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In recent years, transformer-based models have gained prominence in multivariate long-term time series forecasting (LTSF), demonstrating significant advancements despite facing challenges such as high computational demands, difficulty in capturing temporal dynamics, and managing long-term dependencies. The emergence of LTSF-Linear, with its straightforward linear architecture, has notably outperformed transformer-based counterparts, prompting a reevaluation of the transformer's utility in time series forecasting. In response, this paper presents an adaptation of a recent architecture, termed extended LSTM (xLSTM), for LTSF. xLSTM incorporates exponential gating and a revised memory structure with higher capacity that has good potential for LTSF. Our adopted architecture for LTSF, termed xLSTMTime, surpasses current approaches. We compare xLSTMTime's performance against various state-of-the-art models across multiple real-world datasets, demonstrating superior forecasting capabilities. Our findings suggest that refined recurrent architectures can offer competitive alternatives to transformer-based models in LTSF tasks, potentially redefining the landscape of time series forecasting. [ABSTRACT FROM AUTHOR]

Cloud data centers play a vital role in modern computing infrastructure, offering scalable resources for diverse applications. However, managing costs and resources efficiently in these centers has become a crucial concern due to the exponential growth of cloud computing. User applications exhibit complex behavior, leading to fluctuations in system performance and increased power usage. To tackle these obstacles, we introduce the Modified Feeding Birds Algorithm (ModAFBA) as an innovative solution for virtual machine (VM) consolidation in cloud environments. The primary objective is to enhance resource management and operational efficiency in cloud data centers. ModAFBA incorporates adaptive position update rules and strategies specifically designed to minimize VM migrations, addressing the unique challenges of VM consolidation. The experimental findings demonstrated substantial improvements in key performance metrics. Specifically, the ModAFBA method exhibited significant enhancements in energy usage, SLA compliance, and the number of VM migrations compared to benchmark algorithms such as TOPSIS, SVMP, and PVMP methods. Notably, the ModAFBA method achieved reductions in energy usage of 49.16%, 55.76%, and 65.13% compared to the TOPSIS, SVMP, and PVMP methods, respectively. Moreover, the ModAFBA method resulted in decreases of around 83.80%, 22.65%, and 89.82% in the quantity of VM migrations in contrast to the aforementioned benchmark techniques. The results demonstrate that ModAFBA outperforms these benchmarks by significantly reducing energy consumption, operational costs, and SLA violations. These findings highlight the effectiveness of ModAFBA in optimizing VM placement and consolidation, offering a robust and scalable approach to improving the performance and sustainability of cloud data centers. [ABSTRACT FROM AUTHOR]

Eustachian tube dysfunction (ETD) affects a significant portion of the population, manifesting symptoms that impact the quality of life. Despite the prevalence of ETD, there remains a notable gap in comprehensive studies exploring the condition's dynamics within specific demographic contexts, particularly within Saudi Arabia. This study aimed to assess the prevalence and severity of ETD across different demographic groups, to evaluate the efficacy of various treatment modalities, and to identify key predictors of treatment response in a Saudi Arabian cohort. A cross-sectional study was conducted from June 2022 to May 2023 in tertiary care hospitals in the Aseer region, Saudi Arabia. Participants included adults diagnosed with ETD, assessed through clinical symptoms, otoscopic examinations, audiometric evaluations, tympanometry, and the ETDQ-7 questionnaire. The study incorporated advanced diagnostics such as nasopharyngoscopy and pressure equalization tube function tests and involved 154 participants, revealing significant variations in ETD severity, with the 46–60 age group exhibiting the highest mean ETDQ-7 score of 4.85, and urban residents displaying lower severity scores compared to rural counterparts. Pharmacological interventions were most effective, achieving the highest symptom relief and audiological improvement rates of 87.78%. Multivariate regression highlighted age, geographic location, and treatment modality as key predictors of treatment efficacy, with notable interaction effects between climate conditions and treatment types influencing outcomes. The findings underscore the heterogeneity in ETD presentation and the differential efficacy of treatment modalities. [ABSTRACT FROM AUTHOR]

The rise of artificial intelligence has created and facilitated numerous everyday tasks in a variety of industries, including dentistry. Dentists have utilized X-rays for diagnosing patients' ailments for many years. However, the procedure is typically performed manually, which can be challenging and time-consuming for non-specialized specialists and carries a significant risk of error. As a result, researchers have turned to machine and deep learning modeling approaches to precisely identify dental disorders using X-ray pictures. This review is motivated by the need to address these challenges and to explore the potential of AI to enhance diagnostic accuracy, efficiency, and reliability in dental practice. Although artificial intelligence is frequently employed in dentistry, the approaches' outcomes are still influenced by aspects such as dataset availability and quantity, chapter balance, and data interpretation capability. Consequently, it is critical to work with the research community to address these issues in order to identify the most effective approaches for use in ongoing investigations. This article, which is based on a literature review, provides a concise summary of the diagnosis process using X-ray imaging systems, offers a thorough understanding of the difficulties that dental researchers face, and presents an amalgamative evaluation of the performances and methodologies assessed using publicly available benchmarks. [ABSTRACT FROM AUTHOR]

Time series forecasting has been a challenging area in the field of Artificial Intelligence. Various approaches such as linear neural networks, recurrent linear neural networks, Convolutional Neural Networks, and recently transformers have been attempted for the time series forecasting domain. Although transformer-based architectures have been outstanding in the Natural Language Processing domain, especially in autoregressive language modeling, the initial attempts to use transformers in the time series arena have met mixed success. A recent important work indicating simple linear networks outperform transformer-based designs. We investigate this paradox in detail comparing the linear neural network- and transformer-based designs, providing insights into why a certain approach may be better for a particular type of problem. We also improve upon the recently proposed simple linear neural network-based architecture by using dual pipelines with batch normalization and reversible instance normalization. Our enhanced architecture outperforms all existing architectures for time series forecasting on a majority of the popular benchmarks. [ABSTRACT FROM AUTHOR]

Obesity is increasingly becoming a prevalent health concern among adolescents, leading to significant risks like cardiometabolic diseases (CMDs). The early discovery and diagnosis of CMD is essential for better outcomes. This study aims to build a reliable artificial intelligence model that can predict CMD using various machine learning techniques. Support vector machines (SVMs), K-Nearest neighbor (KNN), Logistic Regression (LR), Random Forest (RF), and Gradient Boosting are five robust classifiers that are compared in this study. A novel "risk level" feature, derived through fuzzy logic applied to the Conicity Index, as a novel feature, which was previously unused, is introduced to enhance the interpretability and discriminatory properties of the proposed models. As the Conicity Index scores indicate CMD risk, two separate models are developed to address each gender individually. The performance of the proposed models is assessed using two datasets obtained from 295 records of undergraduate students in Saudi Arabia. The dataset comprises 121 male and 174 female students with diverse risk levels. Notably, Logistic Regression emerges as the top performer among males, achieving an accuracy score of 91%, while Gradient Boosting lags with a score of 72%. Among females, both Support Vector Machine and Logistic Regression lead with an accuracy score of 87%, while Random Forest performs least optimally with a score of 80%. [ABSTRACT FROM AUTHOR]

In the realm of cancer therapeutics, targeting the hypoxia-inducible factor (HIF) pathway has emerged as a promising strategy. This study delves into the intricate web of HIF-associated mechanisms, exploring avenues for future anticancer therapies. Framing the investigation within the broader context of cancer progression and hypoxia response, this article aims to decipher the pivotal role played by HIF in regulating genes influencing angiogenesis, cell proliferation, and glucose metabolism. Employing diverse approaches such as HIF inhibitors, anti-angiogenic therapies, and hypoxia-activated prodrugs, the research methodologically intervenes at different nodes of the HIF pathway. Findings showcase the efficacy of agents like EZN-2968, Minnelide, and Acriflavine in modulating HIF-1α protein synthesis and destabilizing HIF-1, providing preliminary proof of HIF-1α mRNA modulation and antitumor activity. However, challenges, including toxicity, necessitate continued exploration and development, as exemplified by ongoing clinical trials. This article concludes by emphasizing the potential of targeted HIF therapies in disrupting cancer-related signaling pathways. [ABSTRACT FROM AUTHOR]

Congenital heart disease (CHD) represents a multifaceted medical condition that requires early detection and diagnosis for effective management, given its diverse presentations and subtle symptoms that manifest from birth. This research article introduces a groundbreaking healthcare application, the Machine Learning-based Congenital Heart Disease Prediction Method (ML-CHDPM), tailored to address these challenges and expedite the timely identification and classification of CHD in pregnant women. The ML-CHDPM model leverages state-of-the-art machine learning techniques to categorize CHD cases, taking into account pertinent clinical and demographic factors. Trained on a comprehensive dataset, the model captures intricate patterns and relationships, resulting in precise predictions and classifications. The evaluation of the model's performance encompasses sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. Remarkably, the findings underscore the ML-CHDPM's superiority across six pivotal metrics: accuracy, precision, recall, specificity, false positive rate (FPR), and false negative rate (FNR). The method achieves an average accuracy rate of 94.28%, precision of 87.54%, recall rate of 96.25%, specificity rate of 91.74%, FPR of 8.26%, and FNR of 3.75%. These outcomes distinctly demonstrate the ML-CHDPM's effectiveness in reliably predicting and classifying CHD cases. This research marks a significant stride toward early detection and diagnosis, harnessing advanced machine learning techniques within the realm of ECG signal processing, specifically tailored to pregnant women. [ABSTRACT FROM AUTHOR]

The effect of ferromagnetic CaMnO3 (CMO) addition to structural, magnetic, dielectric, and ferroelectric properties of BiFeO3 is presented. X-ray diffraction and Raman investigation allowed the identification of a single pseudocubic perovskite structure. The magnetic measurement showed that the prepared films exhibit a ferromagnetic behavior at a low temperature with both coercive field and remnant magnetization increased with increasing CMO content. However, a deterioration of magnetization was observed at room temperature. Ferroelectric study revealed an antiferroelectric-like behavior with a pinched P–E hysteresis loop for 5% CMO doping BFO, resulting in low remnant polarization and double hysteresis loops. Whereas, high remnant polarization and coercive field with a likely square hysteresis loop are obtained for 10% CMO addition. Furthermore, a bipolar resistive switching behavior with a threshold voltage of about 1.8 V is observed for high doped film that can be linked to the ferroelectric polarization switching. [ABSTRACT FROM AUTHOR]

Despite the availability of human papillomavirus (HPV) vaccines and screening facilities at various health centers in Saudi Arabia, the annual death rate due to cervical cancer is high. Therefore, knowledge and awareness are essential for self-care and educating others, particularly among healthcare students. The present descriptive, cross-sectional study explored female pharmacy students' knowledge, attitudes, and practices related to cervical cancer. A total of 140 students participated in the survey. The survey was conducted for the period between April 2022 to September 2023. We observed a good knowledge score and positive attitudes among 8.5% and 93.5% of participants, respectively. A total of 10% of the study participants reported good practice scores. Most participants had never been screened for cervical cancer (94.3%). Among the non-screened subjects, feeling healthy and lacking information were the participants' significant reasons for not screening for cervical cancer. A positive history of cancer related to smoking significantly impacted the knowledge score (p = 0.050). The current study reveals that healthcare awareness programs for cervical cancer and HPV vaccination are necessary at the level of educational institutions to improve public health. [ABSTRACT FROM AUTHOR]

Permeability is a crucial property that can be used to indicate whether a material can hold fluids or not. Predicting the permeability of carbonate reservoirs is always a challenging and expensive task while using traditional techniques. Traditional methods often demand a significant amount of time, resources, and manpower, which are sometimes beyond the limitations of under developing countries. However, predicting permeability with precision is crucial to characterize hydrocarbon deposits and explore oil and gas successfully. To contribute to this regard, the current study offers some permeability prediction models centered around ensemble machine learning techniques, e.g., the gradient boost (GB), random forest (RF), and a few others. In this regard, the prediction accuracy of these schemes has significantly been enhanced using feature selection and ensemble techniques. Importantly, the authors utilized actual industrial datasets in this study while evaluating the proposed models. These datasets were gathered from five different oil wells (OWL) in the Middle Eastern region when a petroleum exploration campaign was conducted. After carrying out exhaustive simulations on these datasets using ensemble learning schemes, with proper tuning of the hyperparameters, the resultant models achieved very promising results. Among the numerous tested models, the GB- and RF-based algorithms offered relatively better performance in terms of root means square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) while predicting permeability of the carbonate reservoirs. The study can potentially be helpful for the oil and gas industry in terms of permeability prediction in carbonate reservoirs. [ABSTRACT FROM AUTHOR]

Plant taxonomy is the scientific study of the classification and naming of various plant species. It is a branch of biology that aims to categorize and organize the diverse variety of plant life on earth. Traditionally, plant taxonomy has been performed using morphological and anatomical characteristics, such as leaf shape, flower structure, and seed and fruit characters. Artificial intelligence (AI), machine learning, and especially deep learning can also play an instrumental role in plant taxonomy by automating the process of categorizing plant species based on the available features. This study investigated transfer learning techniques to analyze images of plants and extract features that can be used to cluster the species hierarchically using the k-means clustering algorithm. Several pretrained deep learning models were employed and evaluated. In this regard, two separate datasets were used in the study comprising of seed images of wild plants collected from Egypt. Extensive experiments using the transfer learning method (DenseNet201) demonstrated that the proposed methods achieved superior accuracy compared to traditional methods with the highest accuracy of 93% and F1-score and area under the curve (AUC) of 95%, respectively. That is considerable in contrast to the state-of-the-art approaches in the literature. [ABSTRACT FROM AUTHOR]

YouTube is a popular video-sharing platform that offers a diverse range of content. Assessing the quality of a video without watching it poses a significant challenge, especially considering the recent removal of the dislike count feature on YouTube. Although comments have the potential to provide insights into video content quality, navigating through the comments section can be time-consuming and overwhelming work for both content creators and viewers. This paper proposes an NLP-based model to classify Arabic comments as positive or negative. It was trained on a novel dataset of 4212 labeled comments, with a Kappa score of 0.818. The model uses six classifiers: SVM, Naïve Bayes, Logistic Regression, KNN, Decision Tree, and Random Forest. It achieved 94.62% accuracy and an MCC score of 91.46% with NB. Precision, Recall, and F1-measure for NB were 94.64%, 94.64%, and 94.62%, respectively. The Decision Tree had a suboptimal performance with 84.10% accuracy and an MCC score of 69.64% without TF-IDF. This study provides valuable insights for content creators to improve their content and audience engagement by analyzing viewers' sentiments toward the videos. Furthermore, it bridges a literature gap by offering a comprehensive approach to Arabic sentiment analysis, which is currently limited in the field. [ABSTRACT FROM AUTHOR]

Healthcare professionals consider predicting heart disease an essential task and deep learning has proven to be a promising approach for achieving this goal. This research paper introduces a novel method called the asynchronous federated deep learning approach for cardiac prediction (AFLCP), which combines a heart disease dataset and deep neural networks (DNNs) with an asynchronous learning technique. The proposed approach employs a method for asynchronously updating the parameters of DNNs and incorporates a temporally weighted aggregation technique to enhance the accuracy and convergence of the central model. To evaluate the effectiveness of the proposed AFLCP method, two datasets with various DNN architectures are tested, and the results demonstrate that the AFLCP approach outperforms the baseline method in terms of both communication cost and model accuracy. [ABSTRACT FROM AUTHOR]

Skin cancer represents one of the most lethal and prevalent types of cancer observed in the human population. When diagnosed in its early stages, melanoma, a form of skin cancer, can be effectively treated and cured. Machine learning algorithms play a crucial role in facilitating the timely detection of skin cancer and aiding in the accurate diagnosis and appropriate treatment of patients. However, the implementation of traditional machine learning approaches for skin disease diagnosis is impeded by privacy regulations, which necessitate centralized processing of patient data in cloud environments. To overcome the challenges associated with data privacy, federated learning emerges as a promising solution, enabling the development of privacy-aware healthcare systems for skin cancer diagnosis. This paper presents a comprehensive review that examines the obstacles faced by conventional machine learning algorithms and explores the integration of federated learning in the context of privacy-conscious skin cancer prediction healthcare systems. It provides discussion on the various datasets available for skin cancer prediction and provides a performance comparison of various machine learning and federated learning techniques for skin lesion prediction. The objective is to highlight the advantages offered by federated learning and its potential for addressing privacy concerns in the realm of skin cancer diagnosis. [ABSTRACT FROM AUTHOR]

Congenital heart disease (CHD) is a critical global public health concern, particularly when it comes to newborn mortality. Low- and middle-income countries face the highest mortality rates due to limited resources and inadequate healthcare access. To address this pressing issue, machine learning presents an opportunity to develop accurate predictive models that can assess the risk of death from CHD. These models can empower healthcare professionals by identifying high-risk infants and enabling appropriate care. Additionally, machine learning can uncover patterns in the risk factors associated with CHD mortality, leading to targeted interventions that prevent or reduce mortality among vulnerable newborns. This paper proposes an innovative machine learning approach to minimize newborn mortality related to CHD. By analyzing data from infants diagnosed with CHD, the model identifies key risk factors contributing to mortality. Armed with this knowledge, healthcare providers can devise customized interventions, including intensified care for high-risk infants and early detection and treatment strategies. The proposed diagnostic model utilizes maternal clinical history and fetal health information to accurately predict the condition of newborns affected by CHD. The results are highly promising, with the proposed Cardiac Deep Learning Model (CDLM) achieving remarkable performance metrics, including a sensitivity of 91.74%, specificity of 92.65%, positive predictive value of 90.85%, negative predictive value of 55.62%, and a miss rate of 91.03%. This research aims to make a significant impact by equipping healthcare professionals with powerful tools to combat CHD-related newborn mortality, ultimately saving lives and improving healthcare outcomes worldwide. [ABSTRACT FROM AUTHOR]

Simple Summary: Optimum fertility requires the deposition of sperm at the appropriate time relative to ovulation, which can be achieved by using gonadotrophin-releasing hormone (GnRH) to control the time of ovulation. Following insemination, the establishment and maintenance of pregnancy and embryo survival are promoted by progesterone released from the ovarian corpora lutea, also under the control of GnRH-stimulated gonadotrophin release. This review examines the effect of the application of GnRH on the productivity and fertility of food-producing animals. Over several decades, exogenous GnRH and agonists have been employed for controlling reproductive cascades in animals, and treating some reproductive morbidities. The administration of GnRH is used in animals to counter ovarian dysfunction, induce ovulation, and to increase conception and pregnancy rates. GnRH and its agonists are used in the treatment of cystic ovarian degeneration and repeat breeder syndrome. The development of protocols for GnRH administration by intramuscular injection, intramuscular or subcutaneous implants, and intravaginal deposition has empowered their clinical use worldwide. Currently, exogenous GnRH products are a central part of several pre- and post-breeding programs for the enhancement of fertility, including the control of estrous cycles and timing of ovulation, development of fixed-time artificial insemination protocols, improved embryo survival, and the treatment of reproductive morbidity. The aim of the present review is to summarize the application of exogenous GnRH agonists in food animal production. [ABSTRACT FROM AUTHOR]

The accurate and timely diagnosis of skin cancer is crucial as it can be a life-threatening disease. However, the implementation of traditional machine learning algorithms in healthcare settings is faced with significant challenges due to data privacy concerns. To tackle this issue, we propose a privacy-aware machine learning approach for skin cancer detection that utilizes asynchronous federated learning and convolutional neural networks (CNNs). Our method optimizes communication rounds by dividing the CNN layers into shallow and deep layers, with the shallow layers being updated more frequently. In order to enhance the accuracy and convergence of the central model, we introduce a temporally weighted aggregation approach that takes advantage of previously trained local models. Our approach is evaluated on a skin cancer dataset, and the results show that it outperforms existing methods in terms of accuracy and communication cost. Specifically, our approach achieves a higher accuracy rate while requiring fewer communication rounds. The results suggest that our proposed method can be a promising solution for improving skin cancer diagnosis while also addressing data privacy concerns in healthcare settings. [ABSTRACT FROM AUTHOR]

Over the decades, a tremendous increase has been witnessed in the production of documents available in digital form. The increased production of documents has gained so much momentum that their rate of production jumps two-fold every five years. These articles are searched over the internet via search engines, digital libraries, and citation indexes. However, the retrieval of relevant research papers for user queries is still a pipedream. This is because scientific documents are not indexed based on some subject classification hierarchies. Hence, the classification of these documents becomes a challenging task for the researchers. Classification of the documents can be two-fold: one way is to assign a single label to each document and the other is to assign multi-labels to each document based on its belonging domains. Classification of the documents can be performed by using either the available metadata or the whole content of the documents. While performing classification, there are many challenges which may belong to the dataset, feature selection technique, preprocessing methodology, and which classification model is suitable for the classification of the documents. This paper highlights the issues for single-label and multi-label classification by using either metadata or content of the documents and why metadata-based approaches are better than content-based approaches in terms of feasibility. [ABSTRACT FROM AUTHOR]

Background: Propolis is a natural resinous substance collected by honeybees, chiefly from buds and the leaves, branches, and bark of trees. Its role as a wound-healing gel has been studied, but the use of a propolis hydrogel in the treatment of dentinal hypersensitivity has not been evaluated. Dentin hypersensitivity (DH) is commonly treated via iontophoresis using fluoridated desensitizers. The aim of the present study was to compare and evaluate the effects of a 10% propolis hydrogel, 2% sodium fluoride (NaF), and 1.23% acidulated phosphate fluoride (APF) when used in conjunction with iontophoresis for the treatment of cervical dentin hypersensitivity (DH). Methods: Systemically healthy patients complaining of DH were selected for this single-centre, parallel, double-blind randomized clinical trial. Three substances were selected as desensitizers for study in the present trial: a 10% propolis hydrogel, 2% sodium fluoride, and 1.23% acidulated phosphate fluoride, all in conjunction with iontophoresis. Any decrease in DH following the application of specific stimuli was assessed at baseline, before and after application, on the 14th day following use, and on the 28th day following the intervention. Results: Intra-group comparisons show diminished values of DH at maximum post-op follow-up intervals which were significantly trimmed down from the baseline (p < 0.05). The 2% NaF demonstrated a significant reduction in DH over 1.23% APF and the 10% propolis hydrogel (p < 0.05). However, there was no statistically significant difference in the mean difference between the APF and propolis hydrogel groups assessed via tactile, cold, and air tests (p > 0.05). Conclusion: All three desensitizers have proved to be useful when used in conjugation with iontophoresis. Within the limitations of this study, a 10% propolis hydrogel can be used as a naturally occurring alternative to commercially available fluoridated desensitizers. [ABSTRACT FROM AUTHOR]

Measuring patient safety culture in the community pharmacy can help with identifying areas for development. The current study is a descriptive, cross-sectional, electronic survey conducted among pharmacists working in community pharmacies located in the southern region of Saudi Arabia. The community pharmacy version of the "Pharmacy Survey on Patient Safety Culture" (PSOPSC) was used to collect data. The positive response rate (PRR) was calculated as per the guidance provided by the Agency for Healthcare Research and Quality (AHRQ). Based on the PRR, two least-achieved items (<25%) were taken for further analysis to identify the possible predictors. A sum of 195 pharmacists were included in this study and most of them were working in chain pharmacies. The highest PRRs were observed with teamwork (94.99), and patient counseling (94.13), followed by physical space and environment (93.07). The lowest PRRs were observed with staffing, work pressure, and pace (47.70), followed by communication openness (72.60). Specific characteristics, such as experience and the number of working hours, are significantly related to a poor PRR. The current study results indicate that the scope for improving patient safety exists in various areas of community pharmacies. However, it is necessary to prioritize the need based on a positive response rate. [ABSTRACT FROM AUTHOR]

In this study, we synthesized, characterized, and explored the anti-microbial and anti-cancer effects of albumin–chlorogenic acid nanoparticles (NPs). Characterization studies with a UV-vis spectrophotometer, FTIR, PL spectrum, TEM, FESEM, XRD, and DLA analysis showed patterns confirming the physio–chemical nature of biogenic nanocomposites. Further, anti-microbial studies using bacterial strains Staphylococcus aureus, Streptococcus pneumonia, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Vibrio cholera, and fungal strain Candida albicans showed significant (p < 0.05) anti-bacterial and anti-fungal activities. Next, we used MDA-MB-435s, a human cell line, to evaluate the anti-cancer effects of albumin–chlorogenic acid NPs. Cytotoxic studies revealed its IC50 concentration at 24 μg/mL after a 24 h treatment of MDA-MB-435s cells. We chose this IC50 dose to analyze albumin–chlorogenic acid NPs anti-cancer effects in vitro. MDA-MB-435s cells exposed to our NPs were studied via AO/EtBr staining, cell cycle analyses via PI staining, the status of whole genomic damage via comet assay, levels of apoptotic cells via annexin V/PI staining, ROS generation via DCFH-DA staining, an assay of antioxidant enzymes catalase, superoxide dismutase, and antioxidant GSH, via ELISA analyses of apoptotic markers caspase-3, 8, 9, Bax, Bcl-2, CytC, and p53, PI3/AKT/mTOR pathway. Our results collectively showed albumin–chlorogenic acid NPs induced apoptosis via p53-dependent and PI3/AKT/mTOR inhibition in MDA-MB-435s cells. Our results denote albumin–chlorogenic acid NPs can be used as an effective candidate for anti-microbial and anti-cancer applications; however, further in vivo confirmatory studies are warranted. [ABSTRACT FROM AUTHOR]

Non-traumatic ectopia lentis can be isolated or herald an underlying multisystemic disorder. Technological advances have revolutionized genetic testing for many ophthalmic disorders, and this study aims to provide insights into the clinical utility of genetic analysis in paediatric ectopia lentis. Children that underwent lens extraction for ectopia lentis between 2013 and 2017 were identified, and gene panel testing findings and surgical outcomes were collected. Overall, 10/11 cases received a probable molecular diagnosis. Genetic variants were identified in four genes: FBN1 (associated with Marfan syndrome and cardiovascular complications; n = 6), ADAMTSL4 (associated with non-syndromic ectopia lentis; n = 2), LTBP2 (n = 1) and ASPH (n = 1). Parents appeared unaffected in 6/11 cases; the initial presentation of all six of these children was to an ophthalmologist, and only 2/6 had FBN1 variants. Notably, 4/11 cases required surgery before the age of 4 years, and only one of these children carried an FBN1 variant. In summary, in this retrospective cohort study, panel-based genetic testing pointed to a molecular diagnosis in >90% of paediatric ectopia lentis cases requiring surgery. In a subset of study participants, genetic analysis revealed changes in genes that have not been linked to extraocular manifestations and highlighted that extensive systemic investigations were not required in these individuals. We propose the introduction of genetic testing early in the diagnostic pathway in children with ectopia lentis. [ABSTRACT FROM AUTHOR]

Breast cancer is among the most recurrent malignancies, and its prevalence is rising. With only a few treatment options available, there is an immediate need to search for better alternatives. In this regard, nanotechnology has been applied to develop potential chemotherapeutic techniques, particularly for cancer therapy. Specifically, albumin-based nanoparticles are a developing platform for the administration of diverse chemotherapy drugs owing to their biocompatibility and non-toxicity. Visnagin, a naturally derived furanochromone, treats cancers, epilepsy, angina, coughs, and inflammatory illnesses. In the current study, the synthesis and characterization of albumin visnagin (AV) nanoparticles (NPs) using a variety of techniques such as transmission electron microscopy, UV-visible, Fourier transform infrared, energy dispersive X-ray composition analysis, field emission scanning electron microscopy, photoluminescence, X-Ray diffraction, and dynamic light scattering analyses have been carried out. The MTT test, dual AO/EB, DCFH-DA, Annexin-V-FITC/PI, Propidium iodide staining techniques as well as analysis of apoptotic proteins, antioxidant enzymes, and PI3K/Akt/mTOR signaling analysis was performed to examine the NPs' efficacy to suppress MDA-MB-468 cell lines. The NPs decreased cell viability increased the amount of ROS in the cells, disrupted membrane integrity, decreased the level of antioxidant enzymes, induced cell cycle arrest, and activated the PI3K/Akt/mTOR signaling cascade, ultimately leading to cell death. Thus, AV NPs possesses huge potential to be employed as a strong anticancer therapy alternative. [ABSTRACT FROM AUTHOR]

With the continuous increase in Internet of Things (IoT) device usage, more interest has been shown in internet security, specifically focusing on protecting these vulnerable devices from malicious traffic. Such threats are difficult to distinguish, so an advanced intrusion detection system (IDS) is becoming necessary. Machine learning (ML) is one of the promising techniques as a smart IDS in different areas, including IoT. However, the input to ML models should be extracted from the IoT environment by feature extraction models, which play a significant role in the detection rate and accuracy. Therefore, this research aims to introduce a study on ML-based IDS in IoT, considering different feature extraction algorithms with several ML models. This study evaluated several feature extractors, including image filters and transfer learning models, such as VGG-16 and DenseNet. Additionally, several machine learning algorithms, including random forest, K-nearest neighbors, SVM, and different stacked models were assessed considering all the explored feature extraction algorithms. The study presented a detailed evaluation of all combined models using the IEEE Dataport dataset. Results showed that VGG-16 combined with stacking resulted in the highest accuracy of 98.3%. [ABSTRACT FROM AUTHOR]

Simple Summary: Neuroblastoma (NB) is the most common extracranial tumor in children. Despite the development of new therapeutic options, high-risk patients still have poor long-term survival. Distinguishing viable malignant tissue from necrotic areas and surrounding healthy tissue is pivotal during surgery. Variations in the tumor vascular architecture could guide surgeons to remove only viable tumor regions, achieving a safer and more efficient resection. In this paper, using histopathology scanned slides, we first examined variations in the vasculature density of different tumor regions, showing that viable and necrotic areas are associated with a distinct vascular signature. Then, we scanned an excised human NB specimen using a custom-made high-definition preclinical imaging device based on photoacoustic imaging, which is ideal for imaging vasculature, showing that the acquired images can differentiate macroscopic regions within the tumor. Despite aggressive treatments, the prognosis of high-risk NB remains poor. Surgical oncology needs innovative intraoperative devices to help surgeons discriminate malignant tissue from necrotic and surrounding healthy tissues. Changes within the tumor vasculature could be used intraoperatively as a diagnostic tool to guide surgical resection. Here, we retrospectively analyzed the mean vascular density (MVD) of different NB subtypes at diagnosis and after induction chemotherapy using scanned histological samples. One patient was prospectively enrolled, and an ex vivo photoacoustic imaging (PAI) scan was performed on two representative sections to assess its capacity to discriminate different tumor regions. We found that post-chemotherapy, viable areas of differentiating NBs and ganglioneuroblastomas are associated with higher MVD compared to poorly differentiated NBs. Early necrotic regions showed higher MVD than late necrotic and viable regions. Finally, calcified areas showed significantly lower MVD than any other histological component. The acquired PAI images showed a good high-resolution ex vivo 3D delineation of NB margins. Overall, these results suggest that a high-definition preclinical imaging device such as PAI could potentially be exploited to guide surgical resection by identifying different vasculature signatures. [ABSTRACT FROM AUTHOR]

Clarithromycin (CLR), categorized as a Biopharmaceutical Classification System class II drug, has several gastrointestinal tract side effects and an extremely unpalatable bitter taste. The current study aimed to design transdermal patch-embedded CLR niosomes to overcome the aforementioned CLR-related challenges. Various niosomal formulations were successfully fabricated and characterized for their morphology, size, in vitro release, and antimicrobial efficacy. Subsequently, the CLR niosomes were loaded into transdermal patches using the solvent casting method. The polydispersity index of the niosomes ranged from 0.005 to 0.360, indicating the uniformity of the niosomes. The encapsulating efficiency (EE)% varied from 12 to 86%. The optimal Chol: surfactant ratio for drug release was found to be 0.5:1. In addition, the encapsulation of CLR into niosomal nanovesicles did not reduce the antibacterial activity of the CLR. The niosomal patch had a significantly higher permeability coefficient of CLR than the conventional patch. In addition to that, a shear-thinning behavior was observed in the niosomal gels before loading them into a niosomal patch. The flux (Jss) of the niosomal patch was significantly higher than the conventional patch by more than 200 times. In conclusion, niosome-based transdermal patches could be a promising method for the transdermal drug delivery of class II drugs and drugs experiencing GIT side effects. [ABSTRACT FROM AUTHOR]

Tropical cyclones take precious lives, damage critical infrastructure, and cause economic losses worth billions of dollars in Australia. To reduce the detrimental effect of cyclones, a comprehensive understanding of cyclones using artificial intelligence (AI) is crucial. Although event records on Australian tropical cyclones have been documented over the last 4 decades, deep learning studies on these events have not been reported. This paper presents automated AI-based regression, anomaly detection, and clustering techniques on the largest available cyclone repository covering 28,713 records with almost 80 cyclone-related parameters from 17 January 1907 to 11 May 2022. Experimentation with both linear and logistic regression on this dataset resulted in 33 critical insights on factors influencing the central pressure of cyclones. Moreover, automated clustering determined four different clusters highlighting the conditions for low central pressure. Anomaly detection at 70% sensitivity identified 12 anomalies and explained the root causes of these anomalies. This study also projected parameterization and fine-tuning of AI-algorithms at different sensitivity levels. Most importantly, we mathematically evaluated robustness by supporting an enormous scenario space of 4.737 × 108234. A disaster strategist or researcher can use the deployed system in iOS, Android, or Windows platforms to make evidence-based policy decisions on Australian tropical cyclones. [ABSTRACT FROM AUTHOR]

In this study, we aimed to evaluate the performance of various machine learning (ML) classifiers to predict mode choice of movement-challenged persons (MCPs) based on data collected through a questionnaire survey of 384 respondents in Dhaka, Bangladesh. The mode choice set consisted of CNG-driven auto-rickshaw, bus, walking, motorized rickshaw, and non-motorized rickshaw, which was found as the most prominent mode used by MCPs. Age, sex, income, travel time, and supporting instrument (as an indicator of the level of disability) utilized by MCPs were explored as predictive variables. Results from the different split ratios with 10-fold cross-validation were compared to evaluate model outcomes. A split ratio of 60% demonstrates the optimum accuracy. It was found that Multi-nominal Logistic Regression (MNL), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) show higher accuracy for the split ratio of 60%. Overfitting of bus and walking as a travel mode was found as a source of classification error. Travel time was identified as the most important factor influencing the selection of walking, CNG, and rickshaw for MNL, KNN, and LDA. LDA and KNN depict the supporting instrument as a more important factor in mode choice than MNL. The selection of rickshaw as a mode follows a relatively normal probability distribution, while probability distribution is negatively skewed for the other three modes. [ABSTRACT FROM AUTHOR]

Tropical cyclones devastate large areas, take numerous lives and damage extensive property in Bangladesh. Research on landfalling tropical cyclones affecting Bangladesh has primarily focused on events occurring since AD1960 with limited work examining earlier historical records. We rectify this gap by developing a new Tornado catalogue that include present and past records of Tornados across Bangladesh maximizing use of available sources. Within this new Tornado database, 119 records were captured starting from 1838 till 2020 causing 8735 deaths and 97,868 injuries leaving more than 102,776 people affected in total. Moreover, using this new Tornado data, we developed an end-to-end system that allows a user to explore and analyze the full range of Tornado data on multiple scenarios. The user of this new system can select a date range or search a particular location, and then, all the Tornado information along with Artificial Intelligence (AI) based insights within that selected scope would be dynamically presented in a range of devices including iOS, Android, and Windows. Using a set of interactive maps, charts, graphs, and visualizations the user would have a comprehensive understanding of the historical records of Tornados, Cyclones and associated landfalls with detailed data distributions and statistics. [ABSTRACT FROM AUTHOR]

The present study investigates the preference index of natural fibers such as sisal, coir, and rice straw fibers in stone matrix asphalt mixtures (SMA), using waste marble as filler. Waste marble was used as the filler in asphalt mixtures and was crushed by abrasion machine and sieved according to SMA filler requirements. The SEM topography and EDS analysis of sisal, coir, and rice straw fibers were also carried out. The Marshall test was conducted, which is the most acceptable, cost-effective, and widely adopted method to estimate the optimum bitumen and to examine several Marshall Measures, such as flow value, voids filled with bitumen (VFB), stability, voids in mineral aggregate (VMA), and air voids (VA). Furthermore, tests were performed on the specimen with the optimum amount of bitumen, different percentages of fibers, and waste marble as filler to calculate drain down, moister sensitivity, and Marshall Stability. Multi-criteria decision-making (MCDM) techniques were implemented to obtain subjective and objective weights, which were further used to compute the values of the preference index of natural fiber contents. The outcomes revealed favorable results for the usage of marble dust as filler in Stone Matrix Asphalt (SMA). In addition, the preference index upshots are inclined toward the usage of rice straw over coir followed by sisal fiber. It was observed that the value of the preference index in rice straw at 0.3 varied from 0.918, 0.925, and 0.931 in rice straw using equal, objective, and subjective weights, respectively. The maximum drain down value observed is 0.335 based on ASTM-D 6390 and IRC-SP-79 are against 0.3 percent natural fiber. Moreover, as per the prescribed limit of MoRTH, because of the thin film around aggregates, moisture susceptibility characteristics, i.e., better resistance to moisture, were enhanced by more than 80%. [ABSTRACT FROM AUTHOR]

A growing body of evidence from preclinical and clinical studies has associated alterations of the gut microbiota–brain axis with the progression and development of a number of pathological conditions that also affect cognitive functions. Spinal cord injuries (SCIs) can be produced from traumatic and non-traumatic causes. It has been reported that SCIs are commonly associated with anxiety and depression-like symptoms, showing an incidence range between 11 and 30% after the injury. These psychological stress-related symptoms are associated with worse prognoses in SCIs and have been attributed to psychosocial stressors and losses of independence. Nevertheless, emotional and mental modifications after SCI could be related to changes in the volume of specific brain areas associated with information processing and emotions. Additionally, physiological modifications have been recognized as a predisposing factor for mental health depletion, including the development of gut dysbiosis. This condition of imbalance in microbiota composition has been shown to be associated with depression in clinical and pre-clinical models. Therefore, the understanding of the mechanisms underlying the relationship between SCIs, gut dysbiosis and psychological stress could contribute to the development of novel therapeutic strategies to improve SCI patients' quality of life. [ABSTRACT FROM AUTHOR]

Social media platforms such as Twitter have been used by political leaders, heads of states, political parties, and their supporters to strategically influence public opinions. Leaders can post about a location, a state, a country, or even a region in their social media accounts, and the posts can immediately be viewed and reacted to by millions of their followers. The effect of social media posts by political leaders could be automatically measured by extracting, analyzing, and producing real-time geospatial intelligence for social scientists and researchers. This paper proposed a novel approach in automatically processing real-time social media messages of political leaders with artificial intelligence (AI)-based language detection, translation, sentiment analysis, and named entity recognition (NER). This method automatically generates geospatial and location intelligence on both ESRI ArcGIS Maps and Microsoft Bing Maps. The proposed system was deployed from 1 January 2020 to 6 February 2022 to analyze 1.5 million tweets. During this 25-month period, 95K locations were successfully identified and mapped using data of 271,885 Twitter handles. With an overall 90% precision, recall, and F1score, along with 97% accuracy, the proposed system reports the most accurate system to produce geospatial intelligence directly from live Twitter feeds of political leaders with AI. [ABSTRACT FROM AUTHOR]

Nosocomial resistance in staphylococci and enterococci is challenging. The aim of this work was to conduct a multipoint study using molecular detections, antimicrobial resistances profiles, patient demographics and disease patterns for objective assessments of Staphilococcus aureus and other Gram-positive pathogens recovered from clinical infections in the Ha'il region. We have surveyed 188 non-duplicate Gram-positives against 22 antimicrobials for molecular-differentiation, resistance, patient demographics, and disease patterns from January–April 2021. According to definitions for acquired resistance, Staphylococcus aureus was the most frequent with multidrug resistant (65.4%), where MRSA was 60% (n = 72 out of 121). In age-identified patients, 43% were seniors ≥50 years, 38% 21–49 years, and 19% 0–20 years. In gender-identified patients, 63% were males, and 37% were females. While 25% of specimens were from the ICU, the majority (60%) of specimens were from surgical infection in other wards. Staphylococcus epidermidis was the second (15.4%) species of infection identified with 81% from bloodstream infections at the ICU and other wards. The majority of S. epidermidis patients (69%) were seniors ≥50 years, while other age groups 0–20 and 21–49 each had 14% isolates. Although S. epidermidis was multidrug-resistant, it was susceptible to many drugs. Enterococcus faecalis (13%) ranked third with two major infections; bloodstream (64%) and urinary-tract infections (36%) in mainly seniors (86%). Its isolates were fully resistant to oxacillin, penicillin, cefoxitin, and cefotaxime but nearly 100% susceptible to seven others. Other Gram-positive bacteria (6%) were susceptible to many antibiotics. The use of combinations of objective criteria is a well thought out approach in infection control. While the low-frequency of Gram-positives is an impressive achievement, future large-scale investigations should include all private hospitals, clinics and other cities over a longer sampling time to gain more insights. Although geriatric susceptibility can be justified by age and comorbidities, the staphylococcal infections in young adults and children is a global concern and warrants more vertical studies. [ABSTRACT FROM AUTHOR]

Nowadays, intelligent systems applied to vehicles have grown very rapidly; their goal is not only the improvement of safety, but also making autonomous driving possible. Many of these intelligent systems are based on making use of computer vision in order to know the environment and act accordingly. It is of great importance to be able to estimate the pose of the vision system because the measurement matching between the perception system (pixels) and the vehicle environment (meters) depends on the relative position between the perception system and the environment. A new method of camera pose estimation for stereo systems is presented in this paper, whose main contribution regarding the state of the art on the subject is the estimation of the pitch angle without being affected by the roll angle. The validation of the self-calibration method is accomplished by comparing it with relevant methods of camera pose estimation, where a synthetic sequence is used in order to measure the continuous error with a ground truth. This validation is enriched by the experimental results of the method in real traffic environments. [ABSTRACT FROM AUTHOR]

The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle. [ABSTRACT FROM AUTHOR]

Local government bodies and other concerned agencies in developing countries spend a considerable amount of money on rural road development. However, in the absence of any robust and systematic methodology, road development largely relies on ad-hoc decisions and subjective judgement of public officials. Such a decision-making process often leads to inefficient resource allocation bypassing equity and long-term societal benefits. Although there are some established methodologies for road network planning, complexities exist in applying those methods. First, most of the established methods are not suitable for rural road development, particularly regarding the volume and nature of traffic on them. Second, some methods are highly complex and lack practical applicability. Third, road development planning should not be top-down alone but ensure the participation of local stakeholders. Given these limitations, this study proposes a methodology—Rural Road Planning and Prioritisation Model (RPPM). It consists of two major components (i) developing a core network in participation with local stakeholders and (ii) prioritisation of roads based on Cost-Benefit Analysis (CBA) and Multi-Criteria Analysis (MCA). The proposed method is piloted in one district, and a web-based software is also developed for practical implementation by the Local Government Engineering Department (LGED), Bangladesh. The paper also discusses the results of the pilot study. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) is the fifth leading cause of cancer deaths in the world, with variations across geographical regions and ethnicities. Emerging evidence indicates that miRNA expression is dysregulated in GC and its polymorphisms may contribute to these variations, which has yet to be explored in Latin American populations. In a case-control study of 310 GC patients and 311 healthy donors from Chile, we assessed the association of 279 polymorphisms in 242 miRNA genes. Two novel polymorphisms were found to be associated with GC: rs4822739:C>G (miR-548j) and rs701213:T>C (miR-4427). Additionally, rs1553867776:T>TCCCCA (miR-4274) and rs12416605:C>T (miR-938) were associated with intestinal-type GC, and rs4822739:C>G (miR-548j) and rs1439619:T>G (miR-3175) with TNM I-II stage. The polymorphisms rs6149511:T> TGAAGGGCTCCA (miR-6891), rs404337:G>A (miR-8084), and rs1439619:T>G (miR-3175) were identified among H.pylori-infected GC patients and rs7500280:T>C (miR-4719) and rs1439619:T>G (miR-3175) were found among H. pylori cagPAI+ infected GC cases. Prediction analysis suggests that seven polymorphisms could alter the secondary structure of the miRNA, and the other one is located in the seed region of miR-938. Targets of miRNAs are enriched in GC pathways, suggesting a possible biological effect. In this study, we identified seven novel associations and replicated one previously described in Caucasian population. These findings contribute to the understanding of miRNA genetic polymorphisms in the GC pathogenesis. [ABSTRACT FROM AUTHOR]

The devastating nosocomial resistance is an on-going global concern. Surveillance of resistance is crucial for efficient patient care. This study was aimed to conduct a surveillance in four major Ha'il Hospitals from September to December 2020. Using a multipoint program, records of 621 non-duplicate Gram-negative cultures were tested across 21 drugs belonging to different categories. Major species were Klebsiella pneumoniae (n = 187, 30%), E. coli (n = 151, 24.5%), Pseudomonas aeruginosa, (n = 84, 13.6%), Acinetobacter baumannii (n = 82, 13.3%), and Proteus mirabilis (n = 46, 7%). Based on recent resistance classifications, A. baumanni, P. aeruginosa, and enteric bacteria were defined as pan-resistant, extremely resistant, and multi-drug resistant, respectively. A. baumannii (35%) and K. pneumoniae (23%) dominated among coinfections in SARS-CoV2 patients. The "other Gram-negative bacteria" (n = 77, 12.5%) from diverse sources showed unique species-specific resistance patterns, while sharing a common Gram-negative resistance profile. Among these, Providencia stuartii was reported for the first time in Ha'il. In addition, specimen source, age, and gender differences played significant roles in susceptibility. Overall infection rates were 30% in ICU, 17.5% in medical wards, and 13.5% in COVID-19 zones, mostly in male (59%) senior (54%) patients. In ICU, infections were caused by P. mirabilis (52%), A. baumannii (49%), P. aeruginosa (41%), K. pneumoniae (24%), and E. coli (21%), and most of the respiratory infections were caused by carbapenem-resistant A. baumannii and K. pneumoniae and UTI by K. pneumoniae and E. coli. While impressive IC, hospital performances, and alternative treatment options still exist, the spread of resistant Gram-negative bacteria is concerning especially in geriatric patients. The high selective SARS-CoV2 coinfection by A. baumannii and K. pneumoniae, unlike the low global rates, warrants further vertical studies. Attributes of resistances are multifactorial in Saudi Arabia because of its global partnership as the largest economic and pilgrimage hub with close social and cultural ties in the region, especially during conflicts and political unrests. However, introduction of advanced inter-laboratory networks for genome-based surveillances is expected to reduce nosocomial resistances. [ABSTRACT FROM AUTHOR]

Simple Summary: Although substantial studies exists evaluating the effects of photo-selective nets (PSN) on physiological and morphological aspects of crops in different parts of the world, there is a paucity of studies regarding the effects of PSN on the searching behavior of natural enemies of insect pests. Cydia pomonella is a major insect pest of apples worldwide and the parasitoid Mastrus ridens is an important natural enemy of this pest. We evaluated the effects of pearl and red PSN and black standard nets (SN) on the searching behavior of this parasitoid. The host localization ability of the parasitoid was significantly affected by the net color. The females found their hosts faster under the pearl and red PSN compared to the black SN. However, no effects were found in terms of parasitism or the initial behavior of the parasitoid. Even though parasitism was not affected, host localization was delayed under black SN compared to the other PSN. Therefore, it is necessary to carry out further studies in field conditions to verify whether parasitism or other parameters could be affected. Photo-selective nets (PSN) are used to manipulate the physiology of fruit crops. Besides their advantages to the crop, PSN potentially affect insect pests and their natural enemies. We aimed to assess the effects of these production systems on the searching behavior of the codling moth parasitoid, Mastrus ridens. We hypothesized that PSN and black standard nets (SN) affect the behavior of the parasitoid by delaying host localization and reducing parasitism. Laboratory experiments were carried out in closed cages under four treatment conditions: black SN, pearl PSN, red PSN, and no PSN as control (uncovered cages). Our results showed that the host localization of M. ridens was delayed under black SN and enhanced by pearl and red PSN. The PSN and the black SN did not affect the parasitism levels. In addition, the initial behavior of the parasitoid during the first 30 min of the bioassays was not affected by treatments. However, females spent most of the time walking around the arena, grooming, or resting, regardless of the color of the net. Parasitism was not affected under the PSN or the black SN; however, this must be verified in field conditions. [ABSTRACT FROM AUTHOR]

Conventional natural gas (NG) liquefaction processes remove N2 near the tail of the plant, which limits production capacity and decreases energy efficiency and profit. Engineering calculations suggest that upfront N2 removal could have substantial economic benefits on large-scale liquefied natural gas (LNG) processes. This article provides an overview of the most promising technologies that can be employed for upfront N2 removal in the LNG process, focusing on the process selection and design considerations of all currently available upfront N2 removal technologies. The literature review revealed that although adsorption has proven to be a huge success in gas separation processes (efficiency ≥ 90%), most of the available adsorbents are CH4-selective at typical NG conditions. It would be more encouraging to find N2-selective adsorbents to apply in upfront N2 removal technology. Membrane gas separation has shown growing performance due to its flexible operation, small footprint, and reduced investment cost and energy consumption. However, the use of such technology as upfront N2 removal requires multi-stage membranes to reduce the nitrogen content and satisfy LNG specifications. The efficiency of such technology should be correlated with the cost of gas re-compression, product quality, and pressure. A hybrid system of adsorption/membrane processes was proposed to eliminate the disadvantages of both technologies and enhance productivity that required further investigation. Upfront N2 removal technology based on sequential high and low-pressure distillation was presented and showed interesting results. The distillation process, operated with at least 17.6% upfront N2 removal, reduced specific power requirements by 5% and increased the plant capacity by 16% in a 530 MMSCFD LNG plant. Lithium-cycle showed promising results as an upfront N2 chemical removal technology. Recent studies showed that this process could reduce the NG N2 content at ambient temperature and 80 bar from 10% to 0.5% N2, achieving the required LNG specifications. Gas hydrate could have the potential as upfront N2 removal technology if the is process modified to guarantee significant removals of low N2 concentration from a mixture of hydrocarbons. Retrofitting the proposed technologies into LNG plants, design alterations, removal limits, and cost analysis are challenges that are open for further exploration in the near future. The present review offers directions for different researchers to explore different alternatives for upfront N2 removal from NG. [ABSTRACT FROM AUTHOR]

Boil-off Gas (BOG) generated at the liquefied natural gas (LNG) export terminal causes negative economic and environmental impacts. Thus, the objective of this study is to develop and evaluate various handling schemes to minimize and/or recover the generated BOG from an actual baseload LNG export terminal with a capacity of 554 million standard cubic feet per day (MMSCFD) of natural gas feed. The following three main scenarios were assessed: JBOG re-liquefaction, LNG sub-cooling, and lean fuel gas (LFG) reflux. For the LNG subcooling, two sub-cases were considered; standalone subcooling before LNG storage and subcooling in the prevailing liquefaction cycle. Steady-state models for these scenarios were simulated using Aspen Plus® based on a shortcut approach to quickly evaluate the proposed scenarios and determine the promising options that should be considered for further rigorous analysis. Results indicated that the flow of attainable excess LNG is 0.07, 0.03, and 0.022 million metric tons per annum (MTA) for the standalone LNG sub-cooling, LNG sub-cooling in the main cryogenic heat exchanger (MCHE), and both LFG-refluxing and jetty boil-off gas (JBOG) liquefaction, respectively. This in turn results in a profit of 24.58, 12.24, 8.14, and 7.63 million $/year for the LNG price of 7$ per Metric Million British Thermal Unit (MMBtu) of LNG. [ABSTRACT FROM AUTHOR]

The use of antibiotics in animals for both therapeutic and non-therapeutic purposes is a major driver of the emergence and spread of antimicrobial resistance (AMR). While several studies have investigated prescription and consumption patterns in humans, little attention has been paid to the veterinary sector. A cross-sectional study was conducted in 3002 veterinary students (VS) and non-medical students (NMS) from 12 universities in Bangladesh to explore their knowledge, attitudes and practices (KAP) about antibiotics and AMR using a self-administered questionnaire, and assess the influence of the veterinary curriculum. KAP regarding antibiotic use and AMR was significantly higher in veterinary than non-medical students, and in first-year than final-year students. However, gaps in knowledge and practices were highlighted, suggesting deficiencies in training. Moreover, final-year veterinary students were found to be more likely than first-year students to use antibiotics without instructions, which could indicate deficiencies in their curriculum. Although the study suggested a positive impact of the veterinary curriculum on KAP about antibiotics and AMR in Bangladesh, critical gaps remain that are likely to contribute to inadequate use in their future practice. Therefore, there is scope for improving educational programs on AMR in professional curricula. [ABSTRACT FROM AUTHOR]