On Adjusting the HP-Filter for the Frequency of Observations

This paper studies how the HP-Filter should be adjusted, when changing the frequency of observations. It complements the results of Baxter and King (1999) with an analytical analysis, demonstrating that the filter parameter should be adjusted by multiplying it with the fourth power of the observations frequency ratios. This yields an HP parameter value of 6.25 for annual data given a value of 1600 for quarterly data. The relevance of the suggestion is illustrated empirically.

On Adjusting the HP-Filter for the Frequency of Observations

This paper studies how the HP-Filter should be adjusted, when changing the frequency of observations. It complements the results of Baxter and King (1999) with an analytical analysis, demonstrating that the filter parameter should be adjusted by multiplying it with the fourth power of the observations frequency ratios. This yields an HP parameter value of 6.25 for annual data given a value of 1600 for quarterly data. The relevance of the suggestion is illustrated empirically

Beyond Node Degree: Evaluating AS Topology Models

This is the accepted version of 'Beyond Node Degree: Evaluating AS Topology Models', archived originally at arXiv:0807.2023v1 [cs.NI] 13 July 2008.Many models have been proposed to generate Internet Autonomous System (AS) topologies, most of which make structural assumptions about the AS graph. In this paper we compare AS topology generation models with several observed AS topologies. In contrast to most previous works, we avoid making assumptions about which topological properties are important to characterize the AS topology. Our analysis shows that, although matching degree-based properties, the existing AS topology generation models fail to capture the complexity of the local interconnection structure between ASs. Furthermore, we use BGP data from multiple vantage points to show that additional measurement locations significantly affect local structure properties, such as clustering and node centrality. Degree-based properties, however, are not notably affected by additional measurements locations. These observations are particularly valid in the core. The shortcomings of AS topology generation models stems from an underestimation of the complexity of the connectivity in the core caused by inappropriate use of BGP data

Sentiment analysis of Arabic social media texts: A machine learning approach to deciphering customer perceptions

entiment analysis (SA) is a subfield of artificial intelligence that entails natural language processing. This has become increasingly significant because it discerns the emotional tone of reviews, categorising them as positive, neutral, or negative. In the highly competitive coffee industry, understanding customer sentiment and perception is paramount for businesses seeking to optimise their product offerings. Traditional methods of market analysis often fall short of capturing the nuanced views of consumers, necessitating a more sophisticated approach to sentiment analysis. This research is motivated by the need for a nuanced understanding of customer sentiments across various coffee products, enabling companies to make informed decisions regarding product promotion, improvement, and discontinuation. However, sentiment analysis faces a challenge when it comes to analysing Arabic text due to the language's extraordinarily complex inflectional and derivational morphology. Consequently, to address this challenge, we have developed a new method designed to improve the precision and effectiveness of Arabic sentiment analysis, specifically focusing on understanding customer opinions about various coffee products on social media platforms like Twitter. We gathered 10,646 various coffee products' Twitter reviews and applied feature extraction techniques using the term frequency-inverse document frequency (TF-IDF) and minimum redundancy maximum relevance (MRMR). Subsequently, we performed sentiment analysis using four supervised learning algorithms: k-nearest neighbor, support vector machine, decision tree, and random forest. All the classification statements derived in the analysis were aggregated via ensemble learning to convey the final results. Our results demonstrated an increase in prediction accuracy, with our method achieving over 95.95% accuracy in the Hard voting and soft voting at 94.51 %

An analysis of customer perception using lexicon-based sentiment analysis of Arabic Texts framework.

Sentiment Analysis (SA) employing Natural Language Processing (NLP) is pivotal in determining the positivity and negativity of customer feedback. Although significant research in SA is focused on English texts, there is a growing demand for SA in other widely spoken languages, such as Arabic. This is predominantly due to the global reach of social media which enables users to express opinions on products in any language and, in turn, necessitates a thorough understanding of customers' perceptions of new products based on social media conversations. However, the current research studies demonstrate inadequacies in furnishing text analysis for comprehending the perceptions of Arabic customers towards coffee and coffee products. Therefore, this study proposes a comprehensive Lexicon-based Sentiment Analysis on Arabic Texts (LSAnArTe) framework applied to social media data, to understand customer perceptions of coffee, a widely consumed product in the Arabic-speaking world. The LSAnArTe Framework incorporates the existing AraSenTi dictionary, an Arabic database of sentiment scores for Arabic words, and lemmatizes unknown words using the Qalasadi open platform. It classifies each word as positive, negative or neutral before conducting sentence-level sentiment classification. Data collected from X (formerly known as Twitter, resulted in a cleaned dataset of 10,769 tweets, is used to validate the proposed framework, which is then compared with Amazon Comprehend. The dataset was annotated manually to ensure maximum accuracy and reliability in validating the proposed LSAnArTe Framework. The results revealed that the proposed LSAnArTe Framework, with an accuracy score of 93.79 %, outperformed the Amazon Comprehend tool, which had an accuracy of 51.90 %

Dynamics of Electron Localization in Warm vs. Cold Water Clusters

The process of electron localization on a cluster of 32 water molecules at 20, 50, and 300~K is unraveled using ab initio molecular dynamics simulations. In warm, liquid clusters, the excess electron relaxes from an initial diffuse and weakly bound structure to an equilibrated, strongly bound species within 1.5 ps. In contrast, on cold, glassy clusters the relaxation processes is not completed and the electron becomes trapped in a metastable surface state with an intermediate binding energy. These results question the validity of extrapolations of the properties of solvated electrons from cold clusters of increasing size to the liquid bulk

Domain wall propagation in Permalloy nanowires with a thickness gradient

The domain wall nucleation and motion processes in Permalloy nanowires with a thickness gradient along the nanowire axis have been studied. Nanowires with widths, w = 250 nm to 3 um and a base thickness of t = 10 nm were fabricated by electron-beam lithography. The magnetization hysteresis loops measured on individual nanowires are compared to corresponding nanowires without a thickness gradient. The Hc vs. t/w curves of wires with and without a thickness gradient are discussed and compared to micromagnetic simulations. We find a metastability regime at values of w, where a transformation from transverse to vortex domain wall type is expected

Assessment of esophageal motility disorders by real-time MRI

Purpose To investigate imaging findings of esophageal motility disorders on dynamic real-time. Material and methods 102 patients with GERD-like symptoms were included in this retrospective study between 2015−2018. Dynamic real-time MRI visualized the transit of a 10 mL pineapple juice bolus through the esophagus and EGJ with a temporal resolution of 40 ms. Dynamic and anatomic parameters were measured by consensus reading. Imaging findings were compared to HRM utilizing the Chicago classification of esophageal motility disorders, v3.0. Results All 102 patients completed real-time MRI in a median examination time of 15 min. On HRM, 14 patients presented with disorders with EGJ outlet obstruction (EGJOO) (13.7 %), 7 patients with major disorders of peristalsis (6.9 %), and 32 patients with minor disorders of peristalsis (31.4 %). HRM was normal in 49 patients (48.0 %). Incomplete bolus clearance was significantly more frequent in patients with esophageal motility disorders on HRM than in patients with normal HRM (p = 0.0002). In patients with motility disorders with EGJOO and major disorders of peristalsis, the esophageal diameter tended to be wider (23.6 ± 8.0 vs. 21.2 ± 3.5 mm, p = 0.089) and the sphincter length longer (19.7 ± 7.3 vs. 16.7 ± 3.0 mm, p = 0.091) compared to patients with normal HRM. 3/7 patients with achalasia type II were correctly identified by real-time MRI and one further achalasia type II patient was diagnosed with a motility disorder on MRI films. The other 3/7 patients presented no specific imaging features. Conclusion Real-time MRI is an auxiliary diagnostic tool for the assessment of swallowing events. Imaging parameters may assist in the detection of esophageal motility disorders

Assessment of esophagogastric junction morphology by dynamic real-time MRI: comparison of imaging features to high-resolution manometry

Purpose To assess the esophagogastric junction (EGJ) on real-time MRI and compare imaging parameters to EGJ morphol- ogy on high-resolution manometry (HRM). Methods A total of 105 of 117 eligible patients who underwent real-time MRI and high-resolution manometry for GERD- like symptoms between 2015 and 2018 at a single center were retrospectively evaluated (male n = 57; female n = 48; mean age 52.5 ± 15.4 years). Real-time MRI was performed at a median investigation time of 15 min (1 frame/40 ms). On HRM, EGJ morphology was assessed according to the Chicago classification of esophageal motility disorders. Real-time MRI was performed at 3 T using highly undersampled radial fast low-angle shot acquisitions with NLINV image reconstruction. A 10 mL pineapple juice bolus served as oral contrast agent at supine position. Real-time MRI films of the EGJ were acquired during swallowing events and during Valsalva maneuver. Anatomic and functional MRI parameters were compared to EGJ morphology on HRM. Results On HRM, n = 42 patients presented with EGJ type I (40.0%), n= 33 with EGJ type II (31.4%), and n= 30 with EGJ type III (28.6%). On real-time MRI, hiatal hernia was more common in patients with EGJ type III (66.7%) than in patients with EGJ type I (26.2%) and EGJ type II (30.3%; p < 0.001). Sliding hiatal hernia was more frequent in patients with EGJ type II (33.3%) than in patients with EGJ type III (16.7%) and EGJ type I (7.1%; p = 0.017). The mean esophagus–fundus angle of patients was 85 ± 31° at rest and increased to 101 ± 36° during Valsalva maneuver. Conclusion Real-time MRI is a non-invasive imaging method for assessment of the esophagogastric junction. Real-time MRI can visualize dynamic changes of the EGJ during swallowing events