Reviewerly/exHarmony huggingface dataset:
Usage of Web Scraping in the Pharmaceutical Sector

INTRODUCTION: Web scraping is a technique that provides organizations with the ability to analyse large amounts of information and gather new information. &#x0D; OBJECTIVES: Find a group that is a health check, a full body test, a blood test, and so on. In this way, the pharmaceutical industry should consider how to improve information, information storage, information retrieval, and capture. For example, the healthcare system may decide to standardize the assessment of speech and allow information to be shared across organizations to improve treatment outcomes in web scraping applications.&#x0D; METHODS: Web scraping is based on the pharmaceutical industry. From here, we get information about pharmacies, such as drug names in different categories or drug sales. However, we are dealing with diseases and common medicines. Using this information, we can find the most common viruses. There are many factors to consider when creating a junk website for the pharmaceutical industry, such as drug names, tablet categories, and syrups found in the pharmaceutical industry.&#x0D; RESULTS: As is clearly visible from the output, there are columns for drug names, manufacturers, drug types, and prices. This is the information we get from a website called Net meds, a pharmacy site. With the help of this information, we learn which drugs are most needed, and then we can find the most common diseases today.&#x0D; CONCLUSION: The results of this web scraping can be very useful and powerful. However, the industry's success in web scraping and data extraction techniques depends on the availability of clean chemical data.
Heuristic computing with active set method for the nonlinear Rabinovich–Fabrikant model

The current study shows a reliable stochastic computing heuristic approach for solving the nonlinear Rabinovich-Fabrikant model. This nonlinear model contains three ordinary differential equations. The process of stochastic computing artificial neural networks (ANNs) has been applied along with the competences of global heuristic genetic algorithm (GA) and local search active set (AS) methodologies, i.e., ANNs-GAAS. The construction of merit function is performed through the differential Rabinovich-Fabrikant model. The results obtained through this scheme are simple, reliable, and accurate, which have been calculated to optimize the merit function by using the GAAS method. The comparison of the obtained results through this scheme and the conventional reference solutions strengthens the correctness of the proposed method. Ten numbers of neurons along with the log-sigmoid transfer function in the neural network structure have been used to solve the model. The values of the absolute error are performed around 10-07 and 10-08 for each class of the Rabinovich-Fabrikant model. Moreover, the reliability of the ANNs-GAAS approach is observed by using different statistical approaches for solving the Rabinovich-Fabrikant model.
Artificial Intelligence (AI) can change the way of doing policy modelling

This paper seeks to assess the transformative potential of Artificial Intelligence (AI) in policy modeling. Rapid advancements in AI, encompassing algorithms, advanced programming software, robotics, metadata, sophisticated mathematical models, neural networks, and graphical models are ushering in innovative new research methods for analysing and resolving intricate socio-economic issues. Our focus lies in a comparative evaluation of Artificial Intelligence Response (AIR) versus Human Intelligence Response (HIR) in generating swift and potent solutions to various socio-economic challenges. To achieve this, we propose a fundamental model for appraising the effectiveness of policy modeling, known as the "Policy Modeling Response Evaluator (PMR-Evaluator)." Furthermore, we conducted an experiment to gauge the responsiveness and effectiveness of both AIR and HIR. This experiment revolved around addressing a specific socio-economic problem, namely controlling inflation. Initially, we scrutinized responses from an extensive database of papers published in the Journal of Policy Modeling (JPM) by Elsevier over the past forty-five years (1978–2023) to ascertain HIR's capacity to analyze and resolve inflation-related issues. Concurrently, we utilized ChatGPT, a powerful artificial intelligence application (AI-APP), to explore potential solutions for controlling inflation. Ultimately, we analyzed whether HIR or AIR proved more effective and precise.
Multi-state quantum simulations via model-space quantum imaginary time evolution

Abstract We introduce the framework of model space into quantum imaginary time evolution (QITE) to enable stable estimation of ground and excited states using a quantum computer. Model-space QITE (MSQITE) propagates a model space to the exact one by retaining its orthogonality, and hence is able to describe multiple states simultaneously. The quantum Lanczos (QLanczos) algorithm is extended to MSQITE to accelerate the convergence. The present scheme is found to outperform both the standard QLanczos and the recently proposed folded-spectrum QITE in simulating excited states. Moreover, we demonstrate that spin contamination can be effectively removed by shifting the imaginary time propagator, and thus excited states with a particular spin quantum number are efficiently captured without falling into the different spin states that have lower energies. We also investigate how different levels of the unitary approximation employed in MSQITE can affect the results. The effectiveness of the algorithm over QITE is demonstrated by noise simulations for the H 4 model system.
Relaxing Hardware Requirements for Surface Code Circuits using Time-dynamics

The typical time-independent view of quantum error correction (QEC) codes hides significant freedom in the decomposition into circuits that are executable on hardware. Using the concept of detecting regions, we design time-dynamic QEC circuits directly instead of designing static QEC codes to decompose into circuits. In particular, we improve on the standard circuit constructions for the surface code, presenting new circuits that can embed on a hexagonal grid instead of a square grid, that can use ISWAP gates instead of CNOT or CZ gates, that can exchange qubit data and measure roles, and that move logical patches around the physical qubit grid while executing. All these constructions use no additional entangling gate layers and display essentially the same logical performance, having teraquop footprints within 25% of the standard surface code circuit. We expect these circuits to be of great interest to quantum hardware engineers, because they achieve essentially the same logical performance as standard surface code circuits while relaxing demands on hardware.
Deep learning performance for detection and classification of microcalcifications on mammography

Breast cancer screening through mammography is crucial for early detection, yet the demand for mammography services surpasses the capacity of radiologists. Artificial intelligence (AI) can assist in evaluating microcalcifications on mammography. We developed and tested an AI model for localizing and characterizing microcalcifications.Three expert radiologists annotated a dataset of mammograms using histology-based ground truth. The dataset was partitioned for training, validation, and testing. Three neural networks (AlexNet, ResNet18, and ResNet34) were trained and evaluated using specific metrics including receiver operating characteristics area under the curve (AUC), sensitivity, and specificity. The reported metrics were computed on the test set (10% of the whole dataset).The dataset included 1,000 patients aged 21-73 years and 1,986 mammograms (180 density A, 220 density B, 380 density C, and 220 density D), with 389 malignant and 611 benign groups of microcalcifications. AlexNet achieved the best performance with 0.98 sensitivity, 0.89 specificity of, and 0.98 AUC for microcalcifications detection and 0.85 sensitivity, 0.89 specificity, and 0.94 AUC of for microcalcifications classification. For microcalcifications detection, ResNet18 and ResNet34 achieved 0.96 and 0.97 sensitivity, 0.91 and 0.90 specificity and 0.98 and 0.98 AUC, retrospectively. For microcalcifications classification, ResNet18 and ResNet34 exhibited 0.75 and 0.84 sensitivity, 0.85 and 0.84 specificity, and 0.88 and 0.92 AUC, respectively.The developed AI models accurately detect and characterize microcalcifications on mammography.AI-based systems have the potential to assist radiologists in interpreting microcalcifications on mammograms. The study highlights the importance of developing reliable deep learning models possibly applied to breast cancer screening.• A novel AI tool was developed and tested to aid radiologists in the interpretation of mammography by accurately detecting and characterizing microcalcifications. • Three neural networks (AlexNet, ResNet18, and ResNet34) were trained, validated, and tested using an annotated dataset of 1,000 patients and 1,986 mammograms. • The AI tool demonstrated high accuracy in detecting/localizing and characterizing microcalcifications on mammography, highlighting the potential of AI-based systems to assist radiologists in the interpretation of mammograms.
Creating Location-Based Augmented Reality Games and Immersive Experiences for Touristic Destination Marketing and Education

The aim of this paper is to present an approach that utilizes several mixed reality technologies for touristic promotion and education. More specifically, mixed reality applications and games were created to promote the mountainous areas of Western Macedonia, Greece, and to educate visitors on various aspects of these destinations, such as their history and cultural heritage. Location-based augmented reality (AR) games were designed to guide the users to visit and explore the destinations, get informed, gather points and prizes by accomplishing specific tasks, and meet virtual characters that tell stories. Furthermore, an immersive lab was established to inform visitors about the region of interest through mixed reality content designed for entertainment and education. The lab visitors can experience content and games through virtual reality (VR) and augmented reality (AR) wearable devices. Likewise, 3D content can be viewed through special stereoscopic monitors. An evaluation of the lab experience was performed with a sample of 82 visitors who positively evaluated features of the immersive experience such as the level of satisfaction, immersion, educational usefulness, the intention to visit the mountainous destinations of Western Macedonia, intention to revisit the lab, and intention to recommend the experience to others.
Bandwidth-efficient Microburst Measurement in Large-scale Datacenter Networks

Microburst measurement is essential for diagnosing and mitigating performance problems in datacenter networks. The key is to efficiently identify the flows that contribute the most to queue buildup. However, because the existing microburst measurement systems capture packet-level information, they incur significant bandwidth overhead. We present BurstScope, a bandwidth-efficient microburst measurement system that can profile the microburst characteristics and the contributing flows. BurstScope detects the microburst-involved packets in egress pipeline, then aggregates the measurement granularity from packet level to flow level by an invertible sketch. Finally, by carefully partitioning the measurement and statistic tasks between the data and control plane, we generate only one telemetry packet for each microburst. We have implemented BurstScope on Barefoot Tofino switches. Testbed-based evaluations show that BurstScope keeps low bandwidth overhead (< 0.02%) and high identification accuracy (> 97%). Compared with the state-of-the-art system, BurstScope can reduce 60 × bandwidth overhead.
Blockchain and Healthcare: A Critical Analysis of Progress and Challenges in the Last Five Years

Utilizing the fundamental characteristics of the decentralization, immutability, and transparency of blockchain technology, the healthcare industry has made notable advancements in incorporating it over the past five years. This review examines the progress and challenges encountered in this critical study by assessing 124 articles published by MDPI between 2018 and the current date. Examining blockchain’s potential uses, like safe data exchange and interoperability in supply chain management and electronic health records, provides exciting new directions for the future of healthcare. Blockchain technology can greatly increase efficiency and cost-effectiveness by guaranteeing data integrity, protecting patient privacy, and reducing administrative procedures. This paper objectively evaluates blockchain’s advancement in healthcare through a thorough analysis of real-world applications and research projects. By highlighting both its advantages and disadvantages, this analysis seeks to add to the continuing conversation about how blockchain will influence the way healthcare is managed and delivered in the future.
An efficient detection of non-standard miner behavior using improved YOLOv8

To prevent coal mine safety accidents caused by non-standard mining operations, higher requirements have been proposed for identifying miners' behavior in surveillance videos. In this study, a single-stage detection model, YOLOv8, is employed, which effectively predicts the center of objects and is more suitable for recognizing miner behavior in complex environments. First, the video is segmented sampled to capture long-term information. Afterwards, deformable convolutions are integrated, applying offset learning to enhance fine-grained feature extraction and simultaneously, the introduction of the parameter-free SimAM improves the saliency mapping of miner behavior. Furthermore, an improved cooperative loss function is used for high-quality prior box regression. To validate the performance of non-standard miner behavior recognition, the proposed approach is conducted on a self-built dataset and achieves a mAP of 95.7 %, accuracy of 95.3 %, and recall of 95.1 % compared to the state-of-the-arts. It maintains an effective balance between computational cost and recognition accuracy.
Rolling Shutter Camera: Modeling, Optimization and Learning

Most modern consumer-grade cameras are often equipped with a rolling shutter mechanism, which is becoming increasingly important in computer vision, robotics and autonomous driving applications. However, its temporal-dynamic imaging nature leads to the rolling shutter effect that manifests as geometric distortion. Over the years, researchers have made significant progress in developing tractable rolling shutter models, optimization methods, and learning approaches, aiming to remove geometry distortion and improve visual quality. In this survey, we review the recent advances in rolling shutter cameras from two aspects of motion modeling and deep learning. To the best of our knowledge, this is the first comprehensive survey of rolling shutter cameras. In the part of rolling shutter motion modeling and optimization, the principles of various rolling shutter motion models are elaborated and their typical applications are summarized. Then, the applications of deep learning in rolling shutter based image processing are presented. Finally, we conclude this survey with discussions on future research directions.
Quantum-enhanced greedy combinatorial optimization solver

Combinatorial optimization is a broadly attractive area for potential quantum advantage, but no quantum algorithm has yet made the leap. Noise in quantum hardware remains a challenge, and more sophisticated quantum-classical algorithms are required to bolster their performance. Here, we introduce an iterative quantum heuristic optimization algorithm to solve combinatorial optimization problems. The quantum algorithm reduces to a classical greedy algorithm in the presence of strong noise. We implement the quantum algorithm on a programmable superconducting quantum system using up to 72 qubits for solving paradigmatic Sherrington-Kirkpatrick Ising spin glass problems. We find the quantum algorithm systematically outperforms its classical greedy counterpart, signaling a quantum enhancement. Moreover, we observe an absolute performance comparable with a state-of-the-art semidefinite programming method. Classical simulations of the algorithm illustrate that a key challenge to reaching quantum advantage remains improving the quantum device characteristics.
Incorporating principal component analysis into Hotelling <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si6.svg" display="inline" id="d1e24661"><mml:msup><mml:mrow><mml:mi mathvariant="normal">T</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math> control chart for compositional data monitoring

In the manufacturing industry, compositional data (CoDa) is a vital quality characteristic to be monitored. The proposed study has introduced a Hotelling T2 control chart using principal component analysis to monitor CoDa explicitly. The proposed method overcomes the limitations of previous approaches by utilizing isometric log-ratio transformation to map the CoDa into unrestricted real space. A Monte Carlo simulation procedure is employed to evaluate the performance of the proposed multivariate control chart. The simulation involves varying combinations of the number of variables (p) and subgroup sizes to obtain metrics such as the average run length when the process is out-of-control (OOC) and the upper control limit. The article presents a comprehensive analysis of the proposed control chart’s statistical performance. These visual aids facilitate a deeper understanding of the method’s effectiveness in detecting OOC conditions in CoDa. Furthermore, two illustrative examples using synchronous machines and components of electronic scrap are provided to illustrate the practical implementation and effectiveness of the Hotelling T2-based principal component control chart for p=5-part and p=12-part CoDa in real-world scenarios.
A comparative analysis of super-resolution techniques for enhancing micro-CT images of carbonate rocks

High-resolution digital rock micro-CT images captured from a wide field of view are essential for various geosystem engineering and geoscience applications. However, the resolution of these images is often constrained by the capabilities of scanners. To overcome this limitation and achieve superior image quality, advanced deep learning techniques have been used. This study compares four different super-resolution techniques, including super-resolution convolutional neural network (SRCNN), efficient sub-pixel convolutional neural networks (ESPCN), enhanced deep residual neural networks (EDRN), and super-resolution generative adversarial networks (SRGAN) to enhance the resolution of micro-CT images obtained from heterogeneous porous media. Our investigation employs a dataset consisting of 5,000 micro-CT images acquired from a highly heterogeneous carbonate rock. The performance of each algorithm is evaluated based on its accuracy to reconstruct the pore geometry and connectivity, grain-pore edge sharpness, and preservation of petrophysical properties, such as porosity. Our findings indicate that EDRN outperforms other techniques in terms of the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index, increased by nearly 4 dB and 17%, respectively, compared to bicubic interpolation. Furthermore, SRGAN exhibits superior performance compared to other technqiues in terms of the learned perceptual image patch similarity (LPIPS) index and porosity preservation error. SRGAN shows a nearly 30% reduction in LPIPS compared to bicubic interpolation. Our results provide deeper insights into the practical applications of these techniques in the domain of porous media characterizations, facilitating the selection of optimal super-resolution CNN-based methodologies.
Morphisms Between Aristotelian Diagrams

Abstract In logical geometry, Aristotelian diagrams are studied in a precise and systematic way. Although there has recently been a good amount of progress in logical geometry, it is still unknown which underlying mathematical framework is best suited for formalizing the study of these diagrams. Hence, in this paper, the main aim is to formulate such a framework, using the powerful language of category theory . We build multiple categories, which all have Aristotelian diagrams as their objects, while having different kinds of morphisms between these diagrams. The categories developed here are assessed according to their ability to generalize previous work from logical geometry as well as their interesting category-theoretical properties. According to these evaluations, the most promising category has as its morphisms those functions on fragments that increase in informativity on both the opposition and implication relations. Focusing on this category can significantly increase the effectiveness of further research in logical geometry.
Effects of Extreme Weather Conditions on PV Systems

We are witnessing significant climatic changes and increasingly frequent extreme weather conditions affecting every part of the globe. In order to reduce and stop these unfavourable climate changes, there has been a shift to the use of renewables, and in this sense, a significant contribution of the photovoltaic (PV) power plant is planned. This paper analyses the safety, reliability, and resilience of PV systems to extreme weather conditions such as wind storms, hail, lightning, high temperatures, fire, and floods. In addition to using available information from the literature, temperature measurements were also carried out on the rooftop PV power plant in Slavonski Brod, as well as a numerical stress analysis at extreme wind speeds using Ansys software. The results of the analysis show that existing PV systems are very resilient to extreme weather conditions. Utility-scale PV systems can usually withstand wind speeds of up to 50 m/s without any problems, and only at higher speeds do local stresses occur in certain parts of the structure that are higher than permissible. Resistance to hail is also very high, and manufacturers guarantee resistance to hail up to 25 mm in size. At high air temperatures, the temperature of the panel frame can reach about 70 °C, the panel temperature up to 85 °C, and the temperature of the cable insulation over 60 °C, as measurements have shown. Such high temperatures lead to a drop in electricity production up to 30% but do not pose a fire hazard to the cables and the roof if the roof insulation is conducted correctly. Forest fires do not usually pose a direct threat to PV systems, but the smoke that spreads over a large area reduces the solar radiation reaching the PV panel. It can also cause an unfavourable “wiggle effect”. Lightning strikes to a PV panel are not common, although they are possible. With built-in safeguards, no major damage should occur. Flooding is always a possibility, but with properly designed drainage systems, the damage is minimal in most cases.