Miracle-dz/UniFAR-FTUs huggingface dataset:
Morphological and Molecular Identification of Fungi Isolated from Different Environmental Sources in the Northern Eastern Desert of Jordan

This study is aimed at isolating and identifying filamentous fungi from different environmental sources in the northern eastern Jordanian Desert. The fungal species were isolated from soil and plant parts (Fruits and leaves). The samples were collected from different geographical locations in the northern eastern Desert in Jordan. The isolation of fungi from leaves and fruits was implemented by inoculating (1ml) from serial dilutions (10P-3P10P-6P) on Potato Dextrose Agar (PDA) plates. The plates were incubated at 28 ̊C for one week, then the fungal colonies were observed and pure cultures were maintained. The identification of fungi at the genus level was carried out by using macroscopic and microscopic examinations depending on the colony color, shape, hyphae, conidia, conidiophores and arrangement of spores. For the molecular identification of the isolated fungi at the species level, the extracted fungal DNA was amplified by PCR using specific internal transcribed spacer primer (ITS1/ITS4). The PCR products were sequenced and compared with the other related sequences in GenBank (NCBI). Eight fungal species were identified as: Aspergillus niger, Aspergillus tubingensis, Alternaria tenuissima, Alternaria alternate, Alternaria gaisen, Rhizopus stolonifer, Penicillium citrinum, and Fusarium oxysporum. The results showed that the Aspergillus niger was the most abundant fungus obtained from all the locations and resources, while the Alternaria tenuissima was the less prevalent one. It was also noticed that two of the Alternaria species colonized the leaves of plants at different locations. Rhizopus stolonifer, Aspergillus tubingensis, and Fusarium oxysporum were isolated and identified from all resources and locations.
Evidence from Satellite Data for Export phenomena

The study investigates the potential of satellite observations of atmospheric trace gases for the investigation of export phenomena, in particular intercontinental transport. Atmospheric trace gases are measured by various satellite instruments in different wavelength ranges. Especially nadir viewing instruments operating in the UV/vis (and near IR) spectral range are sufficiently sensitive even for trace gases located in the troposphere (see e.g. Fishman et al., 1986). It was recently demonstrated that several trace gases located even in the planetary boundary layer (e.g. NO2, HCHO, SO2, BrO, H2O) can be measured by such instruments, like e.g. the Global Ozone Monitoring Experiment (GOME) aboard ERS-2 (ESA, 1995). The analysis of tropospheric trace gases from GOME is described in several publications, see e.g. Wagner and Platt (1998), Richter et al. (1998), Eisinger and Burrows (1998), Leue et al. (2001), Velders et al. (2001), Wagner et al. (2001), Richter and Burrows (2002). General description of tropospheric trace gas measurements by GOME can be found in Burrows et al. (2000), Wagner et al. (2002a). An overview on tropospheric satellite data is also given on the TROPOSAT homepage: http://troposat.iup. uni-heidelberg.de/. On March, 1, 2002, the Scanning Imaging Absorption SpectroMeter for Atmospheric ChartographY (SCIAMACHY) was launched aboard ENVISAT (see e.g. Bovensmann et al., 1999). In addition to nadir observations it also performs measurements of the atmosphere in limb viewing geometry. Compared to GOME the SCIAMACHY measuring modes enable several important advantages for the measurement of tropospheric trace gases: a) The spatial resolution is significantly better (typically 30x60km2 and at best 30x15km2 compared to 40x320km2). b) From the combination of limb and nadir observations the tropospheric column density can be directly inferred for trace gases which are located both in the stratosphere and troposphere. c) From the additional near IR channels important greenhouse gases like CH4, N2O and CO2 (also CO) can be retrieved. In this project the general properties of satellite observations of tropospheric trace gases are discussed. The methodology is described in some detail for DOAS observations made by GOME and SCIAMACHY. In the following parts an overview is given on observations of several tropospheric trace gases by GOME. Examples for intercontinental transport are presented in case studies and from averaging global satellite data over several years. Finally the potential of satellite observations for the discrimination of different sources is highlighted.
Artificial Neural Network Application on Determining Chord Composition for Melody Accompaniment

Melody is a sequence of succession of tones and itself is the major part of a song composition. To accompany the melody, chord compositions will be prepared in accordance with the harmonization of tones within it. Composing chords is an unquantifiable process which may only be rated by subjective judgement. Variations are welcomed, however subjectively each person have their own preference thus making a generally likeable composition is a challenge to musicians since long. Simon et al. (2008) proposed a solution on composing chord accompaniment for a melody in real-time as an application named MySong. Machine learning serves a main part on the application, therefore it suggests that other machine learning variations may possibly applicable to the problem and possibly produce better result. Artificial neural network is considered as a potential alternative that may have an advantage on parameter customization and time-series applicable. Based on test results, it is proven that an artificial neural network solution is able to produce a generally both hearable and likeable chord composition.
Predictive analytics in agribusiness industries

Agriculturally related industries are routinely among the most hazardous work environments. Workplace injuries directly impact labor-market outcomes including income reduction, job loss, and health of the injured workers. In addition to medical and indemnity costs, workplace incidents include indirect costs such as equipment damage and repair, incident investigation time, training new personnel for replacement of the injured ones, an increase in insurance premiums for the year following the incidents, a slowdown of production schedules, damage to companies’ reputation, and lowering the workers’ motivation to return to work. The main purpose of incident analysis is the derivation and development of preventative measures from injury data. Applying proper analytical tools aimed at discovering the causes of occupational incidents is essential to gain useful information that contributes in preventing those incidents in future. Insight gained from the analyses of workers’ compensation data can efficiently direct preventative activities at high-risk industries. Since incidents arise from a combination of factors rather than a single cause, research on occupational incidents must go deeper into identifying the underlying causes and their relationship through applying more comprehensive analyses. Therefore, this study aimed at identifying underlying patterns in occupational injury occurrence and costs using data mining and predictive modeling techniques instead of traditional statistical methods. Utilizing a workers’ compensation claims dataset, the objectives of this study were to: investigate the use of predictive modeling techniques in forecasting future claims costs based on historical data; identify distinctive patterns of high-cost occupational injuries; and examine how well machine learning methods work in finding the predictive relationship between factors influencing occupational injuries and workers’ compensation claims occurrence and severity. The results lead to a better understanding of injury
Answer Generation for Community Question Answering System

Community question answering (cQA) services have accomplished quiet popularity over the past years. It helps user to get information from a comprehensive set of well-answered questions. Existing community question-answering forums usually provide only textual answers. However, for many questions, pure texts cannot provide intuitive information, while image or video contents are more appropriate. In this paper, we propose a scheme that is able to improve textual answers in cQA with appropriate multimedia data. MMQA consists of three components in its scheme, those are Answer medium selection, Query generation for multimedia search, and Multimedia data selection and presentation. This approach automatically figures out which type of multimedia information should be added to get an elaborated textual answer. For this, it then automatically collects data from web to enrich the answer. By processing a huge set of questionanswer pairs and adding them to a dataset, our approach can set up a novel multimedia question answering (MMQA) approach as users can find multimedia answers by comparing their questions with those in the dataset. Our approach MMQA is different from rest of the MMQA research efforts as it not only provide direct question answers with image & video data but also is based on community-contributed textual answers and hence it is able to give answers of more complex questions. We have conducted huge experiments on a multi-source QA dataset. The results demonstrate the effectiveness of our approach by providing user more satisfactory answers.
A SELF REGULATING TOOL TO CHARACTERIZE AN ABNORMAL RETINA FROM A NORMAL RETINA USING NEURAL NETWORK

Diabetes Mellitus is the most complicated proliferative health issue all through the world. The International Federation Committee of diabetes had made an analysis with reference to the diabetic sufferer count and had concluded that more than 199 million people deteriorate from diabetic complications globally and it is expected to surge to 366 million by 2030. Diabetes Mellitus is the leading cause for Retinopathy. It is the dominant proliferative eye problem and it is one among the extensive reason for the loss of eyesight. Eyesight problems can be abstained potentially by detecting the signs of retinopathy at an earlier stage. Microaneurosyms and Exudates are the emerging signs of Diabetic Retinopathy. Presently the lesions are observed and detected manually and this detection is not so useful for the mass eye screening programs. A huge group of research community are involved in the process of designing an automatic tool to individualize a deficient retina from the normal retina which provides a complete solution for all the retinal problems and to also solve the issues in terms of time and cost and to satisfy the needs of ophthalmologist. In this paper the necessary region are segmented and are given as the input to the neural network which uses the construction neural network algorithm with back propagation technique in order to classify the retinopathy level. This automatic tool will individualize the patients on the basis of their retinopathy level and it will be useful for the doctors to describe and analyze the inmate conditions more accurately. The prospect of blindness can be probably diminished by detecting the retinopathy at an earlier stage.
Intrepid : an SMT-based Model Checker for Control Engineering and Industrial Automation

Intrepid is an SMT-based model checking library. It provides a rich API for constructing, simulating, and verify circuits. Verification of safety properties is performed in a bit-precise manner, including operations involving integers and doubles. The incremental multi-property engines are suitable for automated test generation tasks, such as MC/DC test generation. Intrepid can parse Lustre specifications, and a subset of IEC-61131 ST language: by using the PLC language kit provided by Mathworks, we show that also Simulink and Stateflow languages can be processed by Intrepid.
Urdu Phonological Rules in Connected Speech

The present work deals with the phonological rules in Urdu language. All these rules have been reported by considering the multiple pronunciations of a word, which has same spellings and parts of speech (POS). For the confirmation of multiple pronunciations, firstly a word list of 13717 words has been extracted from 10 hours speech corpus of a female native Urdu speaker. Secondly, in order to confirm whether these multiple pronunciations are speaker dependent or language dependent, data from 9 more native speakers have been collected for the confirmation of multiple pronunciations. In this paper, phonological rules related to the segment alternation, segment deletion and segment insertion have been investigated. Analysis reports that (i) segment alternation occurs due to stress, (ii) unstressed articulation causes segment deletion and (iii) segment insertion emerges to break consonant cluster at coda position. Keywords—Urdu phonological rules, multiple pronunciations, segment deletion, segment insertion, segment alternation, syllabification and restructuring
A Causal Frequency-Domain Implementation of a Natural Gradient Multichannel Blind Deconvolution and Source Separation Algorithm

Natural gradient adaptation is useful for developing block-based adaptive solutions to convolutive blind source separation tasks, in which frequency-domain fast convolution methods can be exploited for computational simplicity. To maintain causal operation, previouslydeveloped natural gradient convolutive blind source separation algorithms employ approximations to the natural gradient that ultimately limit the separation performance s of these schemes. In this paper, we derive a novel causal frequency-domain implementation of a natural gradient algorithm for convolutive blind source separation tasks. Simulations with convolutive speech mixtures indicate that the proposed method provides robust convergence performance for relatively-short separation filters without creating preor post-echo artifacts in the extracted sources.
A study of Sociodemographic profile and Psychopathology in patients who underwent Amputation ‖ *

Limb loss is one of the most physically and psychologically devastating events that can happen to a person. Attempts to identify specific factors that may account for the diversity of responses to amputation have stimulated investigation of many medical/amputation-related factors, demographic variables and individual psychological variables using semistructured intake proforma and GHQ.The paper reports on the sociodemographic data, aetiology and levels of amputation. Most of the amputees are younger males. The most common cause of amputation was trauma (77.5%), the second being peripheral vascular disease. Lower limb amputation (91.5%), more common than amputation at the upper limb. The amputees have given high scores on all components of GHQ. Many people are suffering from treatable psychiatric problem and they are not being addressed. This study is aimed to get an insight into this issue.
A LOW-ENERGY METHOD OF CONTROLLED NUCLEOSYNTHESIS

Among timely problems of the contemporary science, of the highest priority is the controlled transmutation of elements and isotopes. This problem is a key one for the solution of such urgent tasks as the ecologically safe (without reactors) use of nuclear energy, technological fusion of rare isotopes, safe and efficient neutralization and utilization of radioactive wastes (in particular, worked-out nuclear fuel), and fusion of superheavy transuranium nuclei. Large scientific centers of developed counties have tried to solve these problems during decades by attracting the most skilled experts and huge financial and technical resources. However, any attempts to achieve essential results which would be widely introduced in practice have not met with success. At the same time, a change of conceptual approaches enabled us to get quite reassuring experimental results. Up to now, the field of nuclear transformations is dominated by the conception of the so-called “force” method. Its adherents consider that the problem of controlled nucleosynthesis can be solved by using more and more powerful technical means (accelerators with maximally high energy, neutron sources with maximally high intensity, systems of controlled synthesis of the “Tokamak” type, etc.), which requires high cash investments measured in billions of dollars. However, this way is erroneous in principle, because the technological resources of our civilization are bounded, and the force-based attempts to reproduce the processes, which occur in abnormal astrophysical objects of the type of pulsars or neutron stars, under laboratory conditions are unrealistic. We have tried to find the other method to solve this problem which would ensure the achievement of supercritical conditions not with the use of a strong external action on a nuclear system but through the stimulation of powerful processes of internal self-organization of matter on the nuclear
Fractionally Integrated Multivariate Models for Fat-Tailed Realized Covariance Kernels and Returns

We introduce a new fractionally integrated model for multivariate covariance matrix dynamics based on the long-memory behavior of daily realized covariance matrix kernels and daily return observations. We account for fat-tailedness in both types of data by assuming a matrix-F distribution for the realized kernels and a multivariate Student’s t distribution for the returns. In addition, the score-driven propagation mechanism adopted for the covariance matrix dynamics endows our model with further robustness properties. Using intraday stock data over the period 2001-2012, we construct realized covariance kernels and show that the new fractionally integrated model outperforms recent alternatives such as the Multivariate HEAVY model and the Riskmetrics 2006 (long-memory) model both statistically and economically.
2 GPLM : A General Model for Supervised Learning 2 . 1 The Learning Domain

Supervised learning is characterized by a broad spectrum of learning problems, often involving structured types of prediction, including classification, ranking-based predictions (label and instance ranking), and (ordinal) regression in its various forms. All these different learning problems are typically addressed by specific algorithmic solutions. In this chapter, we propose a general preference learning model (GPLM), which gives an easy way to translate any supervised learning problem and the associated cost functions into sets of preferences to learn from. A large margin principled approach to solve this problem is also proposed. Examples of how the proposed framework has been effectively used by us to address non-standard real-world applications are reported showing the flexibility and effectiveness of the approach.
Low Cost 3 d Rotational Input Devices : the stationary Spinball and the mobile Soap 3 D

This paper introduces a new approach enabling intuitive input of rotational data with low-cost optical sensors. Two devices are built, one for desktop applications and a mobile wireless device for everywhere use. The desktop variant, called Spinball makes the virtual trackball a real device. The mobile device, called Soap3d uses a two-layered approach for device casing, enabling closed hand control. Benefits and construction are illustrated and results of expert reviews are discussed.
Strategies for the Treatment of Osteochondral Lesions

Clinical practice is often challenged by the need for treating lesions of several musculoskeletal tissues, in which osteochondral (OC) tissues have an important share. Figure2.1a shows an X-ray image of a human OC tissue obtained from a patient diagnosed with knee osteoarthritis (OA); Fig. 2.1b and c show, respectively, the 2D and 3D reconstruction of the images that were obtained by the micro-computed tomography (μ-CT) analysis. OC lesions affect both the articular cartilage and the underlying bone. Figure2.1d presents an OC lesion in the human knee. Clinical need for repairing OC lesions is not yet fully satisfied. It is difficult to repair the OC tissue by surgical means alone. Lesions in OC tissues may lead to arthritis if not treated correctly in the early stages [1]. However, usually OA develops without a known cause [2]. OA is the most common disease affecting the joints, and it is seen usually in the knee, hips, hands and spine [3]. The majority of the population over 65years old have evidence of OA [4]. OA is characterized by the imbalance of anabolic and catabolic activities of cartilage cells [5]. It is a progressive degenerative disease with gradual loss of articular cartilage [6] leading to joint deformation and pain [7]. Many tissues can be damaged during OA: articular cartilage, subchondral bone (SB), ligaments, capsule and synovium, and muscles [5, 7]. Degeneration of articular cartilage and remodelling of SB are usually seen in OA [7].
Dynamic Edge Computation Offloading for Internet of Things With Energy Harvesting: A Learning Method

Mobile edge computing (MEC) has recently emerged as a promising paradigm to meet the increasing computation demands in Internet of Things (IoT). However, due to the limited computation capacity of the MEC server, an efficient computation offloading scheme, which means the IoT device decides whether to offload the generated data to the MEC server, is needed. Considering the limited battery capacity of IoT devices, energy harvesting (EH) is introduced to enhance the lifetime of the IoT systems. However, due to the unpredictability nature of the generated data and the harvested energy, it is a challenging problem when designing an effective computation offloading scheme for the EH MEC system. To cope with this problem, we model the computation offloading process as a Markov decision process (MDP) so that no prior statistic information is needed. Then, reinforcement learning algorithms can be adopted to derive the optimal offloading policy. To address the large time complexity challenge of learning algorithms, we first introduce an after-state for each state–action pair so that the number of states in the formulated MDP is largely decreased. Then, to deal with the continuous state space challenge, a polynomial value function approximation method is introduced to accelerate the learning process. Thus, an after-state reinforcement learning algorithm for the formulated MDP is proposed to obtain the optimal offloading policy. To provide efficient instructions for real MEC systems, several analytical properties of the offloading policy are also presented. Our simulation results validate the great performance of our proposed algorithm, which significantly improves the achieved system reward under a reasonable complexity.
Investigating Power and Agency in Singular Diversity-Requirement Education Courses:

Given recent pushes toward homogenous teacher education, teacher educator pedagogy necessitates modeled critical practice. The research presented here provides an analysis to ground the call for a move beyond teacher education pedagogy focused on the dissemination of knowledge about critical issues in classrooms. Utilizing two methodological traditions – namely, critical discourse analysis and phenomenography – this paper reports on the discursive moments enacted in a graduate course that sought to foster critical understandings of issues in urban science education, intending to lead to critical practices employed by this population of teacher candidates and doctoral students. Findings support that not only was there a preponderance of the dissemination model of teaching and learning, but also that the students within this course, even when working in groups, were unable to generate critical unit plans for prospectus science lessons in K-12 classrooms. Implications for this study are discussed in relation to the literature on critical teacher education.