Miracle-dz/UniFAR-FTUs huggingface dataset:
One Wheeled Self-Stable Electric Bike

The goal of this project is to design, analyse and build a self-balancing single wheel bike for use as a transportation tool for someone travelling short distances. The project consists of a research phase in which similar systems have been investigated to help determine a sensible design approach and to establish appropriate design specifications; a design phase in which a model was designed with certain assumptions to meet the aforementioned specifications and a construction phase, in which the vehicle was built and tested.
Introduction of exotic fish into a Mediterranean lake over a 90-year period

The impact of exotic species is a neglected, worldwide ecological problem, particularly for freshwater fish. Examples of historical changes in southern Europe due to fish introduction are rare. Lake Banyoles, the second largest lake of the Iberian Peninsula, has suffered a long history of fish introductions. We reviewed the historical changes in the fish assemblage and assessed its current state in a field study. Before 1910, only five or six native species were present. During this century up to 12 exotic species were introduced, leading to the apparent loss of two native species (three-spined stickleback Gasterosteus aculeatus and, possibly introduced several centuries ago, tench Tinca tinca) and the decline of three others (eel Anguilla anguilla, chub Leuciscus cephalus, and barbel Barbus meridionalis). The current fish assemblage is dominated by exotic species, particularly the largemouth bass (Micropterus salmoides) and the pumpkinseed sunfish (Lepomis gibbosus) in the littoral zone and roach (Rutilus rutilus) in the pelagic zone. The only native species still common nowadays is the freshwater blenny (Salaria (= Blennius)fluviatilis).
On the use of advanced optimization methods in mechanical design

Advanced optimization methods are widely applied to mechanical design, mainly for its abilities to solve complex problems that traditional optimization techniques such as gradient-based methods do not present. With its increasing popularity, the number of algorithms found in the literature is vast. In this work three algorithms are implemented, namely Particle Swarm Optimization (PSO), Differential Evolution (DE) and Teaching-LearningBased Optimization (TLBO). Firstly, the application of these algorithms is analyzed for a composition function benchmark and three mechanical design minimization problems (the weight of a speed reducer, the volume of a three-bar truss and the area of a square plate with a cut-out hole). Furthermore, as the scope of available algorithms increases, the choice of programming tools to implement them is also vast, and generally made considering subjective criteria or difficulties in using enhancing strategies such as parallel processing. Thereby an analysis of programming tools applied to metaheuristic algorithms is carried out using four programming languages with distinct characteristics: Python, MATLAB, Java and C++. The selected algorithms and problems are coded using each programming language, which are initially compared in a sequential processing implementation. Additionally, in order to analyze potential gains in performance, parallel processing procedures are implemented using features of each programming language. The application of the algorithms to the mechanical design problems demonstrates good results in the achieved solutions. In what concerns to the computational time, sequential and processing results present considerable differences between programming languages while the implementation of parallel processing procedures demonstrates significant benefits for complex problems.
Optimization of a circulating multi-car elevator system

Circulating multi-car elevator is a system holding multiple cars in two shafts, where cars move upwards in one shaft and downwards in the other shaft. This system is similar to the paternoster, but cars have to stop on floors and open doors to load and unload passengers. Besides many technical challenges, this system brings algorithmic problems regarding efficient control of all cars. This thesis studies an off-line optimization problem, where the most efficient elevator system is searched for a fixed set of passengers. For this purpose, we created a computer program, implementing a genetic algorithm for searching for the most efficient elevator control and a discrete event simulation for evaluation of the efficiency of the control. The program provides a graphical user interface for input of parameters, generating passengers and displaying the results.
ANTINOCICEPTIVE EFFICACY AND SAFETY OF SUBCUTANEOUS TRAMADOL IN CHINCHILLAS (CHINCHILLA LANIGERA)

&NA; Tramadol has been demonstrated to provide analgesia in rats and mice and has been increasingly used in other small companion mammals for treatment of moderate to severe pain. Currently no evidence is available that supports the use of tramadol as an effective analgesic in chinchillas. The purpose of this study was to evaluate the antinociceptive efficacy and safety of subcutaneously administered tramadol in chinchillas. Initial dose‐escalation studies were performed to determine if dose‐dependent adverse effects occur in chinchillas. At 60 mg/kg subcutaneously (SC), but not at lower doses, tramadol induced clinically significant dose‐limiting adverse effects, consisting of whole body tremors and facial muscle fasciculation. Therefore, in a subsequent analgesimetry experiment, tramadol was evaluated at a single dose of 10, 20, and 40 mg/kg SC in a randomized, blinded, controlled, complete crossover design. Thermal antinociceptive efficacy was determined by measuring hindlimb withdrawal latencies following a thermal noxious stimulus (Hargreaves' method) at 0, 1, 2, 4, and 8 hours after drug administration. Changes in daily food intake and fecal output following tramadol administration were measured. At 10 to 40 mg/kg SC no thermal antinociceptive effects could be demonstrated. A self‐limiting and transient significant reduction in food intake and fecal output occurred in a dose‐dependent manner when tramadol was administered at 20 and 40 mg/kg. Based on the results of this study tramadol cannot be recommended as an analgesic drug in chinchillas.
Short Text Similarity Understanding with Word Embeddings

Understanding short texts is crucial to many applications, but challenges abound. First, short texts do not always observe the syntax of a written language. As a result, traditional natural language processing tools, ranging from part-of-speech tagging to dependency parsing, cannot be easily applied. Second, short texts usually do not contain sufficient statistical signals to support many state-of-the-art approaches for text mining such as topic modeling. Third, short texts are more ambiguous and noisy, and are generated in an enormous volume, which further increases the difficulty to handle them. We argue that semantic knowledge is required in order to better understand short texts. In this work, we build a prototype system for short text understanding which exploits semantic knowledge provided by a well-known knowledge base and automatically harvested from a web corpus. Our knowledge-intensive approaches disrupt traditional methods for tasks such as text segmentation, part-of-speech tagging, and concept labeling, in the sense that we focus on semantics in all these tasks. We conduct a comprehensive performance evaluation on real-life data. The results show that semantic knowledge is indispensable for short text understanding, and our knowledge-intensive approaches are both effective and efficient in discovering semantics of short texts.
Using 3D printed sinonasal models to visualize and optimize personalized sinonasal sinus irrigation strategies.

BACKGROUND Topical sinus irrigations (neti-pot, squeeze bottles) play a critical role in the management of sinonasal disease. However, due to intricate nasal anatomy, penetration of topical irrigations to targeted sinus regions may be highly variable, and difficult to objectively predict. Variables, including head positions, injection angles, flow rates, etc. may vary significantly depending on the individual's anatomy. OBJECTIVE The purpose of this study was to propose a novel idea: using a 3D printed model of sinonasal cavities to visualize and develop a patient-specific irrigation strategy. METHODS As a proof of concept, 3D replicas of one patient's sinonasal cavities pre- and post-surgery were printed with a Form2 SLA 3D printer based on their CT scans. The setup included rubber/silicon seals attached to the model's nostrils to create a watertight seal with the irrigation device and food color dye added for better visualization of irrigation results. RESULTS Irrigations were performed on the 3D models with various head positions, injection angles, and flow rates, and were successful to determine the optimal strategy to targeted sinuses. Significant differences were observed between different targeted sinuses and between pre and post-surgery models. CONCLUSION With more affordable 3D printing, this technology may potentially improve patient care and patient education, allowing clinicians and patients to develop a personalized irrigation strategy and have visual confirmation.
Molecular Decision Tree Algorithms Predict Individual Recurrence Pattern for Locally Advanced Nasopharyngeal Carcinoma

Background: Recurrence remains one of the key reasons of relapse after the radical radiation for locally advanced nasopharyngeal carcinoma (NPC). Here, the multiple molecular and clinical variables integrated decision tree algorithms were designed to predict individual recurrence patterns (with VS without recurrence) for locally advanced NPC. Methods: A total of 136 locally advanced NPC patients retrieved from a randomized controlled phase III trial, were included. For each patient, the expression levels of 33 clinicopathological biomarkers in tumor specimen, 3 Epstein-Barr virus related serological antibody titer and 5 clinicopathological variables, were detected and collected to construct the decision tree algorithm. The expression level of 33 clinicopathological biomarkers in tumor specimen was evaluated by immunohistochemistry staining. Results: Three algorithm classifiers, augmented by the adaptive boosting algorithm for variable selection and classification, were designed to predict individual recurrence pattern. The classifiers were trained in the training subset and further tested using a 10-fold cross-validation scheme in the validation subset. In total, 13 molecules expression level in tumor specimen, including AKT1, Aurora-A, Bax, Bcl-2, N-Cadherin, CENP-H, HIF-1α, LMP-1, C-Met, MMP-2, MMP-9, Pontin and Stathmin, and N stage were selected to construct three 10-fold cross-validation decision tree classifiers. These classifiers showed high predictive sensitivity (87.2-93.3%), specificity (69.0-100.0%), and overall accuracy (84.5-95.2%) to predict recurrence pattern individually. Multivariate analyses confirmed the decision tree classifier was an independent prognostic factor to predict individual recurrence (algorithm 1: hazard ration (HR) 0.07, 95% confidence interval (CI) 0.03-0.16, P < 0.01; algorithm 2: HR 0.13, 95% CI 0.04-0.44, P < 0.01; algorithm 3: HR 0.13, 95% CI 0.03-0.68, P = 0.02). Conclusion: Multiple molecular and clinicopathological variables integrated decision tree algorithms may individually predict the recurrence pattern for locally advanced NPC. This decision tree algorism provides a potential tool to select patients with high recurrence risk for intensive follow-up, and to diagnose recurrence at an earlier stage for salvage treatment in the NPC endemic region.
Computational Rhythm Description

This book is about musical rhythm. More precisely, it is concerned with computer programs that automatically extract rhythmic descriptions from musical audio signals. New algorithms are presented for tempo induction, tatum estimation, time signature determination, swing estimation, swing transformations and classification of ballroom dance music styles. These algorithms directly process digitized recordings of acoustic musical signals. The backbones of these algorithms are rhythm periodicity functions: functions measuring the salience of a rhythmic pulse as a function of the period (or frequency) of the pulse, calculated from selected instantaneous physical attributes (henceforth features) emphasizing rhythmic aspects of sound. This book substantially contributes to the field of computational rhythm description by proposing an unifying functional framework; by reviewing the architecture of many existing systems with respect to individual blocks of this framework; by reporting on the first public evaluation of tempo induction algorithms; and by identifying promising research directions.
Synesthetic Musical Composition using Computational Intelligence

Synesthesia is the combination of two senses in the same perceptive act; see the music, hear a red color or feel the texture of a green sound are some examples. The problem of a bad teaching in music theory turns music into a tedious and boring subject, causing in many cases a desertion. The musical composition with synesthesia will make it easier for the user to learn music through the association of sound with color and exploit a new way of learning with intelligent computing methods, where the advanced user creates visually pleasing musical compositions encouraging musical creativity. This article explains the use of evolutionary algorithms as a novel auxiliary model for musical innovation. In addition, it shows the existing tools of intelligent computation (IC) for musical composition and creation of computational art.
Improving the Performance of Medium Access Control (MAC) Layer Using Virtual Backoff Algorithm (VBA)

Nowadays, there is a wide range of communication over wireless networks. As the need for communication increases, there is a need to prevent wireless communication from collision. This paper presents a scheme, called the Virtual Backoff Algorithm (VBA) for efficient Medium Access Control (MAC) layer. The proposed method minimizes the number of collisions as well as the delays that occur during the backoff periods. The VBA method uses fair distributed mechanism to access the channel. When collision occurs in a MAC layer, it may result in decreased throughput, hence leads to lower bandwidth utilization. Virtual Backoff Algorithm (VBA) is used inorder to prevent collision during multiple channel access in MAC Layer. In this method, we introduce a counter at each node to maintain the discipline of the nodes. AODV is used as the routing protocol in this method. The proposed method minimizes the number of collisions and increases the throughput along the network. Also the proposed scheme shows that the energy requirements are minimum while reducing collisions in the MAC layer. Keyword: Channel access, IEEE 802.11 protocol, CSMA/CA, distributed coordination function (DCF), Medium-access control (MAC), ad hoc on-demand distance vector (AODV).
Temporal Information Retrieval

Temporal dynamics and how they impact upon various components of information retrieval IR systems have received a large share of attention in the last decade. In particular, the study of relevance in information retrieval can now be framed within the so-called temporal IR approaches, which explain how user behavior, document content and scale vary with time, and how we can use them in our favor in order to improve retrieval effectiveness. This survey provides a comprehensive overview of temporal IR approaches, centered on the following questions: what are temporal dynamics, why do they occur, and when and how to leverage temporal information throughout the search cycle and architecture. We first explain the general and wide aspects associated to temporal dynamics by focusing on the web domain, from content and structural changes to variations of user behavior and interactions. Next, we pinpoint several research issues and the impact of such temporal characteristics on search, essentially regarding processing dynamic content, temporal query analysis and time-aware ranking. We also address particular aspects of temporal information extraction for instance, how to timestamp documents and generate temporal profiles of text. To this end, we present existing temporal search engines and applications in related research areas, e.g., exploration, summarization, and clustering of search results, as well as future event retrieval and prediction, where the time dimension also plays an important role.
Generate Football News from Live Webcast Scripts Based on Character-CNN with Five Strokes

Generating the football news from live webcast scripts is one kind of automatic text summarization in the field of natural language processing (NLP). Two typical characteristics in the live webcast scripts make the traditional methods can’t well-generate the football news. Short sentences in live webcast scripts make it difficult to identify all of the keywords. And the sentence category identification is more crucial than the other kinds of automation text summarization. In this paper, the custom dictionary, forward dictionary and reverse dictionary in the football news domain are constructed, which aim to identify all import information from the live webcast. And the character-CNN with five strokes model is designed to classify the sentences category. Finally, the scanning algorithm based on time window is proposed to select the sentences to assemble into football news. Experiments results show that our designed Character-CNN model achieved higher accuracy rate on sentence classification than the state-of-art models. Evaluation conducted on ROUGE-1.5. 5 toolkit showed that generating the football news with our proposed scanning algorithm outperforms than some baseline methods.
Analysing the characteristics of crowdsourcing platforms for improving throughput

Crowdsourcing leverages human intelligence to gather solutions on tasks that cannot be accomplished by automated tools. This system consists of components such as the requester, task, worker and the crowdsourcing platform. Studies do not explore the various features of these components and the dependencies among the same. Hence, we analyse the characteristics of the components of crowdsourcing systems using a trace-driven approach. Additionally, for reproducible research, we have introduced a workload generator for crowdsourcing platforms, which generates an unbiased workload similar to the empirical workload. Finally, the impact of various characteristics on the quality of answers has been analysed using both the empirical and synthetic workloads. The results demonstrate that success rate and activeness positively affect the productivity of workers, while the number of available human intelligence tasks (HITs) and the time duration of the same affect the productivity on each task.
Source localization algorithm based on topographic matching pursuit

Spatio-temporal decomposition methods in combination with source localization algorithms can contribute to an improved description and allocation of neural activity from electrical and magnetic multichannel measurements. We introduce a new algorithm, which combines Topographic Matching Pursuit as spatio-temporal decomposition method with a dipole-source localization. The new algorithm is applied to EEG-data obtained from a photic driving experiment with eleven volunteers. In comparison to a hitherto published Multichannel Matching Pursuit (MMP) source localization the new algorithm shows, for a Mirrored-Dipole configuration, higher Goodness-of-Fit values, if temporal asynchrony exists in the EEG-channels. We conclude that the suggested algorithm is more appropriate for source reconstruction in case of temporal asynchrony than MMP-based procedures used so far.
Cost-effectiveness of Hepatitis B Virus Infection Screening and Treatment or Vaccination in 6 High-risk Populations in the United States

Background Two million individuals with chronic hepatitis B (CHB) in the United States are at risk for premature death due to liver cancer and cirrhosis. CHB can be prevented by vaccination and controlled with treatment. Methods We created a lifetime Markov model to estimate the cost-effectiveness of strategies to prevent or treat CHB in 6 high-risk populations: foreign-born Asian/Pacific Islanders (API), Africa-born blacks (AbB), incarcerated, refugees, persons who inject drugs (PWID), and men who have sex with men (MSM). We studied 3 strategies: (a) screen for HBV infection and treat infected ("treatment only"), (b) screen for HBV susceptibility and vaccinate susceptible ("vaccination only"), and (c) screen for both and follow-up appropriately ("inclusive"). Outcomes were expressed in incremental cost-effectiveness ratios (ICERs), clinical outcomes, and new infections. Results Vaccination-only and treatment-only strategies had ICERs of $6000-$21 000 per quality-adjusted life-year (QALY) gained, respectively. The inclusive strategy added minimal cost with substantial clinical benefit, with the following costs per QALY gained vs no intervention: incarcerated $3203, PWID $8514, MSM $10 954, AbB $17 089, refugees $17 432, and API $18 009. Clinical complications dropped in the short/intermediate (1%-25%) and long (0.4%-16%) term. Findings were sensitive to age, discount rate, health state utility in immune or susceptible stages, progression rate to cirrhosis or inactive disease, and tenofovir cost. The probability of an inclusive program costing <$50 000 per QALY gained varied between 61% and 97% by population. Conclusions An inclusive strategy to screen and treat or vaccinate is cost-effective in reducing the burden of hepatitis B virus among all 6 high-risk, high-prevalence populations.
Don’t Lose Heart: Preliminary Engagement Results in an Ecological Momentary Assessment (EMA) StudyEvaluating Digital Biomarkers for Asthma

Context: In situ patient data over multiple weeks are needed to explore the potential of nocturnal cough and sleep quality as digital biomarkers for asthma. Methods: Ninety-four asthmatics need to complete a 29-day EMA study in which nocturnal smartphone sensor data is recorded and daily questionnaires of 13 to 45 items are delivered by an adapted version of the MobileCoach app. Patients are withdrawn from the study in case of non-adherence on more than five days. Adherence is not financially incentivized. Appointments with health professionals take place on the first and last day. Intervention: Engagement, operationalized as response rates to the questionnaires, is promoted using the following strategies: first, patients discuss with health professionals how they will integrate the study app tasks in their daily routine. Second, working alliance is established through the chat-based interaction with the app’s virtual study nurse. Third, non-adherence is illustrated as lost hearts to elicit loss aversion. Finally, in case of non-adherence (on consecutive days) a notification system sends out reminder SMS to patients (prompts calls from health professionals). Results: The first 29 patients successfully completed 791 of the 810 daily questionnaires (97.65%). 58 reminder SMS were sent to patients and 13 calls by health professionals were triggered. One patient lost all hearts and was withdrawn from the study. The remaining patients completed the study with an average of 4.61/5 hearts (SD = 0.83). Conclusion: The preliminary results suggest that the employed strategies successfully promoted engagement in a population known for non-adherence in clinical practice.