Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal lake‐marsh pattern determination in lake‐marsh systems based on the eco‐hydrological processes management

The major hydrological factors in lake‐marsh systems are water level (depth), water surface area, and water volume. The key index for determining the lake‐marsh pattern is water level, which leads to the variation of lake‐marsh patterns under natural hydrological alternations. In addition, the vegetation structure also affects the lake‐marsh pattern. With socioeconomic development and climate change, the ‘land use’ and ‘water use’ competitions appear more seriously between a lake‐marsh system and its surrounding socioeconomic system, also inside the lake‐marsh system. The possible optimal lake‐marsh pattern could solve the contradictions mentioned above. As few studies focus on this issue, this study proposed an optimal lake‐marsh pattern determination method with eco‐hydrological management on relieving the land use and water use competitions. The optimal lake‐marsh pattern determination method considers the protection objects (water depth demands), water supply (precipitation, surface water, and groundwater), and water demand (especially evapotranspiration) in the system at annual and monthly scales. Calculation and analysis were performed for the optimal pattern of the Wolonghu Wetlands as an example. The results mainly showed that the lake‐marsh pattern of the Wolonghu Wetlands cannot be achieved on meeting both the maximum ecological services values and minimum water shortage amount under present natural condition. With artificial regulation measures, the possible optimal annual lake‐marsh patterns can be obtained based on both ecological and hydrological objectives, with the area ratio of lake and marsh in the range of 0.650:0.350 and 0.726:0.274, the corresponding water level of lake body was of 86.85 to 87.0 m. This study could provide references for the Wolonghu Wetlands management, also for similar lake‐marsh systems and other ecological systems.
20 years of Nordic event and festival research: a review and future research agenda

ABSTRACT The research output during the last two decades suggests that events and festivals are of major importance for society both internationally and in a Nordic context. Existing literature, primarily published in a Nordic context, is reviewed and organized according to three broad areas: The event and festival consumer, the event or festival as organization and the effects and interrelationships of events and festivals with society. We discuss the contribution which Nordic research has made to the Nordic School and to international event and festival research and suggest a future research agenda focusing on methods, context and theories.
Breeding wheat for drought adaptation: Development of selection tools for root architectural traits

i Declaration by author . iii Publications during candidature . iv Publications included in this thesis . iv Contributions by others to the thesis . v Statement of parts of the thesis submitted to qualify for the award of another degree . v Acknowledgements . vi
IoT Network Security: Threats, Risks, and a Data-Driven Defense Framework

The recent surge in Internet of Things (IoT) deployment has increased the pace of integration and extended the reach of the Internet from computers, tablets and phones to a myriad of devices in our physical world. Driven by the IoT, with each passing day, the Internet becomes more integrated with everyday life. While IoT devices provide endless new capabilities and make life more convenient, they also vastly increase the opportunity for nefarious individuals, criminal organizations and even state actors to spy on, and interfere with, unsuspecting users of IoT systems. As this looming crisis continues to grow, calls for data science approaches to address these problems have increased, and current research shows that predictive models trained with machine learning algorithms hold great potential to mitigate some of these issues. In this paper, we first carry out an analytics approach to review security risks associated with IoT systems, and then propose a machine learning-based solution to characterize and detect IoT attacks. We use a real-world IoT system with secured gate access as a platform, and introduce the IoT system in detail, including features to capture security threats/attacks to the system. By using data collected from a nine month period as our testbed, we evaluate the efficacy of predictive models trained by means of machine learning, and propose design principles and a loose framework for implementing secure IoT systems.
Health Problems for Diabetics Disease in Urban Bangladesh

Diabetes mellitus is a group of metabolic disorders characterized by chronic hyperglycemia with disturbance of Carbohydrate, fat and protein metabolism resulting from defects in insulin secretion, insulin action or both. The chronic hyperglycemia of diabetes is associated with long-term damage, dysfunction and failure of various organs, specially the eyes, kidneys, nerves, heart and blood vessels. Several pathogenic processes are involved in the development of diabetes. These range from autoimmune destruction of the beta cells of the pancreas with consequent insulin deficiency to abnormalities that result in resistance to insulin action. The basis of the abnormalities in carbohydrate, fat and protein metabolism in diabetes is deficient action of insulin on target tissues. Deficient insulin action results from inadequate insulin secretion and /or diminished tissue response to insulin at one or more points in the complex pathways of hormone action. Impairment of insulin secretion and defects in insulin action frequently coexist in the same patient, and it is often unclear which abnormality, if either alone or both is the primary cause of the hyperglycemia. Diabetes mellitus may present with characteristics symptoms such as polyuria, polydipsia, weight loss with sometimes polyphagia, and blurred vision. Impairment of growth and susceptibility to certain infections may also accompany with chronic state of hyperglycemia. Acute life-treating consequences of diabetes are hyperglycemia with ketoacidosos or non-ketotic hyperosmoler syndrome. Longterm complications of diabetes include retinopathy with potential loss of vision; nephropathy leading to renal failure; peripheral neuropathy with risk of foot ulcers, amputation and Charcot joints; and autonomic neuropathy causing gastrointestinal, genitourinary, and sexual dysfunction. People with diabetes are also greatly increased risk of cardiovascular disease.
A History of the Credit Market in Central Europe

of editions. Attending to visual cues, Erika Boeckeler discusses widows who used printers’ devices to affirm their role in book production, while Martine van Elk compares the business strategies and self-presentation of stationers in the Dutch Republic and in England. In one of her footnotes, Boeckeler notes that scholars need more reference tools pertaining to female stationers; this is true, and both Farmer and Van Elk provide valuable lists as appendixes. The second section turns to women who labored in making texts as authors and editors. This is familiar terrain for scholars trained in literature departments, but the concept of the author/editor as a maker is enriched as we are prompted to consider literary labor in light of the other kinds of labor involved in book production. Kirk Melnikoff highlights the relations between literary creation and the bookshop as he studies the resonances between Isabella Whitney’s poems and other works sold by her printer and publisher. Sarah Wall-Randell addresses the sexist biases in authorship studies and asks whether it is reasonable to consider the Countess of Pembroke as the coauthor of the 1593 Arcadia, and Molly Yarn draws us into the nineteenth century through a discussion of Katherine Lee Bates, a poet and editor of student editions of Shakespeare’s plays. The third section focuses on women whose labor involved reading, collecting, and marking books. Elizabeth Kolkovich argues that reading, writing, and patronage were all ways that elite women could make books, while Georgianna Ziegler and Lori Newcomb offer ways of detecting meaningful patterns within large sets of annotations and point out that women’s reading was personal and social. The collection is remarkably cohesive, and the reader benefits from the fact that core ideas (from Rebecca Olson, Maureen Bell, and Helen Smith) are discussed in several essays. The essays are of very high quality and the book is well illustrated. It will be useful to a wide range of scholars interested in the history of the book.
Enteral Nutrition In 6 Malaysian Intensive Care Units: A Point Prevalence Study of Prescription Practices

INTRODUCTION: Enteral nutrition (EN) is the first line of nutrition therapy for critically ill patients with an intact gastrointestinal tract. Even though intensive care units (ICUs) generally have established feeding protocols, prescribing practices to achieve nutrition goal is still widely variable among intensive care practitioners. The aim of this study was mainly to investigate commonly prescribed types and energy concentration in EN formulations including protein prescription. MATERIALS AND METHODS: This was an observational, point prevalence study, involving six level 3 ICUs in Malaysia. All patients aged type of EN formulation and protein prescription recorded. RESULTS: A total of 109 patients were included in the analysis out of which 78% of them were mechanically ventilated. At the point of the study, more than 16 years of age in the participating ICUs on the 11th of October 2016 were enrolled in this study and 71.5% (78/109) received EN. Among the EN group, 68% (53/78) received standard formulation feeds (1kcal/ml) while the rest received energy dense formulation (>1kcal/ml). Fluid restriction was the main indication for energy dense formulation prescription, occurring in almost all (24/25) of the patients. There was no report of feeding intolerance. Only 2/109 received parenteral nutrition (PN) giving the EN to PN ratio of 30:1. The mean protein prescription was 0.9g/kg (SD± 0.4). CONCLUSION: Prescription of EN was 30 times more frequent than PN in these ICUs. In those receiving EN, standard formulation feed was 3 times more commonly prescribed than energy dense feeds with fluid restriction being the main indication of the latter. Protein prescription was less than the guideline recommendation.
Improving Computer-Aided Cervical Cells Classification Using Transfer Learning Based Snapshot Ensemble

Cervical cells classification is a crucial component of computer-aided cervical cancer detection. Fine-grained classification is of great clinical importance when guiding clinical decisions on the diagnoses and treatment, which remains very challenging. Recently, convolutional neural networks (CNN) provide a novel way to classify cervical cells by using automatically learned features. Although the ensemble of CNN models can increase model diversity and potentially boost the classification accuracy, it is a multi-step process, as several CNN models need to be trained respectively and then be selected for ensemble. On the other hand, due to the small training samples, the advantages of powerful CNN models may not be effectively leveraged. In order to address such a challenging issue, this paper proposes a transfer learning based snapshot ensemble (TLSE) method by integrating snapshot ensemble learning with transfer learning in a unified and coordinated way. Snapshot ensemble provides ensemble benefits within a single model training procedure, while transfer learning focuses on the small sample problem in cervical cells classification. Furthermore, a new training strategy is proposed for guaranteeing the combination. The TLSE method is evaluated on a pap-smear dataset called Herlev dataset and is proved to have some superiorities over the exiting methods. It demonstrates that TLSE can improve the accuracy in an ensemble manner with only one single training process for the small sample in fine-grained cervical cells classification.
Photoionization of Xe 5s: angular distribution and Wigner time delay in the vicinity of the second Cooper minimum

The angular distribution and photoionization Wigner time delay of Xe 5s photoelectrons are studied in the region of the second Cooper minimum (SCM) using (i) the relativistic multiconfiguration Tamm–Dancoff approximation, (ii) the relativistic-random-phase approximation (RRPA) and (iii) the RRPA-with-relaxation to demonstrate how differing treatments of correlation, and the relativistic interactions, affect the results. The results of the three methods are compared with each other and with available experimental data. The comparison reveals the importance of electron correlations for which a multiconfiguration description of the initial state is essential. The spin-resolved and spin-averaged photoionization time delay results show important signatures in the region of the SCM in the Xe 5s photoionization cross-section.
Effect of Cadmium on Oxidative Enzymes Activity in Persian Clover (Trifolium resupinatum L.)

Heavy metals are among soil pollutant resources that in case of accumulation in the soil and absorption by the plant, enter into the food chain and poison the plants or the people who consume those plants. This research was performed in order to examine the role of cadmium as a heavy metal in the activity of catalase and peroxidase as well as protein concentration in Trifolium resupinatum L. based on a randomized block design with three repetitions. The used treatments included consumption of Cd (NO3)2at four levels, namely, 0, 100, 200, and 300 ppm. The plants under study were treated for 10 days. The results of the study showed that catalase activity decreased by the increase of cadmium. Moreover, peroxidase activity increased by an increase inthe consumption of cadmium. The analysis of protein level showed that plantlet protein decreased in high cadmium concentrations. The findings also demonstrated that cadmium concentration in roots was higher than in shoots.
Differential Equation Model of the LLC Resonant Converter in the Ideal Case

The LLC resonant converter has been widely used in direct current (DC) power supply applications. However, fundamental harmonic approximation or other simplified analyses will introduce inevitable deviations. Therefore, the differential equation model with numerical solution of the LLC converter is proposed in this paper based on the operational principle of the ideal case. In order to solve the differential equation, initial values need to be substituted. The accurate LLC model based on time interval analysis is very complicated and cannot be used. In this paper, solution methods of the initial values corresponding to different switching frequencies are proposed. The initial values can be solved conveniently. Furthermore, the voltage gain curve is modified by the idealized analysis. Lastly, all the above research is verified by PSIM simulation. The work is helpful to understand the operational principle of the LLC resonant converter.
Boosting learning by changing the order and timing of classroom tasks: implications for professional practice

ABSTRACT The timing of what occurs in the classroom can affect how successfully new concepts are learned. This paper analyses two promising ways of modifying the schedule of tasks or examples – the spacing effect and interleaving. The spacing effect refers to improvements in long-term retention if practice sessions are separated by delays. Interleaving refers to the benefits of mixing different types of examples or problems within a series, such that contrasting items appear side by side or consecutively. These techniques are supported by a body of evidence which suggests that they can boost the durability and transferability of learning, and both are straightforward to implement given that they only require manipulating the schedule of learning experiences. However, research into metacognition suggests that classroom adoption of the techniques is likely to be undermined by persistent misconceptions about how learning and memory work, and that while teacher engagement with research evidence could help to promote the techniques, their use is context-specific. As discussed in the paper, this issue implies a need for focused and contextualised professional learning, which could be enhanced via the application of the same techniques to the professional learning activities carried out by new and in-service teachers.
A comparative study of metabolic effects of the corpus allatum in two adult Coleoptera, in relation to diapause

Two problems were studied: a) Whether a lower titre of the corpus allatum hormone had a different influence on the tissues of an adult insect with a diapause mechanism than on those of one without a diapause. b) Whether the metabolic effects of the corpus allatum hormone were caused mainly by its indirect influence upon an organ, e.g. the ovary, or by general influence on the metabolism as a whole. For the research two adult Coleoptera were used: the Colorado beetle (Leptinotarsa decemlineata Say) with a diapause mechanism and the yellow mealworm beetle (Tenebrio molitor L.) without such a mechanism. By tracing the influences of allatectomy or of castration on the respiration it was concluded that in both species the hormone had a gonadotropic influence in females and in♂and♀Colorado beetles also a general influence on the metabolism (direct on the basal resting respiration of active insects and probably indirect on their respiration by forced action through the central nerve system). Further hypotheses were drafted about the location of the diapause mechanism (the flight muscles and the fat body? ) and the function of the corpus allatum in the fat metabolism (probably mainly to speed the making of fatty acids unsaturated).
Structural and nanomechanical properties of porous silicon: Cheap substrate for CMOS process industry

The surface topology of porous silicon (PSi) is a relevant parameter that decides the compatibility of such substrate with CMOS process. Using standard resistivity (1–10 Ω·cm) of Si substrate to fabricate PSi‐S is a low cost solution for the industry. In this paper, through an atomic force microscopy (AFM) analysis, the root mean square (RMS) roughness, the power spectral density and the correlation length were determined for different PSi layers. Furthermore, the measured hardness, Young's modulus, and stress have been made for different thicknesses of PSi: 5, 10, 50, and 200 μm. The obtained results demonstrated that very interesting properties have been achieved with the 50 μm‐thick PSi‐S layer with a maximum porosity around 65%, a surface roughness less than 1 nm and a hardness value of (~1 GPa). The realized results encourage the utilization the PSi‐based substrate into the industry process and thus the development of a Systems‐on‐Chip (SoC).
Charting the Path to Purchase Using Topic Models

In gathering information for an intended purchase decision, consumers submit search phrases to online search engines. These search phrases directly express the consumers’ needs in their own words and thus provide valuable information to marketing managers. Interpreting consumers’ search phrases renders a better understanding of their purchase intentions, which is critical for marketing success. In this article, the authors develop an integrated model to connect the latent topics embedded in consumers’ search phrases to their website visits and purchase decisions. Using a unique data set containing more than 8,000 search phrases submitted by consumers, the model identifies latent topics underlying the searches that led consumers to the firm’s website. Compared with a model lacking any textual information from consumers’ search phrases, a model using textual data in a heuristic approach, and a model based on the latent Dirichlet allocation, the proposed model provides a better evaluation of a consumer’s position on the path to purchase and achieves much better predictive accuracy, which could in turn substantially increase the firm’s revenue. The authors also extend the discussion to aggregators, affiliated websites, and segments of consumers who are exposed to the firm’s outbound ads. Marketing managers can use this method to extract structured information from consumers’ search phrases to facilitate their inference of consumers’ latent purchase states and thereby improve marketing efficiency.
Amharic Document Representation for Adhoc Retrieval

Amharic is the official language of the government of Ethiopia currently having an estimated population of over 110 million. Like other Semitic languages, Amharic is characterized by complex morphology where thousands of words are generated from a single root form through inflection and derivation. This has made the development of tools for Amharic natural language processing a non-trivial task. Amharic adhoc retrieval faces difficulties due to the complex morphological structure of the language. In this paper, the impact of morphological features on the representation of Amharic documents and queries for adhoc retrieval is investigated. We analyze the effects of stem-based and root-based approaches on Amharic adhoc retrieval effectiveness. Various experiments are conducted on TREC-like Amharic information retrieval test collection using standard evaluation framework and measures. The findings show that a root-based approach outperforms the conventional stem-based approachthat prevails in many other languages.
Utilising Deep Learning Techniques for Effective Zero-Day Attack Detection

Machine Learning (ML) and Deep Learning (DL) have been used for building Intrusion Detection Systems (IDS). The increase in both the number and sheer variety of new cyber-attacks poses a tremendous challenge for IDS solutions that rely on a database of historical attack signatures. Therefore, the industrial pull for robust IDSs that are capable of flagging zero-day attacks is growing. Current outlier-based zero-day detection research suffers from high false-negative rates, thus limiting their practical use and performance. This paper proposes an autoencoder implementation for detecting zero-day attacks. The aim is to build an IDS model with high recall while keeping the miss rate (false-negatives) to an acceptable minimum. Two well-known IDS datasets are used for evaluation—CICIDS2017 and NSL-KDD. In order to demonstrate the efficacy of our model, we compare its results against a One-Class Support Vector Machine (SVM). The manuscript highlights the performance of a One-Class SVM when zero-day attacks are distinctive from normal behaviour. The proposed model benefits greatly from autoencoders encoding-decoding capabilities. The results show that autoencoders are well-suited at detecting complex zero-day attacks. The results demonstrate a zero-day detection accuracy of 89–99% for the NSL-KDD dataset and 75–98% for the CICIDS2017 dataset. Finally, the paper outlines the observed trade-off between recall and fallout.