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Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by non-human animals and humans

Requirements to Join the International Federation ofAccountants

Progressive Myoclonic Epilepsy: Lafora Disease - Clinical andGenetic Findings

Chronic inflammation may have a detrimental impact on human health as it tends to result in cancer. In addition, it is often linked to different steps that participate in tumorigenesis, including cellular transformation, survival, promotion, invasion, proliferation, angiogenesis, and metastasis. Hence, inflammation predisposes cancer development and plays a vital role in promoting all tumorigenesis stages. Inflammation is caused by many factors, such as bacterial and viral infections, tobacco smoking, autoimmune diseases, obesity[21], asbestos exposure, and many others, increasing cancer risk. Moreover, cancer can be enhanced by mutations that proceed to cancer progression. Consequently, it leads to immunosuppression and provides a favorable background for tumor development. Although many studies address the question of relationships between inflammation and cancer development, little attention is paid to the link between inflammation and blood cancer. Therefore, the current study reviews the role of inflammation in cancer development, particularly in blood cancer. A meta-analysis research approach meets the research objective and answers the research question. The review results indicate that chronic inflammation directly relates to the development of many cancer types, blood cancer in particular.

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The intensified global Knowledge Economy is frequently reshaped under thedynamics of continuous disruptive technological advancements. Robotic processautomation, artificial intelligence, machine learning, the internet of things, and big dataanalytics are only some of the hot topics on the agendas of organizational decision-makersin strategic development. The quest for knowledge management can no longer beseparated from continuous tech transformations and innovations: knowledge mining,application, dissemination, and protection activities have grown connected to digitaltechnologies. The present study offers a detailed insight into the knowledge managementliterature focused on digital transformations. As such, the relevant timeframe for theanalysis starts from 2006 to 2022, as indicated by our findings. Working within a sampleof 159 documents, the authors enable VOSviewer software 1.6.17 and R coding languagefeatures to identify the main research trends and updates for the selected topic. Theendeavors cover countries' contribution analysis, review of academic interest evolutionacross short to medium timeframes (overlay analysis), as well as identification of potentialfuture research directions which would enrich academic knowledge of the selectedsegment of research. The main contribution of the present bibliometric analysis researchis the visual mapping of knowledge management literature in the context of the digitaltransformation.

AGD-Autoencoder: Attention Gated Deep Convolutional Autoencoder for Brain Tumor Segmentation

By ensuring that security checks and other precautions are taken to reduce the possibility of unwelcome events, airports are very vital to aviation security. Nonetheless, they are not solely responsible for aviation security. The airlines and even the customers are important pieces in the jigsaw puzzle of aviation security. On the one hand, customers want lower prices and higher service levels while on the other, airports like all other businesses want lower cost and improve profits. Thus, practical management tools have become necessary for both airports and airlines in the process of optimizing the security processes while keeping a high customer service level and profitability

Musicology is a growing focus in computer science. Past research has had success in automatically generating music through learning-based agents that make use of neural networks and through model and rule-based approaches. These methods require a significant amount of information, either in the form of a large dataset for learning or a comprehensive set of rules based on musical concepts. This paper explores a model in which a minimal amount of musical information is needed to compose a desired style of music. This paper takes from two concepts, objectness, and evolutionary computation. The concept of objectness, an idea directly derived from imagery and pattern recognition, was used to extract specific musical objects from single musical inputs which are then used as the foundation to algorithmically produce musical pieces that are similar in style to the original inputs. These musical pieces are the product of evolutionary algorithms which implement a sequential evolution approach wherein a generated output may or may not yet be fully within the fitness thresholds of the input pieces. This method eliminates the need for a large amount of pre-provided data as well as the need for long processing times that are commonly associated with machine-learned art-pieces. This study aims to show a proof of concept of the implementation of the described model.

An Efficient Intrusion Detection Approach for Wireless Sensor Networks

Teamwork has been one of the most effective answers to the changes that have occurred in today's environment throughout the years. In all industries, teamwork is being depended on as a preferred performance arrangement to fulfil visions, execute, and achieve goals. It is also a critical component of continuous improvement systems since it allows information exchange, issue resolution, and the growth of employee accountability. The Supervised Learning method was used to model team performance in this study on an intelligent coaching agent. We employed the Object-Oriented System Analysis and Design Methodology. Some web application languages were utilised to effectively execute this study, including Hypertext Markup Language (HTML), Hypertext Preprocessor (PHP), MySQL, Cascaded Style Sheet (CSS), Java Script, Dream weaver, and others

We report a mid-infrared InAs p-i-n photodetector grown on GaAs using an abrupt method through cation exchange at the InAs/GaAs interface, which has resulted in an extremely thin buffer layer. The InAs p-i-n photodetector has a photoresponsivity of 0.327 A/W at $\lambda =2.71\,\,\mu \text{m}$ , which was comparable with the mid-infrared photodetector grown using a compositionally linear-graded process. This result showed that the abrupt method can be used to achieve mid-infrared photodetection on GaAs without the need of a compositionally graded buffer layer.

We report a mid-infrared InAs p-i-n photovoltaic detector on a Si substrate operating at room temperature with a cutoff wavelength at 3.5 $\mu \text{m}$ . The large 11.6% lattice mismatch between the device layer (InAs) and the Si substrate is accommodated by employing a compositionally graded buffer layer technique using solid-source molecular beam epitaxy and a commercially available GeSi substrate. An Al x In1– x As graded buffer layer is used to linearly scale the lattice constant from AlAs to InAs with an increasing $x$ value. The photovoltaic detector has a responsivity of 0.57 A/W, measured using a blackbody source maintained at 700 °C.