Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and evaluation of a multi-heat source deep fat fryer

Deep fat frying constitutes a reasonable percentage in cottage food preparation across the globe. The need to upgrade the local deep fat frying process to be more user friendly, efficient and versatile is the basis of the development of an appropriate portable, low-cost and affordable multi-heat source deep fryer. The machine was evaluated using wheat flour dough to produce chin-chin, a local fried wheat product. Performance of groundnut, corn and soya oils under different heat sources; gas, electricity and charcoal energy were considered. The average capacity of the machine was found to be 7.69 kg/h when using electric energy, 8.52 kg/h when using gas energy and 6.00 kg/h when using charcoal energy. Heat requirement of the machine was 1428 W. The fat uptake of the chin-chin fried with groundnut oil was 0.51, 0.49 and 0.67 g for gas, electric and charcoal energy respectively; for soya oil 0.47, 0.43 and 0.63 g respectively; and for corn oil 0.46, 0.41 and 0.57 g respectively. Vitamin A retention in soya, groundnut and corn oil was 13515, 18485 and 34242 IU/kg respectively when using gas energy; 14727, 22243 and 38182 IU/kg respectively when using charcoal energy; 20606, 27273 and 41030 IU/kg respectively when using electric energy. Thus, corn oil gave the least fat uptake when using electric energy; it also gave the highest vitamin A retention, given the mandatory vitamin A level of 30000 IU/kg in flour and 20000 IU/kg in vegetable oils under the National Food Fortification Programme in Nigeria.
The Egg-Turning Technique on The Hatching Performance of Hybrid Duck Eggs

This research aimed to determine different egg-turning techniques on the hatching performance of hybrid duck eggs. The materials for this research include water, hatching eggs of hybrid ducks, and manual hatching machines. This research used the T-test with 2 treatments and 6 replications, including T1 (swipe egg-turning) and T2 (manual egg-turning), each replication consisting of 10 hatched hybrid duck eggs. The parameters observed included the percentage of mortality, the percentage of hatchability, and the hatching weight of hybrid ducks. Analysis of the resulting data uses an analysis of variance (ANOVA) with the SPSS (Statistical Package for Social Science) application. Data that had significant differences were then tested further using the DMRT test (Duncan Multiple Range Test). The results showed that the egg-turning technique had no significant effect on the hatching performance of hybrid duck eggs, including mortality, hatchability, and hatching weight. The conclusion of the research was the regular turning times with differences in egg-turning techniques did not influence the hatchability of the duck hybrid.
Data representation synthesis

We consider the problem of specifying combinations of data structures with complex sharing in a manner that is both declarative and results in provably correct code. In our approach, abstract data types are specified using relational algebra and functional dependencies. We describe a language of decompositions that permit the user to specify different concrete representations for relations, and show that operations on concrete representations soundly implement their relational specification. It is easy to incorporate data representations synthesized by our compiler into existing systems, leading to code that is simpler, correct by construction, and comparable in performance to the code it replaces.
Distributed generation as Voltage support for single wire Earth return systems

Key issues for distributed generation (DG) inclusion in a distribution system include operation, control, protection, harmonics, and transients. This paper analyzes two of the main issues: operation and control for DG installation. Inclusion of DG in distribution networks has the potential to adversely affect the control of voltage. Both DG and tap changers aim to improve voltage profile of the network, and hence they can interact causing unstable operation or increased losses. Simulations show that a fast responding DG with appropriate voltage references is capable of reduction of such problems in the network. A DG control model is developed based on voltage sensitivity of lines and evaluated on a single wire earth return (SWER) system. An investigation of voltage interaction between DG controllers is conducted and interaction-index is developed to predict the degree of interaction. From the simulation it is found that the best power factor for DG injection to achieve voltage correction becomes higher for high resistance lines. A drastic reduction in power losses can be achieved in SWER systems if DG is installed. Multiple DG can aid voltage profile of feeder and should provide higher reliability. Setting the voltage references of separate DGs can provide a graduated response to voltage correction.
Dimensionality Reduction for large Spatio-Temporal datasets based on SVD

Many models for spatio-temporal measurements Z(s, t) can be written as a sum of a systematic component and a residual component: Z = M + E. The approach presented here incorporates two Singular Value Decompositions (SVD). The first SVD is applied to the space-time data matrix Z with cross-validation to choose the number of smoothed singular vectors to use as temporal basis functions for modelling spatially varying temporal trend in the matrix M . The second SVD is applied to the spatio-temporal matrix E of residuals from the trend models fitted at each site; it represents spatially correlated short time scale temporal processes. The remaining stochastic structure is explained by simple autoregressive models fit to the final residuals. The procedure is applied to 30 years of daily temperature data from Sicily.
Guest Editorial Introduction to the Special Section on M-Health: Beyond Seamless Mobility and Global Wireless Health-Care Connectivity

M-Health can be defined as “mobile computing, medical sensor, and communications technologies for health-care. ” This emerging concept represents the evolution of e-health systems from traditional desktop “telemedicine” platforms to wireless and mobile configurations. Current and emerging developments in wireless communications integrated with developments in pervasive and wearable technologies will have a radical impact on future health-care delivery systems. This editorial paper presents a snapshot of recent developments in these areas and addresses some of the challenges and future implementation issues from the m-Health perspective. The contributions presented in this special section represent some of these recent developments and illustrate the multidisciplinary nature of this important and emerging concept.
Succinct Approximate Counting of Skewed Data

Practical data analysis relies on the ability to count observations of objects succinctly and efficiently. Unfortunately the space usage of an exact estimator grows with the size of the a priori set from which objects are drawn while the time required to maintain such an estimator grows with the size of the data set. We present static and on-line approximation schemes that avoid these limitations when approximate frequency estimates are acceptable. Our Log-Frequency Sketch extends the approximate counting algorithm of Morris [1978] to estimate frequencies with bounded relative error via a single pass over a data set. It uses constant space per object when the frequencies follow a power law and can be maintained in constant time per observation. We give an (e, δ)-approximation scheme which we verify empirically on a large natural language data set where, for instance, 95 percent of frequencies are estimated with relative error less than 0.25 using fewer than 11 bits per object in the static case and 15 bits per object on-line.
Introducing Darwinian Networks

Darwinian networks (DNs) are introduced to simplify and clarify working with Bayesian networks (BNs). Rather than modelling the variables in a problem domain, DNs represent the probability tables in the model. The graphical manipulation of the tables then takes on a biological feel. It is shown how DNs can unify modeling and reasoning tasks into a single platform.
Prediction of wiring space requirements for LSI

A stochastic model is developed for estimating wiring space requirements for one-dimensional layouts. This model uses as input the number of devices in the complex to be wired, the average length of a connection, and the average number of connections per device, to compute the probability of successfully wiring the devices as a function of the number of tracks provided. A heuristic approach is used to extend this model to the two-dimensional case, and tested against experimen-tal studies. Satisfactory agreement is found between a priori calculations of track requirements for the two-dimensional case against global wiring solutions for artificially generated problems, and for some layouts of actual logic complexes.
Modeling the Origin of Anthropogenic Black Carbon and Its Climatic Effect Over the Tibetan Plateau and Surrounding Regions

Black carbon (BC) in snow/ice induces enhanced snow and glacier melting. As over 60% of atmospheric BC is emitted from anthropogenic sources, which directly impacts the distribution and concentration of BC in snow/ice, it is essential to assess the origin of anthropogenic BC transported to the Tibetan Plateau (TP) where there are few direct emissions attributable to local human activities. In this study, we used a regional climate‐atmospheric chemistry model and a set of BC scenarios for quantitative evaluation of the impact of anthropogenic BC from various sources and its climate effects over the TP in 2013. The results showed that the model performed well in terms of climatology, aerosol optical properties, and near‐surface concentrations, which indicates that this modeling framework is appropriate to characterize anthropogenic BC source‐receptor relationships over the TP. The simulated surface concentration associated with the anthropogenic sources showed seasonal differences. In the monsoon season, the contribution of anthropogenic BC was less than in the nonmonsoon season. In the nonmonsoon season, westerly winds prevailed and transported BC from central Asia and north India to the western TP. In the monsoon season, BC aerosol was transported to the middle‐upper troposphere over the Indo‐Gangetic Plain and crossed the Himalayas via southwesterly winds. The majority of anthropogenic BC over the TP was transported from South Asia, which contributed to 40%–80% (mean of 61.3%) of surface BC in the nonmonsoon season, and 10%–50% (mean of 19.4%) in the monsoon season. For the northeastern TP, anthropogenic BC from eastern China accounted for less than 10% of the total in the nonmonsoon season but can be up to 50% in the monsoon season. Averaged over the TP, the eastern China anthropogenic sources accounted for 6.2% and 8.4% of surface BC in the nonmonsoon and monsoon seasons, respectively. The anthropogenic BC induced negative radiative forcing and cooling effects at the near surface over the TP.
Adolescent Carotenoid Intake and Benign Breast Disease

BACKGROUND: Carotenoids may reduce risk of benign breast disease (BBD), an independent risk factor for breast cancer, through antioxidative or antiproliferative mechanisms. Exposure to carotenoids may be most important during adolescence when breast tissue is still developing. We examined adolescent carotenoid intake in relation to BBD in young women. METHODS: In 6593 adolescent girls in the prospective Growing Up Today Study cohort, intakes of α-carotene, β-carotene, β-cryptoxanthin, lutein/zeaxanthin, and lycopene were assessed by using the means from food-frequency questionnaires in 1996, 1997, and 1998. Girls reported biopsy-confirmed BBD on questionnaires in 2005, 2007, and 2010 (n = 122). We conducted logistic regression of energy-adjusted carotenoid intakes in relation to BBD, adjusted for age, family history of breast cancer or BBD, age at menarche, nulliparity, alcohol intake, BMI, and physical activity. RESULTS: Mean (SD) age at baseline was 12.0 (1.6) years. β-Carotene intake was inversely associated with BBD; comparing the highest to lowest quartile, the multivariate-adjusted odds ratio was 0.58 (95% confidence interval: 0.34–1.00; P-trend = .03). α-Carotene and lutein/zeaxanthin were also inversely associated with BBD, but the associations were not statistically significant. CONCLUSIONS: Adolescent carotenoid intake may be associated with lower BBD risk; these findings warrant further study.
PerfXplain: Debugging MapReduce Job Performance

While users today have access to many tools that assist in performing large scale data analysis tasks, understanding the performance characteristics of their parallel computations, such as MapReduce jobs, remains difficult. We present PerfXplain, a system that enables users to ask questions about the relative performances (i.e., runtimes) of pairs of MapReduce jobs. PerfXplain provides a new query language for articulating performance queries and an algorithm for generating explanations from a log of past MapReduce job executions. We formally define the notion of an explanation together with three metrics, relevance, precision, and generality, that measure explanation quality. We present the explanation-generation algorithm based on techniques related to decision-tree building. We evaluate the approach on a log of past executions on Amazon EC2, and show that our approach can generate quality explanations, outperforming two naive explanation-generation methods.
Pengelolaan Self-disclosure Generasi Z melalui Penggunaan Multiple Accounts di Instagram

This research extensively explores the phenomenon of Self-disclosure among Generation Z in the context of using Multiple Accounts on the Instagram platform, aiming to gain a deeper understanding of how Generation Z carefully manages their self-disclosure. Within the theoretical framework of Self-disclosure theory and the Johari Window, a qualitative constructivist phenomenological approach is applied, focusing on individuals from Generation Z who actively use more than two Instagram accounts. The research findings indicate that Generation Z consciously categorizes their accounts into three separate categories: a personal account for general content, a private account for more profound self-disclosure to trusted followers, and a professional account for specialized content showcasing their expertise. These findings reflect Generation Z's early awareness of the importance of shaping and maintaining their personal branding and digital identity through tailored openness based on the type of account they manage. This research provides deeper insights into how Generation Z manages Multiple Accounts, and these implications can be used to understand their digital self-disclosure dynamics in this modern era. Penelitian ini secara rinci mengeksplorasi fenomena Self-disclosure Generasi Z dalam konteks penggunaan Multiple Accounts di platform Instagram, bertujuan untuk memahami secara lebih mendalam bagaimana Generasi Z secara hati-hati mengelola pengungkapan diri mereka. Dalam kerangka teoretis teori Self-disclosure dan Johari Window, pendekatan kualitatif fenomenologi konstruktivisme diterapkan dengan fokus pada individu Generasi Z yang aktif menggunakan lebih dari dua akun Instagram. Hasil penelitian menunjukkan bahwa Generasi Z dengan sadar mengelompokkan akun mereka menjadi tiga kategori yang terpisah, akun personal untuk konten umum, akun privat untuk pengungkapan yang lebih mendalam kepada followers terpercaya, dan akun profesional untuk konten khusus yang menunjukkan keahlian mereka. Temuan ini mencerminkan kesadaran dini Generasi Z akan pentingnya membentuk dan merawat personal branding serta identitas digital mereka melalui keterbukaan yang disesuaikan dengan jenis akun yang mereka kelola. Penelitian ini memberikan wawasan yang lebih mendalam tentang bagaimana Generasi Z dalam mengelola Multiple Accounts, dan implikasi ini dapat digunakan untuk memahami dinamika pengungkapan diri mereka secara digital dalam era modern ini.
GPDL: a framework-independent problem definition language for grammar-guided genetic programming

Defining custom problem types in genetic programming (GP) software systems is a tedious task that usually involves the implementation of custom classes and methods including framework-specific code. Users who want to solve a custom problem have to know the details of the targeted framework, for instance cloning semantics, and often have to write a lot of boilerplate code in order to implement the necessary functionality correctly. This can lead to frustration and hinders new developments and the application of GP to solve interesting problems. In this contribution we propose a framework-independent definition language for GP problems that can reduce the required effort and facilitate the integration of new problem types. We draw a parallel between the implementation of compilers for programming languages and the implementation of GP problems and reuse the well-established concept of attributed grammars with semantic actions to define computational symbols, semantics and structural constraints for GP. This goes beyond previous work in the area of context-free-grammar GP and grammatical evolution, because we also interweave the definition of symbol semantics and the target function with the definition of the grammar. This paper describes the proposed GP problem definition language (GPDL) and exemplary definitions of two popular benchmark problems using GPDL. We also describe a reference implementation of a GPDL compiler for HeuristicLab.
Buffer Overflow Attack Blocking Using MCAIDS- Machine Code Analysis Intrusion Detection System

MCAIDS-Machine Code Analysis Intrusion Detection System for blocking code-injection buffer overflow attack messages targeting at various Internet services such as web service. With the increasing access of Internet,the Internet threat takes a form of attack, targetting individuals users to gain control over network and data. Buffer overlow is one of the most occuring security vulnerability in computer's world. Buffer overflow attack typically contains executables where as legitmate client request never contains executables in most Internet services. MCAIDS blocks attack by detecting the presense of code. MCAIDS uses new data - flow analysis technique called code abstraction. MCAIDS is signature free , thus it can block new and unknown buffer overflow attack. This MCAIDS simulate by using Network Simulater NS2 on the linux platform to analyze the expected results.
Drivers and Spatio-Temporal Extent of Hyporheic Patch Variation: Implications for Sampling

The hyporheic zone in stream ecosystems is a heterogeneous key habitat for species across many taxa. Consequently, it attracts high attention among freshwater scientists, but generally applicable guidelines on sampling strategies are lacking. Thus, the objective of this study was to develop and validate such sampling guidelines. Applying geostatistical analysis, we quantified the spatio-temporal variability of parameters, which characterize the physico-chemical substratum conditions in the hyporheic zone. We investigated eight stream reaches in six small streams that are typical for the majority of temperate areas. Data was collected on two occasions in six stream reaches (development data), and once in two additional reaches, after one year (validation data). In this study, the term spatial variability refers to patch contrast (patch to patch variance) and patch size (spatial extent of a patch). Patch contrast of hyporheic parameters (specific conductance, pH and dissolved oxygen) increased with macrophyte cover (r2 = 0.95, p<0.001), while patch size of hyporheic parameters decreased from 6 to 2 m with increasing sinuosity of the stream course (r2 = 0.91, p<0.001), irrespective of the time of year. Since the spatial variability of hyporheic parameters varied between stream reaches, our results suggest that sampling design should be adapted to suit specific stream reaches. The distance between sampling sites should be inversely related to the sinuosity, while the number of samples should be related to macrophyte cover.
User-Optimizer Communication using Abstract Plans in Sybase ASE

Query optimizers are error prone, due to both their nature and the increased search space that modern query processing requires them to manage. This paper introduces the Sybase Adaptive Server Enterprise (ASE) Abstract Plan (AP) language, a novel technology that puts together a set of proven techniques to palliate optimizer mistaken decisions. The AP language is a 2-way user-optimizer communication mechanism based on a physical level relational algebra. AP expressions are used both by the optimizer to describe the plan that it selected and by the user to direct the optimizer choices. APs are not textually part of the query. They are persistent objects stored in the system catalogs. APs yield important performance gains by eliminating all optimizer errors.
Object Detection Based on Plane Segmentation and Features Matching for a Service Robot

With the aging of the world population and the continuous growth in technology, service robots are more and more explored nowadays as alternatives to healthcare givers or personal assistants for the elderly or disabled people. Any service robot should be capable of interacting with the human companion, receive commands, navigate through the environment, either known or unknown, and recognize objects. This paper proposes an approach for object recognition based on the use of depth information and color images for a service robot. We present a study on two of the most used methods for object detection, where 3D data is used to detect the position of objects to classify that are found on horizontal surfaces. Since most of the objects of interest accessible for service robots are on these surfaces, the proposed 3D segmentation reduces the processing time and simplifies the scene for object recognition. The first approach for object recognition is based on color histograms, while the second is based on the use of the SIFT and SURF feature descriptors. We present comparative experimental results obtained with a real service robot. Keywords—Service Robot, Object Recognition, 3D Sensors, Plane Segmentation.
Porous Polyethylene Terephthalate Optical Waveguide for Sensing Applications

We report the fabrication and characterization of optical waveguides using porous polyethylene terephthalate (PET) film. An optical waveguide is produced by exposing the PET film to cyclotron followed by selective chemical etching. Considering that the average refractive index of porous regions is lower than that of non-porous regions, a waveguide can be formed using a single optical material system. A single-material channel waveguide and an optical splitter were demonstrated using our proposed technique. Through placing a liquid crystal inside the pores, an ethanol gas sensor was obtained with a single-material channel waveguide structure. These PET film waveguides can create new opportunities in various optical applications, particularly in chemical sensing applications.
A music recommendation system based on music data grouping and user interests

With the growth of the World Wide Web, a large amount of music data is available on the Internet. In addition to searching expected music objects for users, it becomes necessary to develop a recommendation service. In this paper, we design the Music Recommendation System (MRS) to provide a personalized service of music recommendation. The music objects of MIDI format are first analyzed. For each polyphonic music object, the representative track is first determined, and then six features are extracted from this track. According to the features, the music objects are properly grouped. For users, the access histories are analyzed to derive user interests. The content-based, collaborative and statistics-based recommendation methods are proposed, which are based on the favorite degrees of the users to the music groups. A series of experiments are carried out to show that our approach is feasible.