Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Cost Printer for DLP Stereolithography

A general research direction of stereolithography based on digital light processing (DLP) is to reduce the production time and to increase manufacturing accuracy. Compared to fused deposition modelling (FDM) machines, machines for DLP stereolithography are expensive andthus not available to a broad range of users as it is the case with FDM 3D printers. Comparing technologies, DLP stereolithography offers quicker and more accurate production. In this paper, performances of a low cost DLP stereolithographic printer is presented. Three main challenges are treated: an uneven illumination of commercial DLP projectors, a direction of illumination and a selection of optimal 3D printing parameters. Uneven illumination of DLP projector results in smaller usable working area and poor printing quality. The problem is solved by implementing an appropriate software mask, thus the product quality is not influenced by its position on a working table. The direction of illumination has a key role in DLP stereolithography. It is shown that constrained surface (illumination through a transparent bottom of the vat) gives better 3D printing accuracy compared to free surface (illumination of the photopolymer surface) stereolithography. To further improve the product quality, the optimal process parameters are determined. Using the Taguchi based surface response methodology optimal process parameters are defined and by using them, the deviation of the actual dimensions from the specified dimensions is less than 80 μm.
On strong domination number of corona related graphs

Let G = (V (G),E(G)) be a graph and uv ∈ E(G) be an edge. A vertex u strongly dominates v if dG(u)≥ dG(v). A set S⊆V (G) is a strong dominating set (sd− set) if every vertex v ∈V (G)−S is strongly dominated by some u in S. The minimum cardinality of a strong dominating set is called the strong domination number of G which is denoted by γst(G). We investigate strong domination number of some corona related graphs.
Pattern recognition using hidden Markov models in nancial time series

Our aim consists in developing a software which can recognize M trading patterns in real time using Hidden Markov Models (HMMs). A trading pattern is a prede ned gure indicating a speci c behavior of prices. We trained M + 1 HMMs using Baum Welch Algorithm combined with Genetic Algorithm. In particular, with HMMs we describe M trading patterns while the other one, called threshold model, can recognize all the not prede ned patterns. The classi cation algorithm correctly recognizes 93% of the provided patterns. Thanks to the analysis of the false positive examples, we nally designed some more lters to reduce them.
Characterization of Waste Frying Oils Obtained from Different Facilities

Biodiesel cannot economically compete with petroleum-based diesel fuel because of its high cost problem. This problem may be solved with the use of low cost feedstocks in biodiesel production. Waste frying oils are one of the low cost feedstocks. However, the feedstocks’ properties to be processed must be controlled in detail prior to transesterification reaction, since the physical and chemical properties of the feedstock significantly influence biodiesel production reaction as well as fuel properties. Frying oils which are used in various facilities in different conditions (such as frying temperature, time, and kind of food) have significantly different physico-chemical properties. Therefore, in this study, 30 different waste frying oil samples (14 from fish restaurants, 5 from fast-foods, 5 from hospitals, 4 from pastry shops, and 2 from restaurants) were collected and their density, viscosity, total polar material, water content, acid value, iodine value, peroxide value, and heating content were determined and compared to each others. The correlations between the total polar material content (which has to be legally determined to monitor frying oil’s degradation level) and density, viscosity, acid value and water content were remarkable. The usage of peroxide value to decide the quality of an oil was misleading.
Electrical Resistivity Tomography for Coastal Sea Water Intrusion Characterization along Rafah Area, South of Gaza Strip, Palestine

Resistivity methods mainly electrical resistivity tomography 2D and resistivity sounding 1D represent the main geophysical tools to delineate seawater intrusion into freshwater aquifers. Thirty 2D imaging sections were applied with Wenner-Schlumberger array (NW-SE direction), distributed on six profiles (perpendicular to the coast) in southern part of the coastal aquifer in Gaza Strip (Rafah Governorates), supported by 4 Vertical electrical soundings points with Schlumberger array, to map shallow-depth seawater intrusion into freshwater aquifer and study the subsurface geologic formation. Analysis and interpretation of the electrical resistivity tomography 2D and 1D sounding results reveal that seawater intrusion zone appears at the western part of the study area and extends along Rafah coast with a difference distance and depths. The depths are ranging between (few meters to 20 m) in the western sections, while they are betwee 40 to more than 65 m in the eastern section. The resistivity data results were correlates well with the 3 boreholes data and 3 chemical analyses water samples.
An Application of ‘Willingness to Pay’ Method as a Quantifier for Environmental Impact Assessment

The preservation/restoration of natural environment is frequently entailing excessive cost (paid by people through taxation) while it is a source of additional income for both, the State and the people, due to tourism. Since the evaluation of this good cannot be in market terms, it is applied here in a modified version of the CVM (Contingent Valuation Method), which is used in experimental economics in order to investigate the significance that people put on this good and how much they might be WTP (Willing to Pay) for supporting activities concerning the preservation/restoration of Lake Kastoria. The WTP dependence on (i) external diseconomies; (ii) the expectations for property values’ rise as a result of the restoration; (iii) the proximity of interviewees’ residence to the lake; (iv) the opinion of the interviewee on the time and money spent to visit the lake; (v) the time and money the interviewees spent to visit the lake, as well as other dependencies (all taken as independent variables) are estimated by means of logit, probit, logistic and linear regression models. The optimal concentration Copt of a pollutant in the environment can be determined as an equilibrium point in the tradeoff between (i) environmental cost, due to impact on man/ecosystem/economy; and (ii) economic cost for environmental protection, as it can be expressed by Pigouvian tax. These two conflict variables are internalized within the same techno-economic objective function of total cost, which is minimized. In this work, the first conflict variable is represented by a WTP index. A methodology is developed for the estimation of this index by using fuzzy sets to count for uncertainty. Implementation of this methodology is presented, concerning odor pollution of air round an olive pomace oil mill.
Experimental investigation of effect of specimen thickness on fracture toughness of Al-TiC composites

In this paper, the macro and micro-mechanical fracture behavior was studied for aluminum (Al6061) alloy matrix, reinforced with various proportions of TiC particles such as 3wt%, 5wt% and 7wt%. The Al6061-TiC metal matrix composites were produced by stir casting method to ensure uniform distribution of the TiC particulates in the Al matrix. The compact tension (CT) specimens were machined according to ASTM E399 specifications to evaluate the fracture toughness for Al6061-TiC metal matrix composites. The CT specimens were machined for crack to width (a/W) ratio of 0.5 and thickness to width (B/W) ratios of 0.2 to 0.7 with an increment of 0.1. Load versus crack mouth opening displacement (CMOD) data was plotted to estimate stress intensity factor KQ for various thicknesses of the specimen. The fracture toughness KIC was obtained by plotting stress intensity factor versus thickness to width ratios of specimen data. The fracture toughness of these composites varied between 16.4-19.2 MPa? m. Scanning Electron Microscope (SEM) studies was made on the fractured surface of the specimens to understand the micro-mechanisms of failure involved in these composites. Void initiation is more significant in the matrix near the interface. The micro-cracks grow from these micro-voids and crack propagates by linking these micro cracks locating the crack path preferentially in the matrix adjacent to the interface indicating ductile fracture.
Fair Clearing of Reactive Power Market

In this paper, new reactive power market structure is studied and presented. Active power flow by itself causes active and reactive losses. Considering reactive losses of active power flow in the reactive power market without paying any costs is the main purpose of this paper. For this purpose, new methodology for reactive power market structure is proposed. Hence, this study tries to improve reactive power market and create fair competition in reactive power generation through modifying the market structure. The advantage of this method is determining the mandatory region of units based on both active power output and its distance from the load. In order to stimulate the proposed method, IEEE 24 is used, and this method is compared with each the conventional reactive power market. As it will be shown, the total payment by ISO will be reduced by using this method.
Dynamic Alarm Correlation based on Cellular Learning Automata in Telecommunication Networks

† , ††† †† Summary Powerful fault management systems are increasingly required to ensure robustness and qualitative services. Though alarms are usually useful for identifying faults in such systems, huge numbers of alarms generated as a result of some major network event require efficient management methods and algorithms in order to discover the root cause in a timely manner. In this paper, we propose a robust algorithm for recognizing root cause faults in a reasonable time window by dynamically clustering alarms and events. Our algorithm is composed of three stages and uses cellular learning automaton in all stages. Simulations testify to the high efficiency of this algorithm with different parameters.
Possible articulatory reasons for sex-specific differences in vowel duration

X-ray microbeam data of five tokens of the word light from 26 female and 22 male English speakers are examined in an attempt to find some of the articulatory correlates of observed acoustic differences in male and female diphthong duration (female > male). Upward vertical displacement of the tongue body associated with the diphthong glide and downward tongue tip movement around lateral release are analysed. Expected relationships between displacement magnitude, peak velocity, and movement duration are found in tongue body and tip data. Tongue body movement is found to be slightly longer for male speakers. Significant differences in the temporal onset of tongue tip movement relative to tongue body movement are found, such that the tongue tip release of the female lateral begins earlier.
Personalized and Accurate QoS Prediction Approach Based on Online Learning Matrix Factorization for Web Services

Quality of Service (QoS) prediction has played an important role in service computing. However, in the real-world scenario of Web service, many user-observed QoS values are unknown and vary over time. In order to provide high accurate and efficient QoS prediction performance for Web services, we propose a personalized and accurate QoS prediction approach namely PAOMF. Our prediction model is built by employing matrix factorization and online stochastic gradient descent algorithm. Extensive experiments are conducted on real world public datasets, which demonstrate the effectiveness and efficiency of our proposed approach.
Vertical swimming behavior influences the dispersal of simulated oyster larvae in a coupled particle-tracking and hydrodynamic model of Chesapeake Bay

Because planktonic organisms have swimming speeds that are orders of magnitude lower than horizontal current velocities, it is unclear whether behavior of weak-swimming bivalve larvae could influence dispersal distance, encounters with suitable habitat, or subpopulation connec- tivity. We used a numerical approach to investigate whether these processes could be affected by species-specific differences in larval vertical swimming behavior of 2 oyster species (Crassostrea vir- ginica and C. ariakensis) in Chesapeake Bay, a partially mixed estuary. A coupled particle-tracking and hydrodynamic model was forced with observed winds and freshwater flow and included the best available estimate of present-day oyster habitat. Model scenarios were conducted with hydrody- namic predictions from June to September, 1995 to 1999, to simulate a range of environmental con- ditions. Simple larval swimming behaviors were parameterized for the 2 oyster species with results from preliminary laboratory experiments and literature. To isolate the effect of circulation, settlement habitat, and larval behavior on the spatial trajectories of particles, vertical swimming velocity was the only biological process represented in the model; egg production and larval growth were not included. Differences in larval swimming behavior had significant consequences for particle trans- port in Chesapeake Bay by influencing dispersal distances, transport success, and the degree of con- nectivity between 'subpopulations' in different tributaries. Most particles (>96%) did not return to the same reef on which they were released, and there were behavior-dependent differences in spa- tial patterns of the 'source' and 'sink' characteristics of oyster reefs. Simulated larval behavior had greater influence on spatial patterns of transport success than did interannual differences in circula- tion patterns. These model results have implications for fisheries management and oyster restoration activities.
Isolation and Characterization of Mosquitocidal Bacillus Thuringensis, Analysis of Its Crystal Proteins and Plasmid

Today, India ranks second worldwide in farm output. Agriculture sector employs nearly 58% of India’s workforce and constitutes 22% of total Indian economy. With sowing and building up at a rapid pace for the majority of crops, crop production is also anticipated to increase by 2.5 % during the next financial years to come. The growth in the GDP of agriculture and allied sectors is anticipated at 3.2% during the financial year 2009, as stated by the Centre for Monitoring Indian Economy (CMIE). India is the largest producer in the world of milk, cashew nuts, coconuts, tea, ginger, turmeric and black pepper (Agribusiness in India, 2009). Our country is the second largest producer of wheat, rice, sugar, cotton, silk, peanuts and the third largest producer of tobacco. India is the largest fruit producer, accounting for 10% of the world fruit production. It is the leading producer of bananas, sapotas and mangoes (Indian agriculture ,2008) Out of 750,000 identified species , about 10,000 species of insects are important as pest throughout the world. Over 1,800 species of weeds out of the known 30,000 cause serious economic loss. About 15,000 species of nematodes produce more than 1,500 serious deleterious effects on plants. Over 1,00,000 species of pests destroy food which could be food for 135 million people. The word pest has no biological meaning. Pests are organisms that diminish the value of resources in which we are interested. In India, crops are affected by over 200 major pests, 100 plant diseases, hundreds of weeds and other pests like nematodes, harmful birds, rodents and the like. About 4,800 million rats cause havoc in India. Approximately, 30% of Indian crop yield potential is being lost due to insects, disease and weeds which in terms of quantity would mean 30 million tones of food grain. The value of total loss has been placed at Rs 50,000 million, represents about 18% of the gross national agriculture production. (Manncsa, 2009).
PERILAKU OPORTUNISTIK LEGISLA TIF DAN FAKTOR-F AKTOR YANG MEMPENGARUHINY A: Bukti Empiris dari PenganggaranPemerintah Daerah di Indonesia

Berlakunya autonomi daerah di Indonesia, yang dimulai pada I Januari 2001 berdasarkan UU NO.22/1999 tentang Pemerintahan Daerah dan UU NO.25/1999 tentang Perimbangan Keuangan Antara Pemerintah dan Pemerintahan Daerah, telah membawa perubahan yang sangat mendasar terhadap hubungan Pemerintah Daerah dengan Dewan Perwakilan Rakyat Daerah (DPRD). UU No.22/1999 menempatkan DPRD pada posisi yang sangat kuat karena diberi kewenangan untuk memilih dan memberhentikan kepala daerah, memiliki hak untuk meminta pertanggungjawaban kepala daerah, dan menentukan anggaran belanja DPRD sendiri. Jika dibanding konsep autonomi daerah menurut UU No.5/1974, UU NO.22/1999 menggeser dominasi eksekutif daerah (executive heavy) menjadi dominasi legislatif daerah (legislative heavy) (Wasistiono, 2003: 19). Namun, pada tanggal 15 Oktober 2004, UU NO.22/1999 digantikan oleh UU No.32/2004 tentang Pemerintahan Daerah. Dominasi legislatifyang terlalu besar dipandang menyebabkan penyelenggaraan pemerintahan daerah tidak dapat berjalan dengan baik, termasuk dalam penganggaran dan pengelolaan keuangan di daerah. Ungkapan Lord Acton yang mengatakan bahwa "
The relation between the symmetry of vibrational modes and the potential curve displacement associated with electronic transition studied by using real-time vibrational spectroscopy

The relation between the vibronic coupling strength of the vibronic state combined with the optical transitions with short pulses and the symmetry of the coupled vibrational modes was studied by using multi-wavelength vibrational real-time spectroscopy. A pump–probe experiment of thiophene oligomers using 6.7 fs pulsed laser as an ultrashort pulse light source and a multi-channel lock-in amplifier as a broadband detector was performed to obtain simultaneously the real-time traces at 128 wavelengths. At all wavelengths, the absorbance changes were plotted against pump–probe delay time, which was between −200 and 2000 fs. The vibronic coupling in both the ground and excited states was classified into two types: the displaced (D) type with displaced potential surfaces and the non-displaced (ND) type with non-displaced potential surfaces between the two electronic studies. The D and ND types were found to be related to inversion anti-symmetric and symmetric molecular vibrations, respectively, with respect to the center of the pseudo-inversion symmetry of the molecule. The symmetry center is located at the middle point of the bond connecting the two quinoid-type thiophene rings. It was shown that multi-wavelength measurement of the vibrational real-time spectroscopy provides information on the mechanism of vibronic coupling in the process of transition from the ground state to the excited state and that between the two excited states.
Histopathological breast-image classification with restricted Boltzmann machine along with backpropagation

Deaths due to cancer have increased rapidly in recent years. Among all the cancer diseases, breast cancer causes many deaths in women. A digital medical photography technique has been used for the detection of breast cancer by physicians and doctors, however, they need to give more attention and spend more time to reliably detect the cancer information from the images. Doctors are heavily reliant upon Computer Aided Diagnosis (CAD) for cancer detection and monitoring of cancer. Because of the dependence on CAD for cancer diagnosis, researchers always pay extra attention to designing an automatic CAD system for the identification and monitoring of cancer. Various methods have been used for the breast-cancer image-classification task, however, state-of-the-art deep learning techniques have been utilised for cancer image classification with success due to its self-learning and hierarchical featureextraction ability. In this paper we have developed a Deep Neural Network (DNN) model utilising a restricted Boltzmann machine with “scaled conjugate gradient” backpropagation to classify a set of Histopathological breast-cancer images. Our experiments have been conducted on the Histopathological images collected from the BreakHis dataset.
Automated financial multi-path GETS modelling

General-to-Specific (GETS) modelling has witnessed major advances over the last decade thanks to the automation of multi-path GETS specification search. However, several scholars have argued that the estimation complexity associated with financial models constitutes an obstacle to multi-path GETS modelling in finance. We provide a result with associated methods that overcome many of the problems, and develop a simple but general and flexible algorithm that automates financial multi-path GETS modelling. Starting from a general model where the mean specification can contain autoregressive (AR) terms and explanatory variables, and where the exponential variance specification can include log-ARCH terms, log-GARCH terms, asymmetry terms, Bernoulli jumps and other explanatory variables, the algorithm we propose returns parsimonious mean and variance specifications, and a fat-tailed distribution of the standardised error if normality is rejected. The finite sample properties of the methods and of the algorithm are studied by means of extensive Monte Carlo simulations, and two empirical applications suggest the methods and algorithm are very useful in practice.
Nonprojectable Horava-Lifshitz gravity without the unwanted scalar graviton

We consider a way of eliminating the unwanted scalar graviton from Horava-Lifshitz gravity that is achieved via introduction of certain additional constraints. We perform canonical analysis of both projectable and nonprojectable versions of the theory. We obtain the structure of constraints in each case, and analyze its dependence on the values of the coupling constants involved in the additional constraints. In the nonprojectable theory, the scalar graviton is absent when the coupling constants have certain values, while for other values the scalar graviton appears. The projectable theory is free from the scalar graviton regardless of the values of the coupling constants, even though the structure of the constraints does depend on the value of a coupling constant.
A ‘standard joint’? The role of quantity in predicting cannabis-related problems

The ‘standard drink’ concept is widely used as a standardized measure of alcohol consumption. There is no equivalent measure of cannabis consumption, perhaps due to challenges such as varied joint size, tetrahydrocannabinol content, and means of delivery. This study introduces a new measure of cannabis quantity and examines whether it predicts cannabis-related social problems with and without controlling for frequency of use. Cannabis-related problems, measured by the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST), were predicted from cannabis use frequency (days in past month) and quantity (one joint = 0.5 g, five bong or pipe hits, 10 puffs), controlling for age and gender. The sample consisted of 665 participants aged 15–67 (mean = 28.2, SD = 11.8) from the British Columbia Alcohol and Other Drug Monitoring Project, High Risk Group Surveys, 2008 to 2009. Cannabis use frequency and quantity were positively associated with cannabis-related problems. Individuals who consumed cannabis daily and consumed more than one joint per day were at the greatest risk of problems. Controlling for frequency, the effect of quantity remained significant for failure to do what is expected due to cannabis use. This study suggests that quantity, above and beyond frequency, is an important predictor of cannabis problems. We discuss the potential usefulness and validity of this new measure in harm reduction.