Miracle-dz/UniFAR-FTUs huggingface dataset:
Body Composition Estimation in Breeding Ewes Using Live Weight and Body Parameters Utilizing Image Analysis

Simple Summary Monitoring animal condition is integral for maintaining a healthy flock, increasing ewe productivity, refining animal nutrition, and identifying suitable animals for slaughter. Accurate determination of the body compositions (the amount of fat, muscle, and bone) of ewes can be used to evaluate their conditions, which provides key information to make management decisions. Farmers currently rely on live weight (LW) and body condition score (BCS) to evaluate the health statuses of ewes. This research proposed the use of visual imaging to determine body dimensions, which are then used in combination with LW to predict the body compositions of ewes. The results showed a correlation between fat, muscle, and bone weight determined by computerized tomography (CT) and the fat, muscle, and bone weight estimated by the live weight and body parameters calculated using the image processing application. The results showed an optimal fat of 9% of LW for ewes during the production cycle. If the percentage of fat is less than or more than 9%, farmers have to take action to improve the conditions of the animals to ensure the best performance during weaning and ewe and lamb survival during the next lambing. Abstract Farmers are continually looking for new, reliable, objective, and non-invasive methods for evaluating the conditions of ewes. Live weight (LW) and body condition score (BCS) are used by farmers as a basis to determine the condition of the animal. Body composition is an important aspect of monitoring animal condition. The body composition is the amount of fat, muscle, and bone; knowing the amount of each is important because the information can be used for better strategic management interventions. Experiments were conducted to establish the relationship between body composition and body parameters at key life stages (weaning and pre-mating), using measurements automatically determined by an image processing application for 88 Coopworth ewes. Computerized tomography technology was used to determine the body composition. Multivariate linear regression (MLR), artificial neural network (ANN), and regression tree (RT) statistical analysis methods were used to develop a relationship between the body parameters and the body composition. A subset of data was used to validate the predicted model. The results showed a correlation between fat, muscle, and bone determined by CT and the fat, muscle, and bone weight estimated by the live weight and body parameters calculated using the image processing application, with r2 values of 0.90 for fat, 0.72 for muscle, and 0.50 for bone using ANN. From these results, farmers can utilize these measurements to enhance nutritional and management practices.
Convergence of the class of methods for solutions of certain sixth-order boundary value problems

The Class of various order numerical methods based on non-polynomial spline have been developed for the solution of linear and non-linear sixth-order boundary value problems. We developed non-polynomial spline which contains a parameter $\rho$, act as the frequency of the trigonometric part of the spline function, when such parameter tends to zero the dened spline reduce into the septic polynomial spline, the consistency relation of non-polynomial spline derived in such a way that, to be fitted to approximate the solution of the given sixth-order boundary value problems. Boundary formulas are developed to associate with presented spline methods. Truncation errors are given, we developed the class of second, fourth, sixth and eight order methods. Convergence analysis has been proved. The obtained methods have been tested on nine examples, to illustrate practical usefulness of our approach. The results of our higher eight order method compare with the existing methods so far.
Knowledge-Graph-Based IoTs Entity Discovery Middleware for Nonsmart Sensor

Internet-of-Things (IoTs) entity discovery plays an important role in the Industrial IoTs, especially with the rapidly increasing and updating of IoT sensors in the industrial environment driven by the era of Industry 4.0 and intelligent manufacturing. However, large numbers of nonsmart sensors are required in the industrial environment, causing IoT entity discovery challenges. Unlike the smart sensor, the nonsmart sensor with limited computation and communication ability is hard to discover and recognize by traditional IoT platforms. Aiming at the challenge, this work proposes a novel IoT entity discovery middleware for nonsmart sensor discovery in the industrial environment. The proposed middleware combines both sensor knowledge graphs and sensor data values to build an IoT entity discovery and recognition model. A knowledge–data fused learning network is proposed for the model to identify the data type, function, and other information of the nonsmart sensor. At last, a prototype middleware with the discovery and recognition model is produced to implement nonsmart sensor discovery. In the experimental evaluations, the prototype middleware tests various nonsmart sensors and achieves 87.6% recognition accuracy. In real-world case studies, the prototype middleware proves the feasibility and effectiveness of nonsmart sensor discovery in the industrial environment.
Metallicity distribution functions using Gaia-DR1 data

Abstract The metallicity distribution function (MDF) of the stellar components of the Milky Way hold valuable information regarding the processes that have taken place in the evolution of our Galaxy. In this proceeding, we investigate updates concerning the MDF now that the Tycho-Gaia Astrometric Solution (TGAS) catalogue has been released and that trigonometric distances are available. In particular, vertical changes and skewness of the MDF are investigated, together with the properties of the metal-rich stars in the sample, at different positions in the Galaxy.
Choosing the group size for group testing to estimate a proportion

Group testing, which tests individuals in groups or batches rather that one-at-a-time, can be very effective in estimating the proportion (P) of individuals that are infected or defective in some sense, especially when P is small. However, choosing an appropriate group size (Ie) to use is critical if the potential benefits of group testing are to be realized. Three criteria for selecting Ie are: (1) choose Ie to make the probability that a group contains one or more defective individuals equal to 1/2. This is equivalent to equating the influence of a defective group (one with one or more defective individuals) and a nondefective group on the calculation of the maximum likelihood estimator Pof Pi (2) choose Ie to minimize the asymptotic variance of Pi (3) choose Ie to minimize the exact mean squared error ofp. For implementation, all three approaches require, the user to specify an initial value Po for P, and the value of Ie determined will depend in part on Po. This paper compares the largeand small-sample properties of these three approaches in detaili none is uniformly best, but (3) is recommended overall. Having determined Ie in advance, the typical group-testing procedure . fixes Ie for the entire experiment, even though there might -The authors thank Bibhuti B. Bhattacharyya. and Richard A. Johnson for their many helpful comments and suggestions.
Acute And Chronic Disease Diagnosis Using ML For Post Covid 19 Medical Issues

Everyone is attempting to be health aware now that the epidemic is over. There is a chance that we'll have a sickness in our bodies but are not known to the person affected. And it may be inner damage. At any stage of its manifestations, it might create any kind of sickness. In this contest in this paper As a result, we have such a datasets depending on medical health history, and developing the model by using a machine learning technique which utilizes the above said data. The proposed algorithm will be able to forecast certain diseases before they show the severe symptoms. By this the sufferers will be susceptible and they can be monitored and the treatment can be given on time.
ANALYSIS OF SUCCESS FACTORS IN THE IMPLEMENTATION OF THE DIRECORATE GENERAL OF TAXES INFORMATION SYSTEM DEVELOPMENT (SIDJP) USING THE DELONE AND MCLEAN MODEL

In this era of globalization, information technology has developed rapidly along with the increasing human needs. The traffic of goods, services, and information can be accessed through internet services and communication media without any limitation of time and place. User satisfaction is the overall evaluation of the user experience in using the information system and the potential impact of the information system. The purpose of this study was to analyze the effect of system quality, information quality, and service quality on SIDJP user satisfaction at KPP Pratama Padang. The number of samples in this study were 72 respondents. In collecting data using a questionnaire. The data analysis method used in this research is multiple linear regression. Based on the results of the study indicate that the quality of the system, the quality of information, and the quality of services have a positive and significant effect on the satisfaction of SIDJP users. Keywords: User satisfaction, SIDJP, system quality, information quality, service quality
Kautz Function Based Continuous-Time Model Predictive Controller for Load Frequency Control in a Multi-Area Power System

A continuous-time Model Predictive Controller was proposed using Kautz function in order to improve the performance of Load Frequency Control (LFC). A dynamic model of an interconnected power system was used for Model Predictive Controller (MPC) design. MPC predicts the future trajectory of the dynamic model by calculating the optimal closed loop feedback gain matrix. In this paper, the optimal closed loop feedback gain matrix was calculated using Kautz function. Being an Orthonormal Basis Function (OBF), Kautz function has an advantage of solving complex pole-based nonlinear system. Genetic Algorithm (GA) was applied to optimally tune the Kautz function-based MPC. A constraint based on phase plane analysis was implemented with the cost function in order to improve the robustness of the Kautz function-based MPC. The proposed method was simulated with three area interconnected power system and the efficiency of the proposed method was measured and exhibited by comparing with conventional Proportional and Integral (PI) controller and Linear Quadratic Regulation (LQR).
Teaching assistive technology in undergraduate courses in occupational therapy in the State of São Paulo

Abstract Introduction Knowledge about assistive technology is related to aspects of the field of occupational therapy, thus, it is questioned whether such knowledge has been addressed in undergraduate courses. Objectives To identify the undergraduate courses in occupational therapy in the state of São Paulo that offer disciplines related to assistive technology, identify the disciplines, their respective workloads and the content present in each of them. Method The sample consisted of eight universities, five public and three private. The search was made in the curricular matrices, teaching plans of specific and non-specific disciplines, objectives and contents available online or requested from the course coordinator. Descriptive data analysis was performed. Results It was found that assistive technology content was offered in the investigated universities, with a difference between the workload and the content offered. The analysis by assistive technology application area showed that the content on orthoses is offered in 100% of the analyzed courses, followed by assistive products for the performance of daily activities and prostheses, both contents found in 75% of the courses; mobility equipment; accessibility and universal design in 62.5%; alternative communication in 50% of the courses. Conclusion The results point to the relevance of greater homogeneity of content between the investigated universities and suggest that this study has the potential to foster discussions about the formation of occupational therapists in the assistive technology area, since the results come from the analysis of undergraduate courses located in the state which has the largest number of occupational therapy courses in Brazil.
Automatic change detection using very high-resolution SAR images and prior knowledge about the scene

Change detection using very high resolution SAR images is an important source of information for reconnaissance applications. Modern SAR sensors are capable of acquiring many images in short periods of time, which creates the need for a reliable automatic change detection method. In this paper, we will describe a new automatic change detection approach that combines very high resolution SAR images with prior knowledge about the imaged scene. In this case, the prior knowledge about the scene will come from vector maps, which can be obtained from a Geographic Information System (GIS). These vector maps will allow us to determine which regions are of interest for the change detection, and what kind of changes/objects can be expected there. The algorithm described in this paper will be applied to a time series of high resolution TerraSAR-X images of a port with military shipyards, and used to automatically detect ship activity and extract information about the detected ships. In this case, the vector maps were obtained from a Geographic Information System (GIS) containing map data from OpenStreetMap
Attenuation of decimeter and centimeter range radio waves under low atmospheric temperatures

The paper considers the impact of hydrometeors and extremely low air temperatures on the weakening of the radio frequency range 0.8 GHz–10 GHz. The calculations of the frequency dependence of the total attenuation of the radio wave intensity for the temperature range from 0° C to 60 °C below zero have been carried out using the designed for this purpose applied computer program.
A 300-320 GHz Sliding-IF I/Q Receiver Front-End in 130 nm SiGe Technology

This paper presents a 300–320 GHz sliding-IF I/Q receiver front-end in 130 nm SiGe BiCMOS technology with $f_{t}/f_{\max}$ of 300 GHz/450 GHz. The architecture, unlike direct conversion receiver at sub-THz/THz frequency range, removes the need of LO frequency same as carrier frequency. Consequently, power consumption of the LO chain is significantly reduced. Signal amplification is performed at IF stage. LO frequency at two-third and one-third of carrier frequency is generated, from external 50 GHz LO signal, using on-chip frequency doublers for RF and I/Q mixers, respectively. The receiver provides 15.2 dB of conversion gain at 310 GHz. The 3-dB RF and BB bandwidths are measured to be 26 GHz and 8 GHz, respectively. Input referred compression point (ICP) and SSB noise figure of the receiver are measured to be -17 dBm and 29.5 dB, respectively. RF and LO chain of the receiver consume 296 mW and 110 mW, respectively.
Agricultural Solid Wastes Based Adsorbent Materials in the Remediation of Heavy Metal Ions from Water and Wastewater by Adsorption: A Review

Adsorption has become the most popular and effective separation technique that is used across the water and wastewater treatment industries. However, the present research direction is focused on the development of various solid waste-based adsorbents as an alternative to costly commercial activated carbon adsorbents, which make the adsorptive separation process more effective, and on popularising the sustainable options for the remediation of pollutants. Therefore, there are a large number of reported results available on the application of raw or treated agricultural biomass-based alternatives as effective adsorbents for aqueous-phase heavy metal ion removal in batch adsorption studies. The goal of this review article was to provide a comprehensive compilation of scattered literature information and an up-to-date overview of the development of the current state of knowledge, based on various batch adsorption research papers that utilised a wide range of raw, modified, and treated agricultural solid waste biomass-based adsorbents for the adsorptive removal of aqueous-phase heavy metal ions. Metal ion pollution and its source, toxicity effects, and treatment technologies, mainly via adsorption, have been reviewed here in detail. Emphasis has been placed on the removal of heavy metal ions using a wide range of agricultural by-product-based adsorbents under various physicochemical process conditions. Information available in the literature on various important influential physicochemical process parameters, such as the metal concentration, agricultural solid waste adsorbent dose, solution pH, and solution temperature, and importantly, the adsorbent characteristics of metal ion removal, have been reviewed and critically analysed here. Finally, from the literature reviewed, future perspectives and conclusions were presented, and a few future research directions have been proposed.
Vascular epiphytes in the Amazon: main gaps, limitations and perspectives for studies on the subject

Vascular epiphytes stand out in tropical forests in terms of diversity. However, no comprehensive review of the group in the Amazon region has been performed so far. We carried out a literature review on the scientific knowledge of vascular epiphytes in the Amazon aiming to identify the main gaps, limitations and perspectives for studies on the subject. Searches were conducted in Google Scholar, Scopus and Web of Science using inclusion and exclusion criteria. 291 articles published in the period 1933-2022, mostly the 21st century, were included in the review. Brazil was the most studied country. However, knowledge gaps were found in regions located in the Brazilian arc of deforestation as well as in areas of Bolivia, Guyana, French Guiana and Suriname. There was a predominance of studies related to the floristics, systematics and biogeography of spermatophytes and ferns, focusing on the diversity and taxonomy of certain families (e.g. Orchidaceae). However, we found gaps for more comprehensive research, considering population dynamics, dominance (biomass), guidelines for evaluation of epiphyte and systematization of data for Amazon. We indicate the need of studies focused on ecology, floral and reproductive biology, biochemistry, phytochemistry, anatomy and physiology. Future research should also consider the impacts of current trends in deforestation and climatic changes on the diversity of vascular epiphytes in the Amazon.
Effect of tissue thickness and liquid composition on the penetration of long-lifetime reactive oxygen and nitrogen species (RONS) generated by a plasma jet

Two of the key questions in plasma medicine are how deep the reactive oxygen and nitrogen species (RONS) generated by a plasma can penetrate into tissue and how the liquid (extracellular and intracellular fluid) composition affects the concentration of RONS. In this paper, different thicknesses of pig muscle tissue are used as a tissue mode to investigate the effect of tissue thickness on the penetration of RONS through tissue. Six different types of liquid (inorganic group: double-distilled water (DDW), 1% phosphate-buffered saline, 0.9% NaCl; organic group: 5% glucose, 2% serum and 10% serum solution) are used in the receiving chamber under the tissue in order to try to understand the effect of liquid composition on the penetration of RONS (H2O2, and ) generated by the plasma. It is found that when a tissue thickness of 500 µm is used the H2O2 concentrations in organic liquids are about 20–30 times higher than in DDW. The and concentrations in serum liquid are much higher than in all other liquids, which might be due to the plasma reacting with amino acids and proteins. Besides, the concentration in organic solution is higher than the concentration for the same experimental conditions. Furthermore, when the serum percentage is increased from 2% to 10%, the concentration increases dramatically but the concentration decreases significantly. This is especially true for a tissue thickness of 500 µm. One novel discovery is the RONS do not only penetrate the tissue by diffusion—there are also reactions between the plasma and the liquid which affect the final RONS concentration.
Goldstone models of modified gravity

We investigate scalar-tensor theories where matter couples to the scalar field via a kinetically dependent conformal coupling. These models can be seen as the low-energy description of invariant field theories under a global Abelian symmetry. The scalar field is then identified with the Goldstone mode of the broken symmetry. It turns out that the properties of these models are very similar to the ones of ultralocal theories where the scalar-field value is directly determined by the local matter density. This leads to a complete screening of the fifth force in the Solar System and between compact objects, through the ultralocal screening mechanism. On the other hand, the fifth force can have large effects in extended structures with large-scale density gradients, such as galactic halos. Interestingly, it can either amplify or damp Newtonian gravity, depending on the model parameters. We also study the background cosmology and the linear cosmological perturbations. The background cosmology is hardly different from its $\Lambda$-CDM counterpart whilst cosmological perturbations crucially depend on whether the coupling function is convex or concave. For concave functions, growth is hindered by the repulsiveness of the fifth force whilst it is enhanced in the convex case. In both cases, the departures from the $\Lambda$-CDM cosmology increase on smaller scales and peak for galactic structures. For concave functions, the formation of structure is largely altered below some characteristic mass, as smaller structures are delayed and would form later through fragmentation, as in some warm dark matter scenarios. For convex models, small structures form more easily than in the $\Lambda$-CDM scenario.
Preparation and Characterization of Patuletin-Loaded Chitosan Nanoparticles with Improved Selectivity and Safety Profiles for Anticancer Applications

Objective. This study aimed to investigate the preparation of patuletin-encapsulated chitosan nanoparticles (PT-CS-NPs) using the ionic gelation method, evaluate their potential as anticancer agents, and invent a new method of differential pulse voltammetric analysis for patuletin (PT). Methods. Computational studies were conducted to assess the affinity of PT for chitosan, confirming a promising interaction. PT-CS-NPs were synthesized based on the computational outputs, resulting in nanoparticles with an angular structure and an average size of 6.168 nm. The cytotoxicity of PT and PT-CS-NP was evaluated in breast and colon cancer cell lines, and selectivity indices were calculated to assess safety profiles. The electrochemical behavior of PT was also investigated using the Britton–Robinson buffer. Results. PT-CS-NP exhibited both potent cytotoxicity and a favorable safety profile, showing the most active and safest pattern of cytotoxicity among the tested compounds. The electrochemical oxidation of PT was observed at 0.531 V vs. Ag/AgCl at pH 4.0. 0. The concentration of PT showed a linear relationship with the corresponding peak current over the range of 10.0 x 10 − 9 : 10.0 x 10 − 5 M M, with a minimum limit of detection of 3.4 × 10(−9)M. The proposed method successfully measured PT, with a relative standard deviation below 2%. Conclusion. The preparation of PT-CS-NP via the ionic gelation method resulted in angular nanoparticles with a promising anticancer activity and safety profiles. The electrochemical behavior of PT was characterized, and a reliable method for PT quantification was established.