Miracle-dz/UniFAR-FTUs huggingface dataset:
Climate change impacts on summer flood frequencies in two mountainous catchments in China and Switzerland

Flood events are difficult to characterize if available observation records are shorter than the recurrence intervals, and the non-stationarity of the climate adds additional uncertainty. In this study, we use a hydrological model coupled with a stochastic weather generator to simulate the summer flood regime in two mountainous catchments located in China and Switzerland. The models are set up with hourly data from only 10–20 years of observations but are successfully validated against 30–40-year long records of flood frequencies and magnitudes. To assess the climate change impacts on flood frequencies, we re-calibrate the weather generator with the climate statistics for 2021–2050 obtained from ensembles of bias-corrected regional climate models. Across all assessed return periods (10–100 years) and two emission scenarios, nearly all model chains indicate an intensification of flood extremes. According to the ensemble averages, the potential flood magnitudes increase by more than 30% in both catchments. The unambiguousness of the results is remarkable and can be explained by three factors rarely combined in previous studies: reduced statistical uncertainty due to a stochastic modelling approach, hourly time steps and the focus on headwater catchments where local topography and convective storms are causing runoff extremes within a confined area.
A Qualitative Study Investigating the Management of Cybersecurity in Swedish Online Fashion Companies

​Due to a world-wide digitalisation, the fashion segment has experienced a shift from offline to online shopping. Consequently, more companies choose to interconnect digitally with consumers and suppliers. This highlights cyber risks and cybersecurity issues more than ever, which becomes specifically apparent amongst online companies. Through qualitative semi-structured interviews with three different Swedish online fashion companies, the purpose of investigating how cybersecurity currently is prioritised and managed was reached. In addition to this, two cybersecurity experts gave their view of the most important aspects in the field, which companies should consider. Results showed a fairly well-managed cybersecurity amongst Swedish online fashion companies, even though knowledge in the field is scarce. Through educating everyone at the company and implementing a group of people in charge of these questions, a more holistic view could be attained. By offering thoughts on how online fashion companies can enhance their current cybersecurity, this paper contributes to the literature of cyber risk management as well as provides meaningful knowledge to all types of online companies.
Eco-hotels as an example of environmental responsibility and innovation in savings in the hotel industry

Green hotels are ecological buildings whose managers set up programs to save water and energy, and reduce solid waste, thereby saving money and aiding preservation of the environment. These hotels have guidelines so that the guests can stay in a safe, non-toxic and energy-efficient lodging. Green hotels are introducing environmental standards and use elements of environmentally friendly new technologies, thereby making a significant contribution to the protection of the environment and achieving better operational results. Looking at global trends in green tourism and hospitality, caused by turbulent development and carrying many negative consequences, the purpose of this paper is to point out and affirm the advantages provided by the green hotels. It also suggests that Serbia can become a more competitive destination considering its potential and resources.
Microsatellite DNA Polymorphism of Channa marulius

Wild populations of C. marulius have undergone a steady decline due to habitat deterioration, overexploitation and aquatic pollution. Genetic polymorphism by using microsatellite DNA markers was analyzed in natural populations of C. marulius. Samples were collected from different sites of river Jhelum viz. Trimu Headworks, Rasul Barrage, Mangla Dam, Jhelum Bridge, and Marala Dhand. A total of five microsatellite loci (CA05, CA07, CA08, CA09, and CA10) were used to evaluate genetic polymorphism. The overall populations of C. marulius showed a moderate level of heterozygosity. The mean observed heterozygosity (Ho) at all the five loci was 0.43 whereas expected heterozygosity (He) was 0.54. The pairwise population genetic differentiation (FST) ranged in between 0.000001 to 0.231955. Analysis of molecular variance (AMOVA) revealed that majority of variations among populations than within populations 62.05% and 33.03%, respectively. Significant deviation (P<0.05) from Hardy-Weinberg equilibrium was seen in all the populations with clear deficit heterozygosity suggesting inbreeding. A UPGMA tree based on Nei’s genetic distance matrix revealed that C. marulius populations were divided into two major clusters. Principle component analysis showed same clustering pattern. The present study about genetic structure of C. marulius populations will be fruitful for effective management and conservation of fisheries resources.
A Framework for a Five-Axis Stylus for Design Fabrication

This paper proposes a new workflow between design and fabrication phases through the introduction of a novel framework centered around a stylus that is tracked in real-time for five-axis by a single RGB-D camera. Often misconceived as a linear process, urgent reinterpretation of design and fabrication tools is discussed briefly. Similar to how industrial robots have become an enabler for fabrication process in the field of architecture and construction, the necessity for providing a similar tool that would reform the "design" process is underlined. A generic stylus is proposed with interchangeable operations which allows for intuitive, non-obstructive grasp of the user serves as the physical avatar that transform into a virtual representation of a fabrication tool mounted on a six-axis industrial robot arm. User interaction with the apparatus is simulated for the user, and the user is notified of any errors as the interaction is translated for motion planning of a KUKA KR20-3 industrial robot.
Histogram Based Clustering for Nonlinear Compensation in Long Reach Coherent Passive Optical Networks

In order to meet the increasing capacity requirements, network operators are extending their optical infrastructure closer to the end-user while making more efficient use of the resources. In this context, long reach passive optical networks (LR-PONs) are attracting increasing attention.Coherent LR-PONs based on high speed digital signal processors represent a high potential alternative because, alongside with the inherent mixing gain and the possibility of amplitude and phase diversity formats, they pave the way to compensate linear impairments in a more efficient way than in traditional direct detection systems. The performance of coherent LR-PONs is then limited by the combined effect of noise and nonlinear distortion. The noise is particularly critical in single channel systems where, in addition to the the elevated fibre loss, the splitting losses should be considered. In such systems, Kerr induced self-phase modulation emerges as the main limitation to the maximum capacity. In this work, we propose a novel clustering algorithm, denominated histogram based clustering (HBC), that employs the spatial density of the points of a 2D histogram to identify the borders of high density areas to classify nonlinearly distorted noisy constellations. Simulation results reveal that for a 100 km long LR-PON with a 1:64 splitting ratio, at optimum power levels, HBC presents a Q-factor 0.57 dB higher than maximum likelihood and 0.21 dB higher than k-means. In terms of nonlinear tolerance, at a BER of 2×10 − 3 , our method achieves a gain of ∼2.5 dB and ∼1.25 dB over maximum likelihood and k-means, respectively. Numerical results also show that the proposed method can operate over blocks as small as 2500 symbols.
Improving stroke diagnosis accuracy using hyperparameter optimized deep learning

Stroke may cause death for anyone, including youngsters. One of the early stroke detection techniques is a Computerized Tomography (CT) scan. This research aimed to optimize hyperparameter in Deep Learning, Random Search and Bayesian Optimization for determining the right hyperparameter. The CT scan images were processed by scaling, grayscale, smoothing, thresholding, and morphological operation. Then, the images feature was extracted by the Gray Level Co-occurrence Matrix (GLCM). This research was performed a feature selection to select relevant features for reducing computing expenses, while deep learning based on hyperparameter setting was used to the data classification process. The experiment results showed that the Random Search had the best accuracy, while Bayesian Optimization excelled in optimization time.
Associations Between Psychopathy and the Trait Meta-Mood Scale in Incarcerated Males: A Combined Latent Variable- and Person-Centered Approach

The present study sought to replicate and extend current knowledge on the relevance of emotion regulation (ER) for psychopathy. In a large sample of incarcerated adult males (N = 578), latent profile analysis (LPA) and structural equation modeling (SEM) were employed to examine person- and variable-centered associations between self-reported ER and both self-report and clinical ratings of psychopathy. With LPA, participants were classified into three profiles corresponding to low, medium, and high ER. The low-ER profile displayed higher affective traits across psychopathy assessments compared with the other profiles. The same pattern of findings was evident for overt behavioral features of psychopathy, but not for interpersonal traits. SEM results were consistent with LPA findings: interpersonal (positively), affective, and lifestyle (negatively) facets had unique associations with a superordinate ER latent variable. Findings replicate and extend prior associations between psychopathy and ER and suggest differential links between ER and affective and interpersonal traits of psychopathy.
The Measuring of Quality Services on Pasyandu Desa as The Effort to Enhance Mother and Children's Health Degree

The state health burden comes from the high population growth, infectious diseases, non-communicable diseases, and the higher mental health disorder. In a long period of time, this will undermine the nation’s productivity level and the economy in a country. The effort to promote the population services, health, and disease prevention need to be taken seriously. Most morbidity, mortality, population explosion, and disability as the disease effect can be prevented through promotive and preventive efforts. However, the efforts to enhance public health quality in primary service level are currently not optimal. The loose health promotion and disease prevention result the high number of morbidity and mortality. In the quarter IV 2017 there were 3.597 mothers’ died during childbirth and 22.327 infants’ death. The prevalence of the stunting toddlers or short stature was 27.6 percent, 3.4 percent of children under five were malnourished, and 14.4 percent of children under five were less malnourished [1]. This study is based on the consideration that it is necessary to conduct evaluation research on the quality services of Posyandu Desa through the Servqual model test, the importance of continuous research, and the consistency of the previous researcher. In particular, this study aims to determine the gap value and the efforts to close the biggest gap through Sevqual test. The population in this study are housewives who have children under five as the receiver of Posyandu Desa services. This study uses purposive sampling of 60 respondents in the village area of Klaten Regency. The attributes in the quality service questionnaire include tangibles, reliability, responsiveness, assurance, and empathy [2]. Quantitative tests to be conducted include the item validity test and Servqual Analysis confirmation test. From the result, it is obtained that there is still negative value with the largest negative value on the tangible dimension. The improvement efforts must be continued with emphasis on improving the value which have the largest negative dimension to enhance the quality service of Posyandu KB in the community
Stochastic excitation for high-resolution atomic force acoustic microscopy imaging: a system theory approach

In this work, a high-resolution atomic force acoustic microscopy imaging technique is developed in order to obtain the local indentation modulus at the nanoscale level. The technique uses a model that gives a qualitative relationship between a set of contact resonance frequencies and the indentation modulus. It is based on white-noise excitation of the tip–sample interaction and uses system theory for the extraction of the resonance modes. During conventional scanning, for each pixel, the tip–sample interaction is excited with a white-noise signal. Then, a fast Fourier transform is applied to the deflection signal that comes from the photodiodes of the atomic force microscopy (AFM) equipment. This approach allows for the measurement of several vibrational modes in a single step with high frequency resolution, with less computational cost and at a faster speed than other similar techniques. This technique is referred to as stochastic atomic force acoustic microscopy (S-AFAM), and the frequency shifts of the free resonance frequencies of an AFM cantilever are used to determine the mechanical properties of a material. S-AFAM is implemented and compared with a conventional technique (resonance tracking-atomic force acoustic microscopy, RT-AFAM). A sample of a graphite film on a glass substrate is analyzed. S-AFAM can be implemented in any AFM system due to its reduced instrumentation requirements compared to conventional techniques.
Assessing attention in a 3D environment: Preliminary evidence from a pilot study using the Computerized Battery for the Assessment of Attention Disorders (CBAAD)

Neuropsychological assessment is traditionally performed with the use of either paper-pencil administered tests or 2D computerized tests. Both testing procedures have been associated with limitations in reflecting real world situations. While 3D environments have been used increasingly in the field of neuropsychological rehabilitation with promising results, neuropsychological assessment has largely retained traditional tools. Thus, in order to provide a reality-based attention assessment, we developed the Computerized Battery for the Assessment of Attention Disorders (CBAAD). In the current study, we present preliminary data from the administration of the CBAAD to 50 healthy (N = 26 females) adult participants, as well as their feedback regarding their experience taking the test and its usefulness. Preliminary findings suggest that participants generally performed well and rated their experience with the CBAAD as pleasant, motivating, real-life resembling, and time-preserving. Results are discussed with respect to the aims of the battery development. In conclusion, this is the first 3D attention assessment battery to our knowledge, and the present study provides strong preliminary evidence that it is user-friendly.
Echocardiographic evaluation of right heart functions in hemodialysis patients

Aim: Volume load assessment is an important problem in patients with chronic renal failure (CRF). Apart from classical volume assessment methods, the number of studies on echocardiographic evaluation of right heart functions is limited. In this study, we aimed to evaluate right heart functions echocardiographically in end-stage renal failure patients receiving hemodialysis (HD) therapy and its utility in volume load determination. Methods: This case-control study included 49 patients receiving HD treatment as the study group. The control group consisted of 46 healthy individuals whose age and gender were matched. Echocardiographic evaluation was performed for all participants. Tricuspid annular plane systolic excursion (TAPSE), systolic pulmonary artery pressure (SPAB), tricuspid E wave velocity, A wave velocity, E / A ratio, right ventricular ejection fraction (RVEF) and left ventricular ejection fraction (LVEF) were measured. Results: Right ventricular diastolic diameter (2.55 (0.3) vs 2.30 (0.29) respectively, P<0.001) SPAP (37.4 (8.3) vs 19 (6.8), P<0.001, respectively) were statistically significant higher in the study group, while TAPSE (1.61 (0.35) vs 2.09 (0.27), P<0.001, respectively), A rate, (0.63 (0.27) vs 0.45 (0.26), P<0.001, respectively), E / A ratio (0.93 (0.25) vs 1.37 (0.24), P<0.001, respectively) were significantly lower. Right ventricular (RV) E velocity was insignificantly lower in the study group (P=0.523). There were no statistically significant differences between the groups in terms of age, gender, and body mass index. Conclusion: In our study, we found that TAPSE was low and SBAP and RV diastolic diameter were high in HD patients. We think that TAPSE and SPAB can be used together with conventional methods for evaluating hypervolemia in HD patients. Further studies are required to explore this clinical tool.
Photosynthetic Microbial Desalination Cell to Treat Oily Wastewater Using Microalgae Chlorella Vulgaris

Microbial desalination cell (MDC) offers a new and sustainable approach to desalinate saltwater by directly utilizing the electrical power generated by bacteria during organic matter oxidation. In this study, we used microalgae Chlorella Vulgaris in the cathode chamber to produce oxygen as an electron accepter by photosynthesis process for generate bioelectricity power and treat oil refinery wastewater by microorganisms in both anode and cathode. The power density generated by this Photosynthetic Microbial Desalination Cell (PMDC) with 1KΩ external resistance at the first 4 th hr. of operation period was 0.678 W/m 3 of anode volume and 0.63 W/m 3 of cathode volume. It increased after one day to a peak value of (4.32 W/m 3 of anode volume and 4.013 W/m 3 of cathode volume). The microalgae growth in the biocathode chamber followed in terms of optical density. The optical density increased from 0.546 at the beginning of the system operation to 1.71 after 24 days of operation period. The percentage removal of chemical oxygen demand (COD) of oil refinery wastewater was 97.33% and 79.22% in anode and cathode chamber, respectively. The microalgae in the biocathode were able to remove volatile compounds causing odor from the influent wastewater. TDS removal rate 159.722 ppm/h with initial TDS in desalination chamber of 35000 ppm.
The dynamic causality between corporate social responsibility and corporate political activity: a panel vector autoregression approach

The objective of this study is to assess the dynamic causality between corporate social responsibility (CSR) and corporate political activity (CPA). This research uses panel vector autoregression (VAR) model within the framework of generalized method of moments (GMM) with consideration to the time invariant characteristics of each organisation. Based on a sample of 100 World’s Most Admired Companies (WMAC) listed in Fortune between 2007 and 2016, this study provides empirical evidences that the CPA negatively affects the CSR, while enhanced CSR does not warrant an enhanced CPA. The findings of this study contradict the notion propounded by the hypothesis of virtuous circle which states that there is a positive relationship and mutual reinforcement between CSR and CPA.
Perfume Encapsulation via Vapor Induced Phase Separation

In this study we explored the implementation of the vapor induced phase separation (VIPS) to produce cellulose acetate microcapsules for the encapsulation of a complex mix of fragrances. VIPS is a technique used for membrane preparation, but barely mentioned for microencapsulation. We compared the products from VIPS and a more common microencapsulation process, the immersion precipitation technique (IPS). The capsules prepared via VIPS show a core-shell structure with a thin polymeric shell surrounding the internally empty space, conversely to those produced via IPS, showing an incomplete spherical morphology. This can be attributed to a better control of the precipitation rate of the encapsulation material in the non-solvent thanks to the longer exposition time to the vapor. The activity and encapsulation efficiency of the capsules, obtained through TGA analysis, reached a maximum of ≈75% and ≈90%, respectively. Moreover, a growing trend between the initial active concentration and the encapsulation efficiency is noticed.
Critical Point Behavior of Deep Networks

where Wk ∈ Rdk×dk−1 are the weight matrices, while X ∈ Rdx×m and Y ∈ Rdy×m are the input and output data respectively. Previous results state that L̄ is convex in each Wk when all others are fixed, which is obvious. Moreover, Baldi & Hornik [2] proved in 1988 that if H = 1 (a ”shallow” network), p < dy = dx, and XX T , XY T are invertible, then every local minimum is a global minimum. Here p = min{d1, . , dH} is the smallest width of a hidden layer. The proof is mainly done by using first order conditions and eigenvalue perturbation arguments.
TRANSFORMATION AND METHODS OF USING SAME

The invention is directed to a promoter, designated MuB, sequences which hybridize to same and functional fragments thereof. The regulatory element of the invention provide improved expression in plants of operably linked nucleotide sequences. Expression vectors with the regulatory element is the subject of the invention, which may further include an operably linked nucleotide sequence. The invention is further directed to transformed plant tissue including the nucleotide sequence and to transformed plants and seeds thereof. The regulatory element is useful for driving gene or antisense expression or the like for the purpose of imparting agronomi cally useful traits such as, but not limited to, increase in yield, disease resistance, insect resistance, herbicide tolerance, drought tolerance and salt tolerance in plants.
Speech Recognition Using Historian Multimodal Approach

This paper proposes an Audio-Visual Speech Recognition (AVSR) model using both audio and visual speech informationto improve recognition accuracy in a clean and noisy environment. Mel frequency cepstral coefficient (MFCC) and DiscreteCosine Transform (DCT) are used to extract the effective features from audio and visual speech signal respectively. TheClassification process is performed on the combined feature vector by using one of main Deep Neural Network (DNN)architecture, Bidirectional Long-Short Term Memory (BiLSTM), in contrast to the traditional Hidden Markov Models (HMMs). The effectiveness of the proposed model is demonstrated on a multi-speakers AVSR benchmark dataset named GRID. Theexperimental results show that the early integration between audio and visual features achieved an obvious enhancement in therecognition accuracy and prove that BiLSTM is the most effective classification technique when compared to HMM. The obtainedresults when using integrated audio-visual features achieved highest recognition accuracy of 99.07%, this result demonstrates anenhancement of up to 9.28% over audio-only recognition for clean data. While for noisy data, the highest recognition accuracy forintegrated audio-visual features is 98.47% with enhancement up to 12.05% over audio-only. The main reason for BiLSTMeffectiveness is it takes into account the sequential characteristics of the speech signal. The obtained results show the performanceenhancement compared to previously obtained highest audio visual recognition accuracies on GRID, and prove the robustness ofour AVSR model (BiLSTM-AVSR).