Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental study on the detection of trace H2S and H2O in high-voltage combination appliances based on TDLAS technology

H2S and moisture content are important indicators to evaluate the potential faults and insulation performance of H2S electrical appliances. Currently, there is no report on the simultaneous detection of H2S gas and moisture content. In order to realize the accurate detection of H2S gas and H2O at the same time, Lorentz simulation analysis was performed on the absorption spectra of H2S gas and H2O molecules. The absorption was strongest near 2684nm, and there was no cross interference of SF6 other decomposition products. H2S and moisture content at different concentrations were measured by TDLAS technology in a customized CW-DFB laser with a central wavelength of 2. 68μm combined with a small-volume Herriott long-range cell with multiple reflections. In order to verify the detection ability of selected spectral lines to H2S gas and H2O molecules at low concentration, trace H2S and H2O vapor with SF6 as background were detected by direct absorption technology and wavelength modulation technology respectively. In the second harmonic absorption, the minimum detection lower limit of H2S and H2O is 9.01×10-6 and 4.54×10-6, respectively. The maximum harmonic signal amplitude has a good linear correlation with concentration, and the linear fitting degree R2 is 0.978 and 0.997, respectively. SF6 gas displacement experiments with different flow rates showed that the equilibrium time of H2S and moisture content at 708mL/min was 90s and 100s, respectively. The response time of H2S and H2O at the same concentration and injection velocity is 30s and 45s, respectively. Wavelength modulation technology can provide a reliable experimental basis for the simultaneous detection of trace H2S and H2O in high voltage combination electrical appliances.
Rates and Microbial Players of Iron-Driven Anaerobic Oxidation of Methane in Methanic Marine Sediments

The flux of methane, a potent greenhouse gas, from the seabed is largely controlled by anaerobic oxidation of methane (AOM) coupled to sulfate reduction (S-AOM) in the sulfate methane transition (SMT). S-AOM is estimated to oxidize 90% of the methane produced in marine sediments and is mediated by a consortium of anaerobic methanotrophic archaea (ANME) and sulfate reducing bacteria. An additional methane sink, i.e., iron oxide coupled AOM (Fe-AOM), has been suggested to be active in the methanic zone of marine sediments. Geochemical signatures below the SMT such as high dissolved iron, low to undetectable sulfate and high methane concentrations, together with the presence of iron oxides are taken as prerequisites for this process. So far, Fe-AOM has neither been proven in marine sediments nor have the governing key microorganisms been identified. Here, using a multidisciplinary approach, we show that Fe-AOM occurs in iron oxide-rich methanic sediments of the Helgoland Mud Area (North Sea). When sulfate reduction was inhibited, different iron oxides facilitated AOM in long-term sediment slurry incubations but manganese oxide did not. Especially magnetite triggered substantial Fe-AOM activity and caused an enrichment of ANME-2a archaea. Methane oxidation rates of 0.095 ± 0.03 nmol cm–3 d–1 attributable to Fe-AOM were obtained in short-term radiotracer experiments. The decoupling of AOM from sulfate reduction in the methanic zone further corroborated that AOM was iron oxide-driven below the SMT. Thus, our findings prove that Fe-AOM occurs in methanic marine sediments containing mineral-bound ferric iron and is a previously overlooked but likely important component in the global methane budget. This process has the potential to sustain microbial life in the deep biosphere.
The Second Case of Death in the Same Family from Chronic Renal Failure and Systemic Oxalosis as a Result of Primary Hyperoxaluria Type1

Background: Primary hyperoxaluria is a rare cause of chronic renal failure in children worldwide, but it is an important cause of chronic renal failure among children under 15 years old in Jenin district (Palestine) contributing about 67% of the causes of chronic renal failure among these children as was found in a previous study. The infantile form of hyperoxaluria manifests early with nephrocalcinosis and leads to end-stage renal failure in early childhood and the most severe form may lead to death during the first year of life. Case presentation: We report the case of a 8-years old boy with a history of primary hyperoxaluria type1 since the age of three months presented with ascites, generalized edema, fever, myopathy, limited movement ability, general malaise, severe malnutrition, and respiratory difficulties. Clinical, laboratory, ultrasound, cardiac investigations revealed end-stage renal failure accompanied by heart failure, liver failure, liver cirrhosis, and systemic oxalosis. The patient was in a bad condition, stayed 4 days in the ICU and died in the fourth day from complications of systemic oxalosis. Conclusion: History revealed that this is the second case of death in the same family from ESRD and complications of primary hyperoxaluria. The first death was for his brother since about 10 years who died at age of three months. It seems that the primary hyperoxaluria type 1 in this family is very severe and leads to end-stage renal disease and systemic oxalosis in early childhood despite conservative treatment. Since, sometimes, there are more than one case of primary hyperoxaluria in the same family, all children in the family with a history of primary hyperoxaluria - even who looks like healthy - must be screened for the disease because early diagnosis is important in treating strategies and delaying complications such as end-stage renal disease and systemic oxalosis.
Improvements on Probability Model for Sightseeing Route Recommendation Method Employing Generalized Formulation in terms of Edges

This paper proposes an improved formulation of probability model for sightseeing route recommendation method based on a generalized formulation in terms of edges. In sightseeing route recommendation, at least, methods has to predict spots to be visited by a user and a route to travel them. Recommendation process can be modeled as a optimization problem, such as Traveling Salesman Problem or Orienteering Problem. Simulated annealing (SA) is one of the meta-heuristics to solve optimization problem, in which problems can be modeled as sampling from a probability model. A method using SA on Edge vector-based formulation (EV) has been proposed, which has introduced a concept of a real and virtual edge to deal with a non-complete graph like a real world road map. Using this formulation, EV has generalized both spots and roads passed by a user to edges, then applied SA to its formulated problem. The shortage of EV is that this model deals with the real and virtual edges asymmetrically, which causes decreasing of performance. This paper proposes an improved formulation of the objective function of EV. Experimental result shows that asymmetry treatment actually decreases performance, and proposed formulation gives grater performance than that of original formulation.
Underdetermined Blind Source Separation for Hard Clipped Stereophonic Mixtures

Underdetermined source separation becomes more challenging when the observed mixtures are distorted. A novel framework for source separation from mixtures affected by clipping distortion is presented. Our method exploits knowledge about the geometry of clipped mixtures when the sources are disjoint in time. This knowledge constrains a convex optimisation problem, which we formulate and solve to recover the sources. This paper develops work previously published substantially by implementing novel preprocessing steps inspired by geometric observations of clipped mixtures of time-disjoint sources. Evaluation experiments showed a significant increase in objective recovery performance for the proposed method compared with sequential approaches for speech and synthetic sources.
A Fast Encoding of 3GPP MBMS Raptor Codes Based on Operation Instruction

In this letter, we propose an efficient encoding scheme for 3GPP Raptor codes. When encoding numerous information blocks with the same size, conventional encoder is similar to matrix multiplication in the first stage of the encoding. The main idea of the proposed method is to utilize the potential redundancy in the multiplication. A series of ordered operation instructions, stored in a variable called <inline-formula> <tex-math notation="LaTeX">${\it\text { Index}}$ </tex-math></inline-formula>, are established to achieve the goal. To construct <inline-formula> <tex-math notation="LaTeX">${\it\text { Index}}$ </tex-math></inline-formula>, two schemes are introduced in this letter. Analysis demonstrates that the proposed schemes have less complexity than the conventional one. Furthermore, the simulation results show that both of them take less than 1/3 the encoding time of the original method.
Performance Analysis of Agile-Beam NOMA in Millimeter Wave Networks

We propose an agile-beam non-orthogonal multiple access (NOMA) scheme for millimeter wave (mmWave) communication networks. The agile-beam NOMA scheme flexibly switches between the single/multi-beam NOMA depending on the user pairing results. In particular, a novel user pairing strategy employing both distance and angle information is designed to facilitate the agile-beam NOMA transmission. More specifically, the base station first selects one user from the far user group randomly and then pairs it with the user that has the minimum angle difference in the near user group. This pairing strategy efficiently exploits the channel sparsity in user domain while supporting flexible mmWave transmission with agile-beam NOMA. Then an in-depth performance analysis is carried out to reveal the interplay between the key system parameters and the coverage probability of the proposed agile-beam NOMA by using the tool of stochastic geometry. Moreover, numerical results demonstrate the superiority of the proposed agile-beam NOMA over the conventional NOMA and orthogonal multiple access (OMA) in mmWave communication networks.
Speech Emotion Recognition of Merged Features Based on Improved Convolutional Neural Network

The correlation between speech and emotions provides the possibility for the realization of psychological forewarning system. Because of the unexpected noise when transferring speech into semantic information, directly obtaining emotion from voice information may perform more efficient and natural in speech emotion recognition. The key of improving the effect of speech emotion recognition is to introduce more reliable features and realized better accuracy. Based on this, 384 dimension hand-crafted features extracted from speech acting as the basic features of speech. At the same time, the multi-dimension learning features obtained from log- mel spectrogram through the improved convolutional neural network combined with long short term memory recurrent neural network will act as the highly features. By applying this method into speech emotion recognition, merged features have been proved to be effective. Compared with the results of only relying on features learning from the log-mel spectrogram, the merged features showed better performance.
Enabling Continuous Improvement of a Continuous Integration Process

Continuous Integration (CI) is a widely-adopted software engineering practice. Despite its undisputed benefits, like higher software quality and improved developer productivity, mastering CI is not easy. Among the several barriers when transitioning to CI, developers need to face a new type of software failures (i.e., build failures) that requires them to understand complex build logs. Even when a team has successfully introduced a CI culture, living up to its principles and improving the CI practice are also challenging. In my research, I want to provide developers with the right support for establishing CI and the proper recommendations for continuously improving their CI process.
An Assessment of Utilization of Intermittent Preventive Treatment of Malaria among Pregnant Women in Lusaka Province of Zambia

Malaria in pregnancy is one of the major global health concerns more especially in malaria-endemic regions where an estimated 30 million pregnancies occur every year. Malaria infection in pregnancy is a major risk factor for maternal and child health. A comparative cross-sectional study was conducted to assess the utilization and determinants of IPT of malaria by pregnant women in the rural and urban districts of Lusaka Province of Zambia. Four health centers in each study setting were conveniently selected. A systematic sampling technique was then applied to select the sample from the rural and urban health centers. Full IPT utilization was low in Lusaka Province. There was no significant difference between rural and urban levels of full utilization of IPT/SP in the Province (P = 0.288). There was however, a significant association between availability of fansidar in health facilities and IPT utilization in Lusaka (P = 0.012) while Chongwe showed insignificant association (P = 0.373). This study demonstrated that utilization levels of IPT/SP of malaria were very low in both the rural and urban districts despite the availability of Fansidar in health facilities. Education level and economic status were not significant factors for pregnant women to utilize IPT/SP services.
Optical fiber vacuum sensor based on modal interferometer and PDMS coating

This work studies the behavior of polydimethylsiloxane (PDMS) as a transducer for optical vacuum pressure measurements. The optical structure chosen for this device is a modal interferometer achieved by splicing a coreless multimode optical fiber between two single mode fibers. Then, an etching process is applied to the obtained device, in order to decrease the diameter of the fiber and increase the sensitivity. Finally, the fiber is coated by dip-coating with a layer of PDMS, which changes its volume with pressure changes. The device has been studied in the 1x10-3 mbar to 10 mbar range with a wavelength shift of 4 nm. A maximum sensitivity of 35 nm/mbar was obtained. The simple fabrication process, which can be applied to more sensitive structures, suggest that PDMS can be a good choice for the development of optical fiber vacuum sensors.
Novel Alternatives and Derivations of Linear Error-Functions for Phase-Compensator Design

A phase-compensator (PC) is needed in compensating for the phase distortions of a digital system such as digital-signal-processing (DSP) systems and telecommunication systems. This indicates that a PC must be designed to meet a given phase design specification. The intrinsic nonlinearity of the phase of an allpass system inevitably makes the PC design nonlinear. This paper discusses the nonlinear issue involved in the design of a digital allpass PC. To design an allpass PC using a linear-optimization method, it is desirable to develop an approximate linear error expression so that the error expression (cost function) is linear with respect to the allpass PC parameters (coefficients). This paper aims to derive and show two different linear error expressions that can be used to design an allpass using a linear-optimization technique. Consequently, the problem of designing an allpass PC can be approximately reduced to a linear-optimization problem. Using a linear-optimization method and the newly developed linear error expressions to design an allpass PC yields an approximate design with reasonable design accuracy, but the design procedure becomes much simpler than the case using a nonlinear error function. That is, the linear error expressions facilitate the PC design and can achieve a reasonably accurate PC with reduced design complexity through employing a linear-optimization approach.
How Intellectual Capital Combination Method Can Improve Corporate Performance in China’s Information Technology Industry

This study aims to illustrate how intellectual capital (IC) dimensions can improve corporate performance, and explore ideal IC combination methods in diversified firm situations to distribute IC dimensions effectively with limited resources. A universal theoretical framework based on Balanced Scorecard (BSC) theory is proposed to illustrate the way in which IC dimensions can enhance corporate performance from the perspective of stakeholders. Then, the rough set method is introduced to empirically explore ideal IC combination methods in diversified situations using 539 valid samples from China’s information technology industry. The theoretical analysis shows that IC dimensions work as a combination rather than as isolated parts to affect corporate performance, and different levels of corporate performance can be achieved by different IC combination methods under diverse firm factors. Ideal IC combination method is obtained through empirical study. Relational capital is the most important IC dimension, whereas leverage is the most important influence factor. This study extends IC management theory by profoundly illustrating the internal logical between IC dimensions and corporate performance through the theoretical framework. Additionally, this study introduces the rough set method to IC empirical research to explore ideal IC combination methods. Findings will deepen insight of practitioners about the essence of IC dimension combinations to corporate performance, and how they can apply the IC combination results to effectively distribute IC resources in diversified firm situations. The theoretical and practical research of this study extends both the third and fourth stages of IC research.
Facial Expression Recognition by Jointly Partial Image and Deep Metric Learning

The performance of facial expression recognition (FER) tends to deteriorate due to high intraclass variations and high interclass similarities. To address this problem, an expression recognition model based on a joint partial image and deep metric learning method (PI&DML) is proposed. First, we propose cropping the active units (AU) that are most closely related to the expression to generate a partial image for feature extraction, which is conducive to mitigating the negative impact of the abovementioned problems to some extent. Second, a novel expression metric loss function (EMLF) is suggested to enhance the intraclass similarities and interclass variations. Finally, superior performance is achieved by jointly optimizing the expression metric loss and classification loss. As demonstrated by the visualization results, the proposed EMLF is effective at increasing the distance between various expressions and reducing the distance between the same expressions. The evaluations on three public expression databases have demonstrated that our method is capable of achieving better results than the state-of-the-art methods.
Crack Segmentation on UAS-based Imagery using Transfer Learning

The vast number of images that typically arise from automated structure inspection, e.g. by means of unmanned aircraft systems (UAS), pose a challenge for inspectors. In order to support the human responsible, automated crack detection can help determine grave defects that require close-up inspection. The available crack datasets showed to be unsuited for crack segmentation on UAS-acquired imagery. Thus, a crack dataset was created to reflect the difficulties connected to the UAS-based imagery. The challenges mainly are low resolution and intensity of the cracks and re-occurring planking patterns. A convolutional neural network (CNN) for crack segmentation was derived from TERNAUSNET [1] applying transfer learning. It achieved an average precision (AP) of 75% on the challenging dataset. The code and dataset are available at https://github.com/khanhha/crack_segmentation.
Does a friendly robot make you feel better?

As robots are taking a more prominent role in our daily lives, it becomes increasingly important to consider how their presence influences us. Several studies have investigated effects of robot behavior on the extent to which that robot is positively evaluated. Likewise, studies have shown that the emotions a robot shows tend to be contagious: a happy robot makes us feel happy as well. It is unknown, however, whether the affect that people experience while interacting with a robot also influences their evaluation of the robot. This study aims to discover whether people’s affective and evaluative responses to a social robot are related. Results show that affective responses and evaluations are related, and that these effects are strongest when a robot shows meaningful motions. These results are consistent with earlier findings in terms of how people evaluate social robots.
Experimental Comparison of Decentralized Task Allocation Algorithms Under Imperfect Communication

We compare the performance of five state of the art decentralized task allocation algorithms under imperfect communication conditions. The decentralized algorithms we consider are CBAA, ACBBA, DHBA, HIPC and PI. All algorithms are evaluated using three different models of communication, including the Bernoulli model, the Gilbert-Elliot model, and the Rayleigh Fading model. All 15 of the resulting combinations of an algorithm with a communication model are evaluated in two different problem scenarios: (1) Collaborative visit, a scenario in which the agents have to collaboratively visit known stationary targets. (2) Collaborative search and visit, a scenario in which the agents have to collaboratively search for and then visit unknown stationary target locations. Each algorithm is evaluated in each scenario using two performance measures: (1) the maximum distance traveled by any agent (2) the maximum number of messages sent by any agent. Real-time experimental simulations show the trade-offs that exists between these five algorithms at different communication conditions.
The PLATINO Experience: A LoRa-based Network of Energy-Harvesting Devices for Smart Farming

Framed within the PLATINO research project we have prototyped an energy-harvesting device specifically designed for supporting a set of smart farming applications. To this purpose, our prototype is equipped, among other components, with several sensors for environmental and energy conditions monitoring and a LoRa communication module to enable a Low-Power Wide Area Network. The physical network will be composed of dozens of PLATINO devices acting as end-nodes and of a drone with limited time of flight acting as a mobile gateway, which will receive the data temporally stored on the end-devices. This paper analyzes the set of constraints imposed by the European LoRa regulations and by the drone itself to design an efficient communication protocol between the drone and the end-devices.
Deployable Microwave Imaging System

Non-invasive imaging is critical for component integrity assessment and structural health monitoring in a wide range of applications. Near-field microwave non-destructive techniques have been successfully used in the past to detect and map defects in opaque dielectric materials. This paper reports the development of a deployable microwave imaging system explicitly designed to alleviate some of the deployment limitations and expand the utility of microwave inspection systems in industrial settings. In the developed system, the microwave probe is moved by the operator on the gantry positioner above the specimen surface while relaying the probe’s location as well as its raw measurements to a backend subsystem for image rendering. The functionality of the proposed imaging system is demonstrated herein based on the imaging results of various samples.