Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnosis of Noise Inside Neonatal Incubators under Free-Field Conditions

The study aims to diagnose the sound pressure levels inside incubators in a controlled environment under free-field conditions. The tests were carried out in a semi-anechoic room under the standard UNE-EN ISO 3745:2012/A1:2018 in three different operating states: off, on, and on with a temperature alarm triggered. Sound pressure levels were analyzed in three different models of incubators, both inside and outside. The main noise indices analyzed were the corrected equivalent continuous level (LKeq) and the equivalent continuous level (Leq) in third-octave bands. The results obtained under normal operating conditions showed variations among the different incubators, with overall values between 48.8 and 56.3 dBA. The influence of the alarm considerably worsened these data. The values obtained showed that premature newborns are exposed to noise levels above international recommendations. All incubators tested showed the presence of tonal components, both outside and inside the incubator cabin, and, in some cases, low-frequency components, but no impulsivity components were observed in any case.
Image Approach to Uzbek Speech Recognition

This paper proposes the method to extract the features of speech signal from its spectrograms by applying a two-dimensional discrete-cosine transform over them. The calculated informative features are classified by three machine learning algorithms with a teacher, i.e. KNN, RF and SVM. As part of the scientific research, a database of Uzbek syllables, words and sentences is formed. Experiments have shown that when the proposed method is applied in combination with KNN classification method, the recognition accuracy of syllables, words and sentences together averaged 98,6%, thus outperforming RF (91,2%) and SVM (94,5%) algorithms.
Glacial meltwater and sediment resuspension can be important sources of dissolved and total dissolvable aluminum and manganese to coastal ocean surface waters

The supply of aluminum (Al) and manganese (Mn) to the Gulf of Alaska from coastal sources is poorly constrained. Here, we investigate the seasonality of sources to better constrain Al and Mn cycling in the coastal Gulf of Alaska region and add to our understanding of seasonal and interannual inputs. We examine Mn and Al behavior over the shelf to distinguish between redox‐induced release of dissolved trace metals (i.e., diffusion from sediments), sediment resuspension, and meltwater release. Data suggest that, prior to the onset of stratification in the spring, shelf sediment resuspension from deep mixing is an important mechanism for trace metal delivery to surface waters. As spring and summer ensue, increased meltwater discharge coupled with increased surface water temperatures result in stratification of the water column within the coastal Gulf of Alaska, and meltwater becomes a more important source of Al and Mn to the surface waters. The limited data available suggest that a redox‐driven flux of Mn from shelf sediments is not as important as the meltwater flux during the summer. In addition, dissolved trace metal concentrations in meltwater‐influenced plumes over the shelf exhibit conservative mixing, while particulate trace metal concentrations do not behave conservatively. This indicates that there are different physical controls (particle settling vs. mixing) on the spatial distributions of dissolved and particulate Al and Mn in coastal waters, the manifestation of which are likely highly variable and dependent on the trace metal composition of the river and the hydrodynamics governing this interaction at any given time.
A Fast and Effective Classification Method for Missing Data

Missing data is common in life. The preprocessing of missing data is the premise of pattern classification. Therefore, it is necessary to use the existing reliable training data set to attribute missing data. These methods have a significant impact on dealing with ambiguity in data sets. Therefore, it is necessary and effective to use accurate data and estimation methods to imput missing data. This paper presents a fast and effective method for missing data classification. Specifically, we propose two strategies to estimate incomplete data, namely, nearest class-center imputation (NCCI) and weighted class-center imputation (WCCI). At the same time, in order to further eliminate the influence of noise in the training set, we also propose a method to optimize the training set. Finally, a conventional classifier is used to classify the estimated incomplete data. The effectiveness of the proposed method is verified by testing different datasets with related methods.
A Little Nickel Goes a Long Way: Ni Incorporation into Rh2P for Stable Bifunctional Electrocatalytic Water Splitting in Acidic Media

In acidic media, many transition-metal phosphides are reported to be stable catalysts for the hydrogen evolution reaction (HER) but typically exhibit poor stability toward the corresponding oxygen evolution reaction (OER). A notable exception appears to be Rh2P/C nanoparticles, reported to be active and stable toward both the HER and OER. Previously, we investigated base-metal-substituted Rh2P, specifically Co2–xRhxP and Ni2–xRhxP, for HER and OER as a means to reduce the noble-metal content and tune the reactivity for these disparate reactions. In alkaline media, the Rh-rich phases were found to be most active for the HER, while base-metal-rich phases were found to be the most active for the OER. However, Co2–xRhxP was not stable in acidic media due to the dissolution of Co. In this study, the activity and stability of our previously synthesized Ni2–xRhxP nanoparticle catalysts (x = 0, 0.25, 0.50, 1.75) toward the HER and OER in acidic electrolyte are probed. For the HER, the Ni0.25Rh1.75P phase was found to have comparable geometric activity (overpotential at 10 mA/cmgeo2) and stability to Rh2P. In contrast, for OER, all of the tested Ni2–xRhxP phases had similar overpotential values at 10 mA/cmgeo2, but these were >2x the initial value for Rh2P. However, the activity of Rh2P fades rapidly, as does Ni2P and Ni-rich Ni2–xRhxP phases, whereas Ni0.25Rh1.75P shows only modest declines. Overall water splitting (OWS) conducted using Ni0.25Rh1.75P as a catalyst relative to the state-of-the-art (RuO2||20% Pt/C) revealed comparable stabilities, with the Ni0.25Rh1.75P system demanding an additional 200 mV to achieve 10 mA/cmgeo2. In contrast, a Rh2P||Rh2P OWS cell had a similar initial overpotential to RuO2||20% Pt/C, but is unstable, completely deactivating over 140 min. Thus, Rh2P is not a stable anode for the OER in acidic media, but can be stabilized, albeit with a loss of activity, by incorporation of nominally modest amounts of Ni.
Building renovation business: the effects of specialization on profitability

Abstract The importance of renovations is widely recognized, for example, due to renovation backlogs in the developed countries. The urbanization megatrend, among many other factors, is still increasing the need for renovations in the long run. One approach to review the renovation issue is the profitability of the companies that will tackle the increasing demand in the sector. By applying mainly quantitative methods, using the 15-year timeframe (2005–2019) and earnings before interest, taxes, depreciation, and amortization (EBITDA) and return on assets (ROA) as measures, this study reviewed the profitability of the building renovation (BR) companies from two perspectives: how does focusing on certain special services (specialized BR) fare compared to focusing on a wide range of services (wide BR) and what are the profitability differences among specialized BR companies? The results show that, when reviewing the research timeframe in total, there are no differences in profitability between wide BR and specialized BR companies. However, an annual review reveals that specialized BR companies are profitability-wise more vulnerable to economic cycles. Among the specialized BR companies, there are several differences in profitability; classically, specialization in a niche market with a deliberate customer base and low competition level is gainful. The research provides new information about an unresearched area encouraging companies to re-think their strategic choices considering service specialization and performance.
Affordance-Based Human–Robot Interaction With Reinforcement Learning

Planning precise manipulation in robotics to perform grasp and release-related operations, while interacting with humans is a challenging problem. Reinforcement learning (RL) has the potential to make robots attain this capability. In this paper, we propose an affordance-based human-robot interaction (HRI) framework, aiming to reduce the action space size that would considerably impede the exploration efficiency of the agent. The framework is based on a new algorithm called Contextual Q-learning (CQL). We first show that the proposed algorithm trains in a reduced amount of time (2.7 seconds) and reaches an 84% of success rate. This suits the robot’s learning efficiency to observe the current scenario configuration and learn to solve it. Then, we empirically validate the framework for implementation in HRI real-world scenarios. During the HRI, the robot uses semantic information from the state and the optimal policy of the last training step to search for relevant changes in the environment that may trigger the generation of a new policy.
Corporate giving as earnings quality signal: some new evidence from Nigeria

PURPOSE – The purpose of this paper is to re-examine the effect of CSR disclosures on earnings quality in Nigeria beyond the conventional wisdom of statistical significance. DESIGN/METHODOLOGY/APPROACH – The sample consists of 300 company-year observations from 2013 to 2018 of listed companies on the Nigerian Stock Exchange. The research hypothesis was tested using multivariate linear regression on the total sample and subsamples. Pre-study statistical power analysis was carried out to ensure that the study is adequately powered to detect an effect if it exists. FINDINGS – The main results suggest that corporate giving is not related to earnings quality. Though, additional analysis for the income-decreasing subsample was statistically significant, the effect size for both the primary and additional analyses is weak, negligible, and unlikely to be of any practical significance. The results retained their robustness after further analysis. PRACTICAL IMPLICATIONS – The findings could inspire policymakers and regulators to shift attention to other areas of CSR that matters. It could also serve as an input to the current debate on CSR, especially the ongoing consideration of a Bill for an Act to regulate corporate giving in Nigeria. ORIGINALITY/VALUE – This is probably the first papers to provide critical results index needed for substantive comparison of future studies. Hence, the paper serves as a baseline for future research on the topic.
Upper Bounds on Optimal Correct Probability of Quantum State Discrimination with and without Inconclusive Results

We propose an upper bound on the maximum correct probability of quantum measurements. The proposed bound is obtained by a suboptimal solution to the dual problem of the optimal state discrimination problems. We derive that a slightly modified version of the proposed bound is tighter than that proposed by Qiu et al. [Phys. Rev. A 81, 042329 (2010)]. We also propose an upper bound on the maximum correct probability with a fixed rate of inconclusive results. The performance of the proposed bounds are evaluated through numerical experiments.
Penapisan Derau Gaussian, Speckle dan Salt&Pepper Pada Citra Warna

Quality of digital image can decrease becouse some noises. Noise can come from lower quality of image recorder, disturb when transmission data process and weather. Noise filtering can make image better becouse will filtering that noise from the image and can improve quality of digital image. This research have aim to improve color image quality with filtering noise. Noise ( Gaussian, Speckle, Salt&Pepper) will apply to original image, noise from image will filtering use Bilateral Filter method, Median Filter method and Average Filter method so can improve color image quality. To know how well this research do, we use PSNR (Peak Signal to Noise Ratio) criteria with compared original image and filtering image (image after using noise and filtering noise). This research result with noise filtering Gaussian (variance = 0 . 5) , highest PSNR value found in the Bilateral Filter method is 27.69. Noise filtering Speckle (variance = 0 . 5) , highest PSNR value found in the Average Filter method is 34.12. Noise filtering Salt&Pepper (variance = 0. 5), highest PSNR value found in the Median Filter method is 31 . 27 . Keywords — Bilateral Filter, image restoration, derau Gaussian, Speckle dan Salt&Pepper
Preparation of nanofibrous poly (L-lactic acid) scaffolds using the thermally induced phase separation technique in dioxane/polyethylene glycol solution

ABSTRACT Porous nanofibrous poly (L-lactic acid) (PLLA) scaffolds were fabricated in combination with a thermally induced phase separation technique using a dioxane/polyethylene glycol (PEG) system. The effect of factors such as molecular weight of PEG, aging treatment, aging or gelation temperature, and the ratio of PEG to dioxane were investigated. The results revealed that all scaffolds had high porosity, and had a significant impact on the formation of nanofibrous structures. The decrease in the molecular weight and aging or gelation temperature leads to a thinner and more uniform fibrous structure.
Joint Optimization of Transmit Beamforming and Base Station Cache Allocation in Multi-Cell C-RAN

This paper studies a downlink cloud radio access network (C-RAN) consisting of a centralized processor (CP) and multiple cells each one of which has one cache-enabled base station (BS) connected to the CP through wireless backhaul links. In such a network, we aim to determine sizes of the cache allocated to the BSs such that the long-term delivery time of the available files is minimized. Considering the two-stage nature of this problem, we formulate a beamforming optimization problem and a cache allocation problem. The purpose of the first one is to deliver un-cached portions of files to the BSs in the shortest possible time during the beamforming stage. The second problem aims to minimize long-term expectation of delivery time through optimal cache allocation during the cache allocation stage. We rigorously prove that the time-varying beamformers can be assumed to be piece-wise constant functions of time. Based on this finding, the beamforming optimization problem does not appear to be amenable to a computationally efficient solution. Hence, we resort to the zero-forcing (ZF) beamforming approach to tackle the beamforming optimization problem. Using the results of the beamforming optimization problem, we prove that the cache allocation problem is a convex optimization problem; hence it can be solved by any convex optimization solvers. Simulation results show that the loss of optimality due to adopting ZF beamforming is negligible. Moreover, the superiority of our proposed cache allocation scheme over several other heuristic schemes including proportional cache size allocation is shown.
Biotransformations Performed by Yeasts on Aromatic Compounds Provided by Hop—A Review

The biodiversity of some Saccharomyces (S.) strains for fermentative activity and metabolic capacities is an important research area in brewing technology. Yeast metabolism can render simple beers very elaborate. In this review, we examine much research addressed to the study of how different yeast strains can influence aroma by chemically interacting with specific aromatic compounds (mainly terpenes) from the hop. These reactions are commonly referred to as biotransformations. Exploiting biotransformations to increase the product’s aroma and use less hop goes exactly in the direction of higher sustainability of the brewing process, as the hop generally represents the highest part of the raw materials cost, and its reduction allows to diminish its environmental impact.
Modelling and identification for non-uniformly periodically sampled-data systems

The authors state the non-uniformly periodically sampling pattern and derives the state-space models of non-uniformly sampled-data systems with coloured noises, and further obtains the corresponding transfer function models. Difficulties of identification are that there exist unknown inner variables and unmeasurable noise terms in the information vectors. By means of the auxiliary model method, an auxiliary model based multi-innovation generalised extended stochastic gradient (SG) algorithm is presented by expanding the scalar innovation to the innovation vector and introducing the innovation length. The proposed algorithm provides higher parameter estimation accuracy and faster convergence rate than the SG algorithm due to repeatedly using the system innovation.
FPGA-specific decimal sign-magnitude addition and subtraction

ABSTRACT The interest in sign-magnitude (SM) representation in decimal numbers lies in the IEEE 754-2008 standard, where the significand in floating-point numbers is coded as SM. However, software implementations do not meet performance constraints in some applications and more development is required in programmable logic, a key technology for hardware acceleration. Thus, in this work, two strategies for SM decimal adder/subtractors are studied and six new Field Programmable Gate Array (FPGA)-specific circuits are derived from these strategies. The first strategy is based on ten’s complement (C10) adder/subtractors and the second one is based on parallel computation of an unsigned adder and an unsigned subtractor. Four of these alternative circuits are useful for at least one area-time-trade-off and specific operand size. For example, the fastest SM adder/subtractor for operand sizes of 7 and 16 decimal digits is based on the second proposed strategy with delays of 3.43 and 4.33 ns, respectively, but the fastest circuit for 34-digit operands is one of the three specific implementations based on C10 adder/subtractors with a delay of 4.65 ns.
Risk Assessment of Pre-dialysis Chronic Kidney Disease (CKD) Patients for Cardiovascular Disease (CVD) in a Tertiary Hospital in Nigeria: A Case-Controlled Cross-Sectional Study

Introduction: Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in the chronic kidney disease (CKD) population. CKD patients are more likely to die from CVD before ever reaching end-stage renal disease (ESRD). The study, therefore, seeks to identify the prevalence of risk factors of CVD in CKD patients such as systemic hypertension, anemia, dyslipidemia, hypoalbuminemia, albuminuria, and abnormal calcium/phosphate products. Methods: The study was a case-control cross-sectional study where one hundred fifty hypertensive CKD patients and age- and sex-matched hypertensive non-CKD subjects were consecutively enrolled at the renal unit of Delta State University Teaching Hospital (DELSUTH), Oghara. Results: The findings of the study revealed the mean ages of cases and controls to be 48.91±11.93 years and 51.0±15.45 years respectively (p-value 0.182). There was an equal number of males and females among the study group and controls (92 males and 58 females) making a male-to-female ratio of 3:2. The prevalence of CVD risk factors such as diabetes mellitus, hypercholesterolemia, hypertriglyceridemia, elevated low-density lipoprotein, anemia, hypocalcemia, hyperphosphatemia, albuminuria, and hypoalbuminemia was significantly higher among the CKD group compared to controls. Similarly, the prevalence of reduced high-density lipoprotein (HDL) was higher among cases than controls, the difference was however not statistically significant. Conclusion: The study has shown that systemic hypertension, diabetes, anemia, dyslipidemia, hypoalbuminemia, albuminuria, and abnormal calcium/phosphate products increases the risk for CVD in the general population but is more expressed and significant in CKD patients.
A Review of Barbed Sutures—Evolution, Applications and Clinical Significance

Surgical ligatures are a critical component of any surgical procedure since they are the device that provides immediate post-surgical tissue apposition. There have been several studies to improve the design and use of these wound closure devices for different surgical procedures. Yet, there is no standardized technique or device that can be used for any specific application. Over the last two decades, there has been an increased focus on the innovative surgical sutures known as knotless or barbed sutures, along with studies focusing on their advantages and disadvantages in clinical environments. Barbed sutures were invented to reduce the localized stress on the approximated tissues as well as facilitating the surgical technique and improving the clinical outcome for the patient. This review article discusses how barbed sutures evolved from the first patent published in 1964 and how these barbed sutures influence the surgical outcomes in different procedures ranging from cosmetic surgery to orthopedic surgery performed on both human patients and animals.
Application of Markov chain Monte Carlo methods to projecting cancer incidence and mortality

Summary. Projections based on incidence and mortality data collected by cancer registries are important for estimating current rates in the short term, and public health planning in the longer term. Classical approaches are dependent on questionable parametric assumptions. We implement a Bayesian age–period–cohort model, allowing the inclusion of prior belief concerning the smoothness of the parameters. The model is described by a directed acyclic graph. Computations are carried out by using Markov chain Monte Carlo methods (implemented in BUGS) in which the degree of smoothing is learnt from the data. Results and convergence diagnostics are discussed for an exemplary data set. We then compare the Bayesian projections with other methods in a range of situations to demonstrate its flexibility and robustness.
Perceived anthropomorphism and purchase intention using artificial intelligence technology: examining the moderated effect of trust

PurposeThis study explores features that impact consumers' purchase intention through artificial intelligence (AI), because it is believed that through artificial intelligence, consumers' intention to purchase grows significantly, especially in the retail sector, whereby retailers provide lucrative offers to motivate consumers. The study develops a theoretical framework based on media-richness theory to investigate the role of perceived anthropomorphism toward an intention to purchase products using AI.Design/methodology/approachThe study is based on cross-sectional data through an online survey. The data have been analyzed using PLS-SEM and SPSS PROCESS macro.FindingsThe results show that consumers tend to demand anthropomorphized products to gain a better shopping experience and, therefore, demand features that attract and motivate them to purchase through artificial intelligence via mediating variables, such as perceived animacy and perceived intelligence. Moreover, trust in artificial intelligence moderates the relationship between perceived anthropomorphism and perceived animacy.Originality/valueThe study investigates and concludes with managerial and academic insights into consumer purchase intention through artificial intelligence in the retail and marketing sector.