Miracle-dz/UniFAR-FTUs huggingface dataset:
The application of optimal estimation to lidar

Lidars are ideally placed to investigate the effects of aerosol and cloud on the climate system due to their unprecedented vertical and temporal resolution. Dozens of techniques have been developed in recent decades to retrieve the extinction and backscatter of atmospheric particulates in a variety of conditions. These methods, though often very successful, are fairly ad hoc in their construction, utilising a wide variety of approximations and assumptions that makes comparing the resulting data products with independent measurements difficult and their implementation in climate modelling virtually impossible. As with its application to satellite retrievals at the turn of the century, the methods of non-linear regression can improve this situation by providing a mathematical framework in which the various approximations, estimates of experimental error, and any additional knowledge of the atmosphere can be clearly defined and included in a mathematically `optimal' retrieval method, providing rigorously derived error estimates. In addition to making it easier for scientists outside of the lidar field to understand and utilise lidar data, it also simplifies the process of moving beyond extinction and backscatter coefficients and retrieving microphysical properties of aerosols and cloud particles. A technique to estimate the lidar's overlap function using an analytic model of the optical system and a simple extinction profile has been developed. This is used to calibrate the system such that the profile of extinction and backscatter coefficients can be retrieved using the elastic and nitrogen Raman backscatter signals. These methods have been used to extract value from compromised data collected with a prototype Raman lidar system. Selected events will be presented, with the hope that others may be inspired to apply the techniques to a more robust system.
Nyhetens behag: nyhetskonsumtion hos svenska teknologistudenter

The purpose of this study was to examine news habits of students of technology in Stockholm, Sweden. In today’s ever-changing news climate, studies quickly lose their relevance. Therefore, continous research is necessary. 40 students responded to a survey and interviews were conducted with 16 of these. The results showed that “modern” news channels, such as websites and social media, were more popular than “traditional” news channels, such as newspapers and TV news. However, the “traditional” channels were perceived as the most credible, and they were the most popular channels for major news events. Generally, the partypants felt that news filled an important role in society, but they didn’t keep up with the news as well as they would have liked.
Comments on 'Totally self-checking CMOS circuit design for breaks and stuck-on faults'

The commenters argue that in the above-titled paper by M.S. Cheema and P.K. Lala (ibid., vol.27, no.8, p.1203-6, Aug. 1992) a reference has been wrongly interpreted by the authors, resulting in an incorrect impression of the defect densities. Another problem, concerning the propagation of faults to the final outputs, is also discussed. Furthermore, the commenter points out that the dynamic behavior of the method is not addressed in the original paper and that the design technique presented in the paper requires a high area overhead with which not all possible defects will be detected. >
Rotor position estimation for permanent magnet synchronous motor using saliency-tracking self-sensing method

This paper presents an implementation of a position estimation method using a carrier signal injection and a single saliency machine model for permanent magnet (PM) synchronous machines. The method using rotating vector, carrier frequency excitation and heterodyning, tracking observers for position estimation is discussed. The experimental results for the case of a buried PM machine are shown in the paper. It was demonstrated that acceptable results could be obtained from the buried PM machine using the simplest, single saliency-tracking method.
Minimally invasive proctectomy for locally advanced rectal cancer: is less always more?

Colorectal cancer (CRC) is the 3 rd most common cancer worldwide, with rectal cancer accounting for approximately 1/3 of CRC cases (1). Although there has been increasing interest in the primary treatment of rectal cancer with chemoradiotherapy in patients who experience complete clinical response, surgical management remains the gold standard treatment for curable cases and has seen significant advances in the past 4 decades (2). Total mesorectal excision (TME) which was first described by Prof. Bill Heald in 1982, saw a remarkable improvement in rates of local control for rectal cancer and paved the way for future rectal surgery (3). Advances in neoadjuvant therapies have seen a further reduction in local recurrence rates, particularly for more locally advanced tumours.
Expand the Range of Traffic Offloading with Multi-Hop D2D Communication

Device-to-device (D2D) communication is a promising technique for traffic offloading in next-generation cellularsystems, in response to the sharp increase in traffic and unevendistribution. In this paper, in the case of meeting the user’s delay, we propose a multi-hop D2D traffic offloading scheme to expand the range of traffic offloading. In consideration of the load balancing problem, we introduce load fairness of Base Stations to restraint the traffic that is offloaded to each BS. And, based on the constraints of delay and fairness, we formulate an optimization model that maximizes the range of offloading. We also proposed joint algorithm to achieve traffic offloading. And simulation results show that our scheme can further expand the range of traffic offloading under the condition of ensuring load fairness.
Resynthesis of Coupled Piano String Vibrations Based on Physical Modeling

This paper presents a technique to resynthesize the sound generated by the vibrations of two piano strings tuned to a very close pitch and coupled at the bridge level. Such a mechanical system produces doublets of components, thus generating beats and double decays on the amplitudes of the partials. We design a digital waveguide model by coupling two elementary digital waveguides. This model is able to reproduce perceptually relevant sounds. The parameters of the model are estimated from the analysis of real signals collected directly on the strings by laser velocimetry. Sound transformations can be achieved by modifying relevant parameters in order to simulate various physical situations.
PRETHODNA SAOPŠTENJA PRELIMINARY REPORTS

Sažetak – Napredak u razumevanju patofiziologije akutnog koronarnog sindroma doveo je do pronalaženja niza biomarkera za dijagnozu, procenu rizika, terapeutsko odlucivanje i procenu klinickih ishoda, međutim jos uvek postoji potreba za razvojem ranog markera koji pouzdano može da dokaže ili iskljuci postojanje akutnog koronarnog sindroma. Cilj naseg istraživanja bio je ispitivanje vrednosti Fon Vilebrandovog faktora kao moguceg markera nekroze miokarda u ranoj dijagnostici akutnog koronarnog sindroma. Prvog dana od verifikovanja bolesti, 115 sukcesivno primljenih pacijenata sa dijagnozom akutnog koronarnog sindroma podvrgnuto je uzimanju uzoraka krvi radi određivanja vrednosti Fon Vilebrandovog faktora antigena i Fon Vilebrandovog faktora aktivnosti. Rezultati studije jasno pokazuju da bolesnici imaju znacajno povisene vrednosti oba parametra u odnosu na kontrolne vrednosti i da Fon Vilebrandov faktor predstavlja izuzetno senzitivan i specifican marker, koji korelira sa stepenom nekroze miokarda i klinickom slikom. Ove rezultate bi trebalo potvrditi daljim istraživanjima u multicentricnim studijama na vecem broju bolesnika. Kljucne reci: von Willebrand faktor; Akutni koronarni sindrom, Biomarker
A NEW ALGORITHM FOR AUTOMATIC IMAGE RETRIEVAL AND SEGMENTATION USING WAVELET

In the past decade, remarkable progress has been made towards the multimedia data retrieval; however there is a challenging need for efficient and realistic image retrieval model. One such approach for image retrieval is content based technique. In this pilot study, we have proposed a new algorithm for digital image indexing and retrieval using wavelet based block processing approach. Original image has been converted into HSV co-ordinate and it has been divided into 4x4 pixel block. A block clustering algorithm is applied for image segmentation using K-means algorithm. Feature parameters were extracted from each cluster and have been stored for future use. At the time of image query, the feature vectors of the query image were collected and Euclidean distance between each region is computed. The result has been sorted in descending order based on vector value. Results demonstrate that, our image retrieval system improves the accuracy and robustness. In future, same technique could be used for automatic diagnosing of medical images and also for health care sector; for patient database maintenance.
Acceleration-based Control Strategy and Design for Hybrid Electric Vehicle Auxiliary Energy Source

The paper presents a new control strategy and design for auxiliary energy source (AES) used in battery hybrid electric vehicle (BHEV) based on the acceleration power. The control strategy takes actual speed and acceleration of the vehicle and system losses into account for regulating the energy and power supply to the propulsion load. The design of AES and its dynamic control design are demonstrated. Cascade control is availed in this work in order to control the terminal voltage and current of supercapacitors (SCs). The benefits of AES in which recapture of regenerative braking energy are examined by the numerical simulation and verified by a small scale experiment. The comparison of energy consumption and DC bus voltage regulation between pure battery and battery with supercapacitors (BSCs) propulsion system declares the theoretical results and confirms the benefits of the proposed method.
Nucleotide-binding and molecular interactions of plant disease resistance proteins

Planten worden continu blootgesteld aan ziekteverwekkers. Om zich hiertegen te verdedigen bevatten planten onder meer eiwitten die ‘op wacht staan’ en onregelmatigheden in de cel kunnen detecteren die door ziekteverwekkers worden veroorzaakt. Deze eiwitten worden resistentie-eiwitten (R-eiwitten) genoemd. Zodra een R-eiwit een ziekteverwekker waarneemt wordt een sterke afweerreactie opgewekt. Ewa Lukasik-Shreepaathy deed onderzoek naar R-eiwitten die zogeheten NB-LRR-domeinen bevatten (NB-LRR staat voor nucleotide binding and leucine rich repeat). Deze NB-LRR-eiwitten fungeren als moleculaire schakelaars die plantafweer aan en uit kunnen zetten. Lukasik-Shreepaathy beschrijft de intra- en intermoleculaire interacties van een aantal R-eiwitten en keek naar de rol van specifieke R-eiwitdomeinen in het functioneren van het eiwit. Ze laat onder andere zien dat een R-eiwit uit gerst, MLA27, een stabiele binding vormt met ADP (chemische verbinding die betrokken is bij energiehuishouding). Dit ondersteunt het idee dat R-eiwitten functioneren als aan-uitschakelaars.
Providing lifestyle advice to people with type 2 diabetes from different cultures: A qualitative investigation

ICross-cultural communication between clinicians and local people, especially in relation to type 2 diabetes, is often under researched. The referral system in Saudi Arabia is similar to that of the NHS, and international medical graduates (IMGs) are heavily relied on in the country to provide diabetes care to local people. IMGs often do not share the same culture as the local people, yet are required to deliver culturally sensitive care. This study explores the experiences of general practitioner IMGs and people with type 2 diabetes in regards to how lifestyle advice is provided in the clinic. It is hoped that the recommendations arising from this study will improve cross-cultural interaction and raise awareness of the challenges not only in Saudi Arabia but also in the UK.
Plasticity in secondary sexual characteristics in the male of the river 1 blenny

27 Alternative reproductive tactics (ARTs) driven by environmental factors are common 28 among fish. However, the flexibility of fish to adopt distinct tactics in response to the 29 characteristics of their environment has received little attention. The aim of this study 30 was to study phenotypic plasticity in the adoption of dominant behaviour (“bourgeois 31 tactic”) by the male of the river blenny Salaria fluviatilis (Asso, 1801). For this purpose, 32 two simultaneous experiments in aquaria were performed to examine the effect of 33 social cues and nest abundance on the acquisition of secondary sexual characteristics 34 (SSCs). Experiments were conducted with small (individuals without SSCs), medium35 sized (one year old individuals), and large older dominant males (more than two years 36 old), all collected in the wild. In Experiment 1 the three sizes of males were combined 37 to compare their SSCs development depending on intrasexual context. In Experiment 2 38 the effect of nest abundance (two nests vs. six nests) was tested for each size of male. 39 Medium-sized males showed phenotypic plasticity in response to the environmental 40 conditions simulated in the two experiments. The absence of larger dominant males 41 was found to be the main factor enhancing SSCs and the onset of parental behaviour. 42 Nest shortage also influenced the degree of cephalic crest development among 43 medium-sized males. This knowledge helps to understand how the population of 44 S.fluviatilis still persists when it is reduced to young individuals during the summer 45 droughts in Mediterranean streams. 46 3
Spatio-temporal Probabilistic Short-term Forecasting on Urban Networks

The probabilistic forecasting method described in this work is designed to leverage spatial and temporal dependency of urban traffic networks in order to provide accurate predictions for an horizon up to a few hours. By design it can deal with missing data both for training and running the model. It is able to forecast the state of the whole network in one pass with an execution time scaling linearly with the size of the network. The method consists in two learn a sparse Gaussian Copula of traffic variables, compatible with the Gaussian belief propagation algorithm. The model is learned automatically from an historical dataset through an iterative proportional scaling procedure well suited to impose this compatibility constraint. It is tested on three different datasets of increasing sizes ranging from 250 to 2000 detectors corresponding to flow or/and speed and occupancy measurements. The results show very good ability to predict flow variables and a reasonably good performance on speed or occupancy variables. Some element of understanding of the observed performance are given by a careful analysis of the model allowing to some extend to disentangle modelling bias from intrinsic noise of the traffic phenomena and its measurement process.
The Comparative Application of Fuzzy Logic and Genetic Algorithm for Intelligent Navigation of Mobile Robot in Dynamic Unknown Environment in the Case of Fixed and Moving Obstacles

Introduction : In recent years, topics related to robotics have become one of the areas of research and development. In the meantime, smart robots are very popular, but the control and navigation of these devices are very difficult, and the lack of handling and staggering obstacles and avoidance of them, due to safe and secure routing, outweigh the basic needs of these systems. Material and Methods : In this research, for the purpose of solving the intelligent navigation problem, a moving robot in a dynamic unknown environment (conditions at any moment in the range of moving and obstacles in motion) and the choice of optimal path, the methods of genetic algorithm and fuzzy logic are used comparatively. Results : By using genetic algorithm and fuzzy logic methods, the robot can move in the dynamic and unknown environment to the optimal path to the target. Conclusion : Information about the environment is also necessary to avoid obstacles, optimal path design and environment exploration, and to establish a clever relationship between perception and practice that requires the use of appropriate algorithms such as the genetic algorithm and fuzzy logic (fuzzy controller) are also needed to manage the control and navigation.
An air freight forwarder’s resource planning and revenue management

Abstract Forwarders acquire cargo space from airlines, partners, and subcontracting agents. Their customers may be categorised into two segments: regular and ad hoc. This paper examines the relationship between a forwarder’s resource planning and revenue management decisions. We first consider a static scenario where regular demand occurs before ad hoc demand with a three-stage model. We then consider a dynamic scenario where demands of different segments may overlap using a Markov decision process model. In both scenarios, revenue management (via control limits) decreases resources acquired, and the contribution of control limits to expected profit is unimodal with respect to capacity level.
Frugal Forests : learning a dynamic and cost sensitive feature extraction policy for anytime activity classification

Many approaches to activity classiﬁcation use supervised learning and so rely on extracting some form of features from the video. This feature extraction process can be computationally expensive. To reduce the cost of feature extraction while maintaining acceptable accuracy, we provide an anytime framework in which features are extracted one by one according to some policy. We propose our novel Frugal Forest feature extraction policy which learns a dynamic and cost sensitive ordering of the features. Cost sensitivity allows the policy to balance features’ predictive power with their extraction cost. The tree-like structure of the forest allows the policy to adjust on the ﬂy in response to previously extracted feature values. We show through several experiments that the Frugal Forest policy exceeds or matches the classiﬁcation accuracy per unit time of several baselines, including the current state of the art, on two challenging datasets and a variety of feature spaces.
Imbalanced Fault Diagnosis of Rolling Bearing Based on Generative Adversarial Network: A Comparative Study

Due to the real working conditions and data acquisition equipment, the collected working data of bearings are actually limited. Meanwhile, as the rolling bearing works in the normal state at most times, it is easy to raise the imbalance problem of fault types which restricts the diagnosis accuracy and stability. To solve these problems, we present an imbalanced fault diagnosis method based on the generative adversarial network (GAN) and provide a comparative study in detail. The key idea is utilizing GAN, a kind of deep learning technique, to generate synthetic samples for minority fault class and then improve the generalization ability of the fault diagnosis model. First, this method applies fast Fourier transform to pre-process the original vibration signal and then obtains the frequency spectrum of fault samples. Second, it uses the spectrum data as the input of GAN to generate the synthetic minority samples following the data distribution of the real samples. Finally, it puts the synthetic samples into the training set and builds a stacked denoising auto encoder model for fault diagnosis. To testify the effectiveness of the proposed method, a series of comparative experiments is carried out on the CWRU bearing dataset. The results show that the proposed method can provide a better solution for imbalanced fault diagnosis on the basis of generating similar fault samples. As a comparative study, the proposed method is compared to several diagnostic methods with traditional time-frequency domain characteristics. Moreover, we also demonstrate that the proposed method outperforms three widely used sample synthesis techniques, such as random oversampling, synthetic minority oversampling technique, and the principal curve-based oversampling method in terms of diagnosis accuracy and numerical stability.
An Innovative Approach of Control and Simulation Based Analysis of Doubly Fed Electrical Machines for Harnessing Wind Energy

Background/Objectives : Wind energy systems are need of the hour from electrical energy system point of view. Doubly fed electric machines are best suited for wind systems due to their ability to work under variable speed conditions. Methods/Statistical Analysis : This paper proposes a new configuration of doubly fed induction generator (DFIG) called as single external feeding of DFIG (SEF-DFIG) which has significant merits over the former system. Findings : The operating mode is presented with detailed analysis of the proposed system. Extensive simulation study is carried out by considering a hybrid energy system which consists of wind as well as other renewable energy sources. Application/Improvements : The results obtained during the simulation case study demonstrate the usefulness of the proposed system.
Technological and design constraints for multilevel flash memories

This paper discusses basic constraints for multilevel storage in flash memories. Aspects such as programming algorithms, threshold voltage distribution, data retention, read disturbs, sense amplifier sensitivity and cell transconductance spread are considered. Experimental results and design considerations are provided. Guidelines for the evaluation of multilevel storage feasibility are given. The feasibility of four-level storage with present technologies using a read voltage around 6 V is demonstrated.
Patient-centered Asthma: Severe Disease and Its Impact on Lifestyle

Life with severe asthma is difficult for patients. The condition and its common comorbidities have a significant impact on quality of life and many people with severe asthma also suffer from depression or anxiety. In addition, treatment side effects also affect patients considerably more than many clinicians realize. Poor adherence to medication—often attributed to these side effects by patients—is a common problem and significantly worsens asthma outcomes. However, we believe that it can be tackled by making asthma care more patient-centric. This can be done by improving patient involvement in decision making, and making sure that services are in place to deliver patient education, self-management, and peer support. There is a moral imperative to improve cooperation between patients and clinicians by involving patients in decision making; it is particularly important in light of the emergence of new treatments which are costly and which have unknown long-term effects and which should consequently only be given to those who really need them.
Aerobic training combined with nutritional guidance as an effective strategy for improving aerobic fitness and reducing BMI in inactive adults

The aim of this study was to examine the impact of a 33-week program, which included two weekly intensive running sessions, on aerobic capacity, BMI, waist circumference and body composition in overweight and obese, inactive adults. An additional aim was to examine the additional impact of lifestyle modification education. Twenty-four participants in the Training Group (exercise only) and 27 in the Nutritional Guidance and Training Group (exercise + nutritional guidance) completed the study. Anthropometric measurements (BMI, waist circumference, visceral fat, muscle mass and fat percentage) and 3000 m running time were measured at start, after 15 weeks (BMI, waist circumference and 3000 m running time) and after 33 weeks of intervention. Significant reductions in BMI, waist circumference, fat percentage and 3000 m running time were found for both groups. A small, but insignificant, increase was found for muscle mass in both groups and visceral fat in the training group. A significant interaction effect for visceral fat was also found. Both groups improved their running time and anthropometric measurements. No significant differences were found between the groups.