Manifold dynamics and periodic orbits in a multiwell potential

In this article, we explore the dynamics as well as the geometry of the invariant manifolds that determine the escapes from a multiwell potential. We also present the network of both symmetric and asymmetric solutions of the system, while at the same time we extract valuable information about the periodic solutions, such as their locations, multiplicity, and linear stability.The present research work was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number PNURSP2022R106, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Penis auto-amputation and chasm of the lower abdominal wall due to advanced penile carcinoma: a case report

<p>Abstract</p> <p>Introduction</p> <p>Penile cancer is uncommon. When penile cancer is left untreated, at an advanced stage it can have tragic consequences for the patient.</p> <p>Case presentation</p> <p>Our case report does not concern a new manifestation of penile cancer, but an interesting presentation with clinical significance that emphasizes the need to diagnose and treat penile cancer early. It is an unusual case of a neglected penile cancer in a 57-year-old Greek man that led to auto-amputation of the penis and a large chasm in the lower abdominal wall. The clinical staging was T4N3M0 and our patient was treated with a bilateral cutaneous ureterostomy, chemotherapy and radiotherapy. Our patient died 18 months after his first admission in our clinic.</p> <p>Conclusions</p> <p>Emphasis must be placed on early diagnosis and treatment of penile cancer, so further development of the disease can be prevented.</p

Employing a dichotomous choice model to assess Willingness to Pay (WTP) for organically produced products

This study employs a dichotomous choice model to identify the socio-economic characteristics and attitudes that influence consumers' willingness to pay (WTP) a premium for organically produced products (OPP). Considering consumers' growing interest in quality and safety of food, the study attempts to identify consumers level of awareness on OPP, the intention to buy OPP and the WTP a higher price for OPP. Results highlight consumers awareness and intention, revealing useful information for the development of OPP market. The estimated maximum WTP indicates that consumers are willing to pay a substantial extra price to purchase OPP. Finally, WTP seems to be affected by certain consumer attitudes and socio-economic factors. © 2006 by The Haworth Press, Inc. All rights reserved

Effect of pre-treatment and smoking process (cold and hot) on chemical, microbiological and sensory quality of mackerel(Scomber scombrus)

The sensory, hygienic, toxicological and nutritional profiles of hot- and cold-smoked mackerel samples were studied with various pre-treatments. The panellists assessed all smoked samples as barely to quite acceptable products whilst the product immersed in 120 g kg−1 sodium chloride and 60 g kg−1 fructose prior to smoking was assessed as very acceptable regarding its sensory characteristics. The available lysine in all hot smoked samples was reduced to the same extent (32%) whilst a very good correlation (r = 0.912) was observed between loss of available lysine and colour formation of the cold-smoked products, indicating the high contribution of lysine in the interactions with carbonyls. Histamine was found in highly unacceptable levels even in the unprocessed samples (600 mg kg−1) and strongly increased (2220 and 2250 mg kg−1) in the cold- and hot-smoked samples, respectively, due to all treatments. These are levels which would be expected to cause symptoms of scombrotoxin poisoning. Benzo(a)pyrene, fluoranthene and perylene were at high levels both in cold- (2.1, 4.3 ± 0.04 and 7.2 ± 0.05 µg kg−1) and hot-smoked samples (9.2, 7.8 ± 0.03 and 9.4 ± 0.14 µg kg−1, respectively) and were, as expected, influenced by the temperature. The aerobic bacteria remained at acceptable levels, since salt and high temperature prevent bacterial growth. Copyright © 2004 Society of Chemical Industr

Chapter A WiMAX Network Architecture Based on Multi-Hop Relays

Communications engineering / telecommunication

Chapter A WiMAX Network Architecture Based on Multi-Hop Relays

Communications engineering / telecommunication

Deep Learning Approaches for Big Data-Driven Metadata Extraction in Online Job Postings

This article presents a study on the multi-class classification of job postings using machine learning algorithms. With the growth of online job platforms, there has been an influx of labor market data. Machine learning, particularly NLP, is increasingly used to analyze and classify job postings. However, the effectiveness of these algorithms largely hinges on the quality and volume of the training data. In our study, we propose a multi-class classification methodology for job postings, drawing on AI models such as text-davinci-003 and the quantized versions of Falcon 7b (Falcon), Wizardlm 7B (Wizardlm), and Vicuna 7B (Vicuna) to generate synthetic datasets. These synthetic data are employed in two use-case scenarios: (a) exclusively as training datasets composed of synthetic job postings (situations where no real data is available) and (b) as an augmentation method to bolster underrepresented job title categories. To evaluate our proposed method, we relied on two well-established approaches: the feedforward neural network (FFNN) and the BERT model. Both the use cases and training methods were assessed against a genuine job posting dataset to gauge classification accuracy. Our experiments substantiated the benefits of using synthetic data to enhance job posting classification. In the first scenario, the models’ performance matched, and occasionally exceeded, that of the real data. In the second scenario, the augmented classes consistently outperformed in most instances. This research confirms that AI-generated datasets can enhance the efficacy of NLP algorithms, especially in the domain of multi-class classification job postings. While data augmentation can boost model generalization, its impact varies. It is especially beneficial for simpler models like FNN. BERT, due to its context-aware architecture, also benefits from augmentation but sees limited improvement. Selecting the right type and amount of augmentation is essential