Assessment Methods Research Articles

Comparison of WASPAS and VIKOR methods to determine non-cash food assistance recipients

Non-cash food assistance or bantuan pangan non-tunai (BPNT) is a government program of the Republic of Indonesia by distributes food assistance in non-cash to beneficiary families. The process of distributing BPNT still needs to be done with the data and criteria set, because the existing BPNT distribution is considered not right on target. We need a method that can help provide an objective decision. One method that can be used in making decisions is the weighted aggregated sum product assessment (WASPAS) and Vlsekriterijumsko Koompromisno Rangiranje (VIKOR) methods. The results of the calculations from the two methods will then be chosen which is the best, by conducting sensitivity tests and accuracy tests. This study uses 100 sample data and 16 criteria. The sensitivity test results are 9.780678997% for the WASPAS method and -0.0759182% for the VIKOR method, while the results of the accuracy test show that both methods have the same level of accuracy, which is 80%. Based on the comparison of the sensitivity test and accuracy test of the two methods, the WASPAS method is considered more accurate in determining the recipients of the BPNT program because the WASPAS method has a higher sensitivity test value than the VIKOR method.

Environmentally safe chemometric evaluation and data analysis for the kinetic investigation of mirabegron stability

The physicochemical characteristics of different pharmaceuticals may change with transportation, storage, and usage which directly affect their efficacy, quality, and other features. It is crucial to investigate the kinetic action of pharmaceutical compounds to produce predictive data that aid in the computation of their shelf lives. Several conventional analytical methods introduce unwanted dangerous risks, like employing unsafe solvents, that harm both the analyst and the environment. This created the motive for analysts to apply green approaches that consider using safe chemicals and producing as little waste as possible. In this study, powerful green assessment methods were investigated to study the kinetic process of Mirabegron (MIR) in different hydrolytic conditions giving its main degradation product. First, the optimal solvent was identified for the given kinetic procedures with the aid of Principal Component Analysis. Then two advanced chemometric methods were applied to estimate the kinetic parameters of the MIR degradation process namely: Multivariate Curve Resolution-Alternating Least Squares and Artificial Neural Networks. By data manipulation, both algorithms could provide an explanation of the MIR kinetic process in different hydrolytic conditions. Moreover, modified National Environmental Methods Index (NEMI), eco-scale, Hexagon algorithm, Complementary Green Analytical Procedure Index (Complex GAPI), and analytical greenness metric for sample preparation (AGREEprep) were adopted to ensure the greenness of the proposed study. Eventually, the presented investigation could successfully present an eco-friendly chemometric approach for monitoring the different kinetic processes of pharmaceutical compounds with minimum effort and solvent consumption and maximum time saving and analyst safety compared to other traditional methods.

Voltage stability index: a review based on analytical method, formulation and comparison in renewable dominated power system

In an interconnected complex power system network voltage stability evaluation is indispensable to guarantee secure power system operation. To further increase the system performance and to make the system safer assessment, the voltage stability improvement is obligatory. In the various literature different voltage security assessment method using the voltage stability index has been presented. Different lists proposed in literature can be utilized to realize the weak buses and weak transmission lines to enacting the countermeasures against issues of voltage insecurity. Additionally, the arrangement and measuring of inexhaustible assets, on the web and disconnected observing of force framework and the measure of load to be shed at whatever point essential. This paper shows a survey on the different voltage stability index from various perspectives. The audit results on various record gives a far and wide logical to perceive the impending works in this field and to choose the best index for variety of applications such as voltage security assessment, renewable energy integration, distributed generation (DG) placement and sizing, online monitoring of the power system, and shedding of load.

Environmental Impact Analysis in the Cement Industry with Life Cycle Assessment Method

Environmental Impact Analysis in the Cement Industry with Life Cycle Assessment Method

Solder Defect Assessment Method for Power Electronic Devices Based on Mechanical Stress Wave Signals

The health of a power electronic device is closely related to the mechanical stress waves released at the moment when the device is turned on and off. Research has shown the potential of mechanical stress waves in power electronic device health monitoring. However, the correlation between power device defect severity and mechanical stress wave signal characteristics has not been fully established and an effective evaluation method is currently lacking. This article uses finite element simulation methods to explore the impact of void defects in the chip solder and base plate solder of insulated gate bipolar transistor (IGBT) devices on mechanical stress wave signals. By analyzing the mechanical stress wave curves of cavity defects of multiple sizes, a method for assessing the health status of power devices based on principal component analysis (PCA) is proposed. By calculating the projection of the mechanical stress wave signal on the principal component, the characteristic quantity representing the severity of the defect is obtained. Simulation results indicate that for chip soldering defects, as the severity of defects increases, the feature values gradually decrease. Therefore, this method exhibits good evaluation performance for defects throughout the entire lifecycle. In the case of plate solder defects, this approach can effectively identify mid-to-late-stage defects in devices and can also be utilized for reliability prediction before device failure.

Heart Disease Prediction using Machine Learning

Abstract: Heart disease remains a leading cause of morbidity and mortality worldwide. Early detection and accurate prediction of heart disease risk factors are crucial for effective prevention and timely intervention. In recent years, machine learning techniques have emerged as powerful tools for predictive analytics in healthcare. This paper presents a comprehensive review of recent advances in the application of machine learning algorithms for heart disease prediction. The review begins by discussing the significance of heart disease prediction and the limitations of traditional risk assessment methods.

Non-invasive Scores and Serum Biomarkers for Fatty Liver in the Era of Metabolic Dysfunction-associated Steatotic Liver Disease (MASLD): A Comprehensive Review From NAFLD to MAFLD and MASLD.

The prevalence of non-alcoholic fatty liver disease (NAFLD) is rapidly increasing worldwide, making it the leading cause of liver related morbidity and mortality. Currently, liver biopsy is the gold standard for assessing individuals with steatohepatitis and fibrosis. However, its invasiveness, sampling variability, and impracticality for large-scale screening has driven the search for non-invasive methods for early diagnosis and staging. In this review, we comprehensively summarise the evidence on the diagnostic performance and limitations of existing non-invasive serum biomarkers and scores in the diagnosis and evaluation of steatosis, steatohepatitis, and fibrosis. Several non-invasive serum biomarkers and scores have been developed over the last decade, although none has successfully been able to replace liver biopsy. The introduction of new NAFLD terminology, namely metabolic dysfunction-associated fatty liver disease (MAFLD) and more recently metabolic dysfunction-associated steatotic liver disease (MASLD), has initiated a debate on the interchangeability of these terminologies. Indeed, there is a need for more research on the variability of the performance of non-invasive serum biomarkers and scores across the diagnostic entities of NAFLD, MAFLD and MASLD. There remains a significant need for finding valid and reliable non-invasive methods for early diagnosis and assessment of steatohepatitis and fibrosis to facilitate prompt risk stratification and management to prevent disease progression and complications. Further exploration of the landscape of MASLD under the newly defined disease subtypes is warranted, with the need for more robust evidence to support the use of commonly used serum scores against the new MASLD criteria and validation of previously developed scores.

Life Cycle Assessment Framework for Diagnostic Imaging

The increasing focus on environmental sustainability is becoming essential in the diagnostic imaging sector, which is accredited for about 10% of the healthcare industry’s carbon footprint. A multitude of research initiatives investigated the environmental impacts of diagnostic imaging, encompassing factors like electricity consumption, carbon emissions, and waste generation from these procedures. Life Cycle Assessment (LCA) stands as a prominent method for structural assessment of environmental impacts, offering a detailed framework for examining the environmental consequences of specific processes. The aim of this study includes analyzing existing LCA approaches in literature to identify their limitations and to suggest an elaborate framework for LCA application in diagnostic imaging.Out of 17 original articles on environmental sustainability in radiology published since 2014, but only a part, 29.4% (5/17), described an LCA approach. The different characteristics of these studies provide valuable insights, enabling the proposal of a more comprehensive LCA research methodology.The reviewed articles did not present a uniform research outcome. According to the GreenHouse Gas (GHG) Protocol Corporate Standard, the optimal outcome for assessing environmental impact is the calculation of greenhouse gas emissions. For a thorough methodological approach in LCA, it is essential to cover all direct and indirect emissions associated with diagnostic imaging. All studies (100%, 5/5) considered the electricity consumption of imaging equipment. Usage of consumables was included in 80% (4/5) of the studies. Only 40% (2/5) of articles considered auxiliary equipment like computers and contrast-medium injectors, as well as heating, ventilation and air conditioning (HVAC) systems. Factors like equipment manufacture, staff travel, and waste generation, though crucial to overall greenhouse gas emissions, were each covered in only 20% (1/5) of the studies. The articles also varied in their LCA versions, with two employing the detailed Cradle-to-Grave approach, while others used partial Cradle-to-Gate and Input-Output LCA methods.The insights from this analysis could lead to a valuable framework for a new LCA methodological approach in diagnostic imaging. This novel approach is designed to overcome the limitations observed in existing research, offering a more comprehensive analysis. By enhancing the LCA framework, it will be possible identify the phases in diagnostic imaging that have the most substantial environmental impact, allowing for the development of more targeted strategies to reduce GHG emissions associated with diagnostic procedures.

Enhancing the Evaluation of District Heating System Resilience: A Literature Review

Ensuring an uninterrupted supply of energy, electricity, and heat is becoming an increasingly pressing necessity. Energy provides both social and economic growth therefore, the energy systems should be resilient to different external conditions. Resilience is the ability of a system to recover from adversity [1] and in connection with the critical infrastructure - the ability to prepare for and adapt to changing conditions and withstand and recover rapidly from disruptions. Resilience includes the ability to withstand and recover from deliberate attacks, accidents, or naturally occurring threats or incidents. Several factors influence the resilience of the energy supply system: energy security, reliability, robustness, flexibility, and resilience. If energy resilience has not been well considered or even ignored in the planning, design, and operation stages in district energy communities can lead to system fragility and vulnerability. Therefore, the research assesses the different methods for resilience assessment in energy systems by focusing on district heating. During climate change mitigation and adaptation energy supply system resilience is unclear. Based on current studies, the energy resilience factor for most is considered in the context of the electricity transmission system, and the heat transmission resilience study is limited. During the research of the literature, it has been concluded that there is no consistent quantitative and quality approach to resilience because there is no consistent treatment of the concept of resilience. The available literature sources about energy resilience are limited in their scope and usability and hence are not amenable for use outside the scope where they have been developed. The metric and formula used for calculating resilience, if any, and the input data required for such calculations are also dependent on the research scope. There is a need for a fundamental generic quantitative and quality approach for resilience that would be usable and useful across various scopes or sectors in a consistent manner, which can be used for the development of resilient systems and effective resilience strategies for district heating systems.

Environmental Performance of a Polyamide-Based Thermoplastic Compound with Brominated Flame Retardants

Thermoplastic compounds are present in various industrial, domestic and consumer applications thanks to their strength, lightness, flexibility and processing properties. However, thermoplastics are often derived from polymers formed from non-renewable sources, such as petroleum. In addition, the production processes have a high consumption of energy. For this reason, assessing the environmental impacts caused by the production process is of primary importance. This research evaluates the environmental performance of a polyamide-based thermoplastic compound with brominated flame retardants. The objective is to understand which phase of the production process has the most significant impacts. The assessment of environmental impacts was carried out through the Life Cycle Assessment methodology using SimaPro v9.5, Ecoinvent v3.9 database and ReCiPe 2016 v1.07 impacts assessment method. The primary data were generated and collected in a factory in northern Italy, and the Functional Unit considered is 1 kg of final product. The results show that the upstream phase accounts for more than 90% of the environmental profile, especially in human non-carcinogenic toxicity, with more than 56% of the total impacts and fine particulate matter formation with 18% shares. At the climate change level, the product delivers a total impact of 6.17 kg CO2 eq per kg of the final product. This study indicates that most of the impacts are caused by the production of raw materials. For this reason, it is necessary to introduce more sustainable and environmentally friendly raw materials.

Comparison and Influence of a Multicomponent Educational Program on the Patient-Reported Outcomes of a Group of Patients With Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic disease that affects different areas of the patient's body. Patient education and health literacy is essential for them to participate actively in follow-up. The aim of this study was to assess differences between clinimetric measurements done by a medical team and patient-reported outcome measures (PROMs) in RA and understand the impact of patient education strategies in order to identify differences between RA assessment methods. This is a longitudinal cohort study. It included adult patients with RA and access to digital tools. These were divided into 3 groups by type of education. Group 1 included patients who participated in a multicomponent RA educational program. Group 2 did not have this multicomponent RA education. Group 3 did not receive any education. The 3 groups performed PROMs. Disease activity scales, functional class, and quality of life were measured. Univariate and bivariate analysis (χ2 and Wilcoxon for paired data) were done. Twenty-eight patients were included in group 1, 26 in group 2, and 37 in group 3. All were women. In group 1, there were no significant differences in clinimetrics between the medical team and patient's PROMs except for fatigue. In group 2 and group 3, significant differences were found. The RAPID3 and PAS variables did not show significant differences when analyzed by intervention subgroups. This study shows no differences between clinimetrics/PROMs for patients with a high-level education on RA and physicians. On the other hand, when patient did not have any RA education, the clinimetric results differed from physician measurement.

An integrated cost based approach for warehouse performance evaluation: A new multiphase model

Warehouses represent key links in domestic and international commodity flows. The increasing shortage of workers and increasing costs on the one hand, and the increasing number and stricter demands of users on the other hand lead warehouse managers to realize their operations as efficiently as possible. A proposed model has an objective of enabling companies to monitor warehouse performance in an authoritative, reliable, and simple way and define appropriate corrective measures accordingly. The proposed empirical research consists of three stages, where in the first stage a combination of Principal Component Analysis-Data Envelopment Analysis methods was applied in order to determine efficient warehouses based on 90 decision making units. In the second phase, a completely new method called Interval Fuzzy Rough Pivot Pair-wise Relative Criteria Importance Assessment method used for determining criteria weights was developed and applied, which is one of the most important novelties of this study. In the last phase, the Interval Fuzzy Rough Measurement of Alternatives and Ranking according to the Compromise Solution method was applied to rank the alternatives. Twelve criteria were observed to evaluate 21 alternatives. Based on the results, it was concluded that salary stood out as the most important criterion, while amortization stood out as the least significant criterion. On the other hand, alternatives A9 and A10 stood out as the best-ranked alternatives while A1, A2, and A3 stood out as the least efficient ones. The paper provides clear scientific contributions that are reflected in the reduction of the gap that was observed after reviewing the literature where there is a lack of papers dealing with this task. Also, the combination of methods applied in the paper has not been used so far, so it can be said that this paper represents an excellent basis for further research. The model has practical contributions as it allows decision-makers to make quality decisions regarding the operation of their warehouses in different time periods or observation periods, as well as it represents a decision support tool that can be used for better warehouse management.

How to assess a university campus planning — a case study based on students’ spatio-temporal behavior

ABSTRACT The planning of university campus directly impacts students’ quality of study and living, and there is a considerable amount of research on campus planning assessment, most of which uses post-occupancy evaluation methods. This paper tries to propose a new comprehensive assessment method for campus planning, which collects spatio-temporal behavioral data of students in their daily life through GPS software, then assesses the actual effect of campus planning comprehensively by analyzing the spatio-temporal behavioral characteristics of students. This paper also verifies the reliability of the student behavior characteristics analysis through questionnaire and explores the possible biases. The results indicate that, in addition to significant advantages in reliability, accuracy, and comprehensive judgment, the proposed method can visually reflect specific issues existing within tangible campus spaces and their impact on student behavior. Therefore, it can provide practical and reliable evidence for the practice of campus planning renewal. This method used in this paper is also applicable to other fixed group living spaces within a certain scale, like communities and neighborhoods.

MRI-based Deep Learning Models for Preoperative Breast Volume and Density Assessment Assisting Breast Reconstruction.

The volume of the implant is the most critical element of breast reconstruction, so it is necessary to accurately assess the preoperative volume of the healthy and affected breasts and select the appropriate implant for placement. Accurate and automated methods for quantitative assessment of breast volume can optimize breast reconstruction surgery and assist physicians in clinical decision making. The aim of this study was to develop an artificial intelligence model for automated segmentation of the breast and measurement of volume. A total of 249 subjects undergoing breast reconstruction surgery were enrolled in this study. Subjects underwent preoperative breast MRI, and the breast region manually outlined by the imaging physician served as the gold standard for volume measurement by the automated segmentation model. In this study, we developed three automated algorithms for automatic segmentation of breast regions, including a simple alignment model, an alignment dynamic encoding model, and a deep learning model. The volumetric agreement between the three automated segmentation algorithms and the breast regions manually segmented by imaging physicians was evaluated by calculating the mean square error (MSE) and intragroup correlation coefficient (ICC), and the reproducibility of the automated segmentation of the breast regions was assessed by the test-retest step. The three breast automated segmentation models developed in this study (simple registration model, dynamic programming model, and deep learning model) showed strong ICC with manual segmentation of the breast region, with MSEs of 1.124, 0.693, and 0.781, and ICCs of 0.975 (95% CI, 0.869-0.991), 0.986 (95% CI, 0.967-0.996), and 0.983 (95% CI, 0.961-0.992), respectively. Regarding the test-retest results of breast volume, the dynamic programming model performed the best with an MSE of 0.370 and an ICC of 0.993 (95% CI, 0.982-0.997), followed by the deep learning algorithm with an MSE of 0.741 and an ICC of 0.983 (95% CI, 0.956-0.993), and the simple registration algorithm with an MSE of 0.763 and an ICC of 0.982 (95% CI, 0.949-0.993). The reproducibility of the breast region segmented by the three automated algorithms was higher than that of manual segmentation by different radiologists. The three automated breast segmentation algorithms developed in this study generate accurate and reliable breast regions, enable highly reproducible breast region segmentation and automated volume measurements, and provide a valuable tool for surgical selection of appropriate prostheses. This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Offistretch: camera-based real-time feedback for daily stretching exercises

AbstractIn this paper, we present OffiStretch, a camera-based system for optimal stretching guidance at home or in the workplace. It consists of a vision-based method for real-time assessment of the user’s body pose to provide visual feedback as interactive guidance during stretching exercises. Our method compares the users’ actual pose with a pre-trained target pose to assess the quality of stretching for a number of different exercises. We utilize angular and spatial pose features to perform this comparison for each individual exercise. The result of this pose assessment is presented to the user as real-time visual feedback on an "augmented mirror" display. As our method relies simply on a single RGB camera, it can be easily utilized in everyday training scenarios. We validate our method in a user study, comparing users’ performance and motivation in stretching when receiving audio-visual guidance on a TV screen both with and without our live feedback. While participants performed equally well in both conditions, feedback boosted their motivation to perform the exercises, highlighting its potential for increasing users’ well-being. Moreover, our results suggest that participants preferred stretching exercises with our live feedback over the condition without the feedback. Finally, an expert evaluation with professional physiotherapists reveals that further work must target improvements of the feedback to ensure correct guidance during stretching.

An investigation into the impact of vocabulary retrieval practice as a method of formative assessment in a Latin AS-level unseen translation context

Abstract Unseen translation forms a central part of assessment and teaching both in the Latin GCSE and A Level. Developing skills for the unseen translation presents several challenges for the classroom teacher; unseens can be introduced using a scaffolded approach, yet pupils must learn to develop independence for the examination. Unseens can often take an entire lesson, or more to translate, precluding the opportunity for meaningful, immediate feedback. Furthermore, classes of mixed- ability students often suffer from staggered completion rates and unequal attention being divided among students. Within the curriculum, unseen classes can additionally suffer from feeling severed from the specification as passages contain unfamiliar material, the content is discrete from the set texts, and lack of an overarching framework for approaching unseens can make them feel irrelevant to pupils. Therefore, striking a balance between productive support for in-class unseen translation practice and nurturing pupil confidence requires a clear strategy. This article investigates the effect of two methods of formative assessment used in preparation for an unseen translation lesson with a year 12 class. Reflecting on the results of the investigation, this article discusses the opportunities different tasks may afford the Latin teacher for developing a vocabulary curriculum that supports long-term retention of vocabulary, increases the efficiency of unseen classes, and allows the unseen passage to be received as part of a wider framework of learning.

The Future of Obesity Management through Precision Nutrition: Putting the Individual at the Center.

PURPOSEOF REVIEW: The prevalence of obesity continues to rise steadily. While obesity management typically relies on dietary and lifestyle modifications, individual responses to these interventions vary widely. Clinical guidelines for overweight and obesity stress the importance of personalized approaches to care. This review aims to underscore the role of precision nutrition in delivering tailored interventions for obesity management. RECENT FINDINGS: Recent technological strides have expanded our ability to detect obesity-related genetic polymorphisms, with machine learning algorithms proving pivotal in analyzing intricate genomic data. Machine learning algorithms can also predict postprandial glucose, triglyceride, and insulin levels, facilitating customized dietary interventions and ultimately leading to successful weight loss. Additionally, given that adherence to dietary recommendations is one of the key predictors of weight loss success, employing more objective methods for dietary assessment and monitoring can enhance sustained long-term compliance. Biomarkers of food intake hold promise for a more objective dietary assessment. Acknowledging the multifaceted nature of obesity, precision nutrition stands poised to transform obesity management by tailoring dietary interventions to individuals' genetic backgrounds, gut microbiota, metabolic profiles, and behavioral patterns. However, there is insufficient evidence demonstrating the superiority of precision nutrition over traditional dietary recommendations. The integration of precision nutrition into routine clinical practice requires further validation through randomized controlled trials and the accumulation of a larger body of evidence to strengthen itsfoundation.

Joint operational situation assessment and firepower allocation method based on enhanced potential energy model

The assessment of battlefield situations and the allocation of firepower are pivotal prerequisites for conducting joint operational planning. In response to the challenges posed by multi-service joint operations, this paper presents a methodology based on enhanced operational potential energy model for the assessment of battlefield situations and the generation of optimal firepower allocation plan. We focuses on the three-fold factors influencing operational effectiveness: spatial, informational, and environmental factors, and completes the enhanced operational potential energy model by integrating the assessment of joint operational situations and the generation of optimal firepower allocation plans. Finally, the effectiveness of this methodology is demonstrated through operational simulations in a joint anti-ship combat scenario.

Safety Assessment Method of High-speed Rail Interval Structure Based on Weighted Bayesian Network

Safety Assessment Method of High-speed Rail Interval Structure Based on Weighted Bayesian Network

Enhanced Network Security Protection through Data Analysis and Machine Learning: An Application of GraphSAGE for Anomaly Detection and Operational Intelligence

With the Internet's rapid expansion, network security challenges have become increasingly complex and prominent. Traditional protection methods, largely dependent on predefined rules and patterns, demonstrate limited effectiveness against sophisticated and unknown network attacks, failing to harness the full potential of extensive network data. This study addresses the challenges faced by modern cybersecurity, particularly the limitations of traditional defense methods in countering unknown and complex attacks, by proposing a solution that integrates data analysis and machine learning technologies. The focus of this research is placed on network security anomaly detection as well as on intelligent network operations and maintenance exception management based on graph network algorithms, aiming to enhance security defense capabilities and operational efficiency. Specifically, the main contributions and innovations of this paper include: 1. Innovations in sampling, aggregation, and loss functions within the Graph Sample and Aggregation (GraphSAGE) model to improve the accuracy and robustness of the model for network anomaly detection; 2. The introduction of a novel network anomaly root cause analysis and localization model, which, combined with an optimized root cause likelihood assessment method and search scheme, significantly enhances the speed and accuracy of anomaly localization; 3. The design of an integrated decision support system that can automatically adjust protection strategies as network conditions change, achieving a high level of automation and intelligence in cybersecurity management. This work not only provides effective technical support for network security protection but also opens new avenues for future cybersecurity research.