Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Unsupervised Single-Channel Speech Separation Algorithm for Processing Speech Sensor Signals

As network supporting devices and sensors in the Internet of Things are leaping forward, countless real-world data will be generated for human intelligent applications. Speech sensor networks, an important part of the Internet of Things, have numerous application needs. Indeed, the sensor data can further help intelligent applications to provide higher quality services, whereas this data may involve considerable noise data. Accordingly, speech signal processing method should be urgently implemented to acquire low-noise and effective speech data. Blind source separation and enhancement technique refer to one of the representative methods. However, in the unsupervised complex environment, in the only presence of a single-channel signal, many technical challenges are imposed on achieving single-channel and multiperson mixed speech separation. For this reason, this study develops an unsupervised speech separation method CNMF+JADE, i.e., a hybrid method combined with Convolutional Non-Negative Matrix Factorization and Joint Approximative Diagonalization of Eigenmatrix. Moreover, an adaptive wavelet transform-based speech enhancement technique is proposed, capable of adaptively and effectively enhancing the separated speech signal. The proposed method is aimed at yielding a general and efficient speech processing algorithm for the data acquired by speech sensors. As revealed from the experimental results, in the TIMIT speech sources, the proposed method can effectively extract the target speaker from the mixed speech with a tiny training sample. The algorithm is highly general and robust, capable of technically supporting the processing of speech signal acquired by most speech sensors.
Effects of Controlled Release Urea on the Yield and Nitrogen Nutrition of Flooded Rice

Nitrogen (N) loss is one of the key problems faced by rice farmers, and Nitrogen-use efficiency in rice is often poor as a result of high N loss through volatilization, leaching, and denitrification. One of the ways to improve N efficiency is by using controlled-release urea (CRU). The CRU generally outperformed granular urea fertilizer in reducing N losses, stimulating plant growth, and increasing N concentrations. A field experiment with the flooded rice variety MR220 was conducted to compare the effect of six different types of CRU fertilizers on yield and N nutrition of a flooded rice cultivar. Bakau series soil (Typic Tropaquept) was used in this study. Rice plants were grown in a cylindrical culvert measuring 90 cm in diameter by 60 cm in height, and all culverts were filled with soil (approx. 210 kg). The soil was flooded and preincubated for 3 weeks to stabilize physiochemical properties before sowing. The experiment was carried out over two planting seasons on the same plot using a completely randomized design (CRD) and was replicated three times. The CRUs evaluated were CDU Uber-10, Meister-20, Meister-27, humate-coated urea, Duration type V, and sulfur-coated urea (gold–N). Fertilizer was applied once throughout the study. For both seasons, CRU-treated plants had significantly greater rice yields [6 t ha−1 (first planting harvest) and 6.2 t ha−1 (second planting harvest)] than urea-treated plants [3.7 t ha−1 (first planting harvest) and 2.2 t ha−1 (second planting harvest)], respectively. The N accumulations in rice straw and rice grains of the CRU-treated plot were significantly greater than in the control. It can be inferred that CRU performs significantly better than granular urea. This finding is important, considering the usually high N losses in rice-growing areas.
Running Head: Intentions to Self-Manage Mental Health Athlete and non-athlete Intentions to Self-Manage Mental Health: Applying the Integrated Behaviour Change Model to the State of Mind Programme

University students are vulnerable to mental health issues, and stigma remains a barrier to engagement with mental health care services (O'Neill, Mc Lafferty, Ennis, Lapsley, Bjourson, Armour, Murphy, Bunting, & Murray, 2018). It has been argued that student athletes may be less likely than non-athletes to seek help (Donohue et al, 2018), partly due to a sport culture that celebrates mental toughness, winning at all costs, and not showing weakness (Bauman, 2016). To our knowledge, theory-based psychoeducational programmes that promote self-management are lacking for athletes (Breslin and Leavey, 2019). The present study is in response to the lack of theory-based interventions, with two aims: (1) to determine whether a mental health awareness and self-management psychoeducational programme called State of Mind Ireland (SOMI) could improve intentions to self-manage mental health for both athletes and non-athletes; and (2) to apply the Integrated Behaviour Change Model (IBCM) framework to determine what mechanisms inherent within IBCM contributed to self-management of mental health. Two hundred students (Mean age = 21.10 years, SD=3.73, male = 53%) took part, 146 received the SOMI intervention programme (101 athletes and 45 non-athletes), while 54 (38 athletes and 16 non-athletes) were an inactive control group. Baseline and post-intervention motivation and belief-based measures were collected via a self-report questionnaire. Two regression models subscribing to IBCM processes were specified. In comparison to the control group, the intervention group showed an increase in self-management intentions (p <.05), which was facilitated indirectly through the intervention’s direct changes in autonomous (β=.13, p<.05) and controlled motivation (β=.18, p<.05), and direct (β =.28, p <.05) and indirect (β=.14, p<0.05) changes in the attitude factor of the Theory of Planned Behaviour. Model 1 (autonomous motivation) and 2 (controlled motivation) explained R=. 20 and R=. 23 of the variance predicting selfRunning Head: Intentions to Self-Manage Mental Health management intentions respectively. This is the first study to incorporate the IBCM into a mental health promotion intervention among student athletes.
ARCHITECTURAL FEATURES CONSTITUTING HINDRANCES TO ACHIEVING UNIVERSAL DESIGN IN SELECTED SHOPPING MALLS IN LAGOS STATE, NIGERIA

The introduction of Universal Design in the field of architecture has brought about a change in design approach, from planning for normal situations that do not usually cater for all user groups, to designing to accommodate the needs of everyone from the planning stage. This study investigated architectural features constituting hindrances to achieving Universal Design in selected shopping malls in Lagos State, Nigeria, with a view to making contributions on how to enhance accessibility and usability in such buildings for all user groups, regardless of their ability or inability, towards promoting social inclusion in the development of public environments. The study adopted a qualitative research approach that gathered data with an observation guide designed for the study in four selected shopping malls in the study area. The data was content analysed, and the result presented using a descriptive approach with the aid of photographic images for easy understanding. Several architectural features were discovered to constitute hindrances to achieve Universal Design in the malls. The said features were mostly accessibility features useful for people with disabilities to gain access to use the buildings on equal terms with able-bodied individuals. In addition to recommending retrofitting of the malls with appropriate accessible features where they are lacking or inappropriately provided, the study recommended that the building industry design professionals should pay more attention to such areas in the planning of future schemes, towards achieving social inclusion in the development of public environments. The study is useful for better understanding of issues pertaining to developing inclusive public environments.
An investigation of antifungal susceptibilities of the Candida species isolates from blood cultures using the Sensititre YeastOne microdilution method

ÖZET Amaç: Candida türlerinin neden olduğu invaziv fungal enfeksiyonlar önemli ölçüde artmıştır. Bu nedenle türlerin hızlı ve doğru tanımlanması ve duyarlılık testleri, özellikle fırsatçı enfeksiyonları içeren vakalarda oldukça önemlidir. Mikrodilüsyon esasına dayanan Sensititre YeastOne yöntemi, antifungal duyarlılık testi için Klinik ve Laboratuvar Standartları Enstitüsü referans yöntemine uygun bir alternatiftir. Sensititre YeastOne liyofilize halde antifungal içeren kuyucuklara sahip olup Candida izolatlarının Minimal İnhibitör Konsantrasyonu (MİK) değerlerinin belirlenmesi için geliştirilen bir yöntemdir. Çalışmamızda kan kültürlerinden izole edilmiş Candida izolatlarının Sensititre YeastOne kolorimetrik sistem kullanılarak antifungal ilaç duyarlılığının belirlenmesi amaçlanmıştır.
Automated driving for an UGV for agriculture 4.0

This thesis report presents an approach to create autonomy for a mobile robot and then focuses on the planning module of the autonomous four-wheel steering (4WS) mobile robot. The planning module is composed of a global path planner and a local path planner. The global path planner, working with Rapid Random exploring Tree (RRT), provides local goals to a local path planner, which is implemented with the Dynamic-Window approach (DWA). DWA is a receding horizon technique that can produce a smooth and optimized path. The path is later given as a reference signal to a motion controller that another student worked on.
Cuffless Blood Pressure Measurement

Continuous vital signals monitoring has gained a huge relevance for disease prevention that afflict a large part of the world population, for this reason the healthcare equipment should be easy-wear and convenient-operate. Nonintrusive and noninvasive detective methods are the basic requirement for the wearable medical devices, especially when the devices are used in sports applications or by the elderly for selfmonitoring. The aim of this thesis is to measure continuous arterial blood pressure through a cuffless non-intrusive approach. The arterial blood pressure is an essential physiological parameter for health monitoring. Most blood measurement devices determine the systolic and diastolic arterial blood pressure through the inflation and the deflation of a cuff. This method is uncomfortable to the user and may cause anxiety which in turns can affect the blood pressure. The approach utilized in this thesis is based on deep learning techniques: different neural networks are used to infer ABP starting from photoplethysmogram and electrocardiogram. In particular we predicted ABP first utilizing only PPG and then PPG and ECG, we demonstrated that adding ECG improved performance in every configuration achieving, after personalization, a MAE equal to 4.118 mmHg on systolic blood pressure 2.228 mmHg on diastolic blood pressure with a modified ResNet followed by 3 LSTM layers. Results were compliant with the American National Standards of the Association for the Advancement of Medical Instrumentation. ECG, PPG and blood pressure measurements are extracted from the MIMIC database that contains clinical signal data reflecting real measurements and validates the results on a custom dataset created at Neuronica Lab, Politecnico di Torino.
Multiprobe Time Reversal for High-Fidelity Vortex-Mode-Division Multiplexing Over a Turbulent Free-Space Link

The orbital angular momentum (OAM) of photons presents a degree of freedom for enhancing the secure key rate of free-space quantum key distribution (QKD) through mode-division multiplexing (MDM). However, atmospheric turbulence can lead to substantial modal crosstalk, which is a long-standing challenge to MDM for free-space QKD. Here, we show that the digital generation of time-reversed wavefronts for multiple probe beams is an effective method for mitigating atmospheric turbulence. We experimentally characterize seven OAM modes after propagation through a 340-m outdoor free-space link and observe an average modal crosstalk as low as 13.2% by implementing real-time time reversal. The crosstalk can be further reduced to 3.4% when adopting a mode spacing $\mathrm{\ensuremath{\Delta}}\ensuremath{\ell}$ of 2. We implement a classical MDM system as a proof-of-principle demonstration, and the bit error rate is reduced from $3.6\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}3}$ to be less than $1.3\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}7}$ through the use of time reversal. We also propose a practical and scalable scheme for high-speed, mode-multiplexed QKD through a turbulent link. The modal crosstalk can be further reduced by using faster equipment. Our method can be useful to various free-space applications that require crosstalk suppression.
LifeStream: a high-performance stream processing engine for periodic streams

Hospitals around the world collect massive amounts of physiological data from their patients every day. Recently, there has been an increase in research interest to subject this data to statistical analysis to gain more insights and provide improved medical diagnoses. Such analyses require complex computations on large volumes of data, demanding efficient data processing systems. This paper shows that currently available data processing solutions either fail to meet the performance requirements or lack simple and flexible programming interfaces. To address this problem, we propose LifeStream, a high-performance stream processing engine for physiological data. LifeStream hits the sweet spot between ease of programming by providing a rich temporal query language support and performance by employing optimizations that exploit the periodic nature of physiological data. We demonstrate that LifeStream achieves end-to-end performance up to 7.5× higher than state-of-the-art streaming engines and 3.2× than hand-optimized numerical libraries on real-world datasets and workloads.
A Graphene Platform for Sensitive Electrochemical Immunoassay of Carcinoembryoninc Antigen Based on Gold-Nanoflower Biolabels

A new graphene immunosensing platform based on a new electrochemical immunosensor was designed for sensitive screening of carcinoembryoninc antigen (CEA) as a model biomarker in clinical immunoassay. Gold nanoflowers and single-stranded DNA (ssDNA) molecules were initially assembled onto the surface of graphene for the fabrication of the electrochemical immunosensor using layer-by-layer strategy, and then a sandwich-type immunoassay format was developed for CEA detection based on gold nanoflower-labeled signal antibodies. Graphene nanosheets with high conductivity and biocompatibility could greatly enhance the bound force with ssDNA, and improve the analytical properties of the immunosensor. Gold nanoflowers with electrochemical activity provide a large surface area for the label of biomolecules. The graphene platform displayed a good electrochemical behaviour toward the detection of CEA. Under optimal conditions, the electrochemical immunosensor exhibited a wide dynamic range of 0.05 to 45 ng mL−1 with a relatively low detection limit of 0.01 ng mL−1 CEA at signal-to-noise ratio of 3. Intra- and interassay coefficients of variation were below 12 %. The feasibility of the electrochemical immunosensor was evaluated for real-life clinical specimens, and no significant differences at the 95 % confidence level were encountered between the immunosensor and commercially available Roche Elecsys 2010 Electrochemiluminescent Automatic Analyzer. In addition, the analytical properties of the immunosensor were comparatively favourable with those of other electrochemical immunosensors.
Robust Stability of Uncertain Quantum Input-Output Networks

This paper presents a systematic method to analyze stability and robustness of uncertain Quantum Input-Output Networks (QIONs). A general form of uncertainty is introduced into quantum networks in the SLH formalism. Results of this paper are built up on the notion of uncertainty decomposition wherein the quantum network is decomposed into nominal (certain) and uncertain sub-networks in cascade connection. Suﬃcient conditions for robust stability are derived using two diﬀerent methods. In the ﬁrst approach, a generalized small-gain theorem is presented and in the second approach, robust stability is analyzed within the framework of Lyapunov theory. In the second method, the robust stability problem is reformulated as feasibility of a Linear Matrix Inequality (LMI), which can be examined using the well-established systematic methods in the literature.
Resistivity study and computer modelling of the isothermal transformation kinetics of Ti-6AI-4V and Ti-6AI-2Sn-4Zr-2Mo-0.08Si

A resistivity technique is used to study the kinetics of b (cid:222) a1b transformation for Ti–6Al–4V and Ti–6Al–2Sn–4Zr–2Mo–0.08Si alloys at isothermal conditions. The kinetics are modeled in the framework of the Johnson–Mehl–Avrami (JMA) theory. The JMA kinetic parameters for different temperatures and conditions of the above transformation are derived. A good agreement between the calculated and the experimental kinetics is demonstrated Two different mechanisms of a -phase formation depending on the temperature of isothermal exposure are proposed. The calculation of the thermodynamic equilibria showed good agreement with the experimental data for the Ti–6Al–4V alloy and disagreement for the Ti–6Al–2Sn–4Zr–2Mo–0.08Si alloy. Based on the resistivity results, time– temperature–transformation diagrams for both alloys are designed. (cid:211)
Revealing the trajectories of KLAIM tuples, statically (cid:63)

K LAIM (Kernel Language for Agents Interaction and Mobility) has been devised to design distributed applications composed by many components deployed over the nodes of a distributed infrastructure and to offer programmers primitive constructs for communicating, distributing and retrieving data. Data could be sensitive and some nodes could not be secure. As a consequence it is important to track data in their traversal of the network. To this aim, we propose a Control Flow Analysis that over-approximates the behaviour of K LAIM processes and tracks how tuple data can move in the network.
Removal of dissolved Tri n-butyl phosphate from aqueous streams of reprocessing origin: engineering scale studies

Abstract Tri-n-butyl phosphate is extensively used in aqueous reprocessing of spent nuclear fuel for separating the fissile elements from fission products and other wastes. Presence of tri-n-butyl phosphate in these acidic aqueous solutions leads to a limitation with respect to evaporation of these streams for achieving volume reduction. Process development for removal of dissolved tri-n-butyl phosphate from acidic solutions has been addressed in the present work using solvent extraction route. Batch studies were followed with continuous runs using Combined Airlift based Mixer-Settler Unit (CAL-MSU). These studies have revealed that the dissolved TBP in aqueous solutions can be brought down from 160 ppm to 15 ppm using dodecane with a maximum loading of 3.6% TBP. All these studies on CAL-MSU have demonstrated the applicability of diluent in removing dissolved TBP from reprocessing aqueous solutions. The results obtained reveal an alternate method for stripping out dissolved TBP from reprocessing solutions.
Efficient data-driven encoding of scene motion using Eccentricity

This paper presents a novel approach of representing dynamic visual scenes with static maps generated from video/image streams. Such representation allows easy visual assessment of motion in dynamic environments. These maps are 2D matrices calculated recursively, in a pixel-wise manner, that is based on the recently introduced concept of Eccentricity data analysis. Eccentricity works as a metric of a discrepancy between a particular pixel of an image and its normality model, calculated in terms of mean and variance of past readings of the same spatial region of the image. While Eccentricity maps carry temporal information about the scene, actual images do not need to be stored nor processed in batches. Rather, all the calculations are done recursively, based on a small amount of statistical information stored in memory, thus resulting in a very computationally efficient (processor- and memory-wise) method. The list of potential applications includes video-based activity recognition, intent recognition, object tracking, video description, and so on.
Efficient UAV Trajectory-Planning using Economic Reinforcement Learning

Advances in unmanned aerial vehicle (UAV) design have opened up applications as varied as surveillance, firefighting, cellular networks, and delivery applications. Additionally, due to decreases in cost, systems employing fleets of UAVs have become popular. The uniqueness of UAVs in systems creates a novel set of trajectory or path planning and coordination problems. Environments include many more points of interest (POIs) than UAVs, with obstacles and no-fly zones. We introduce REPlanner, a novel multi-agent reinforcement learning algorithm inspired by economic transactions to distribute tasks between UAVs. This system revolves around an economic theory, in particular an auction mechanism where UAVs trade assigned POIs. We formulate the path planning problem as a multi-agent economic game, where agents can cooperate and compete for resources. We then translate the problem into a Partially Observable Markov decision process (POMDP), which is solved using a reinforcement learning (RL) model deployed on each agent. As the system computes task distributions via UAV cooperation, it is highly resilient to any change in the swarm size. Our proposed network and economic game architecture can effectively coordinate the swarm as an emergent phenomenon while maintaining the swarm's operation. Evaluation results prove that REPlanner efficiently outperforms conventional RL-based trajectory search.
Searching for Mg ii absorbers in and around galaxy clusters

To study environmental effects on the circumgalactic medium (CGM), we use the samples of redMaPPer galaxy clusters, background quasars and cluster galaxies from the SDSS. With ∼82 000 quasar spectra, we detect 197 Mg ii absorbers in and around the clusters. The detection rate per quasar is 2.7±0.7 times higher inside the clusters than outside the clusters, indicating that Mg ii absorbers are relatively abundant in clusters. However, when considering the galaxy number density, the absorber-togalaxy ratio is rather low inside the clusters. If we assume that Mg ii absorbers are mainly contributed by the CGM of massive star-forming galaxies, a typical halo size of cluster galaxies is smaller than that of field galaxies by 30±10 per cent. This finding supports that galaxy haloes can be truncated by interaction with the host cluster.
Synthesis and Characterization of Hexagonally Packed Mesoporous Tantalum Oxide Molecular Sieves

The synthesis and characterization of Ta-TMS1, a new member of a growing family of hexagonally packed transition-metal oxide mesoporous molecular sieves (termed TMS1) is described. Ta-TMS1 exhibits a hexagonal array of pores which can be varied in size from 20 to 40 A and surface areas of over 500 m2/g. The thermal and hydrothermal stabilities of Ta-TMS1 are 500 and 450 °C, respectively, making this system the most stable transition-metal oxide molecular sieve yet isolated. The high hydrocarbon adsorption capacities of this material make it a promising candidate as a catalyst support for hydrocarbon re-forming processes. The synthesis of this material was achieved by a novel approach involving the careful hydrolysis of long-chain primary amine complexes of Ta(OEt)5. This ligand-assisted templating mechanism represents a new approach to the synthesis of porous materials in that the inorganic precursor is covalently bonded to the template throughout synthesis. The high thermal stability, ease of synthesis a.