered at every visit, and all vaccines were accessible in the clinic. The mean age was 50 y (±SD 13), male gender was 78.6%, and black race was 74.3%. The overall adherence to all recommended vaccines was 63.6%. Vaccine adherence was >90% for influenza, pneumococcal, and tetanus, >80% for HAV and HBV, and ≥60% for HPV and zoster vaccines. The main predictor of adherence to all vaccines was ≥2 annual clinic visits (odds ratio [OR] 3.45; 95% confidence interval [CI] 2.36–5.05; p 200 cell/mm3 at entry into care (OR 1.91 [95% CI 1.24–2.94, p = .0003]). Retention in care combined with vaccine reminders and accessibility of vaccines in the clinic can achieve high vaccine uptake in PWH.

Automated Scoring is a developing technology. The accuracy and reliability of these systems have been proven to be much higher. Besides being a time-and money-saver, there are a number of studies which are being conducted to provide variety in feedback, not only on grammatical issues, but also on semantic related issues. This will reduce not only the paper load of the teachers, but as well as teachers’ assessment related issues. In this paper, we remove those dummy note bins, thereby having 183 dimensions in total. We augmented the input by concatenating it with the first-order difference of the semitone filtered spectrogram. We observed a significant increase of the transcription performance with this addition. Compared to feedforward neural networks, recurrent neural network (RNN) are capable of learning temporal dependency of sequential data, which is the property found in music answer scoring.Also, the Long Short-Term Memory (LSTM) unit has a memory block updated only when an input or forget gate is open, and the gradients can propagate through memory cells without being multiplied each time step. Throughout backward and forward layers together, the networks can access to both history and future of the given time frame. Comparative analyses show that the proposed technique outperforms existing techniques. Keywords: Natural Language Processing, Automated scoring system, Ambiguity

This article contains data on the effects of seagrass decline on wave energy along the shoreline of Barnegat Bay (USA) previously evaluated in Donatelli et al., 2019. This study was carried out applying the Coupled-Ocean-Atmosphere-Wave-Sediment Transport (COAWST) numerical modelling framework to six historical maps of seagrass distribution. A new routine recently implemented in COAWST was used, which explicitly computes the wave thrust acting on salt marsh boundaries. The numerical modelling results are reported in terms of wind-wave heights for different seagrass coverages, wind speeds and directions. From a comparison with a numerical experiment without submerged aquatic vegetation, we show how the computed wave thrust on marsh boundaries can be reduced by seagrass beds. Keywords: Vegetation, COAWST, Wave thrust

Amniotic fluid embolism (AFE) is one of the catastrophic complications of pregnancy in which amniotic fluid, fetal cells, hair, or other debris enters into the maternal pulmonary circulation, causing cardiovascular collapse. Etiology largely remains unknown, but may occur in healthy women during labour, during cesarean section, after abnormal vaginal delivery, or during the second trimester of pregnancy. It may also occur up to 48 hours post-delivery. It can also occur during abortion, after abdominal trauma, and during amnio-infusion. The pathophysiology of AFE is not completely understood. Possible historical cause is that any breach of the barrier between maternal blood and amniotic fluid forces the entry of amniotic fluid into the systemic circulation and results in a physical obstruction of the pulmonary circulation. The presenting signs and symptoms of AFE involve many organ systems. Clinical signs and symptoms are acute dyspnea, cough, hypotension, cyanosis, fetal bradycardia, encephalopathy, acute pulmonary hypertension, coagulopathy etc. Besides basic investigations lung scan, serum tryptase levels, serum levels of C3 and C4 complements, zinc coproporphyrin, serum sialyl Tn etc are helpful in establishing the diagnosis. Treatment is mainly supportive, but exchange transfusion, extracorporeal membrane oxygenation, and uterine artery embolization have been tried from time to time. The maternal prognosis after amniotic fluid embolism is very poor though infant survival rate is around 70%.

Deep brain stimulator (DBS) devices are used for unilateral or bilateral stimulation of thalamus, subthalamus and globus pallidus to treat movement disorders. DBS can interfere with domestic and medical equipment such as electrocardiography (ECG), slow wave diathermy, electrocautery, peripheral nerve stimulators, pacemakers, external and implantable cardioverters and defibrillators. This case report describes a patient with such a device who presented for bilateral (B/L) cataract surgery.

Dilated cardiomyopathy (DCM) is a form of cardiomyopathy characterized by left ventricular or biventricular dilatation and impaired ventricular contractility. This results in systolic dysfunction of the heart with decreased left ventricular ejection fraction and progressive congestive cardiac failure. Pregnancy in patients with pre-existing dilated cardiomyopathy can flare up the disease and can be fatal. This case report describes the successful anaesthetic management of a parturient with dilated cardiomyopathy undergoing caesarean section under spinal anaesthesia.

Neurofibromatosis is a multisystem genetic disorder that is associated with cutaneous, neurologic and orthopedic manifestations. Type 1 neurofibromatosis is characterized by dermatological lesions, such as benign neurofibromas of the skin and cafι-au-lait spot, and is more common than type 2 neurofibromatosis. Anesthesiologists must be aware of and consider each of the multisystemic complications of the disorder when evaluating and managing patients for surgical procedures. Anesthesia for the pregnant patient with neurofibromatosis is not well documented. We report a successful case of spinal anesthesia for cesarean section in a parturient with type 1 neurofibromatosis.

Dengue is a distinctive and fatal infectious disease that spreads through female mosquitoes called Aedes aegypti. It is a notable concern for developing countries due to its low diagnosis rate. Dengue has the most astounding mortality level as compared to other diseases due to tremendous platelet depletion. Hence, it can be categorized as a life-threatening fever as compared to the same class of fevers. Additionally, it has been shown that dengue fever shares many of the same symptoms as other flu-based fevers. On the other hand, the research community is closely monitoring the popular research fields related to IoT, fog, and cloud computing for the diagnosis and prediction of diseases. IoT, fog, and cloud-based technologies are used for constructing a number of health care systems. Accordingly, in this study, a DengueFog monitoring system was created based on fog computing for prediction and detection of dengue sickness. Additionally, the proposed DengueFog system includes a weighted random forest (WRF) classifier to monitor and predict the dengue infection. The proposed system's efficacy was evaluated using data on dengue infection. This dataset was gathered between 2016 and 2018 from several hospitals in the Delhi-NCR region. The accuracy, F-value, recall, precision, error rate, and specificity metrics were used to assess the simulation results of the suggested monitoring system. It was demonstrated that the proposed DengueFog monitoring system with WRF outperforms the traditional classifiers. [ABSTRACT FROM AUTHOR]

In the case of the Hindi language, the technology that underpins automated scoring is still in its infancy in terms of its development. These systems have shown much better accuracy and reliability in their operations Nowadays, several studies are being carried out in addition to saving individuals money and time. with the intention of providing nuanced feedback on grammatical as well as semantic problems. This paper's main objective is to develop a hybrid methodology for automated answer scoring using semantic analysis for long Hindi text. Deep Learning and Recurrent Neural Network method have been taken into consideration throughout this research study. The ability of recurrent neural networks, to learn the temporal dependency of sequential input gives them an edge over feed forward neural networks, when it comes to the scoring of musical responses. Research work has integrated PSO and Roberta to improve accuracy. Based on the research findings, the recommended approach has been shown to outperform the currently recognized revolutionary techniques. It shows that the Hybrid PSO-Roberta based deep learning strategy performs better than the old system in terms of precision, recall, and f1 score. It reduce the amount of paperwork they need to do, teachers won't have to be concerned about any evaluation issues going away either. [ABSTRACT FROM AUTHOR]

Superhydrophobicity is a remarkable adaptation observed in various natural surfaces, which can potentially play a crucial role in the efficient functioning of numerous applications. The attainment of long-lasting or "durable" superhydrophobicity has significant implications for global energy production, water conservation, and the overall carbon footprint of industrialized and developing nations. Many applications requiring superhydrophobicity have been limited to laboratory scale experiments due to poor durability. In this article, we discuss important recent advancements and challenges to the development of durable superhydrophobic surfaces, including scalable fabrication, efficacy with low surface tension fluids, and the absence of standardized durability testing methods. To help address these challenges that hinder the implementation of superhydrophobicity, we conclude the article by providing a roadmap that outlines the crucial areas where the community should focus its research and development efforts, both from a fundamental and applied perspective. [ABSTRACT FROM AUTHOR]

Growth of malignant tumors in the breast results in breast cancer. It is a cause of death of many women across the world. As a part of treatment, a woman might have to go through painful surgery and chemotherapy that may further lead to severe side effects. However, it is possible to cure it if it is diagnosed in the initial stage. Recently, many researchers have leveraged machine learning (ML) techniques to classify breast cancer. However, these methods are computationally expensive and prone to the overfitting problem. A simple single-layer neural network, i.e., functional link artificial neural network (FLANN), is proposed to overcome this problem. Further, the F-score is used to reduce the issue of overfitting by selecting features having a higher significance level. In this paper, FLANN is proposed to classify breast cancer using Wisconsin Breast Cancer Dataset (WBCD) (with 699 samples) and Wisconsin Diagnostic Breast Cancer (WDBC) (with 569 samples) datasets. Experimental results reveal that the proposed models can diagnose breast cancer with higher performance. The proposed model can be used in the early breast cancer diagnosis with 99.41% accuracy. [ABSTRACT FROM AUTHOR]

Pulmonary fibrosis is a severe chronic lung disease that causes irreversible scarring in the tissues of the lungs, which results in the loss of lung capacity. The Forced Vital Capacity (FVC) of the patient is an interesting measure to investigate this disease to have the prognosis of the disease. This paper proposes a deep learning-based FVC-Net architecture to predict the progression of the disease from the patient's computed tomography (CT) scan and the patient's metadata. The input to the model combines the image score generated based on the degree of honeycombing for a patient identified based on segmented lung images and the metadata. This input is then fed to a 3-layer net to obtain the final output. The performance of the proposed FVC-Net model is compared with various contemporary state-of-the-art deep learning-based models, which are available on a cohort from the pulmonary fibrosis progression dataset. The model showcased significant improvement in the performance over other models for modified Laplace Log-Likelihood (−6.64). Finally, the paper concludes with some prospects to be explored in the proposed study. [ABSTRACT FROM AUTHOR]

This paper shows the importance of automated scoring (AS) and that it is better than human graders in terms of degree of reproducibility. Considering the potential of the automated scoring system, there is further a need to refine and develop the existing system. The paper goes through the state of the art. It presents the results concerning the problems of existing systems. The paper also presents the semantic features that are indispensable in the scoring system as they have complete content. Moreover, in the present research, a huge deviation has been exhibited by the system which has been shown later in performance analysis of the study, and this clearly indicates the novelty and improved results of the system. It explains the algorithms included in the methodology of this proposed system. The novelty of our work consists in the use of its own similarity function and its notation mechanism. It does not use the cosine similarity function between two vectors. This paper describes and develops a more accurate system which employs a statistical method for scoring. This system adopts and integrates rule-based semantic feature analysis. [ABSTRACT FROM AUTHOR]

Background. Severe viral encephalitis in children causes a viral infection that damages their central nervous system. This situation arises the mental abnormalities, sudden rise in body temperature, disturbance of consciousness, and so forth in children, which can be life-threatening. Objective. This work aimed at exploring the effect of diffusion weighted MRI on children with severe viral encephalitis and myocarditis. Methods. This work presents a diffusion weighted MRI scanning method that involves scanning through a serial imaging device, axial scanning, and sagittal and coronal scanning. 60 children with severe viral encephalitis and myocarditis who admitted to Brain Hospital of Hunan Province from April 2017 to May 2020 were deemed as research subjects. All the children underwent CT and MRI examination, blood routine examination, and cerebrospinal fluid examination after admission. This work uses the random number table method to classify the subjects into control group and observation group, each consisting of 30 cases. Children in the control group were provided with the routine nursing intervention, whereas children in the observation group were subjected to incentive nursing intervention. The baseline data, ECG monitoring indicators, body abnormalities, and clinical symptom relief time of the two groups of children were compared and the results of diffusion weighted MRI scans were analyzed and the ADC values were counted. Results. The two groups of children were compared on the basis of baseline data, and the variation was not statistically substantial (P > 0.05). The cases of children in the control group had higher heart rate and respiration, and physical dysfunction, language dysfunction, unconsciousness, and nervous dysfunction were more than those in the observation group. However, the cases of blood oxygen saturation were less than those of the observation group. After nursing intervention done for the control group, remission time of clinical symptoms such as convulsion, physical dysfunction, unconsciousness, and nerve dysfunction was longer relative to the observation group (all P < 0.05 are considered). Conclusion. The diffusion weighted MRI had diagnostic significance for severe viral encephalitis and myocarditis. For children with severe viral encephalitis and myocarditis, clinical incentive nursing intervention was particularly imperative. It can not only help children to relieve symptoms and control the deterioration of the disease in a short time but also help improve the quality of life of the children and the confidence of family members to cope with the disease. [ABSTRACT FROM AUTHOR]

Salt marshes are valuable ecosystems that must trap sediments and accrete in order to counteract the deleterious effect of sea level rise. Previous studies have shown that the capacity of marshes to build up vertically depends on both autogenous and exogenous processes including ecogeomorphic feedbacks and sediment supply from in‐land and coastal ocean. There have been numerous efforts to quantify the role played by the sediments coming from marsh edge erosion on the resistance of salt marshes to sea level rise. However, the majority of existing studies investigating the interplay between lateral and vertical dynamics use simplified modeling approaches, and they do not consider that marsh retreat can affect the regional‐scale hydrodynamics and sediment retention in back‐barrier basins. In this study, we evaluated the fate of the sediments originating from marsh lateral loss by using high‐resolution numerical model simulations of Jamaica Bay, a small lagoonal estuary located in New York City. Our findings show that up to 42% of the sediment released during marsh edge erosion deposits on the shallow areas of the basin and over the vegetated marsh platforms, contributing positively to the sediment budget of the remaining salt marshes. Furthermore, we demonstrate that with the present‐day sediment supply from the ocean, the system cannot keep pace with sea level rise even accounting for the sediment liberated in the bay through marsh degradation. Our study highlights the relevance of multiple sediment sources for the maintenance of the marsh complex. Key Points: The sediment eroded from marsh edges helps to build the remaining marshesMarsh erosion has a twofold effect: more sediment available for the remaining marshes but decrease in the trapping capacity of the lagoonManagement actions should consider the effects associated with wetland edge erosion at basin scale [ABSTRACT FROM AUTHOR]

Answer Scoring is defined as an act of assigning a score to an answer by a human grader. This scoring technique is costly and requires deep logical efforts and it depends on less-than-perfect human assessment. However, the Automated Scoring (AS) System has its importance in providing the student with a score as well as feedback within seconds. This paper describes an AS system in which scores are assigned to essays automatically based upon predefined algorithms. Most of the educational sectors carry out an important examination process, i.e. to examine and assess the capabilities of the student based on his/her given answers. To accomplish this process, the human graders can apply this Automated Answer Scoring system. The paper goes through the existing techniques for automated answer scoring systems and then goes on to explain the newly developed system in which scoring is done by the statistical method adopting and integrating rule-based semantic quantum-based features analysis resulting in more accuracy. It is in a way a hybrid system suitable for short answer type scoring. It also presents the methodology and architecture of AS. [ABSTRACT FROM AUTHOR]