Miracle-dz/UniFAR-FTUs huggingface dataset:
Decent Living Emissions: A Conceptual Framework

There is very little elaboration in literature of the phrase “equitable access to sustainable development” that is referenced in the Cancun Agreement on climate change. We interpret this at a minimum as people’s right to a decent living standard, which gives rise to claims by countries to an exemption from mitigation for the energy and emissions needed to provide a decent life to all. We elaborate a conceptual framework for a comprehensive quantification of such an energy requirement, including the energy required to build out infrastructure to support these living standards. We interpret decent living as the consumption by households of a set of basic goods including adequate nutrition, shelter, health care, education, transport, refrigeration, television and mobile phones. We develop universal indicators for these activities and their infrastructure requirements, and specify a methodology to convert these to energy requirements using energy input-output analysis. Our main recommendations include estimating bottom-up, country-specific energy and emissions requirements, incorporating a minimum for methane emissions, and using international benchmarks at the sector level to encourage the reduction of countries’ energy and emissions intensity.
Ras , and the classic MAPK pathway couple muscarinic receptor activation to increased Na-HCO 3 cotransport activity in renal epithelial cells

R. BROOKS ROBEY,1,2,4* OFELIA S. RUIZ,1,3,4* JESSICA BANIQUED,1 DOLORES MAHMUD,1,4 DORIS JOY D. ESPIRITU,1,4 ANGELITO A. BERNARDO,1,4 AND JOSE A. L. ARRUDA1,2,4 With the Technical Assistance of Yi-Yong Qiu 1Section of Nephrology, Department of Medicine, 2Department of Physiology and Biophysics, and 3Department of Pathology, University of Illinois at Chicago College of Medicine, and 4Veterans Affairs Chicago Health Care System, West Side Division, Chicago, Illinois 60612
RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process

An RNN-based forecasting approach is used to early detect anomalies in industrial multivariate time series data from a simulated Tennessee Eastman Process (TEP) with many cyber-attacks. This work continues a previously proposed LSTM-based approach to the fault detection in simpler data. It is considered necessary to adapt the RNN network to deal with data containing stochastic, stationary, transitive and a rich variety of anomalous behaviours. There is particular focus on early detection with special NAB-metric. A comparison with the DPCA approach is provided. The generated data set is made publicly available.
SAR Data Classification of Urban Areas by Means of Segmentation Techniques and Ancillary Optical Data

The classification of urban areas in terms of land-use/land-cover (LULC) maps is a challenging as well as essential task in order to monitor how the urban sprawl is changing the environment. This paper is devoted to the description of a novel procedure designed to exploit coarse-resolution SAR images and obtain both the built-up area extents and a LULC map of the individuated urban area. The approach starts from the previously developed BuiltArea algorithm to produce the built-up area extent map, exploiting the spatial correlation among neighboring pixels by means of local indicators of spatial association and gray level co-occurrence matrix (GLCM) features. After discriminating between urban and nonurban areas, a novel approach is presented that exploits segmentation techniques, spatial feature selection, and a supervised classifier to generate urban LULC maps. A robust chain, considering SAR data and using ancillary optical data is proposed and validated using data sets available in two test cases, the megacities of Shanghai and Beijing.
The Utilization of Carbon Compounds by Some Actinomycetales as an Aid for Species Determination

Attention has recently been focused on the identification of the actinomycetes because of the ability of some members of this group to produce antibiotics. The determinative procedures now in use are based primarily on morphology, pigmentation, and growth characteristics on media composed of complex natural substrates. With such media one can often observe a variety of pigments and colony types produced by the same organism. In inexperienced hands, this leads to difficulties in interpretation of results, and one is often forced to designate a particular isolate as one of several species rather than one distinct species. To aid in the identification of a group of antibiotic-producing actinomycetes under study, an intensive investigation of their utilization of carbon compounds in chemically defined media was undertaken. Prior to the current investigations a number of workers had studied the utilization of carbon compounds by some species in this group. Beijerinck (1900), Sames (1900), Caminiti (1913), Fousek (cited by Waksman, 1919), Krainsky (1914), and Gray and Thornton (1928) each tested a few materials and found that various members of this group could grow on media containing different carbon sources. More extensive tests on carbon compounds were made by Salzmann (1907), Munter (1913), and Waksman (1919). Recently Taylor and Decker (1947) showed that various plant pathogenic actinomycetes could be separated from nonpathogenic isolates on the basis of their reactions on carbon compounds, as well as other criteria. The studies of Cochrane (1947) also indicated that different species in the genus Actinomyces might be separated on the basis of acid production.
TEMPORAL CHANGES OF CORROSION LOSSES AND CORROSION RATES IN WATER PIPES

Water distribution systems are often constructed of metal tubes, which in addition to its good properties, especially mechanical, has also tendency to corrosion. Increased activity of corrosion processes significantly affect not only the time- life of the pipe, its failure rate and reliability, but also changes hydraulic conditions of water supply system and may result in poor water quality. Price increase of the water caused a reduction of water consumption and the extension of the travel time of the water in distribution system. This also influences the interaction of the transported water and pipe material. For the reasons, stated above, we observed the corrosive effect of the water from water source Pernek on the pipe with the short- and long- term corrosion tests. Tests are based on measurements of the weight loss difference of the test sample after 30 and 60 day exposure to flowing water. Since short time (30-60 day) test does not always give an accurate picture of the interaction between water and pipe, we extend the test up to two years. During these long term test we have observed not only the corrosion losses and the corrosion speed, but also the formation of the corrosion layer and its thickness. Corrosion losses between 30 and 60 day were almost in linear course, but during longer tests corrosion losses speed decreases and the average corrosion rate for 2-year test reached only about 39% of the corrosion rate determined from short term tests.
Out-of-Band Radiation Comparison of GFDM, WCP-COQAM and OFDM at Equal Spectral Efficiency

GFDM and WCP-COQAM are amongst the candidate physical layer modulation formats to be used in 5G, whose claimed lower out-of-band (OOB) emissions are important with respect to cognitive radio based dynamic spectrum access solutions. In this study, we compare OFDM, GFDM and WCP-COQAM in terms of OOB emissions in a fair manner such that their spectral efficiencies are the same and OOB emission reduction techniques are applied to all of the modulation types. Analytical PSD expressions are also correlated with the simulation based OOB emission results. Maintaining the same spectral efficiency, carrier frequency offset immunities will also be compared.
Non-intrusive objective speech quality and intelligibility prediction for hearing instruments in complex listening environments

A non-intrusive objective speech quality and intelligibility measure tailored to hearing restoration instruments is proposed and evaluated in complex listening environments. The measure builds upon the previously-proposed “speech-to-reverberation modulation energy ratio” (SRMR) by incorporating hearing impairment percepts, such as hearing loss thresholds and altered modulation frequency selectivity. Performance is assessed using speech data corrupted by additive noise, reverberation, and noise-plus-reverberation which were subjectively rated by cochlear implant and hearing aid users. Experimental results show that the developed measures outperform the original SRMR metric for hearing impaired listeners and achieve performance levels inline with existing intrusive quality and intelligibility metrics, but with the advantage of not requiring access to a clean reference signal. As such, the measure may be used to develop qualityor intelligibility-aware speech enhancement algorithms for advanced hearing restoration instruments.
A survey on audio analysis: Text characterization and summarization

The integration of cutting-edge natural language processing (NLP) technology for smooth audio-to-text conversion and summarization is examined in this survey. Utilizing Facebook’s BART model for succinct summaries and Google’s Speech-to-Text API for precise transcription. The report highlights the value of sophisticated summarization models and precise transcription. It talks about how the system can be used in a variety of fields, such as podcast and video transcript generation, automated meeting transcription and summarization, content indexing and search, and more. In addition to addressing issues like context preservation and bias reduction, the survey assesses relevant research on text generation, LSTM networks, and summarization techniques. Overall, by incorporating state-of-the-art technology, this study advances the processing of audio content and eventually makes it easier to extract valuable information.
Focus Constraints on Language Generation

Computer generation of natural language requires the ability to make reasoned choices from a large number of possible things to say as well as from a large number of expressive possibilties. This paper examines in detail how one influence on a generated text, focus of attention, can be used to constrain the many possibilities that a generation system must consider. A computational treatment of focus of attention is presented that can be used to constrain what the system needs to consider when deciding what to say next. In this process, information is produced that provides constraints on which words and syntactic structures best express the system's intent, thus ensuring that its resulting text is coherent. This analysis has been used in the fully implemented TEXT system which generates paragraph length responses to questions about database structure.
Analysis of the Existence of Environmental Kuznets Curve (EKC) Hypothesis on CO2 Emission, Energy Consumption, and Economic Growth in Indonesia

This research explains environmental issues, in the context of Kuznets' theory that links economic growth, energy consumption and CO2 emissions. The focus of the research is to test the Kuznets Environment Curve (EKC) hypothesis in Indonesia in the period 1990-2019 using time series analysis Vector Error Correction Model (VECM). Research findings show that, both in the long and short term, CO2 emissions and energy consumption have a significant impact, while economic growth has no significant impact. These results indicate that the ECC hypothesis is not supported in Indonesia. Emerging policy implications include the transition to clean energy, sustainable farming and forestry management, waste and pollution control, and increased environmental awareness and education. Faced with the complexity of environmental challenges, the implementation of this policy has become crucial toining a balance between environmental sustainability and economic growth.
Effect of Occupational Transfers on Employee Engagement in Private Universities in Western Kenya By IJISRT

According to Makori, M. & Kariuki, N. , (2015), a study was conducted in Presbyterian University of East Africa (PUEA) by the HR Office in 2014 and disclosed that about 50% of employees in Kenyan institutions of Higher Learning merely come to work to do what is needed of them. The study showed that 47% of workers came to work and ended up using most of their time on social websites during work hours. Hence workforce turnover was registered at 43%. The major cause of these situations is not known. Scholars have given many suggestions that some Occupational changes in private universities are linked with digitalization, how information is shared, relocation of workers, change in task objectives and production costs. Nevertheless, it is not known whether these changes have any significant effect on employee engagement in Kenyan private universities. This study therefore was particularly concerned with the Effect of Occupational Transfers on Employee Engagement in Private Universities in Western Kenya. The conclusions of this study might be very useful to many stakeholders involved in the management of Private universities in Kenya. The study was based on the Job Characteristics theory. Correlational research design was used. Target population was 1440 workers of four selected Private Universities. Stratified random sampling techniques were employed. The sample of 313 workers from different sections was used. Questionnaires were given to Respondents to complete for the purpose of data collection. Questionnaire were developed using items from literature reviews. SPSS and Excel tools were used during the data analysis process. Data was presented using charts and Frequency tables. From the results, conclusions and recommendations were made. A pilot study was carried out with 10 respondents from Uzima University to assess the validity of the research instruments. The 10 respondents were not involved in the main study. The results revealed that occupational transfers had a negative effect (β=-.541, p<.05) and accounted for 29.3% variance in employee engagement (R2=.293, p<.05). It was concluded that Occupational Transfers have a significant effect on employee engagement. The study suggested that private universities in western Kenya should communicate to their employees before they are transferred.
Adaptive Fuzzy-Neural-Network Control for Maglev Transportation System

A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.
Transcoding live adaptive video streams at a massive scale in the cloud

More and more users are watching online videos produced by non-professional sources (e.g., gamers, teachers of online courses, witnesses of public events) by using an increasingly diverse set of devices to access the videos (e.g., smartphones, tablets, HDTV). Live streaming service providers can combine adaptive streaming technologies and cloud computing to satisfy this demand. In this paper, we study the problem of preparing live video streams for delivery using cloud computing infrastructure, e.g., how many representations to use and the corresponding parameters (resolution and bit-rate). We present an integer linear program (ILP) to maximize the average user quality of experience (QoE) and a heuristic algorithm that can scale to large number of videos and users. We also introduce two new datasets: one characterizing a popular live streaming provider (Twitch) and another characterizing the computing resources needed to transcode a video. They are used to set up realistic test scenarios. We compare the performance of the optimal ILP solution with current industry standards, showing that the latter are sub-optimal. The solution of the ILP also shows the importance of the type of video on the optimal streaming preparation. By taking advantage of this, the proposed heuristic can efficiently satisfy a time varying demand with an almost constant amount of computing resources.
Morphological segmentation of histology cell images

Two algorithms for segmentation of cell images are proposed. They have a unique part that contains computation of morphological gradient to extract object borders and thinning the obtained borders to get a line of one-pixel thickness. For this task, we propose the fast gray-scale thinning algorithm that is based on the idea of the analysis of binary image layers. Then, the obtained one-pixel lines are used to extract cells and compute their characteristics. The algorithms based on morphological and split/merge segmentation are developed and used for this task.
Impact of fragmentation on tree diversity, density and structure of Pentadesma butyracea Sabine Clusiaceae population in Benin (West-Africa)

Fragmentation affects population structure and tree diversity. However, the main causes of fragmentation and their consequences on population structure are still unclear. This study evaluated some causes of gallery forest (GFs) fragmentation and their effect on Pentadesma butyracea population structures in Benin. Twelve P. butyracea GFs were selected in semi-arid and sub-humid ecological zones. Populations were identified varying fragmentation indicators (gallery width, distance to the nearest population, current area, shape of GFs and rate of area loss during the last three decades) to assess the species diversity, tree densities and shape of diameter class distribution (SDCD) of trees. Generalized linear regression was used to test the effect of fragmentation on species diversity, density and SDCD. The proximity of villages, farms to GFs increased fruit harvest rate and GFs area reduction. Interaction between gallery width, area loss rate and ecological zone had a significant effect on species diversity, density and marginally on SDCD. The results are valuable in the perspective that they showed the importance of the combined effect of habitat fragmentation and provide a better understanding of the actions to be considered for sustainable conservation of GFs and P. butyracea trees. Keywords : Fragmentation, gallery forest, Pentadesma butyracea , ecological zone, population structure, tree diversity.
Research on Performance Improvement of Emulsified Asphalt Mixture Based on Innovative Forming Process

Bulk density and porosity have great influence on the technical performance of an emulsified asphalt mixture, so in order to enhance the strength of the asphalt mixture, bulk density should be improved and porosity should be reduced. Considering the forming process of the emulsified asphalt mixture, the decrease in porosity can ensure the state of the mixture. In order to reduce the porosity of the emulsified asphalt mixture, an innovative forming process is proposed to improve the performance of the emulsified asphalt mixture, and its strength formation mechanism is explored in this paper. Three groups of emulsified asphalt mixtures (ARC-8 + SBR, SMA-5 + EVA, SMA-5 + SBR) were prepared by a conventional mixing process and novel mixing process. Marshall test of the emulsified asphalt mixture, CT scanning test of the emulsified asphalt mixture, workability test and analysis were manufactured and tested. The results show that, compared with conventional methods, the innovative forming method can increase the bulk density of the mixture and reduce the porosity, and thus improve its technical performance. The reason is that most of the water in the mixture of the innovative forming method sticks to the outer surface of the fine aggregate, and the water is more easily discharged. Secondly, the fine aggregate of the innovative forming method is directly mixed with the emulsion, and the volume is smaller. The emulsion wraps the fine aggregate in it due to the surface tension, which enhances the adhesion effect, thus improving the strength of the mixture.
The challenges of implementing cultural-based teaching in remedial education: analyses and recommendations

In remedial education, teachers play a crucial role in ensuring students from diverse backgrounds master language literacy skills. The current implementation of cultural-based teaching is perceived as an effort to assist remedial students from diverse backgrounds and cultures to learn effectively. The study aims to analyze teachers’ perceptions of challenges in implementing cultural-based teaching in remedial education for language literacy. The study employed a quantitative approach by distributing questionnaires to 252 remedial teachers recruited randomly from primary schools in the Malaysian middle-state zone. The collected data were evaluated descriptively, and the results revealed that these teachers experienced challenges in four areas, namely teaching resources, remedial curricula, teacher knowledge, and time allocation. Moreover, the findings demonstrated that demographic factors, such as gender, school location, and years of teaching experience, did not produce significant impacts on the aforementioned challenges. Simultaneously, this study explored teachers’ perceptions of recommending culturally responsive teaching approaches in remedial education as an alternative to the current cultural-based teaching methodology.
Proximal Alternating Direction Network: A Globally Converged Deep Unrolling Framework

Deep learning models have gained great success in many real-world applications. However, most existing networks are typically designed in heuristic manners, thus lack of rigorous mathematical principles and derivations. Several recent studies build deep structures by unrolling a particular optimization model that involves task information. Unfortunately, due to the dynamic nature of network parameters, their resultant deep propagation networks do not possess the nice convergence property as the original optimization scheme does. This paper provides a novel proximal unrolling framework to establish deep models by integrating experimentally verified network architectures and rich cues of the tasks. More importantly,we prove in theory that 1) the propagation generated by our unrolled deep model globally converges to a critical-point of a given variational energy, and 2) the proposed framework is still able to learn priors from training data to generate a convergent propagation even when task information is only partially available. Indeed, these theoretical results are the best we can ask for, unless stronger assumptions are enforced. Extensive experiments on various real-world applications verify the theoretical convergence and demonstrate the effectiveness of designed deep models.