Miracle-dz/UniFAR-FTUs huggingface dataset:
Continual Learning with Deep Neural Networks in Physiological Signal Data: A Survey

Deep-learning algorithms hold promise in processing physiological signal data, including electrocardiograms (ECGs) and electroencephalograms (EEGs). However, healthcare often requires long-term monitoring, posing a challenge to traditional deep-learning models. These models are generally trained once and then deployed, which limits their ability to adapt to the dynamic and evolving nature of healthcare scenarios. Continual learning—known for its adaptive learning capabilities over time—offers a promising solution to these challenges. However, there remains an absence of consolidated literature, which reviews the techniques, applications, and challenges of continual learning specific to physiological signal analysis, as well as its future directions. Bridging this gap, our review seeks to provide an overview of the prevailing techniques and their implications for smart healthcare. We delineate the evolution from traditional approaches to the paradigms of continual learning. We aim to offer insights into the challenges faced and outline potential paths forward. Our discussion emphasizes the need for benchmarks, adaptability, computational efficiency, and user-centric design in the development of future healthcare systems.
Impact of mussel seed fishery on subtidal macrozoobenthos in the western Wadden Sea

Within the framework of PRODUS, the effect of mussel fishery on the macrofauna species sampled with a box-corer was investigated following a so-called split-plot design. Within areas where mussel seed fishery was allowed, 40 plots/locations were chosen within which one part was open for commercial fisheries and one part was closed of fisheries. Box-corers were taken in 21 of these locations, sampled one to several times.
Comparative analyses of complete chloroplast genomes reveal interspecific difference and intraspecific variation of Tripterygium genus

The genus Tripterygium was of great medicinal value and attracted much attention on the taxonomic study using morphological and molecular methods. In this study, we assembled 12 chloroplast genomes of Tripterygium to reveal interspecific difference and intraspecific variation. The sequence length (156,692–157,061 bp) and structure of Tripterygium were conserved. Comparative analyses presented abundant variable regions for further study. Meanwhile, we determined the ndhB gene under positive selection through adaptive evolution analysis. And the phylogenetic analyses based on 15 chloroplast genomes supported the monophyly of Tripterygium hypoglaucum and the potential sister relationship between Tripterygium wilfordii and Tripterygium regelii. Molecular dating analysis indicated that the divergence time within Tripterygium was approximately 5.99 Ma (95% HPD = 3.11–8.68 Ma). The results in our study provided new insights into the taxonomy, evolution process, and phylogenetic construction of Tripterygium using complete plastid genomes.
Wilderness Visitors, Experiences, and Management Preferences: How They Vary With Use Level and Length of Stay

We explore the extent to which visitor experiences and management preferences vary between the most heavily used places in wilderness and places that are less popular. We also contrast day and overnight users. The study was conducted in Forest Service administered wildernesses in Oregon and Washington using both on-site and mailback questionnaires. The on-site questionnaires were administered as visitors exited the wilderness at 36 trailheads in 13 wildernesses. The trail use ranged from very high to moderate. To include visitors who selected low use trails, we sent mailback questionnaires to self-issue permit holders. We describe visitor characteristics, trip characteristics, motivations and experiences, encounters with other groups, attitudes toward recreation management, and opinions about the Forest Service. Differences related to use level were surprisingly small. Differences between day and overnight users were also small. We found evidence that wilderness experiences were adversely affected at high use locations but most visitors consider these effects to be of little importance. Most visitors to the more popular places make psychological adjustments to heavy use, allowing most of them to find solitude and have what they consider “a real wilderness experience. ” Consequently, most are not supportive of use limits to avoid peoplerelated problems. We draw conclusions about potential indicators, standards, and management actions for heavily-used places in wilderness.
Assimilação de Dados de Vazão na Previsão de Cheias em Tempo-Real com o Modelo Hidrológico MGB-IPH

This paper presents an empirical data assimilation method applied to a real-time flood forecasting hydrological model based on the MGB-IPH hydrological model. A medium-size basin located in Southeastern Brazil was selected as a case study, primarily because of the availability of hourly hydrologic information. Streamflow forecasts were calculated with lead times up to 48 hours, at hourly intervals. Several forecast scenarios were simulated with the MGB-IPH model: (1) forecasting with data assimilation and "perfect" precipitation forecasts (in which observed precipitation was used as a forecast of precipitation); (2) forecasting with data assimilation and zero precipitation forecasts; and (3) forecasting without data assimilation and "perfect" precipitation forecasts. Forecasts from these three scenarios were compared to observed streamflows and to forecasts from a naive model which assumes that the last recorded streamflow will be held constant up to the end of the forecast horizon. Several performance measures such as the Nash—Sutcliffe efficiency coefficient, the mean absolute error and the mean relative error were used to assess the relative performance of the models. Results show that the data assimilation method has positive impacts on real-time flood forecasting, increasing the forecast accuracy for all lead-times
SMES Response Strategies and Performance In Covid-19 Pandemics: Implications For Trade And Industry In Computer Village, Lagos, Nigeria

The global outbreak of COVID-19, commonly referred to as the coronavirus, has profoundly influenced enterprise operations and consumer behaviors. The pandemic's rapid spread has resulted in immediate socioeconomic repercussions for both industrialized and developing nations. This research explores the impact of the COVID-19 pandemic on enterprise performance within the portable electronic devices sector, focusing on businesses in Computer Village at Ikeja, Lagos. The resource-based theory, behavioral and psychological theory
Flavor Characterization and Imitation Flavor Identification Using Gas Chromatography-Orbitrap Mass Spectrometry Fingerprinting

Gao et al. : Gas Chromatography-Orbitrap Mass Spectrometry Fingerprinting Flavor is commonly used as a food additive to impart a distinctive taste. Minor differences in the chemical profile of imitation flavors may severely affect the final food quality. Thus, similarity evaluation of flavor compounds is key to maintaining quality control in the food industry. In this study, a similarity evaluation method was developed, which incorporates non-targeted gas chromatography-Orbitrap for high-resolution mass-based fingerprinting and multivariate statistical analysis, including the Pearson product-moment correlation coefficient, hierarchical cluster analysis, and principal component analysis. Pearson product-moment correlation coefficient of historical batches greater than 0.8 were integrated to the chromatographic fingerprint, while hierarchical cluster analysis and principal component analysis were used to evaluate the similarities between the reference samples and imitation flavors. Eight batches of samples were tested, and the results of all samples were included in the chromatographic fingerprint. Three imitation samples were successfully identified by hierarchical cluster analysis and principal component analysis. The developed method provides a tool for the accurate evaluation of the quality of imitation flavor compounds in food.
Dynamic Clustering and Scaling Behavior of Active Particles under Confinement

A systematic investigation of the dynamic clustering behavior of active particles under confinement, including the effects of both particle density and active driving force, is presented based on a hybrid coarse-grained molecular dynamics simulation. First, a series of scaling laws are derived with power relationships for the dynamic clustering time as a function of both particle density and active driving force. Notably, the average number of clusters N¯ assembled from active particles in the simulation system exhibits a scaling relationship with clustering time t described by N¯∝t−m. Simultaneously, the scaling behavior of the average cluster size S¯ is characterized by S¯∝tm. Our findings reveal the presence of up to four distinct dynamic regions concerning clustering over time, with transitions contingent upon the particle density within the system. Furthermore, as the active driving force increases, the aggregation behavior also accelerates, while an increase in density of active particles induces alterations in the dynamic procession of the system.
Market Segmentation Based on Materialistic, Conspicuous, and Traditionalist Goals in Support of an Effective Industrial Design

One important line of action in the marketing field is the conception of products attractive to consumers. Industrial design plays a significant role in this action. However, the traditional market segmentation is not very useful from the industrial design point of view. Also, because the traditional criteria for market segmentation are becoming less efficient in general, the experts from the field are looking for new criteria. The research presented here is focused on the relationship between industrial design, consumer profile, and product contribution to materialistic, conspicuous, and traditionalist goals.
Integration of metabolomics and transcriptomics reveals the regulation mechanism of the phenylpropanoid biosynthesis pathway in insect resistance traits in Solanum habrochaites

Abstract Solanum habrochaites (SH), a wild species closely related to ‘Ailsa Craig’ (AC), is an important germplasm resource for modern tomato breeding. Trichomes, developed from epidermal cells, have a role in defense against insect attack, and their secretions are of non-negligible value. Here, we found that the glandular heads of type VI trichomes were clearly distinguishable between AC and SH under cryo-scanning electron microscopy, the difference indicating that SH could secrete more anti-insect metabolites than AC. Pest preference experiments showed that aphids and mites preferred to feed near AC compared with SH. Integration analysis of transcriptomics and metabolomics data revealed that the phenylpropanoid biosynthesis pathway was an important secondary metabolic pathway in plants, and SH secreted larger amounts of phenylpropanoids and flavonoids than AC by upregulating the expression of relevant genes in this pathway, and this may contribute to the greater resistance of SH to phytophagous insects. Notably, virus-induced silencing of Sl4CLL6 not only decreased the expression of genes downstream of the phenylpropanoid biosynthesis pathway (SlHCT, SlCAD, and SlCHI), but also reduced resistance to mites in tomato. These findings provided new genetic resources for the synthesis of phenylpropanoid compounds and anti-insect breeding in S. habrochaites and a new theoretical basis for the improvement of important traits in cultivated tomato.
A study on entrepreneurial behaviour of self-help group members under National Rural Livelihood Mission (NRLM) in Lakhimpur Kheri District of Uttar Pradesh

The study was conducted during the year 2022-23 in Lakhimpur Kheri district of Uttar Pradesh state to measure the entrepreneurial behaviour of Self Help Group Members under National Rural Livelihood Mission. The three components of entrepreneurial behaviour were undertaken to evaluate the status of entrepreneurial behaviour such as, innovativeness, leadership ability and self-confidence. In Lakhimpur Kheri district, Nighasan and Pasgawan block was purposefully selected for the present study, since it is dominated with the maximum self-help group members were working under National Rural Livelihood Mission in areas. A sample of 100 Respondents was selected by using proportionate random sampling technique with personnel interview from two selected gram panchayat and 10 SHGs from each gram panchayat. Total 20 SHGs (each block total fifty respondents) were selected randomly. The results showed that majority of SHG members (57.00 percent) had a medium level of innovativeness, majority (55.00 percent) of SHG members had a medium level of leadership ability, large proportion (72.00 percent) of SHG members had a medium level of self-confidence and 64.00 percent of SHG members belonged to medium level of overall entrepreneurial behaviour. The study also revealed that variables i.e., age, caste, education, occupation, land holding and social participation was found highly significant and positively correlated with entrepreneurial behaviour of SHG members.
Multimodel ensemble-based Pneumonia x-ray image classification

Pneumonia is a life-threatening respiratory infection that affects millions of individuals worldwide. Early and accurate diagnosis of pneumonia is crucial for effective treatment and patient care. In recent years, deep learning techniques have shown remarkable promise in automating the diagnosis of pneumonia from X-ray images. However, the inherent variability in X-ray images and the complexity of pneumonia patterns pose significant challenges to achieving high classification accuracy. In this paper, we propose a novel approach for pneumonia X-ray image classification based on multiple model ensemble. Our method leverages the strengths of diverse deep learning architectures and achieves superior classification performance compared to single models. We conducted extensive experiments on both public and private datasets, and the proposed method achieved accuracy improvements of 7.53 and 3.36, respectively. The experimental results indicate that the proposed method has high usability.
Carbon Dioxide Uptake Estimation for Spanish Cement-Based Materials

The Intergovernmental Panel on Climate Change (IPCC), which is the United Nations body for assessing the science related to climate change, has recently recognized the natural carbonation process as a way of carbon offsetting with mortar and concrete. Accordingly, this activity could be recognized as a carbon removal process for which certification should be granted. The aim of the certification of carbon removal is to promote the development of adequate and efficient new carbon removal processes. Therefore, the main objective of this study is to provide reliable results on carbon dioxide uptake by cement-based materials in Spain. Yearly, greenhouse gas emissions are reported to the United Nations Framework Convention on Climate Change (UNFCCC) by each country, and the natural carbonation should be added up to the carbon accounting. Therefore, natural carbonation should be included in the IPCC Guidelines for National Greenhouse Gas Inventories, and such accounting information should be made available promptly to the national regulatory authorities. This paper provides the results of carbon dioxide uptake by Spanish cement-based materials from 1990 to 2020 by using an easy method of estimating the net carbon dioxide emissions (simplified method) considering the carbon dioxide released by the calcination during clinker production (process emissions). The outcome of this study reveals that there was 93,556,000 tons of carbon dioxide uptake by the mortar and concrete manufactured in Spain from 1990 to 2020.
THE NEXUS BETWEEN FOREIGN DIRECT INVESTMENT AND NOMINAL EXCHANGE RATE, REAL GDP, AND CAPITAL STOCK IN TANZANIA

Aim: The paper aims to examine the relationship between FDI and the nominal exchange rate, real GDP, and capital stock in Tanzania using quantitative research methods and an econometric analysis. The analysis aims to provide insights into the factors that affect FDI and contribute to the existing literature on the relationship between FDI and economic growth. Methods: This study examines the relationship between FDI inflow, real GDP, capital stock, and the normal exchange rate in Tanzania using a robust research methodology. The study employs STATA 15 software and Akaike’s Information Criteria (AIC), Schwarz Information Criteria (SC), Final Prediction Error (FPE), and the Hannan Quinn (HQ) Information Criteria. In addition, the autoregressive model, the Johansen co-integration test, and the Toda-Yamamoto Granger causality (modified WALD) test were employed to determine the optimal lag. Results: The results indicate a bidirectional relationship between the nominal exchange rate and FDI in Tanzania, with FDI inflows influencing the nominal exchange rate volatility and vice versa. Furthermore, the results indicate that real GDP, capital stock, and the nominal exchange rate exert a unidirectional influence on FDI influx in Tanzania. Conclusions: The nominal exchange rate and capital stock have positive and negative correlations with foreign direct investment. Like many other African economies, Tanzania remains vulnerable to external forces despite making significant strides in stabilizing the exchange rate. It is recommended that the Central Bank of Tanzania – along with those of other African nations with similar economic structures – maintain a stable nominal exchange rate level as an incentive for foreign investors in order to increase the inflow of foreign direct investment.
Diabetes Diagnosis from Big Data using Fuzzy-Neural Chaotic Tree

—Today, diabetes is considered an important disease in the world. The statistics show that this disease is developing worldwide. For this reason, smart and automated systems are considered as a challenge in the medical world. The methods available in computer science meet this need. To date, various methods have been proposed to diagnose and predict diabetes, but there are some errors that researchers are seeking to resolve. Data mining is used as a technical science in identifying and extracting new knowledge of data. In this research, a new method has been developed for categorizing diabetic data, which consists of three parts: the first part is the preprocessing, in which data normalization operations are performed, then the extraction and selection of attributes. Finally, classification operations is done based on the principles of data mining. The classification results can be used to predict diabetes in different individuals. The use of criteria such as sensitivity, specificity, accuracy is used to evaluate the results. The proposed approach is based on the combination of fuzzy-neural in chaotic mode along with K-means tree. The results indicate that the proposed method is more suitable than the previous methods.
Internal auditors’ artificial intelligence capabilities and sustainable competitive advantage

The aim of this research is to provide empirical evidence regarding the influence of internal auditor capabilities in artificial intelligence on sustainable competitive advantage in large companies in Indonesia. By using Porter’s (1985) competitive advantage model and the resource-based view of the firm, this research uses the basic assumption of thinking that a company can only achieve sustainable high performance if the company has “superior resources” and also has “capabilities” that can drive the company to improve its performance. This research finds empirical evidence that internal auditors’ ability to work with artificial intelligence can increase sustainable industrial competitive advantage. This finding also succeeded in confirming the theory of competitive advantage introduced by Porter (1985) which explains that a company’s human resource capabilities can improve the company’s performance higher than other companies in the same industry or market. This research contributes that as an assurance of corporate governance, accountants must improve their ability to work using artificial intelligence to improve their functions beyond just preparing financial reports.
Outcome of High-Dose Chemotherapy Followed by Autologous Stem Cell Transplantation in Relapsed/Refractory Hodgkin Lymphoma after Different Numbers of Salvage Regimens

The introduction of novel drugs (PD-1 inhibitors and/or brentuximab vedotin) into salvage regimens has improved the response rate and the outcome of patients with relapsed/refractory Hodgkin lymphoma. However, the impact of new drugs on the outcome has not been adequately investigated so far. We retrospectively analyzed 42 consecutive patients treated at our institution with high-dose chemotherapy/autologous stem cell transplantation after either one standard chemotherapy represented by BEGEV (n = 28) or >1 salvage therapy (ST) comprising novel drugs (n = 14). With a median follow-up of 24 months, the 2-year cumulative incidence of relapse was similar between the two cohorts: 26% for 1 ST and 18% for >1 ST (p = 0.822). Consistently, overall survival and progression-free survival did not differ among the two groups: 3-year overall survival was 91% and 89% (p = 0.731), respectively, and 3-year progression-free survival was 74% and 83% (p = 0.822) for only one and more than one salvage regimens, respectively. Of note, the post-transplant side effects and engraftment rates were similar between the 1 ST and >1 ST cohorts. In conclusion, consolidation with high-dose chemotherapy/autologous stem cell transplantation is a safe and curative option, even for patients achieving disease response after more than one rescue line of therapy.