bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effect of Marketing Mix and Agricultural Incentives on Farmers' Participation in Organic Rice Cultivation in Bakti Village, Ponrang Selatan District, Luwu Regency
Authors: Nur Iqbal M. , Yusuf Q. M. , Mahmud Andi Nadirah
Published: July 2022

This study examines the effect of the marketing mix and agricultural incentives on farmer participation in organic rice cultivation. This research method uses descriptive quantitative research through survey techniques. The data collection uses observation and interview methods. The data were analyzed using structural equation modeling (SEM) with the help of the Smart PLS application. The results showed that the marketing mix affected farmer participation of -0.082 units with a T arithmetic value of 0.441 < 1.697 or a p-value of 0.660 > 0.005. Agricultural incentives affect farmer participation by 0.923 units with a t-count value of 3.491 > 1.697 or a p-value of 0.001 <0.05. The combined effect of the marketing mix and agricultural incentives on farmer participation is 57.2%, while 42.8% is influenced by other variables not observed in this study.
Comparison of Properties of Different Materials by Adsorptions N2 and Ar at Temperature –196 °C
Authors: Kyselová Veronika , Ciahotný Karel
Published: July 2022

The article is focused on comparing of the structural properties of different adsorption materials. The measurement of adsorption isotherms was carried out in nitrogen and argon at a temperature of –196 °C. The individual isotherms using selected samples were measured on an Autosorb ASiQ instrument. BET surfaces and total pore volumes of individual samples were calculated and compared from the resulting isotherms. The results show relatively high differences between the adsorption isotherm measured by using nitrogen and argon. For the Envisorb sample, which consists of up to 85% silicagel, the BET surface area calculated from the nitrogen adsorption isotherm was 45 % higher than when measuring the adsorption isotherm using argon as the adsorptive. For silicagel SGR 50 was the difference between BET surface area measurements with nitrogen and argon only 4 %. The opposite phenomenon was calculated for the material activated carbon SC 40, where the BET surface measured by argon was higher than BET surface measured by nitrogen.
Of the adsorbents used, nitrogen appears to be more suitable; only for carbonbased microporous materials is it better to use argon as an adsorptive.
The evaluation of the measured adsorption isotherms by the t-plot method showed a good usability of this method when it is applied similarly to the BET method for adsorption isotherms in the range of adsorptive rel. pressure ranged from 0.05 to 0.35. This method is a reliable tool for determining the proportion of the smallest pores (micropores) in the total surface area of a given adsorbent. However, in the case of adsorbents with a very low proportion of micropores, its accuracy is lower.
Pemanfaatan Teknologi Informasi untuk Peningkatan Pelayanan pada UPT TIK Politeknik Negeri Cilacap
Authors: Is dahlia Laely Fitri , Zakaria Andriansyah , Prasetyanti Dwi Novia
Published: July 2022

UPT Teknologi Informasi dan Komunikasi (UPT TIK) is a technical implementing unit that provides information and communication technology services for the academic community at the Cilacap State Polytechnic. At UPT TIK has services such as email submission, subdomain and website submission, installation submission and network disturbance submission. In the management of UPT TIK, there are problems, including the lack of information about standard operating procedures, the head of UPT TIK finds it difficult to collect data because users are still doing it via telephone, users have difficulty monitoring submissions, forms that are paper based so that data loss occurs every year approximately 5 sheets and storage takes up a lot of space. At UPT TIK there are problems, so there is a need for a system that overcomes these problems, so the UPT TIK information system was developed which aims to assist the submission process, manage data submissions, and monitor submissions. This web-based system was developed using the RAD method because this method is very short work and is designed from the results of user responses with existing prototypes. In addition, the RAD method consists of the requirements planning stage, RAD design workshop, and implementation. The output of this system is the submission process is better controlled, users can monitor submissions and file management can be properly archived. From the results of the questionnaire data analysis with 20 respondents, it was found that 1% disagreed, 8% disagreed, 81% agreed and 10% stated strongly agree that this system is said to be successful in helping improve services at UPT TIK
Contribution to the Design and Implementation of a Reflexive Cyber-Physical System: Application to Air Quality Prediction in the Vallees des Gaves
Authors: Poupry Sylvain , Beler Cedrick , Medjaher Kamal
Published: July 2022

This thesis aims to set up a scientific approach to monitor and take preventive actions on the air quality for the actors of a territory not covered by conventional measuring stations. Thus, a Cyber-Physical System (CPS) approach combined with Prognostics Health Management (PHM) methodologies is chosen to move toward a self-monitoring and self-reconfiguration system. To collect data in an inexpensive manner, measurement stations with low-cost sensors (LCS) are developed. LCS have drawbacks and the first part of this thesis is the use of redundancy and a proposed algorithm to increase their hardware and data reliability. A first station is deployed as proof of concept and the region is already receiving real-time data. The next phase is to build forecasting models to help authorities make decisions.
Hybrid Fault Prognostics for Nuclear Applications: Addressing Rotating Plant Model Uncertainty
Authors: Blair Jennifer , Stephen Bruce , Brown Blair , Forbes Alistair , Mcarthur Stephen
Published: July 2022

Nuclear plant operators are required to understand the uncertainties associated with the deployment of prognostics toolsin order to justify their inclusion in operational decision making processes and satisfy regulatory requirements. Operationaluncertainty can cause underlying prognostics models to underperform on assets that are subject to evolving impactsof age, manufacturing tolerances, operating conditions, and operating environment effects, of which may be capturedthrough a condition monitoring (CM) system that itself may be degraded. Sources of uncertainty in the data acquisitionpipeline can impact the health of CM data used to estimate the remaining useful life (RUL) of assets. These uncertaintiescan disguise or misrepresent developing faults, where (for example) the fault identification is not achieved until it hasprogressed to an unmanageable state. This leaves little flexibility for the operator’s maintenance decisions and generallyundermines model confidence.

One method to quantify and account for operational uncertainty is calibrated hybrid models, employing physics, knowledgeor data driven methods to improve model accuracy and robustness. Hybrid models allow known physical relations tooffset full reliance on potentially untrustworthy data, whilst reducing the need for an abundance of representative historicaldata to reliably identify the monitored asset’s underlying behavioural trends. Calibration of the model then ensuresthe model is updated and representative of the real monitored asset by accounting for differences between the physics orknowledge model and CM data.

In this paper, an open-source bearing knowledge informed machine learning (ML) model and CM datasets are utilizedin an illustrative bearing prognostic application. The uncertainty incurred by the decisions made at key stages in thedevelopment of the model’s data acquisition and processing pipeline are assessed and demonstrated by the resultant impacton RUL prediction performance. It was shown that design decisions could result in multiple valid pipeline designswhich generated different predicted RUL trajectories, increasing the uncertainty in the model output.
State of Health and Lifetime Prediction of Lithium-ion Batteries Using Self-learning Incremental Models
Authors: Camargos Murilo , Angelov Plamen
Published: July 2022

Lithium-ion batteries are key energy storage elements in the context of environmental-aware energy systems representing a crucial technology to achieve the goal of zero carbon emission. Therefore, its conditions must be monitored to guarantee the safe and reliable operation of the systems that use these components. Furthermore, lithium-ion batteries’ prognostics and health management policies must cope with the nonlinear and time-varying nature of the complex electrochemical dynamics of battery degradation. This paper proposes an incremental-learning-based algorithm to estimate the State of Health (SoH) and the Remaining Useful Life (RUL) of lithium-ion batteries based on measurement data streams. For this purpose, a two-layer framework is proposed based on incremental modeling of the SoH. In the first layer, a set of representative features are extracted from voltage and current data of partial charging and discharging cycles; these features are then used to train the proposed model in a recursive procedure to estimate the battery’s SoH. The second layer uses the capacity data for incremental learning of an Autoregressive (AR) model for the SoH, which will be used to propagate the battery’s degradation through time to make the RUL prediction. The proposed method was applied to two datasets for experimental evaluation, one from CALCE and another from NASA. The proposed framework was able to estimate the SoH of 8 different lithium-ion cells with an average percentage error below 1.5% for all scenarios, while the lifetime model predicted the cell’s RUL with a maximum average error of 25%.
Data Driven Seal Wear Classifications using Acoustic Emissions and Artificial Neural Networks
Authors: Noori Nadia. S. , Shanbhag Vignesh. V. , Kandukuri Surya. T. , Schlanbusch Rune
Published: July 2022

The work presented in this paper is built on a series of experiments aiming to develop a data-driven and automated method for seal diagnostics using Acoustic Emission (AE) features. Seals in machineries operate in harsh conditions, and seal wear in hydraulic cylinders results in fluid leakage, and instability of the piston rod movement. Therefore, regular inspection of seals is required using automated approaches to improve productivity and to reduce unscheduled maintenance. In this study, we implemented a data-driven diagnostics approach which utilizes AE measurements along with light weight Artificial Neural Networks (ANN) as a classifier to investigate the performance and resources (hardware & software) required for implementing a real-time soft sensor unit for monitoring seal wear condition. We used a feedforward multilayer perceptron ANN (Scaled Conjugate Gradient- SCG algorithm) that is trained with the back propagation algorithm, which is a popular network architecture for a multitude of applications (automotive, oil and gas, electronics). We benchmark the developed method against previous work conducted based on Support Vector Machine (SVM), and we compare ANN performance in classifying the running condition of seals in hydraulic cylinders by applying it to both raw (full frequency spectrum) and down sampled frequency measurements. The experiments were performed at varying pressure conditions on a hydraulic test rig that can simulate fluid leakage conditions like that of hydraulic cylinders. The test cases were generated with seals of three different conditions (unworn, semi-worn, worn). From the AE spectrum, the frequency bands were identified with peak power and by heterodyning the signal. This technique results in 10X down sampling without losing the information of interest. Further, the signal was divided into smaller “snapshots” to facilitate rapid diagnosis. In these tests, the diagnosis was made on short-time windows, as low as 0.3 seconds in length. A general set of 16 time and frequency domain features were designed. Then a training set was developed using relevant set of features (4, 5, and 16 features). The data was used to train the ANN (70% training – 30% test & validation) and SVM (60 % training - 40% test and validation). Classification of down sampled measurements, both ANN and SVM were able to accurately classify the status irrespective of the pressure conditions, with an accuracy of ~99% with execution time less than seconds. Therefore, the proposed approach can be applied as part of an automated seal wear classification technique based on AE and ANN/SVM and can be used for real-time monitoring of seal wear in hydraulic cylinders.
Estimation of Wind Turbine Performance Degradation with Deep Neural Networks
Authors: Mathew Manuel S , Kandukuri Surya Teja , Omlin Christian W
Published: July 2022

In this paper, we estimate the age-related performance degradation of a wind turbine working under Norwegian environment, based on a deep neural network model. Ten years of high-resolution operational data from a 2 MW wind turbine were used for the analysis. Operational data of the turbine, between cut-in and rated wind velocities, were considered, which were pre-processed to eliminate outliers and noises. Based on the SHapley Additive exPlanations of a preliminary performance model, a benchmark performance model for the turbine was developed with deep neural networks. An efficiency index is proposed to gauge the agerelated performance degradation of the turbine, which compares measured performances of the turbine over the years with corresponding bench marked performance. On an average, the efficiency index of the turbine is found to decline by 0.64 percent annually, which is comparable with the degradation patterns reported under similar studies from the UK and the US.
Fault Detection in a Wind Turbine Hydraulic Pitch System Using Deep Autoencoder Extracted Features
Authors: Korkos Panagiotis , Kleemola Jaakko , Linjama Matti , Lehtovaara Arto
Published: July 2022

A wind turbine is equipped with lots of sensors whose measurements are recorded by the supervisory control and data acquisition (SCADA) system and stored every 10 minutes. The pitch subsystem of a wind turbine is of critical importance as it presents the highest failure rate. Thus, selecting the most essential features from the SCADA system is performed in order to detect faults efficiently. In this study, a feature space of 49 features is available, referring to the condition of a hydraulic pitch system. The dimensionality of this feature space (original input space) is reduced using a Deep Autoencoder in order to extract latent information. The architecture of the Autoencoder is investigated regarding its efficiency on fault detection task. This way, effect of new extracted features on the performance of the classifier is presented. A Support Vector Machine (SVM) classifier is trained using a set of healthy (fault free) and faulty data, representing different kind of pitch system failures. The data are acquired from a wind farm of five 2.3MW fixed-speed wind turbines. The performance metric used to evaluate their effect on data is F1-score.  Results show that SVM using new extracted feature by Autoencoder outperforms SVM classifier using the original feature set, underlining the power of Autoencoders to unveil latent information.
Certainty Groups: A Practical Approach to Distinguish Confidence Levels in Neural Networks
Authors: Lodes Lukas , Schiendorfer Alexander
Published: July 2022

Machine Learning (ML), in particular classification with deep neural nets, can be applied to a variety of industrial tasks. It can augment established methods for controlling manufacturing processes such as statistical process control (SPC) to detect non-obvious patterns in high-dimensional input data. However, due to the widespread issue of model miscalibration in neural networks, there is a need for estimating the predictive uncertainty of these models. Many established approaches for uncertainty estimation output scores that are difficult to put into actionable insight. We therefore introduce the concept of certainty groups which distinguish the predictions of a neural network into the normal group and the certainty group. The certainty group contains only predictions with a very high accuracy that can be set up to 100%. We present an approach to compute these certainty groups and demonstrate our approach on two datasets from a PHM setting.
Analysis of Vibrations and Currents for Broken Rotor Bar Detection in Three-phase Induction Motors
Authors: Taghiyarrenani Zahra , Berenji Amirhossein
Published: July 2022

Selecting the physical property capable of representing the health state of a machine is an important step in designing fault detection systems. In addition, variation of the loading condition is a challenge in deploying an industrial predictive maintenance solution. The robustness of the physical properties to variations in loading conditions is, therefore, an important consideration. In this paper, we focus specifically on squirrel cage induction motors and analyze the capabilities of three-phase current and five vibration signals acquired from different locations of the motor for the detection of Broken Rotor Bar generated in different loads. In particular, we examine the mentioned signals in relation to the performance of classifiers trained with them. Regarding the classifiers, we employ deep conventional classifiers and also propose a hybrid classifier that utilizes contrastive loss in order to mitigate the effect of different variations. The analysis shows that vibration signals are more robust under varying load conditions. Furthermore, the proposed hybrid classifier outperforms conventional classifiers and is able to achieve an accuracy of 90.96% when using current signals and 97.69% when using vibration signals.
Uncertainty Informed Anomaly Scores with Deep Learning: Robust Fault Detection with Limited Data
Authors: Zgraggen Jannik , Pizza Gianmarco , Goren Huber Lilach
Published: July 2022

Quantifying the predictive uncertainty of a model is an important ingredient in data-driven decision making. Uncertainty quantification has been gaining interest especially for deep learning models, which are often hard to justify or explain. Various techniques for deep learning based uncertainty estimates have been developed primarily for image classification and segmentation, but also for regression and forecasting tasks. Uncertainty quantification for anomaly detection tasks is still rather limited for image data and has not yet been demonstrated for machine fault detection in PHM applications.
In this paper we suggest an approach to derive an uncertaintyinformed anomaly score for regression models trained with normal data only. The score is derived using a deep ensemble of probabilistic neural networks for uncertainty quantification. Using an example of wind-turbine fault detection, we demonstrate the superiority of the uncertainty-informed anomaly score over the conventional score. The advantage is particularly clear in an ”out-of-distribution” scenario, in which the model is trained with limited data which does not represent all normal regimes that are observed during model deployment.
Domain Knowledge Informed Unsupervised Fault Detection for Rolling Element Bearings
Authors: Marx Douw , Gryllias Konstantinos
Published: July 2022

Early and accurate detection of rolling element bearing faults in rotating machinery is important for minimizing production downtime and reducing unnecessary preventative maintenance. Several fault detection methods based on signal processing and machine learning methods have been proposed. Particularly, supervised, data-driven approaches have proved to be very effective for fault detection and diagnostics of rolling element bearings. However, supervised methods rely heavily on the availability of failure data with volume, variety and veracity, which is mostly unavailable in industry. As an alternative data-driven strategy, unsupervised methods are trained on healthy data only and do not require any failure data.
In contrast to supervised and un-supervised data-driven models, physics-based and phenomenological models are based on domain knowledge and not on historical data. Although these models are useful for studying the way in which damage is expected to manifest in a measured signal, they are difficult to calibrate and often lack the fidelity required to model reality. In this paper, an unsupervised data-driven anomaly detection method that exploits informative domain knowledge is proposed. Hereby, the versatility of unsupervised data-driven methods are combined with domain knowledge.
In this approach, supplementary training data is generated by augmenting healthy data towards its possible future faulty state based on the characteristic bearing fault frequencies. Both healthy and augmented squared envelope spectrum data is used to train an autoencoder model that includes regularisation designed to constrain the latent features at the autoencoder bottleneck. Regularisation in the autoencoder loss enforces that the expected deviation of the healthy latent representation towards the augmented latent representation at dam aged conditions, is constrained to be maximally different for different fault modes. Consequently, the likelihood of a new test sample being healthy can be evaluated based on the projection of the sample onto an expected failure direction in the latent representation.
A phenomenological and experimental dataset is used to demonstrate that the addition of augmented training data and a specialized autoencoder loss function can create a separable latent representation that can be used to generate interpretable health indicators.
Processing of Condition Monitoring Annotations with BERT and Technical Language Substitution: A Case Study
Authors: Lowenmark Karl , Taal Cees , Nivre Joakim , Liwicki Marcus , Sandin Fredrik
Published: July 2022

Annotations in condition monitoring systems contain information regarding asset history and fault characteristics in the form of unstructured text that could, if unlocked, be used for intelligent fault diagnosis. However, processing these annotations with pre-trained natural language models such as BERT is problematic due to out-of-vocabulary (OOV) technical terms, resulting in inaccurate language embeddings. Here we investigate the effect of OOV technical terms on BERT and SentenceBERT embeddings by substituting technical terms with natural language descriptions. The embeddings were computed for each annotation in a pre-processed corpus, with and without substitution. The K-Means clustering score was calculated on sentence embeddings, and a Long Short-Term Memory (LSTM) network was trained on word embeddings with the objective to recreate the output from a keywordbased annotation classifier. The K-Means score for SentenceBERT annotation embeddings improved by 40% at seven clusters by technical language substitution, and the labelling capacity of the BERT-LSTM model was improved from 88.3 to 94.2%. These results indicate that the substitution of OOV technical terms can improve the representation accuracy of the embeddings of the pre-trained BERT and SentenceBERT models, and that pre-trained language models can be used to process technical language.
Data-Driven Fault Detection for Transmitter in Logging-While-Drilling Tool
Authors: Sobczak-Oramus Karolina , Mosallam Ahmed , Basci Caner , Kang Jinlong
Published: July 2022

Logging tools widely used in the oil and gas industry are exposed to demanding environmental conditions that can lead to faster degradation and unexpected failures. These events can reduce productivity, delay deliverables, or even bring entire drilling operations to an end. However, such accidents can be avoided using a prognostics and health management  approach. This paper presents a data-driven fault detection method for transmitter in logging-while-drilling tool adopting a support vector machine classifier. The health analyzer determines the component’s physical condition in just a few minutes, demonstrating an exceptional value for both field and maintenance engineers. This work is part of a long-term project aimed at constructing a digital fleet management system for downhole testing tools.
Experimental Assessment of a Broadband Vibration and Acoustic Emission Sensor for Rotorcraft Transmission Monitoring
Authors: Ruiz-Carcel Cristobal , Starr Andrew , Francese Arturo
Published: July 2022

Modern rotorcrafts rely on Health and Usage Monitoring Systems (HUMS) to enhance their availability, reliability, and safety. In those systems, data related to the health of key mechanical components is acquired, in addition to typical flight condition history data such as speed and torque. Commercial HUM systems usually rely on vibration measurements to assess the condition of shafts, gears, and bearings; using techniques such as spectral analysis, harmonic analysis, vibration trend and others. Recent research has shown that acoustic emissions (AE) can be advantageous in the detection of mechanical faults, in particular detecting very early small defects on bearings and gears, providing extra time for maintenance planning. However, the addition of extra sensors adds complexity and weight to the HUMS system, which is undesirable. This research is an experimental study to assess the monitoring capabilities of a broadband sensor, able to cover both low frequency vibration components as well as ultrasonic events, hence combining the benefits of both in a single compact sensing unit. The experimental results obtained from an instrumented rig using healthy components as well as seeded faults show the ability of the sensor to detect high frequency events, and compares the performance of the sensor in the low frequency range with a commercial accelerometer.
Assessment of selected pesticide contamination of surface and ground water samples from an agrarian settlement in Kuje area council, Abuja, Nigeria
Authors: OLU Joshua , YUSUF Hajara Oyiza , DANTANKO Fatima , HILILI Johnson Mathew
Published: July 2022

This study aimed at evaluating the presence and levels of some selected pesticides in both surface and ground water in an agrarian settlement in Kuje area council, Abuja, Nigeria. Samples of water were collected from three areas in Kuje, Abuja, Nigeria namely: Gaube, Kasada and Pasali. The water samples were obtained from borehole, well and stream sources in triplicate. Dissolved oxygen, electrical conductivity, pH, total dissolved solids, and turbidity were the physicochemical parameters determined in the water samples. Five (5) pesticides namely: Lambda-cyhalothrin, Chlorpyrifos, Glyphosate, Dichlorvos and Cypermethrin were investigated using highperformance liquid chromatography-diode array detector (HPLC-DAD). The findings from the study show that pH values were between 5.70±0.30 and 7.82±0.12, electrical conductivity ranged from 61.31±38.41 to 242.81±1.40 μS/cm, dissolved oxygen 5.58±0.69 to 6.40±2.15 mg/L and turbidity values ranged from 1.42±0.51 to 143.11±11.03 NTU. Lambda-cyhalothrin levels were from 0.06±0.00 to 80.30±138.31μg/L, Chlorpyrifos ranged from 139.99±69.07 to 243.49±197.94 μg/L, the concentrations of Dichlorvos were found to be between 86.65±667.12 and 131.22±3.20 μg/L, Glyphosate and Cypermethrin were not detected in any of the samples tested. Lambda-cyhalothrin correlated positively with Dichlorvos (0.721) and Chlorpyrifos (0.951) in stream water sample from Pasali at p<0.05. The concentrations of the three pesticides exceeded the permissible limit except for the Lambda-cyhalothrin in stream water sample from Pasali and this calls for worry because of the potential adverse effects these could cause the residents of Kuje that continuously use and consume water from these sources.
Competência em Informação como Fator de Inovação Social
Authors: De Souza Santos Alessandra , Gomes Maia Luiz Cláudio , Macedo Kerr Pinheiro Marta
Published: July 2022

O presente artigo propõe-se a analisar a competência em informação como sendo um fator de promoção de inovação social, tendo-se em vista uma possível correlação entre as matrizes teóricas dessas duas temáticas. A inovação social visa à transformação social por meio de mudanças nas práticas sociais, satisfazendo necessidades humanas e promovendo inclusão social, com uma consequente mudança de relações de poder, uma vez que o próprio conhecimento pode ser considerado inovação social. A competência em informação é considerada base para a aprendizagem ao longo da vida pois se trata de uma meta-competência capaz de auxiliar o indivíduo a lidar com necessidades informacionais e a compreender, criar e utilizar criticamente a informação nos mais variados contextos. O presente trabalho tem por objetivo delinear o alinhamento dos temas inovação social e competência em informação, realizando uma análise sobre como a perspectiva transformacional de estudos primários realizados na literatura sobre competência em informação pode ser correlacionada à inovação social em uma perspectiva de inclusão e emancipação sociais.
Analisis Kesulitan Guru dalam Melaksanakan Penilaian Autentik pada Kurikulum 2013 di SDN 104208 Cinta Rakyat
Authors: Theo Fanny Lumbanbatu Grace , Arsila Faiza , Marwiyah Tanjung Lince , Sadaria Simangunsong Putri , M. Perangin-Angin Laurensia
Published: July 2022

Along with the implementation of the 2013 curriculum, many changes occurred in the learning components, especially in the assessment system, which in the 2013 curriculum is known as authentic assessment. Authentic assessment emphasizes activity-based learning that covers aspects of attitude, cognitive and psychomotor. The implementation of authentic assessments in the field is still not running as it should. There are still many teachers who have difficulty in carrying out authentic assessments in schools due to several factors. This study aims to describe the difficulties of teachers in carrying out authentic assessments in the 2013 curriculum at SD Negeri 104208 Cinta Rakyat. Data collection techniques were carried out through interviews and documentation. The sample in this research is a VB grade teacher at SD Negeri 104208 Cinta Rakyat. The data analysis technique uses interactive model data analysis techniques. Based on the results of data analysis, the results of the study show that there are several difficulties experienced by teachers in carrying out authentic assessments in the 2013 curriculum including (1) Incompatibility of the assessment rubrics in the teacher's book with current learning (2) Lack of improvisation in using assessment instruments ( 3) The problem of time constraints. The teacher's efforts/solutions to overcome the difficulties of authentic assessment are; (1) the teacher continues to carry out all the assessments in the teacher's book which includes the attitude domain, cognitive domain and pricomotor domain, (2) the teacher conducts discussions with fellow teachers at the school and the teacher practices to vary the existing attitude assessment, (3) teachers practice to pay more attention to how to manage time well so that the implementation of the assessment can be more optimal by dividing the days in conducting this authentic assessment.
Faktor-Faktor yang Memengaruhi Pemilihan Moda Transportasi Berjalan Kaki dan Sepeda di Kota Bogor
Authors: Aji Wibowo Widianto , Herwangi Yori
Published: July 2022

The development of the city as an economic and trade node is very important for the urban system and the regional economy. The distribution of centralized public facilities and services attracts people to work in the city center. In these conditions, the ideal form of the city is needed because to reach work, school, public services and so on can be done only by walking or cycling. This study aims to examine the characteristics and behavior of walking and bicycle mode selection based on the shape of the urban environment, demographic, social and economic conditions of the community. And look at the factors that might influence the decision to choose the mode of transportation. This study uses a quantitative deductive approach. This research method is based on a questionnaire along with interviews and field observations. The study was conducted in eight urban villages in the city of Bogor. The target respondents are individuals who reside/domicile in the area. The results showed that walking mode was the most frequently chosen mode of travel. In addition, the bicycle mode is the second most frequently used mode. The influencing city form factor is the access and affordability of public facilities and services such as bus stops and commercial areas. In addition, population density and the level of land use are also influential factors. Furthermore, demographic, social, and economic factors that influence are income, personal vehicle ownership, days of travel, and ability to walk based on the furthest distance. In addition, there are other factors that influence, namely the travel time to the destination, affordable costs, as well as the safety and comfort of pedestrian and bicycle paths.
Efektivitas Hubungan Kerjasama Indonesia-Australia Melalui Program Ausaid di Sektor Pendidikan Era Jokowi pada Tahun 2014-2019
Authors: Alfiano Jofanka , Hergianasari Putri , Romulo Simanjuntak Triesanto , Fahmi Muhammad
Published: July 2022

This paper will discuss how the cooperative relationship between Indonesia and Australia through AusAID resulted in various assistance programs in the education sector during the Joko Widodo era from 2014 to 2019, in which this research will look at the effectiveness of the cooperation. Indonesia has pressing problems in the education sector. This is due to the declining quality of education in Indonesia and the alarming dropout rate. Therefore, it is necessary to take steps to improve the quality of education in Indonesia, one of which is through cooperation with Australia. Bilateral relations between Indonesia and Australia have existed for a long time so cooperation in the education sector between the two countries is considered to be beneficial. Indonesia has various policies in the education sector such as 9-year compulsory education and character education development. Meanwhile, Australia has a foreign policy in the field of education that focuses on assistance in improving the quality of education and direct assistance. The cooperation between Indonesia and Australia in the field of education can be seen in the implementation of programs such as the Education Partnership, Technical Assistance for Education System Strengthening, ProDEP, and Innovation for Indonesia's School Children. The cooperation program resulted in various achievements, such as the availability of educational infrastructure and facilities, improving the quality of educators and educational services, improving the curriculum for effective learning methods, as well as the accessibility of education quality in achieving gender equality and being disability-friendly. Researchers see that educational cooperation between Indonesia and Australia from 2014 to 2019 was effective in helping to overcome various educational issues in Indonesia.
Financing Strategy to Execute Business Opportunity in Supplying Building Material in Real Estate
Authors: Adhyaksa Muhamad Reynaldi
Published: July 2022

Concerns about distribution and supplier quality are common among developer business owners. In the future, the cost of material transportation could prevent housing developer business owners from making a decision, not to mention the risk of goods availability when housing demand is high, or housing developers lose money when housing demand is high. Delivery damage. Adhyaksa Precast may be the answer, but before establishing a business, the founder must conduct analysis. Before starting, especially in the financial industry, the founder must determine the firm's long-term viability. The initiator must consider what will benefit and hurt his company. This research uses Minitab to predict home sales. A feasibility study is used to properly run the business. The margin between cooperating and not cooperating is small, and the business will last three years. 1. The company's finances. Bank loan plus own equity is the best funding option so far. 2. There is a large potential for income and profit if CV Satria Yaksa Mandiri and its clients can reach a commercial agreement soon. 3. The firm determined that a combination of bank loan and own equity could cover the entire investment (Rp.1.046.000.000). All bank loan feasibilities are positive (IRR, NPV, PI, ROI, PP, US Index, and PP). 5. With the lower price, cooperative business financial calculations are lower. The corporation prefers the cooperative relationship because it has more free cash flow (the rent fee is free).
Perfil do Personal Trainer e sua percepção sobre a avaliação física antes de iniciar um programa de treinamento
Authors: Castro João Paulo Gontijo de , Santos Andrêza Soares dos , Drummond Lucas Rios , Salgado José Vitor Vieira
Published: July 2022

INTRODUÇÃO: O exercício físico (EF) é hoje fonte primária na busca por maior qualidade de vida e saúde, combatendo o sedentarismo e trazendo benefícios relacionados à sua prática. O EF regular pode atuar reduzindo o desenvolvimento de doença coronariana, acidente vascular cerebral, diabetes, dislipidemia, síndrome metabólica e hipertensão. Além disso, promove a perda da massa gorda, desenvolve a aptidão cardiorrespiratória, a força e resistência muscular, atua na redução de depressão e melhora as funções cognitivas. Porém muitas pessoas não conseguem aderir a um programa de EF sem o auxílio do Personal Trainer, que é um profissional de Educação Física (PEF) responsável pelo treinamento individualizado e também por avaliar/reavaliar as condições de saúde dos indivíduos a fim de propor EF adequado. OBJETIVO: Avaliar o perfil do Personal Trainer na cidade de Divinópolis e a importância da avaliação física primária de seus alunos. MÉTODOS: Estudo transversal com 40 PEF, idade média de 33,7±7,52 anos que atuavam como Personal Trainer. Foi aplicado um questionário previamente estruturado, com 24 perguntas elaboradas pelos autores. Na análise dos dados, foi realizada a estatística descritiva, média e desvio padrão, utilizando o programa Microsoft Office Excel. RESULTADOS: Dentre os pesquisados, 67,5% (n=27) eram mulheres, 50% (n=20) estão no mercado a mais de 10 anos e apenas 45% (n=18) possuem especialização. Quanto a avaliação física antes do início do treinamento, 95% (n=38) dos profissionais exigem ou fazem a avaliação e 85% (n=34) vê grande importância e procura fazer uma avaliação completa e detalhada. CONCLUSÃO: Os profissionais atuam no seguimento há mais de 10 anos, atendem um público diversificado, têm as academias como principal local de atuação e possuem parcerias com outros profissionais da saúde. A maioria dos participantes consideram a avaliação física primária dos seus alunos de suma importância e a realizam antes do início dos treinamentos.
Sobre o que é propriamente humano na Ética nicomaqueia
Authors: Santana Juliana
Published: July 2022

Neste artigo, temos a intenção de averiguar o que a Ética nicomaqueia propõe como sendo propriamente humano, dentre os usos da capacidade de razão, qual poderia ser configurado como tal, definindo especificamente o homem. A pergunta surgiu a partir da observação que o tratado às vezes descreve o homem como animal político, às vezes salienta seu aspecto racional, porém, sem associar ou dissociar abertamente essas duas características. Constatamos uma dupla especificação não relacionada no tratado, mas fortemente atrelada à capacidade racional da alma e seus usos prático e teorético. Sendo assim, propomos a pesquisa acerca do que pode ser propriamente humano a partir do que a Ética indica sobre a razão. Para tanto, iniciamos com observações dos primeiros Livros, especialmente do Livro I, porque ali vemos destacado que o homem é naturalmente político, mas também os primeiros traços da discussão sobre a razão humana. Seguimos com a pesquisa mais detida do Livro VI e o que este apresenta sobre as relações e dissenções dos dois tipos de razão, bem como de suas virtudes. Depois, visitamos as linhas do Livro X, em sua segunda metade, que ora enfatiza o uso teorético da razão e a vida de contemplação, ora indica as impossibilidades de se viver plenamente deste modo, reforçando a importância da pesquisa proposta e trazendo elementos cruciais para a resposta que procuramos.
Crítica à instrumentalização do homem
Authors: Tiago Xavier
Published: July 2022

Este trabalho pretende apresentar algumas críticas à ideia de instrumentalização do homem, que se dá a partir de uma tecnologia antropológica de reformulação das qualidades da espécie humana (antropotécnica), vista como instrumento de aprimoramento por ter o potencial de reprogramar e modificar a biologia humana. Nessa toada, destacaremos a necessidade de uma moralização da natureza humana diante do potencial desta tecnologia.
СУДЕБНОЕ РАЗБИРАТЕЛЬСТВО ПО УГОЛОВНЫМ ДЕЛАМ: ПОРЯДОК ОСУЩЕСТВЛЕНИЯ И ПРОБЛЕМЫ СОВЕРШЕНСТВОВАНИЯ В УСЛОВИЯХ ЦИФРОВИЗАЦИИ
Authors: Мальцагов И.Д. , Саидов М.Х. , Шедиев Ш.Б. , Исханов М.А.
Published: July 2022

В статье рассматриваются основные тенденции внедрения искусственного интеллекта в судопроизводство, в условиях цифровой трансформации, реализуемой в Российской Федерации. Рассматривается в контексте цифровизации уголовного судопроизводства феномен этики судьи, включающей внутренние убеждения, нравственность и совесть. В настоящее время применение искусственного интеллекта во многих сферах деятельности человека является важным вопросом, и судебная система не исключение. Освещаются вопросы степени изученности и развития ИИ как технологии; особенностей, связанных с применением технологий искусственного интеллекта в судебной деятельности; проблемы правового регулирования. Анализируется такое понятие как «искусственный интеллект», которое противопоставляется человеческому фактору судьи, с входящим в него не только рациональным мышлением, но и присущим человеку иррациональным мышлением, содержанием которого является духовный, внутренний мир человека, необходимый для реализации этических норм.
АМПЛИТУДНЫЕ ПОКАЗАТЕЛИ ЗУБЦОВ ЭКГ ПОСЛЕ РАБОТЫ С КОЛЬЦАМИ ЛАНДОЛЬТА
Authors: Шахбиев Х.Х. , Мусаева Х.М. , Баматкириева М.Э.
Published: July 2022

В работе исследовано влияние кратковременной умственной нагрузки на электрокардиографические показатели. В частности, исследовано воздействие работы с кольцами Ландольта на амплитуды зубцов ЭКГ, частоту сердечных сокращений, продолжительность сердечного цикла и положение электрической оси сердца. Работа проводилась с участием магистрантов второго года обучения. Во втором отведении амплитуда основных зубцов – P, R, T, Q, S - после нагрузки не изменилась значимо, хотя отмечается незначительное повышение ее величины. Однако, при анализе амплитуды R в трех отведениях после нагрузки было отмечено достоверное увеличение амплитуды данного зубца в третьем отведении. Ментальная нагрузка также приводила к изменению частоты сердечных сокращений, длительности кардиоинтервала, но положение электрической оси сердца при этом не изменилось.
ОСОБЕННОСТИ АДАПТАЦИИ СЕРДЕЧНО – СОСУДИСТОЙ СИСТЕМЫ В УСЛОВИЯХ ВЫСОКОГОРЬЯ
Authors: Дадаева Х.Х. , Межидова Х.А. , Чакаева Р.Р. , Тутуева Х.У.
Published: July 2022

Высота над уровнем моря является одним из самых требовательных факторов окружающей среды для человеческого населения. В последнее время многие люди временно остаются на больших высотах. В основном это связано с ростом популярности регулярных авиаперелетов, а также временных поездок в горные районы. Переменные условия окружающей среды, в том числе перепады давления и температуры, оказывают влияние на организм человека.
Основным фактором, влияющим на функционирование организма в таких условиях, является низкое атмосферное давление и пропорциональное снижение парциального давления кислорода во вдыхаемом воздухе. Организм человека в той или иной степени обладает способностью приспосабливаться к изменениям условий внешней среды, и в этом процессе в значительной степени участвует сердечно-сосудистая система.
ИСПОЛЬЗОВАНИЕ КОНТЕЙНЕРНОГО ОЗЕЛЕНЕНИЯ ГОРОДСКИХ ОТКРЫТЫХ ПРОСТРАНСТВ И ВОЗМОЖНОСТИ ИХ УГЛЕРОДНОЙ НЕЙТРАЛЬНОСТИ
Authors: Байраков И.А.
Published: July 2022

Контейнерное озеленение, естественно, характеризуется своими особенностями. При озеленение открытых городских пространств лучше использовать крупные контейнерные емкости, материалы из которых они сделаны различные: металл, бетон, пластик. Но при оформлении малых садово-парковых комплексов лучше использовать контейнерные изделия из керамики и дерева. Очень важным остается вопрос о почвенном субстрате для контейнерной композиции, чем крупнее она, тем сложнее её приготовить, ведь она всегда отлична от полевой почвы. Контейнерный субстрат состоит в основном из торфа с примесью той или иной доли песка и других ингредиентов, позволяющих создать хорошую структуру, повышающую влагоемкость. Актуальность работы в том, что в современных городах, где значительная часть жизни человека проходит в высотных зданиях, где хорошо чувствуется необходимость создания каких-то зон с контейнерным озеленением, которые улучшили бы экологический фон в окружении человека. Концептуальные и практические основы работы заключаются в том, чтобы сформировать новый подход в озеленение городского и жилищного пространства с применением контейнерного озеленения с его возможностями углеродной нейтральности зон отдыха населения микрорайона.
ТЕОРЕТИЧЕСКОЕ МОДЕЛИРОВАНИЕ МНОГОКОМПОНЕНТНЫХ СИСТЕМ НА ОСНОВЕ ТЯЖЕЛЫХ РЕДКОЗЕМЕЛЬНЫХ ЭЛЕМЕНТОВ
Authors: Умхаева З.С. , Терешина И.С. , Панкратов Н.Ю. , Алиев И.М. , Саид-Ахматова Ф.С.-А.
Published: July 2022

В работе приводятся результаты прогнозирования и теоретического моделирования магнитных свойств сплавов многокомпонентных систем на основе тяжелых редкоземельных элементов (R1-xYx)0.8Sm0.2Fe2, где R = Gd, Dy, Ho и Er. Показано, что сплавы с подобным замещением в редкоземельной подрешетке обладают рядом уникальных свойств в определенных концентрационных областях, обусловленные конкурирующими обменными взаимодействиями, как межрешеточными, так и внутриподрешеточными. Для каждой из представленных систем, (Gd1-хYх)0.8Sm0.2Fe2, (Dy1-хYх)0.8Sm0.2Fe2, (Ho1-хYх)0.8Sm0.2Fe2 и (Er1-хYх)0.8Sm0.2Fe2, в модели трехподрешеточного ферримагнетика определены концентрации иттрия, в области которой наблюдается взаимная магнитная компенсация редкоземельной и 3d-подрешеток. На основе экспериментальных результатов, полученных ранее авторами данной работы для аналогичной системы (Tb1-хYх)0.8Sm0.2Fe2, спрогнозировано наблюдение в представленных системах целого ряда уникальных магнитных явлений по мере изменения параметра замещения х.
ЛИТЕРАТУРНАЯ СКАЗКА КАК ФЕНОМЕН ЧЕЛОВЕЧЕСКОЙ КУЛЬТУРЫ
Authors: Альмурзаева П.Х. , Сакаева М.В. , Махмудов Т.-А.А.
Published: July 2022

Ежедневный контакт с другими культурными сообществами приводит к умножению культурных моделей и, как следствие, к конфликтам и культурным недопониманиям. Люди обычно подходят к незнакомцам или неизвестным со скептицизмом и дискомфортом, что создает стереотипы и расистское поведение и затрудняет совместную жизнь. Именно поэтому тем более важно продвигать социальные и межкультурные навыки, чтобы ценить сосуществование иностранных культурных обществ и обеспечивать соответствующее поведение в межкультурных ситуациях. Поскольку сказки являются важным культурным достоянием, и в них отражено множество культурных и социальных влияний, в ходе этой работы для нас было важно учесть их вклад в культурную коммуникацию.