Acquisition Process Research Articles

Pemberian Ganti Kerugian kepada Masyarakat Terkait Pengadaan Tanah untuk Kepentingan Umum

The success of infrastructure projects is closely related to land acquisition/land acquisition owned by the community, it is not even possible for population relocation to occur. The implementation of development that requires land acquisition is basically a waiver of land rights. Land Procurement for Public Interest is regulated by the Government in Law No. 2 of 2012 and Other Legislations. The purpose of this study was to determine the process of land acquisition and the calculation of fair market value as the basis for providing compensation. The research method used is literature studies and displayed descriptive. Compensation is a proper and fair compensation to the rightful party in the land acquisition process. The results of the research found are based on Law No. 2 of 2012, the land acquisition process consists of 4 stages, namely planning, preparation, implementation and submission of results. Meanwhile, to provide an opinion on the value of the land, an appraisal process is carried out by a Public Appraiser as a professional independent appraiser who has a license from the Land Agency. The appraisal process for compensation includes physical (material) and non-physical (immaterial) compensation where the final value obtained by the Appraiser is Fair Market Value.

Priority radiomic parameters for computed tomography of head and neck malignancies: A systematic review

BACKGROUND: Radiomics is the newest and most promising direction in modern radiographic diagnostics. The number of head and neck cancer studies employing radiomics is increasing annually. A systematic review of recent publications (2021–2023) on computed tomography (CT) of head and neck malignancies was performed. AIM: To present systematized data on parameters for radiomic analysis for head and neck malignancies identified by CT data. MATERIALS AND METHODS: The literature search was carried out in PubMed. The basic characteristics of the selected articles were extracted, and their quality was assessed using RQS 2.0 and the modified QUADAS-CAD questionnaire. The reproducibility level of radiomic parameters selected for predictive models in different studies was assessed. Eleven articles were selected for the review. In most cases, a high risk of systematic error associated with data imbalance in terms of demographic parameters and level of pathologies was noted. RESULTS: The range of RQS 2.0 scores for the included articles varied from 19.44% to 50.00% of the maximum possible score. The decreasing research quality was mainly caused by the lack of external result validation (73% of the analyzed articles) and data accessibility and transparency (82%). Inter-study reproducibility of radiomic parameters was low owing to the wide variety of techniques used for image acquisition, image post-processing, extraction, and statistical processing of radiomic parameters. CONCLUSION: A set of stable radiomic parameters must be successfully introduced into clinical practice. The standardization of radiomics method and creation of an open radiomics database are necessary for this purpose.

Identifying Professional Goals and Promoting Career Development in Nursing Students Through Reflection and Simulation.

Career planning is an important but often neglected component of a nursing program curriculum. To increase retention and encourage professional advancement at the school of nursing of a large, public university in the Northeast, a virtual simulated career development day was established to provide students with the tools needed to navigate the job acquisition process and secure appropriate employment. Course outcomes and student feedback indicated that a successful transition to practice and advancement to higher levels of professional nursing can be promoted through the development of superior interviewing skills, a strong resume, and insight regarding one's professional goals and values.

Domain-specific research methods in instructed second language acquisition: A next step for research integrity and impact

Abstract Instructed second language acquisition (ISLA) is one of the fastest growing areas of applied linguistics. With this tremendous potential comes great responsibility for robust, ethical, and transparent research methods that are responsive to and tailored for the ISLA domain. This article highlights unique characteristics of ISLA research, provides a current landscape of methodological trends within ISLA, and makes specific recommendations for research methods in future ISLA studies. I begin by briefly operationalizing ISLA and articulating some of the main research questions and overarching goals within ISLA, as well as the nature and ultimate aims of ISLA research. Next, the most unique methodological challenges for ISLA research are reviewed, including the use of intact classes and heterogeneous small participant pools, cross-sectional studies, using one’s own students for research, and individual differences. This is followed by a discussion of several current trends in ISLA research methods, including examining the process of learning/development, conducting practice-based research, expanding our conceptualization of instructional contexts, replication studies, especially with bi/multilingual learners in diverse contexts, refining our methods with an eye for ethics and justice, equity, diversity, and inclusion, and conducting open, transparent research that has potential for real-world impact and which dialogues with multiple stakeholders at all stages. I conclude by highlighting that, as ISLA continues as an independent research domain, the development and implementation of strong research methods tailored for ISLA is critical for research integrity and to make the greatest strides in understanding language acquisition processes and effective pedagogical interventions in diverse instructional contexts.

Expert System for Pregnancy Risk Diagnosis Using Decision Tree and Dempster-Shafer Method

The high Maternal Mortality Rate (MMR) remains a severe concern in maternal healthcare. One of the reasons is the delay in recognizing early danger signs during pregnancy. To address this issue, there is a proposed solution in the form of developing an expert system aimed at swiftly and efficiently diagnosing pregnancy risks in pregnant women using the Decision Tree and Dempster Shafer methods. The Decision Tree method is employed for symptom classification, while Dempster Shafer provides confidence values for existing facts. This research collects data from the dataset, the Poedji Rochjati Score Card (KSPR), and qualitative data through expert interviews. From the collected data, knowledge acquisition processes are then carried out to extract knowledge using the ID3 Decision Tree and combine all symptoms from the gathered data. The processed data is then represented as a decision tree and assigned confidence values. The development of this expert system utilizes the Laravel framework with PHP language and MySQL database. System validation involves patients as participants and midwives as experts and testers. Testing was conducted on March 13 and 16, 2024, involving 16 patients at the Gatak Community Health Center. The system evaluation results show an accuracy rate of 93.75%. This value indicates that the system can operate effectively. Thus, it can be recommended for use in diagnosing pregnancy risks.

Investigating spoken classroom interactions in linguistically heterogeneous learning groups – An interdisciplinary approach to process video-based data in second language acquisition classrooms

Speaking the local language is central for successful integration into society. The teacher's language in second language (L2) classrooms serves as a crucial tool in language learning. Heterogeneity of learners’ language proficiency levels challenges teachers to adapt their language and accompanied instructional behavior. We offer an approach to study language acquisition processes and how teachers adapt their instructional language. This article presents our language-independent guidelines for processing video-based data of classroom interactions and demonstrate their reliability in a German as Second Language (GSL) classroom. These guidelines enable transcriptions of spoken language in noisy environments and detailed annotations of non-verbal classroom behavior. We outline research avenues at the intersection of empirical education research and linguistics that become feasible through these resources focusing on studying (non-)verbal adaptation strategies of teachers for learners at different proficiency levels. Our work directly fosters the interdisciplinary study of teacher-learner interactions, teacher competencies, and language acquisition.

PSIII-30 Development of an application programming interface (API) to automate downloading and processing of precision livestock data

Abstract Advancements in technology have ushered in a new era of sensor-based measurement and management of livestock production systems. These sensor-based technologies have the ability to automatically monitor feeding, growth, and enteric emissions for individual animals across confined and extensive production systems. One challenge with sensor-based technologies is the large amount of data generated, which can be difficult to access, process, visualize, and monitor information in real time to ensure equipment is working properly and animals are utilizing it correctly. A solution to this problem is the development of application programming interfaces (APIs) to automate downloading, visualizing, and summarizing datasets generated from precision livestock technology. For this methods paper, we develop three APIs and accompanying processes for rapid data acquisition, visualization, systems tracking, and summary statistics for three technologies (SmartScale, SmartFeed, and GreenFeed) manufactured by C-Lock Inc (Rapid City, SD). Program R markdown documents and example datasets are provided to facilitate greater adoption of these techniques and to further advance precision livestock technology. The methodology presented successfully downloaded data from the cloud and generated a series of visualizations to conduct systems checks, animal usage rates, and calculate summary statistics. These tools will be essential for further adoption of precision technology. There is huge potential to further leverage APIs to incorporate a wide range of datasets such as weather data, animal locations, and sensor data to facilitate decision-making on times scales relevant to researchers and livestock managers.

Research on Weld Seam Marking Based on Machine Vision Technology

The rapid development of machine vision technology has made it widely used in various industrial fields. The detection of welding quality can also be achieved through this technology. The core of welding quality inspection is to realize the marking of weld features. To solve the problem of weld feature marking, the GUI weld marking interface is built based on MATLAB. After the process of image acquisition, image preprocessing, edge detection and feature extraction, the weld marking experiment of is finally completed. Based on data analysis of the experiment, it can be concluded that Canny edge detection operator has the best marking effect and the most stable detection for the research object used.

Investigation on Junction Contacts of Semiconducting Carbon Nanotube Networks Using Conductive Atomic Force Microscopy.

Semiconductor single-walled carbon nanotube (s-SWNT) networks have gained prominence in electronic devices due to their cost-effectiveness, relatively production-naturality, and satisfactory performance. Configuration, density, and resistance of SWNT-SWNT junctions are considered crucial factors influencing the overall conductivity of s-SWNT networks. In this study, we present a method for inferring the lower bounds of the SWNT-SWNT junction resistance in s-SWNT networks based on conductive atomic force microscopy TUNA images. This method further enables the proposal of a classification for SWNT-SWNT junctions based on the current behavior relative to their surroundings. The three types of SWNT-SWNT junctions are denoted as (i) true contact (T), (ii) poor contact (P), and (iii) false contact (F). Of them, the true and poor contacts, respectively, represent good and poor electrical contact for the subject SWNT-SWNT junctions whose electrical conductivity hardly improves under external tip pressure, while that of the false contact can be further improved by external pressure. Statistical analysis demonstrates that while T-type junctions make a significant contribution to network conductivity, their proportion accounts for only approximately 40%. The P-type and F-type junctions, which constitute over 60% of the total, may be a contributing factor that constrains the overall conductivity of the s-SWNT networks. The height ratio of the junction to the sum of two SWNTs was also observed to exhibit variations among the three types. Finally, we propose a three-dimensional model to elucidate the formation mechanism underlying each type of junction. The present study provides insights into the performance of spontaneous contacts between s-SWNTs in the networks, and the systematic image acquisition and junction classification processes may provide support for future advancements in these networks.

Prediction of permeability of various geotechnical materials under different temperatures based on physical characteristics and machine learning

Accurately and efficiently predicting permeability at different temperatures is crucial in the fuel science, as it enables optimization of production efficiency, and ensures safety and stability during storage and transportation. There remains a deficiency in predictive methods using comprehensive and reasonable training datasets and validating their applicability across different materials, particularly when considering stress and temperature. This study presents a machine learning-based method to predict permeability, incorporating physical characteristics such as microscopic image features and damage factors. The image sample data augmentation method was improved for a sufficient and meaningful training dataset, and gas permeability of sandstone under different pressure conditions after various temperature treatments were obtained. After summarizing the variation patterns of each characteristic with temperature, the necessity of using selected predictive features was validated, and sample sets were constructed. We trained an extreme learning machine neural network for sandstone’s permeability under different conditions, and the relative error was compared with other representative machine learning methods. Subsequently, the same data acquisition and prediction process were applied to granite and bentonite to validate the proposed method's applicability. The findings indicate that the proposed predictive method can stably provide accurate gas permeability predictions for various geomaterials, with relatively small prediction error rates (sandstone-4.1782%, granite-4.1782%, bentonite-3.2479%). Moreover, the stability of the relative error is steadily better than other representative machine learning methods. This approach achieves an effective prediction of gas permeability under various conditions, and can potentially simulate the evolution of gas permeability for fuel sector.

Volatile data acquisition and analysis by using Memory Forensics techniques

Memory forensics is a vital component of digital investigations, involving the analysis of volatile memory (RAM) in computer systems to gather evidence, identify malicious activities, and reconstruct cybercrime incidents. This paper provides an overview of memory forensics, highlighting its definition, importance, purpose, and scope. It explores the evolution and significance of memory forensics in response to increasingly complex cyber threats. The memory forensics process is discussed, covering memory acquisition and analysis. Legal and ethical considerations related to the admissibility of memory evidence and privacy protection are examined. The paper also discusses the types of memory, including physical and virtual memory, and their characteristics and significance in memory forensics. Furthermore, it explores the memory acquisition process, different methods, tools, and techniques used, as well as the importance of preserving evidence integrity. Finally, the paper introduces various tools for memory analysis, such as Volatility, Volatility Workbench, FTK Imager, Encase, Hibernation Recon, and Xplico, and highlights their role in extracting valuable evidence from memory dumps.

Development of a Scanning Protocol for Anthropological Remains: A Preliminary Study

Structured-light scanning is a fast and efficient technique for the acquisition of 3D point clouds. However, the extensive and daily application of this class of scanners can be challenging because of the technical know-how necessary to validate the low-cost instrumentation. This challenge is worth accepting because of the large amount of data that can be collected accurately with the aid of specific technical protocols. This work is a preliminary study of the development of an acquisition protocol for anthropological remains performing tests in two opposite and extreme contexts: one characterised by a dark environment and one located in an open area and characterised by a very bright environment. This second context showed the influence of sunlight in the acquisition process, resulting in a colourless point cloud. It is a first step towards the development of a technical protocol for the acquisition of anthropological remains, based on the research of limits and problems associated with an instrument.

Rapid terahertz beam profiling and antenna characterization with phase-shifting holography

In this paper we investigate the application of phase-shifting digital holography for the real-time characterization of electromagnetic sources in the THz frequency range. We use an off-the-shelf terahertz detector array composed of 64×64\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$64 \ imes 64$$\\end{document} power-sensitive pixels, over an area of 96mm×96mm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$96\\,\\hbox {mm} \ imes 96\\,\\hbox {mm}$$\\end{document}, to record intensity interferograms cast between the coherent radiation emitted from a reference source and an unknown antenna under test. This approach parallelizes the acquisition process with respect to conventional near-field point scanning methods, reducing the measurement time by orders of magnitude. In our system, the measurement time is limited only by the refresh rate of the detector array and the speed of a delay line stage that is used to phase-shift the reference wave. As a proof-of-principle demonstration, we map the 2D near-field distribution and estimate the far-field radiation pattern emitted by a plano-convex PTFE spherical lens antenna illuminated by a diagonal horn at 290 GHz frequency with ∼1Hz\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim \\,1\\,\\hbox {Hz}$$\\end{document} refresh rate.

The process of language acquisition: an experimental approach from a minimalist perspective

We present the approach to the study of language acquisition and processing developed in LAPAL (Laboratório de Psicolinguística e Aquisição da Linguagem – PUC-Rio) in the last decades, which incorporates a minimalist conception of language in a procedural model of grammar identification, and in the online computation of sentences. We refer to experimental results regarding infants’ sensibility to elements of functional categories, the early acquisition of gender and number, the early parsing of recursive structures, the role of recursion in cognitive development, and the processing cost of recursive structures and long-distance dependencies. Methodological issues concerning the assessment of children’s ability to process costly structures are considered, which contribute to the identification of the nature of children’s difficulties in comprehension tasks. Particular attention is paid to the application of this approach to the tracking of DLD (Developmental Language Disorder).

How do different goals affect students' internal collaboration script configurations? Results of an epistemic network analysis study.

Research has suggested that how learners act in CSCL environments is considerably influenced by their internal collaboration scripts. These scripts are knowledge structures that reside in an individual's memory and consist of play, scene, scriptlet, and role components. In its "internal script configuration principle," the Script Theory of Guidance suggests that as learners work in a CSCL environment, these components are dynamically (re-)configured, and that this (re-)configuration is influenced by the goals of the individual learner. However, this principle has not yet been tested empirically. In this study, upon entering a CSCL environment, we therefore experimentally manipulated the goals that students pursued while learning. In one condition, we induced learning goals while in the other condition, no goals were induced. A total of 233 pre-service teachers collaborated in dyads on the task to analyze an authentic, problematic classroom situation by aid of educational evidence. We measured their internal scripts both at pre-test (i.e., before collaboration and before goal induction) and post-test (i.e., after collaboration and goal induction), focusing on the scriptlet level. Results show that goal induction had no effects on the kinds of scriptlets participants selected during collaboration. However, results from Epistemic Network Analysis show that learning goal induction led to significantly different combinations of scriptlets (especially to more relations between scriptlets that are indicative of pursuing learning goals) than no goal induction. Furthermore, participants from the learning goal induction acquired significantly more knowledge about educational theories and evidence than students from the control condition. This study is among the first to provide direct evidence for the internal script configuration principle and demonstrates the effectiveness of inducing learning goals as a scaffold to support students' knowledge acquisition processes in CSCL.

Advanced Data Processing of THz-Time Domain Spectroscopy Data with Sinusoidally Moving Delay Lines

We provide a comprehensive technical analysis of the data acquisition process with oscillating delay lines for Terahertz-time domain spectroscopy. The utilization of these rapid stages, particularly in high-repetition-rate systems, is known to enable an effective reduction of noise content through averaging. However, caution must be exercised to optimize the data averaging process, with the goal of significantly optimizing the dynamic range (DR) and signal-to-noise ratio (SNR). Here we discuss some pitfalls to avoid and the effect of improper data handling on the dynamic range obtainable. A free and open-source program, called parrot (Processing All Rapidly & Reliably Obtained THz-traces), is provided alongside this publication to overcome the discussed pitfalls and facilitate the acceleration of experimental setups and data analysis, thereby enhancing signal fidelity and reproducibility.

Control knowledge tracing: Modeling students' learning dynamics from a control-theory perspective

A student's learning system is a system that guides the student's knowledge acquisition process using available learning resources to produce certain learning outcomes that can be evaluated based on the scores of questions in an assessment. Such a learning system is analogous to a control system, which regulates the process of a plant through a controller in order to generate a desired response that can be inferred from sensor measurements. Inspired by this analogy, this study proposes to model the monitoring of students' knowledge acquisition process from a control-theory viewpoint, which is referred to as control knowledge tracing (CtrKT). The proposed CtrKT comprises a dynamic equation that characterizes the temporal variation of students' knowledge states in response to the effects of learning resources and an observation equation that maps their knowledge states to question scores. With this formulation, CtrKT enables tracking students' knowledge states, predicting their assessment performance, and teaching planning. The insights and accuracy of CtrKT in postulating the knowledge acquisition process are analyzed and validated using experimental data from psychology literature and two naturalistic datasets collected from a civil engineering undergraduate course. Results verify the feasibility of using CtrKT to estimate the overall assessment performance of the participants in the psychology experiments and the students in the naturalistic datasets. Lastly, this study explores the use of CtrKT for teaching scheduling and optimization, discusses its modeling issues, and compares it with other knowledge-tracing approaches.

Ten quick tips for clinical electroencephalographic (EEG) data acquisition and signal processing.

Electroencephalography (EEG) is a medical engineering technique aimed at recording the electric activity of the human brain. Brain signals derived from an EEG device can be processed and analyzed through computers by using digital signal processing, computational statistics, and machine learning techniques, that can lead to scientifically-relevant results and outcomes about how the brain works. In the last decades, the spread of EEG devices and the higher availability of EEG data, of computational resources, and of software packages for electroencephalography analysis has made EEG signal processing easier and faster to perform for any researcher worldwide. This increased ease to carry out computational analyses of EEG data, however, has made it easier to make mistakes, as well. And these mistakes, if unnoticed or treated wrongly, can in turn lead to wrong results or misleading outcomes, with worrisome consequences for patients and for the advancements of the knowledge about human brain. To tackle this problem, we present here our ten quick tips to perform electroencephalography signal processing analyses avoiding common mistakes: a short list of guidelines designed for beginners on what to do, how to do it, and what not to do when analyzing EEG data with a computer. We believe that following our quick recommendations can lead to better, more reliable and more robust results and outcome in clinical neuroscientific research.

The skills dimension in the talent acquisition processes of Greek private schools

This paper explores the processes involved in talent acquisition practices, including recruitment, selection, and professional development within the context of private schools in Greece. Unlike the standardized recruitment and selection processes observed in the Greek public educational sector, which assess formal qualifications alongside social criteria, the private sector in Greece adopts a talent acquisition approach primarily based on job-matching theory. The theoretical section of the paper examines the divergence in talent acquisition processes between the Greek public and private educational sectors. In the empirical research section, the study presents findings from qualitative research conducted through semi-structured interviews with 15 managers from private schools in Northern Greece. Thematic analysis was employed for data analysis, with convenience sampling utilized as the sampling method. The research aims to capture managers' perspectives on selection methods, training practices, and professional development initiatives for educational staff. The findings highlight the emphasis placed on soft skills during the recruitment process, the diverse blend of skills and qualifications that form the occupational profile demanded and the significant role of managers in facilitating the professional growth of educational staff.

A multi-task deep learning approach for real-time view classification and quality assessment of echocardiographic images

High-quality standard views in two-dimensional echocardiography are essential for accurate cardiovascular disease diagnosis and treatment decisions. However, the quality of echocardiographic images is highly dependent on the practitioner’s experience. Ensuring timely quality control of echocardiographic images in the clinical setting remains a significant challenge. In this study, we aimed to propose new quality assessment criteria and develop a multi-task deep learning model for real-time multi-view classification and image quality assessment (six standard views and “others”). A total of 170,311 echocardiographic images collected between 2015 and 2022 were utilized to develop and evaluate the model. On the test set, the model achieved an overall classification accuracy of 97.8% (95%CI 97.7–98.0) and a mean absolute error of 6.54 (95%CI 6.43–6.66). A single-frame inference time of 2.8 ms was achieved, meeting real-time requirements. We also analyzed pre-stored images from three distinct groups of echocardiographers (junior, senior, and expert) to evaluate the clinical feasibility of the model. Our multi-task model can provide objective, reproducible, and clinically significant view quality assessment results for echocardiographic images, potentially optimizing the clinical image acquisition process and improving AI-assisted diagnosis accuracy.