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Abstract A significant advancement in artificial intelligence is the development of large language models (LLMs). Despite opposition and explicit bans by some authorities, LLMs continue to play a transformative role, particularly in education, by improving language understanding and generation capabilities. This study explores LLMs’ types, history, and training processes, alongside their application in education, including digital and higher education settings. A novel theoretical framework is proposed to guide the integration of LLMs into education, addressing key challenges such as personalization, ethical concerns, and adaptability. Furthermore, the study presents practical case studies and solutions to barriers, such as data privacy and bias, offering insights into their role in enhancing the teaching–learning process. By providing a systematic analysis and proposing a structured framework, this study advances current knowledge and highlights the significant potential of LLMs in revolutionizing education.

Objectives The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation’s public health authorities in informed decision-making. Methods This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models. Results The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset. Conclusion This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors.

Due to exponential population growth, climate change, and an increasing demand for food, there is an unprecedented need for a timely, precise, and dependable assessment of crop yield on a large scale. Wheat, a staple crop worldwide, requires accurate and prompt prediction of its output for global food security. Traditionally, the development of empirical models for crop yield forecasting has relied on climate data, satellite data, or a combination of both. Despite the enhanced performance achieved by integrating satellite and climate data, the contributions from various sources (Climate, Soil, Socioeconomic, and Remote sensing) remain unclear. The lack of well-defined comparisons between the performance of regression-based approaches and different Machine Learning (ML) methods in yield prediction necessitates further investigation. This study addresses the gaps by combining data from multiple sources to forecast wheat yield in the Multan region in the Punjab province of Pakistan. The findings are compared to the benchmark provided by Crop Report Services (CRS) Punjab, with three widely used ML techniques (support vector machine (SVM), Random Forest (RF), and Least Absolute Shrinkage and Selection Operator (LASSO)) by integrating publicly available data within the GEE (Google Earth Engine) platform, including climate, satellite, soil properties, and spatial information data to develop alternative empirical models for yield prediction using data from 2017 to 2022, selecting the best attribute subset related to crop output. The district-level simulated yield data set was analyzed with three ML models (SVM, RF, and LASSO) as a function of seasonal weather, satellite, and soil. The results indicate that combining all datasets using three ML algorithms achieves better yield prediction performance ( $R^{2}$ : 0.74-0.88). Incorporating spatial information and other properties into benchmark models can improve the prediction from 0.08 to 0.12. Random forest outperformed the competitor models with a Root Mean Square Error (RMSE) of 0.05 q/ha and $R^{2}$ of 0.88. Comparative analysis shows that random forest with 97% and SVM with 93% yielded better results in the study area.

Deepfake synthetic media, manipulated using artificial intelligence to mimic authenticity, has become more dangerous in the modern digital era. Despite significant progress in video deepfake detection, audio deepfake detection relies on specific datasets and machine learning algorithms. This study addresses this limitation by developing a deep ensemble learning approach. The proposed deep ensemble approach for audio deepfake detection is called the Ensemble Convolutional Neural Network-Mel Frequency Cepstral Coefficient (ECN-MF). The ECN-MF model comprises a recurrent neural network, a 1D convolutional neural network, long short-term memory networks, and a convolutional long short-term memory network to extract a wider range of audio features, including mel-frequency cepstral coefficient, chroma features, and zero crossing rate. The data processing step was carried out through the creation of a preprocessing pipeline, inclusive of feature extraction, dimensionality standardization, and data normalization. The summarized features were incorporated into the feature vector, which was then normalized before being standardized in order to enhance consistency and stability across the audios. The investigation of the suggested ECN-MF model was carried out using the Fake-or-Real dataset. The dataset comprises four sub-datasets (‘for-original’, ‘for-norm’, ‘for-2sec’, and ‘for-rerec’), categorized by audio duration and bit rate. To evaluate the performance of the proposed ensemble model, we utilized all sub-datasets with fake and real audio. The proposed ensemble approach achieved state-of-the-art accuracies of 99.5% on the ‘for-original’ sub-dataset, closely matching the CNN model at 99.6%. It also achieved accuracies of 98% on ‘for-norm,’ 96.9% on ‘for-2sec,’ and 92.8% on ‘for-rerec’ sub-datasets. By applying the proposed ensemble model to the ‘for-merged’ dataset, which comprises all sub-datasets, we obtained a state-of-the-art accuracy of 98%. These results demonstrate the effectiveness of the proposed approach, which outperforms the results of the individual models.

Appendiceal neuroendocrine tumors (NET) although rare, but the most common tumors of the gastrointestinal tract in children and adolescents. NET of the appendix is typically undiagnosed preoperatively, are usually not associated with specific neuroendocrine symptoms, and a high percentage are initially identified by pathologists. For well-differentiated tumors of ＜1 cm and complete (R0) resection, no follow-up is required. Unlikely, tumor size ＞2 cm or tumor with high-risk features confer a relevant risk of recurrence and further imaging and surgical procedures are warranted. No consensus, clear recommendation or management guidelines are available for the management of appendiceal NET in children. Herein, current article will provide an overview of literature and suggested guidelines for evaluation and management of childhood neuroendocrine tumors of appendix.

[This corrects the article DOI: 10.1371/journal.pone.0285403.].

Breast cancer is one of the most prevalent causes of death among women across the globe. Early detection is the best strategy for reducing the mortality rate. Currently, mammography is the standard screening modality, which has its shortcomings. To complement this modality, thermal infrared-based Computer-Aided Diagnosis (CADx) tools have been presented as economical, less hazardous, and a suitable solution for various age groups. Although a viable solution, most CADx systems are built primarily from frontal breast thermograms, and are likely to miss lesions that may develop on the sides. Additionally, these systems often disregard critical clinical data, such as risk factors. This paper presents a novel CADx system that utilizes deep learning techniques for breast cancer detection. The system incorporates multiple breast thermogram views and corresponding patient clinical data to improve the accuracy of the diagnosis. We describe the methodology of the system, including the extraction of regions of interest from images and the use of transfer learning to train three different models. We evaluate the performance of the models and compare them to similar works from the literature. The results demonstrate that using multi-inputs outperforms single-input models and achieves an overall accuracy of 90.48%, a sensitivity of 93.33%, and an AUROC curve of 0.94. This approach could offer a more cost-effective and less hazardous screening option for breast cancer detection, particularly for a wide range of age groups.

Machine learning techniques have shown promising results in classifying non-functional requirements (NFR). However, the lack of annotated training data in the domain of requirement engineering poses challenges to the accuracy, generalization, and reliability of ML-based methods, including overfitting, poor performance, biased models, and out-of-vocabulary issues. This study presents an approach for the classification of NFR in software requirements specification documents by extracting features from word embedding pre-trained models. The novel algorithms are specifically designed to extract relevant representative features from pre-trained word embedding models. In addition, each pre-trained model is paired with the four tailored neural network architectures for NFR classification including RPCNN, RPBiLSTM, RPLSTM, and RPANN. This combination results in the creation of twelve unique models, each with its unique configuration and characteristics. The results show that the integration of pre-trained GloVe models with RPBiLSTM demonstrates the highest performance, achieving an impressive average Area Under the Curve (AUC) score of 96%, a precision of 85%, and recall of 82%, highlighting its strong ability to accurately classify NFRs. Furthermore, among the integration of pre-trained Word2Vec models, RPLSTM achieved notable results, with an AUC score of 95%, precision of 86%, and recall of 80%. Similarly, integrated fastText-based pre-trained models the RPBiLSTM yield competitive performance, with an AUC score of 95%, precision of 85%, and recall of 80%. This comprehensive and integrated approach provides a practical solution for effectively analyzing and classifying NFR, thereby facilitating improved software development practices.

This study aims to contribute to the existing literature that explores the impact of market concentration on bank efficiency in emerging economies. Using a sample of 225 banks in 18 countries in the Middle East and North Africa (MENA) region over the period 2006-2020, we empirically investigate the significance of this relationship. Since the evidence of concentration effect on efficiency during the COVID-19 outbreak is ambiguous, we test the hypothesis that the efficiency is positively affected by the level of banking market concentration in the MENA region. We adopt fixed effect model specifications and test the robustness of our results with the two-step Generalized Method of Moments (GMM) estimation technique. Our analysis finds a strong positive association between market concentration and bank efficiency. The analysis of different types of banking systems that co-existing in the MENA region (Islamic and conventional) indicates the market concentration effect is more pronounced when the banking institution is Islamic and during the COVID-19 outbreak. Moreover, the better economic performance of Islamic banks during the initial stage of pandemic further increases their efficiency. Our analysis indicated that the impact of market competitive conditions on bank efficiency varies significantly across banks with different ownership structures and is more pronounced for government-owned banks. The results are robust using different model specifications and alternative estimation techniques.

Coronavirus disease (COVID-19), which has caused a global pandemic, continues to have severe effects on human lives worldwide. Characterized by symptoms similar to pneumonia, its rapid spread requires innovative strategies for its early detection and management. In response to this crisis, data science and machine learning (ML) offer crucial solutions to complex problems, including those posed by COVID-19. One cost-effective approach to detect the disease is the use of chest X-rays, which is a common initial testing method. Although existing techniques are useful for detecting COVID-19 using X-rays, there is a need for further improvement in efficiency, particularly in terms of training and execution time. This article introduces an advanced architecture that leverages an ensemble learning technique for COVID-19 detection from chest X-ray images. Using a parallel and distributed framework, the proposed model integrates ensemble learning with big data analytics to facilitate parallel processing. This approach aims to enhance both execution and training times, ensuring a more effective detection process. The model's efficacy was validated through a comprehensive analysis of predicted and actual values, and its performance was meticulously evaluated for accuracy, precision, recall, and F-measure, and compared to state-of-the-art models. The work presented here not only contributes to the ongoing fight against COVID-19 but also showcases the wider applicability and potential of ensemble learning techniques in healthcare.

The pandemic has significantly affected many countries including the USA, UK, Asia, the Middle East and Africa region, and many other countries. Similarly, it has substantially affected Malaysia, making it crucial to develop efficient and precise forecasting tools for guiding public health policies and approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases. We evaluate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN). We trained these models and assessed them using a detailed dataset of confirmed cases, demographic data, and pertinent socio-economic factors. Our research aims to determine the most reliable and accurate model for forecasting SARS-CoV-2 cases in the region. We were able to test and optimize deep learning models to predict cases, with each model displaying diverse levels of accuracy and precision. A comprehensive evaluation of the models' performance discloses the most appropriate architecture for Malaysia's specific situation. This study supports ongoing efforts to combat the pandemic by offering valuable insights into the application of sophisticated deep-learning models for precise and timely SARS-CoV-2 case predictions. The findings hold considerable implications for public health decision-making, empowering authorities to create targeted and data-driven interventions to limit the virus's spread and minimize its effects on Malaysia's population.

Abstract Background Cemento-ossifying fibroma (COF) and cementoblastoma (CB) are rare benign odontogenic tumors with a predilection for the mandible. Cemento-ossifying fibroma is a fibro-osseous lesion that originates in the tooth bearing areas of jaw and shows cementum-like tissue in a fibrotic stroma. Cementoblastoma is classically related to roots of teeth with the presence of calcified cementum-like material. To date, only a single case of concomitant unilateral COF and CB has been reported in the literature. Case presentation We present an unusual case of a 37-year-old female who presented with two discrete bilateral swellings in the right and left mandible for 10 years. The larger tumor involved the left posterior mandible with extension anteriorly to the left and right anterior mandibles, and the smaller tumor was present in right posterior mandible. Radiology revealed two distinct lesions involving both sides of mandible. Histopathological examination showed characteristic features of cemento-ossifying fibroma in sections of the larger tumor and cementoblastoma in sections of smaller tumor. Conclusion This case shows the very unique bilateral co-existence of COF and CB, the second case reported in literature to date.

Ovarian cancer ranks as the fifth leading cause of cancer-related mortality in women. Late-stage diagnosis (stages III and IV) is a major challenge due to the often vague and inconsistent initial symptoms. Current diagnostic methods, such as biomarkers, biopsy, and imaging tests, face limitations, including subjectivity, inter-observer variability, and extended testing times. This study proposes a novel convolutional neural network (CNN) algorithm for predicting and diagnosing ovarian cancer, addressing these limitations. In this paper, CNN was trained on a histopathological image dataset, divided into training and validation subsets and augmented before training. The model achieved a remarkable accuracy of 94%, with 95.12% of cancerous cases correctly identified and 93.02% of healthy cells accurately classified. The significance of this study lies in overcoming the challenges associated with the human expert examination, such as higher misclassification rates, inter-observer variability, and extended analysis times. This study presents a more accurate, efficient, and reliable approach to predicting and diagnosing ovarian cancer. Future research should explore recent advances in this field to enhance the effectiveness of the proposed method further.

Image encryption techniques protect private images from unauthorized access while they are being transmitted. Previously used confusion and diffusion processes are risky and time-consuming. Therefore, finding a solution to this problem has become necessary. In this paper, we propose a new image encryption scheme that combines the Intertwining Logistic Map (ILM) and Orbital Shift Pixels Shuffling Method (OSPSM). The proposed encryption scheme applies a technique for confusion inspired by the rotation of planets around their orbits. We linked the technique of changing the positions of planets around their orbits with the shuffling technique of pixels and combined it with chaotic sequences to disrupt the pixel positions of the plain image. First, randomly selected pixels from the outermost orbit are rotated to shift the pixels in that orbit, causing all pixels in that orbit to change their original position. This process is repeated for each orbit until all pixels have been shifted. This way, all pixels are randomly scrambled on their orbits. Later on, the scrambled pixels are converted into a 1D long vector. The cyclic shuffling is applied using the key generated by the ILM to a 1D long vector and reshaped into a 2D matrix. Then, the scrambled pixels are converted into a 1D long vector to apply cyclic shuffle using the key generated by the ILM. After that, the 1D long vector is converted into a 2D matrix. For the diffusion process, using ILM generates a mask image, which is then XORed with the transformed 2D matrix. Finally, a highly secure and unrecognizable ciphertext image is obtained. Experimental results, simulation analysis, security evaluation, and comparison with existing image encryption schemes show that it has a strong advantage in defending against common attacks, and the operating speed of this encryption scheme also performs excellently in practical image encryption applications.

Abstract Background Solitary Fibrous Tumor (SFT) is a distinct soft tissue neoplasm associated with NAB2-STAT6 gene fusion. It can involve a number of anatomic sites and exhibits a wide spectrum of histological features. Main body Apart from diversity in morphological features seen even in conventional SFT, two histologic variants (fat-forming and giant cell-rich) are also recognized. In addition, a malignant form and dedifferentiation are well recognized. Owing to diverse histological features and involvement of diverse anatomic locations, SFT can mimic other soft tissue neoplasms of different lineages including schwannoma, spindle cell lipoma, dermatofibrosarcoma protuberans, liposarcoma, gastrointestinal stromal tumor (GIST), malignant peripheral nerve sheath tumor (MPNST), and synovial sarcoma. SFT is classified as an intermediate (rarely metastasizing) tumor according to World Health Organization Classification of Tumors of Soft tissue and Bone, 5th edition. The management and prognosis of SFT differs from its malignant mimics and correct diagnosis is therefore important. Although SFT expresses a distinct immunohistochemical (IHC) profile, the classic histomorphological and IHC profile is not seen in all cases and diagnosis can be challenging. NAB2-STAT6 gene fusion has recently emerged as a sensitive and specific molecular marker and its IHC surrogate marker signal transducer and activator of transcription 6 (STAT6) has also shown significant sensitivity and specificity. However, few recent studies have reported STAT6 expression in other soft tissue neoplasms. Conclusion This review will focus on describing the diversity of histological features of SFT, differential diagnoses and discussing the features helpful in distinguishing SFT from its histological mimics.

Abstract Background Kaposiform Hemangioendothelioma (KHE) is a rare vascular tumor of intermediate malignant potential which shows locally aggressive growth but only rarely metastasizes. It is mostly considered to be a tumor of pediatric population but its occurrence in the adults is not uncommon as once considered. Histologically, KHE can mimic other soft tissue neoplasms of different behaviors (e.g. Kaposi Sarcoma, hemangioma) and establishing the correct diagnosis is important for appropriate treatment. Herein, we describe the clinicopathological features of 8 cases of KHE which will be helpful in making their diagnosis. Methods We reviewed pathology reports, microscopy glass slides and obtained follow up information about 8 cases of KHE which were diagnosed at our institution from January 2008 till June 2020. Immunohistochemical stain for HHV8 was also performed. Results Age ranged from 7 months to 25 years. Seven patients were less than 20 years of age and one patient was 25 years old. Equal gender distribution was observed. Extremities were the most common sites of involvement, followed by head and neck, pancreas and ischiorectal region. 2 cases were resection specimen and all others were incisional biopsies. The largest tumor size was 5.5 cm in one of the resections. The incisional/fragmented tissues were all less than 5 cm in aggregate. Most cases showed predominance of nodular growth and a minor component of spindle cell population along with lymphangiomatosis like vascular channels, with evidence of microthrombi in 2 cases. Few multinucleated giant cells were observed in 2 cases. None of the cases exhibited significant nuclear atypia or mitotic activity. One of the cases arising in dermis showed underlying bone involvement. HHV8 was negative in 7/7 cases. Conclusions KHE can also involve adult population and it should always be considered in the differential diagnoses of a vascular lesion. Presence of multinucleated giant cells is a rare finding. Knowledge about histological features and potential mimics is helpful in avoiding misdiagnosis.

Abstract Background Retiform Hemangioendothelioma (RH) is an extremely rare vascular tumor of intermediate biological behavior, which is prone to local recurrence but rarely shows metastasis to distant sites. It may harbor areas resembling Dabska tumor in some cases and angiosarcoma, which in its well differentiated form may exhibit similar pathological appearance in some areas, making it problematic to rule out a possibility of a malignant diagnosis on a core biopsy. Therefore, complete surgical resection with negative margins is essential for accurate diagnosis and local control. Results In our series, two of the three Pakistani cases were in females, with an age range between 18 and 50 years. Our first patient presented with symptoms of cardiac compromise and pulmonary hypertension. Her computed tomography scan showed multiple tumor masses within the mediastinum. The second patient presented with an ulcerated lesion on his scalp, at right temple. The third patient presented with a hard growth on her left 4th toe which was amputated. Histologically, all cases exhibited retiform arborizing vascular spaces lined by bland endothelial cells with hobnail nuclei, characteristic of retiform hemangioendothelioma. Immunohistochemical markers CD31, CD34 and ERG confirmed the vascular nature of the tumor. The first and the second patient are alive and healthy at 4 and 7 months follow up respectively, while the third patient is lost to follow up. Conclusion Owing to the rate of local recurrence, RH should always be considered in the differential diagnosis of vascular tumors showing arborizing blood vessels, as it may have an atypical presentation and it should be carefully differentiated from Dabska tumor and an angiosarcoma.

Big Data Proteogenomics lies at the intersection of high-throughput Mass Spectrometry (MS) based proteomics and Next Generation Sequencing based genomics. The combined and integrated analysis of these two high-throughput technologies can help discover novel proteins using genomic, and transcriptomic data. Due to the biological significance of integrated analysis, the recent past has seen an influx of proteogenomic tools that perform various tasks, including mapping proteins to the genomic data, searching experimental MS spectra against a six-frame translation genome database, and automating the process of annotating genome sequences. To date, most of such tools have not focused on scalability issues that are inherent in proteogenomic data analysis where the size of the database is much larger than a typical protein database. These state-of-the-art tools can take more than half a month to process a small-scale dataset of one million spectra against a genome of 3 GB. In this article, we provide an up-to-date review of tools that can analyze proteogenomic datasets, providing a critical analysis of the techniques' relative merits and potential pitfalls. We also point out potential bottlenecks and recommendations that can be incorporated in the future design of these workflows to ensure scalability with the increasing size of proteogenomic data. Lastly, we make a case of how high-performance computing (HPC) solutions may be the best bet to ensure the scalability of future big data proteogenomic data analysis.

Primary anaplastic large cell lymphoma of the trachea is a very rare tumor. Diagnosis is often missed for a long time because the presentation often resembles that of obstructive airway disease. A 24-year-old non-smoker male presented at an outpatient clinic complaining of gradual development of stridor and shortness of breath over a recent period of four weeks. Imaging workup revealed a large lobulated, soft tissue, mildly enhancing, eccentrically placed intraluminal proximal tracheal mass, which showed extension outside of the tracheal lumen in the peritracheal fat. The patient underwent surgery. A biopsy of the resected mass reported anaplastic large cell lymphoma. Post-operation, the patient underwent chemotherapy consisting of four cycles of CHOP (cyclophosphamide, doxorubicin, vincristine and prednisolone). Follow-up imaging showed non-visualization of the mass. Primary lymphoma (anaplastic large cell lymphoma, which is a rare type of non-Hodgkin’s lymphoma) of the trachea is a rare tumor. Early curative resection with post-operative chemotherapy has a favorable outcome, as in our case.

Irrigation has an important role in food production worldwide by increasing crop quality and production. As such, the use of fresh water for agricultural or vegetable products is declining in quantity and quality over the coming decades. A major factor in brine water is abiotic stress, which disrupts efficiency and is one of the major sources of significant crop loss worldwide. The vegetable is the most commonly cultivated vegetable crop in Pakistan, which has wide economic potential, but its productivity and quality, diseases, nutritional deficiencies, and insect pests or stresses include cold, heat, salinity, drought, and mild stress. The main objective of this research is to observe the effect of saltwater irrigation on green paper. Assessment of various salt-water regimes in terms of their impact on plant growth. Detection of salinity levels and salt-water regimes. The research was conducted at the Water Management Research Center Agricultural University, Faisalabad. Experimental Station of the Water Management Research Center in the Punjab (Pakistan) Province during the spring and summer of 2019. The results of this study confirm the utility of salt variation in pepper plants. Plants were classified as salt-tolerant and sensitive based on the parameters studied. NaCl nutrient solutions of less than 2 (g) have been shown to tolerate excess salt in all plants, and other plants have proven to be highly salt-sensitive at all salinity levels. Salinity is the major stressor in all of these stresses due to the lack of drainage water or groundwater used for sewage for irrigation. For every part of the salinity level beyond its limit, the yield of pepper decreases by 14%.

As artificial intelligence's potential and pervasiveness continue to increase, its strategic importance, effects, and management must be closely examined. Societies, governments, and business organizations need to view artificial intelligence (AI) technologies and their usage from an entirely different perspective. AI is poised to have a tremendous impact on every aspect of our lives. Therefore, it must have a broader view that transcends AI's technical capabilities and perceived value, including areas of AI's impact and influence. Nicholas G. Carr's seminal paper “IT Does not Matter (Carr, 2003) explained how IT's potential and ubiquity have increased, but IT's strategic importance has declined with time. AI is poised to meet the same fate as IT. In fact, the commoditization of AI has already begun. This paper presents the arguments to demonstrate that AI is moving rapidly in this direction. It also proposes an artificial intelligence-based organizational framework to gain value-added elements for lowering the impact of AI commoditization.

Background: Mycosis fungoides (MF) is the most common primary cutaneous lymphoma. It affects usually the covered areas of the body in elderly males in 6th and 7th decades of life. Atypical dermal lymphoid infiltrate is seen along with epidermotropism. Nuclei of neoplastic cells are convoluted. The neoplastic cells demonstrate positivity for CD3 (Pan T) immunohistochemical stain. Majority show increased CD4 to CD8 ratio. The present study was done to study the clinicopathological features, which might be of help in reaching a correct diagnosis in these cases. Materials and Methods: A retrospective descriptive study was conducted on 60 reported cases of MF. The retrieved slides were reviewed for clinical and histopathological features and immunohistochemical profile. Results: The ages ranged from 20–84 years, mean age was 47 years. Majority (75%) of patients were male. Trunk and extremities were the sites most commonly affected. There was significant inverse correlation between epidermal thickness and tumor stage (P = 0.02). Thickened epidermis was seen in patch stage and thickness reduced with progressing stage. The intensity of dermal infiltrate and cell size was also statistically significantly linked to stage progression (P < 0.001 each). In addition, proliferation index also correlated significantly with tumor stage (P = 0.002). Conclusion: Clinical information and histological features are equally important in the accurate diagnosis of MF. Papillary dermal fibrosis is a useful diagnostic clue. CD4:CD8 ratio is not increased in all cases; it may be decreased or remain unchanged.

The interaction among different Internet of Things (IoT) sensors and devices become massive and insecure over the Internet as we probe to smart cities. These heterogeneous devices produce an enormous amount of data that is vulnerable to various malicious threats. The generated data need to be processed and analyzed in a secure fashion to make smart decisions. The smart urban planning is becoming a reality through the mass information generated by the Internet of Things (IoT). This paper exhibits a novel architecture, SafeCity, that limelight the ecosystem of smart cities consists of cameras, sensors, and other real-world physical devices. SafeCity is a three-layer architecture, i.e., a data security layer, a data computational layer, and a decision-making layer. At the first layer, payload-based symmetric encryption is used to secure the data from intruders by exchanging only the authentic data among the physical devices. The second layer is used for the computation of secured data. Finally, the third layer extracts visions from data. The secured exchange of data is ensured by using Raspberry Pi boards while the computation of data is tested on trustworthy datasets, using the Hadoop platform. The assessments disclose that SafeCity presents precious insights into a secured smart city in the context of sensors based IoT environment.

This paper presents an in-depth literature review on the driving forces and barriers for achieving operational excellence through artificial intelligence (AI). Artificial intelligence is a technological concept spanning operational management, philosophy, humanities, statistics, mathematics, computer sciences, and social sciences. AI refers to machines mimicking human behavior in terms of cognitive functions. The evolution of new technological procedures and advancements in producing intelligence for machines creates a positive impact on decisions, operations, strategies, and management incorporated in the production process of goods and services. Businesses develop various methods and solutions to extract meaningful information, such as big data, automatic production capabilities, and systematization for business improvement. The progress in organizational competitiveness is apparent through improvements in firm’s decisions, resulting in increased operational efficiencies. Innovation with AI has enabled small businesses to reduce operating expenses and increase revenues. The focused literature review reveals the driving forces for achieving operational excellence through AI are improvement in computing abilities of machines, development of data-based AI, advancements in deep learning, cloud computing, data management, and integration of AI in operations. The barriers are mainly cultural constraints, fear of the unknown, lack of employee skills, and strategic planning for adopting AI. The current paper presents an analysis of articles focused on AI adoption in production and operations. We selected articles published between 2015 and 2020. Our study contributes to the literature reviews on operational excellence, artificial intelligence, driving forces for AI, and AI barriers in achieving operational excellence.

Intelligent big data analysis is an evolving pattern in the age of big data science and artificial intelligence (AI). Analysis of organized data has been very successful, but analyzing human behavior using social media data becomes challenging. The social media data comprises a vast and unstructured format of data sources that can include likes, comments, tweets, shares, and views. Data analytics of social media data became a challenging task for companies, such as Dailymotion, that have billions of daily users and vast numbers of comments, likes, and views. Social media data is created in a significant amount and at a tremendous pace. There is a very high volume to store, sort, process, and carefully study the data for making possible decisions. This article proposes an architecture using a big data analytics mechanism to efficiently and logically process the huge social media datasets. The proposed architecture is composed of three layers. The main objective of the project is to demonstrate Apache Spark parallel processing and distributed framework technologies with other storage and processing mechanisms. The social media data generated from Dailymotion is used in this article to demonstrate the benefits of this architecture. The project utilized the application programming interface (API) of Dailymotion, allowing it to incorporate functions suitable to fetch and view information. The API key is generated to fetch information of public channel data in the form of text files. Hive storage machinist is utilized with Apache Spark for efficient data processing. The effectiveness of the proposed architecture is also highlighted.

Abstract External auditory canal, like other epithelialized surfaces is predisposed to malignancies such as Squamous cell carcinomas (SCC), melanomas and adenocarcinomas. In this background, malignancies like adenoid cystic carcinoma (ACC) are a rare occurrence and need to be thoroughly evaluated both locally and for distant extension. The malignancy needs to be addressed with an aggressive approach surgically with adequate marginal clearance. The role of radiation is debatable considering the outcomes in the limited data. Here we present a case of a 35-year-old female who presented with otalgia and otorrhea associated a mass in the external auditory canal. After biopsy and imaging, a diagnosis of adenoid cystic carcinoma was made, and the patient was managed accordingly. Keywords: Adenoid cystic carcinoma, External auditory canal, Neck dissection Continuous...

This paper investigates how banking competition and capital level impact on the risk-taking behavior of banking institutions in the Middle East and North Africa (MENA) region. The topic is perceived to be of significant importance during the COVID-19 pandemic. We use data for more than 225 banks in 18 countries in the MENA region to test whether increased competition causes banks to hold higher capital ratios. Employing panel data techniques, and distinguishing between Islamic and conventional banks, we show that banks tend to hold higher capital ratios when operating in a more competitive environment. We also provide evidence that banks in the MENA region increase their capitalization levels in response to a higher risk and vice versa. Further, banking concentration (measured by the HH-index) and credit risk have a significant and positive impact on capital ratios of IBs, whereas competition does play a restrictive role in determining the level of their capital. The results hold when controlling for ownership structure, regulatory and institutional environment, bank-specific and macroeconomic characteristics. Our findings inform regulatory authorities concerned with improving the financial stability of banking sector in the MENA region to strengthen their policies in order to force banks to better align with capital requirements and risk during the COVID-19 pandemic.

Historically, the database search algorithms have been the de facto standard for inferring peptides from mass spectrometry (MS) data. Database search algorithms deduce peptides by transforming theoretical peptides into theoretical spectra and matching them to the experimental spectra. Heuristic similarity-scoring functions are used to match an experimental spectrum to a theoretical spectrum. However, the heuristic nature of the scoring functions and the simple transformation of the peptides into theoretical spectra, along with noisy mass spectra for the less abundant peptides, can introduce a cascade of inaccuracies. In this paper, we design and implement a Deep Cross-Modal Similarity Network called SpeCollate, which overcomes these inaccuracies by learning the similarity function between experimental spectra and peptides directly from the labeled MS data. SpeCollate transforms spectra and peptides into a shared Euclidean subspace by learning fixed size embeddings for both. Our proposed deep-learning network trains on sextuplets of positive and negative examples coupled with our custom-designed SNAP-loss function. Online hardest negative mining is used to select the appropriate negative examples for optimal training performance. We use 4.8 million sextuplets obtained from the NIST and MassIVE peptide libraries to train the network and demonstrate that for closed search, SpeCollate is able to perform better than Crux and MSFragger in terms of the number of peptide-spectrum matches (PSMs) and unique peptides identified under 1% FDR for real-world data. SpeCollate also identifies a large number of peptides not reported by either Crux or MSFragger. To the best of our knowledge, our proposed SpeCollate is the first deep-learning network that can determine the cross-modal similarity between peptides and mass-spectra for MS-based proteomics. We believe SpeCollate is significant progress towards developing machine-learning solutions for MS-based omics data analysis. SpeCollate is available at https://deepspecs.github.io/.

Abstract First described in 1934, eccrine spiradenoma (ES) is a rare, benign adnexal tumour arising from eccrine sweat glands. It commonly presents as a slow-growing nodule on the upper trunk, and head and neck region, mostly in the age bracket of 15-35 years, with no gender preference. While no established guidelines exist for optimal management of malignant ES, some therapies have been studied. The diagnosis of this entity is extremely important as it can harbour a malignant component with disastrous outcomes which may be missed due to its strong resemblance to benign lesions, such as a papilloma. Here, we present the case of a 35-year-old lady who presented with a papilloma-like growth on the upper medial aspect of the thigh which was diagnosed as eccrine spiradenoma upon excision. Keywords: Eccrine Spiradenoma, adnexal tumour, dermal lesion, case report. Continuous...

Pediatric granulosa cell tumors of the ovary are rare functional neoplasms causing secondary precocious puberty. The tumor possesses significant diagnostic challenge when occurs in a patient approaching puberty due to masking of symptoms. We report a case of ovarian tumor in a 9-year-old girl which was diagnosed on histopathology and the pseudo-puberty signs reverted post resection. Keywords: Precocious puberty, Juvenile granulosa cell tumor, Pediatric

Abstract Objective Lipoblastoma is a rare neoplasm that occurs mostly in infants and children. Although benign, it has a tendency for local recurrence. Results Clinical and pathological features of 23 cases of lipoblastoma described. Patients’ age ranged from 8 months to 18 years with mean and median age 4.1 and 2.5 years, respectively. Male:female ratio was 2.8:1. Most common sites were lower extremities (9 cases), followed by abdominal cavity and retroperitoneum (4 cases), and scrotum/groin (3 cases). Grossly, 22 tumors were well circumscribed and multi nodular. All cases showed lobules composed of adipocytes and lipoblasts with intervening fibrous septa and fine vascular network. Myxoid change, capsule formation and septation were seen in all cases. Zonation was seen in 2 cases. Follow-up was available in 14 out of 23 patients. Of these, 13 were alive and free of disease with no evidence of any recurrent lesion. One patient with a mediastinal infiltrating lipoblastoma experienced 4 recurrences. Lipoblastoma is a benign adipocytic neoplasm of infants and young children. Correlation of clinical and histological features helps in reaching a correct diagnosis. Owing to a high recurrence rate following incomplete resection, a complete resection is essential. Prognosis is excellent after complete resection.

Abstract The rarity of lethal midline granuloma and lack of knowledge by the majority of clinicians about this disease makes this disease a difficult entity to diagnose. Nonspecific symptoms of this disease present obstacles in correct diagnosis and lead to a delay in proper treatment. Surgeons play a limited role in this condition. We present a case report of a 38-year-old man with this rare condition.

Background: Serous cystadenomas of pancreas are rare benign epithelial neoplasms, which predominantly occur in the pancreatic body and tail of elderly females. Majority of these tumors have microcystic appearance. Macrocystic and solid variants have also been described. A number of more aggressive cystic pancreatic lesions are included in the differential diagnosis. Distinction from such lesions is important for optimal management. Objective: Our aim was to study the clinical and histological features of serous cystadenomas which would be helpful in making their correct diagnosis and understanding their behavior. Methods: We reviewed 23 cases of serous cystadenomas diagnosed in our institution between January 2001 and June 2018. Results: Mean age at presentation was 53.43 years. Female to male ratio was 4.75:1. Over half (56.5%) of the cases were diagnosed incidentally. Abdominal pain was the most common symptom. Body and tail (either alone or in combination) were the most common locations. Tumor size ranged from 2 to 16 cm. Central scar was seen in 43.4% cases. Two cases were unilocular (macrocystic). Microscopically, all cases showed simple cuboidal to flattened epithelium with round, uniform nuclei, and glycogen-rich clear cytoplasm. Focal micropapillae formation was seen in eight cases (34.7%). Surgical resection was performed in 82.6% cases. Recurrence occurred in only one single case. Conclusion: Pancreatic serous cystadenomas are benign neoplasms with excellent prognosis. The tumors showed typical morphological features in all cases. Surgical resection was performed in the majority of cases in our study owing to lack of optimal and complete radiological workup pre-operatively and the concern for not missing and adequately treating pancreatic mucinous cystic neoplasms.

The 2016 update of the WHO Classification of Tumours of the Central Nervous System has redefined a number of tumors. Embryonal tumor with multilayered rosettes, C19MC-altered is one such tumor entity which has been newly defined on the basis of a characteristic molecular alteration. We report, to our knowledge, the first case of this rare pediatric brain neoplasm in the Pakistani population. An 8-month-old girl was presented with vomiting and left-sided ptosis, and magnetic resonance imaging scan showed a cerebellar tumor. Histologically, a highly cellular population of primitive cells was seen alternating with hypocellular neuropil-rich regions containing multilayered true rosettes and cells with glial and neuronal differentiation. Amplification of 19q13. 42 chromosome region on fluorescence in situ hybridization analysis confirmed the diagnosis. Post-operative radiological examination revealed widespread central nervous system involvement. Adjuvant treatment was not offered due to complications. Patient expired a week after diagnosis.