Search

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. The diffusion of microplastics in the food supply chain is prompting public concern as their impact on human health is still largely unknown. The aim of this study was to qualitatively and quantitatively characterize microplastics in skim-milk powder samples (n = 16) from different European countries (n = 8) through Fourier-transform infrared microspectroscopy in attenuated total reflectance mode analysis. The present study highlights that the use of hot alkaline digestion has enabled the efficacious identification of microplastics in skim-milk powders used for cheesemaking across European countries. The adopted protocol allowed detection of 29 different types of polymeric matrices for a total of 536 plastic particles. The most abundant microplastics were polypropylene, polyethylene, polystyrene, and polyethylene terephthalate. Microplastics were found in skim-milk powders in 3 different shapes (fiber, sphere, and irregular fragments) and 6 different colors (black, blue, brown, fuchsia, green, and gray). Results demonstrate the presence of microplastics in all skim-milk powder samples, suggesting a general contamination. Results of the present study will help to evaluate the impact of microplastics intake on human health. [ABSTRACT FROM AUTHOR]

In this study, we developed a non-pneumatic tire (NPT) specifically designed for military vehicles, such as the Humvee. Our aim was to enhance performance on challenging terrains crucial for mission success. We selected a novel X-shaped polygonal structure for the spoke design owing its exceptional load-carrying capacity and durability. The equivalent modulus, which is the key factor in spoke design, was analytically derived and formulated. This calculated modulus served as a benchmark for achieving the desired vertical stiffness of the NPT. We manufactured a prototype NPT and evaluated its mechanical properties by using a polyurethane (PU) spoke structure. Multiple finite element models of the novel NPT, considering different parameters such as the number of spoke sections, thickness, and modulus of elasticity, were developed while maintaining a consistent equivalent modulus value. Dynamic finite element analysis was then performed to assess the rolling performance of the NPT. We found that the maximum dynamic stress at the spoke was 2.387 MPa, which is 0.398 times the allowable yield strength of the PU spoke. This finding confirms the durability of the novel NPT under dynamic usage in military vehicles, with promising implications for overcoming challenging terrains. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate linear measurements of the frontal sinus (FS) and sphenoid sinus (SS) for sex identification on cone beam computed tomography (CBCT) images. A comparative CBCT analysis was conducted on 200 full field of view (FOV) scans taken as part of routine dental investigations. Dimensions of the bilateral frontal and sphenoid sinuses were measured. Intra- and interobserver reliability were calculated. Independent t tests were used to compare the various parameters between sexes. Stepwise discriminant function analysis was used to determine sex. Additionally, the receiver operating characteristic (ROC) curve, area under the curve (AUC), sensitivity, and specificity were also determined. A p value < 0.05 was considered significant. A total of 200 CBCT scans were included in the study. The mean age (±SD) among males was 25.66 (±7.11) and that among females was 24.64 (±5.12). The ROC curve revealed that the right length of the frontal sinus showed the greatest accuracy in sex identification in comparison to other linear measurements of the FS and SS. The results of our study indicated that the equation obtained from stepwise discriminant function analysis can aid in sex determination with an accuracy of 76.5 %. Our findings support the sexual dimorphism of linear measurements of FS and SS. There was an improvement in the accuracy of sex prediction when the linear measurements of FS and SS were considered in combination rather than in isolation. The derived equation can be an adjunctive tool for sex identification for the representative population. [Display omitted] [ABSTRACT FROM AUTHOR]

The use of text readability indicators and sentiment factors to asset pricing is practical in investment risk management yield pursuit. We analyzed the polarity of textual sentiment of Management Discussion and Analysis (MD&A) in 10-Ks for firms in the U.S. by constructing Sentiment Entropy, a new sentiment indicator based on L&M Dictionary, which captures the same characteristics of positive and negative emotions from the perspective of polarity. The empirical results show that the polarity of sentiments increases investment risk measured by return volatility, which differs from the traditional one-way emotion indicator. We found through a mediator effect test that emotional polarity significantly increases investment risk by stimulating large changes in trading volume, which is consistent with conventional wisdom. Challenging conventional findings, we find that positive and negative emotions in disclosures may have homogenous effects on the market, providing inspirations for understanding the role of textual sentiment on investor behavior. [ABSTRACT FROM AUTHOR]

Commodities play a pivotal role in the global economy, and their dramatic price fluctuations can affect national security and global economic stability. This paper focus on the field of commodities, utilizing the Web of Science and CNKI databases. Based on the CiteSpace knowledge graph visualization analysis tool, it conducts a bibliometric analysis of the Chinese and English literature on commodity risk published from 2004 to 2023, aiming to reveal the keywords, emerging trends, major research institutions and their collaborative networks, trends in the issuance of articles, and major contributing countries, and authors. This analysis provides insights into the academic landscape of commodity risk and helps to identify future research directions. [ABSTRACT FROM AUTHOR]

Biometrics has permeated modern technology, particularly within mobile devices, integrating into daily life. From the 14th century, biometrics has served as a foundational method for security, aiding in criminal identification through physical characteristics records. Advancements in technology have propelled the evolution of precise and automated systems for analyzing facial, vocal, and ocular biometrics. Major tech companies have adopted biometrics as the primary mode of user authentication, catapulting biometric security into the forefront of cybersecurity discussions. Despite the promising future of biometrics, governments worldwide are grappling with the challenging task of regulating the handling of biometric data. Mismanagement of biometric data could pose significant security risks to both individuals and public safety. Therefore, it is crucial to implement comprehensive regulations and legislative reforms to safeguard privacy and security in the era of biometrics. Towards this goal, this paper contributes to the discourse by identifying security and privacy issues related to biometric data. It conducts a comprehensive analysis of current legal frameworks governing biometric data through the lens of the CIAAA (Confidentiality, Integrity, Availability, Authenticity, and Accountability) security framework. Additionally, it highlights emerging trends and outlines the implications for future policymaking and technological advancements in this field. [ABSTRACT FROM AUTHOR]

Implementing processes for traceability is required in various industries to assure product quality during manufacturing, provide evidence on required processing conditions or facilitate product recalls. Commonly, radio-frequency identification (RFID) or code recognition techniques (e.g. Data Matrix) are applied to track the flow of workpieces through a manufacturing system and link processing data accordingly. Although the analysis of tracking data is well-examined, we still see a gap in the research on the trade-off between data acquisition, data analytics and data quality. Here, we present a framework to increase the value of existing data by enabling data analytics while addressing common pitfalls and reducing the costs of data management. [ABSTRACT FROM AUTHOR]

The era of ubiquitously interconnected, intelligent, and self-driving automobiles approaches as contemporary vehicles undergo gradual enhancements in terms of security catalyzed by security analysis in various modules, algorithms, operating systems, and soft and hardware nodes. This work examines the application of a dynamic Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) in real-time using a Renault T520 Heavy-duty truck. It assesses the effectiveness of the security analysis procedure and its execution, focusing on the quality of the analysis concerning data derived from experiment sources. The validation of the security implementation adheres to a unified inbound and outbound. [ABSTRACT FROM AUTHOR]

Indonesian cuisine, especially Bakso, is one of the trendy foods that many travelers try to consume. Among the variations of Bakso, Bakso Malang is one of the most popular variants that is much talked about in social media. The present study analyzes the sentiment text mining of Bakso Malang business on post covid period from a natural language processing (NLP) social perspective. The data was taken from scraping to social media by "bakso malang" keywords. A total of 331 lexicon-filtering Tweets were gathered in the middle of March 2023. The corpus data were analyzed by valance aware dictionary and sentiment reasoner (Vader) sentiment analysis. K-means conducted the cluster of expressions. The result shows the variation of sentiments expressed in social media by anger, disgust, fear, joy, sadness, and surprise with 1.21%, 1.21%, 4.83%, 32.33%, 3.02%, and 57.40%, respectively. Many contents were discussed with keywords of "malang", "bakso", "enak", "makan", "foodfess2", "mau", and "pengen". Behavioural insight shows joy expression when discussing Bakso Malang. Further insights and recommendations are discussed in the paper. [ABSTRACT FROM AUTHOR]

The infrastructure of the Heliophysics discipline has promising components but with several missing gaps, drastically reducing research and development efficiency. Developing an online discovery and analysis software ecosystem will close several of these gaps. The five main focuses on this ecosystem should be Discovery, Implementation, Analysis, Reproducibility, and Sharing of results (DIARieS). In this paper, we give a detailed description of how the proposed software ecosystem should operate, and point out the large range of possible applications to benefit many disparate groups, such as researchers, operational staff, decision-makers, and educators. The infrastructure components and technological capabilities necessary for its completion are either currently available or in development, making such an ecosystem possible for the first time. One main focus of current infrastructure investments must be to adapt and connect these pieces together into a cohesive whole to increase our research and development efficiency. [ABSTRACT FROM AUTHOR]

Single Point Incremental Forming (SPIF) is an emerging materials processing technology. Owing to a number of merits, like reduced tooling, cycle time and cost and ability to produce sculptured profiles, in comparison to the traditional methods, the recent past has witnessed a growing interest in the application of SPIF to composite materials. This article authoritatively reviews the advancements in this area made since 2008. The review covers several aspects of the process including deformation characteristics, forming limits, failure modes forming forces, strain recovery, surface quality and analytical models. The current state of the technology and challenges are revealed, and many valuable findings are identified thereby setting guidelines for the process users. [ABSTRACT FROM AUTHOR]

Modern technologies have replaced many of the conventional managerial work areas. Blockchain, one of the newest head-turn technologies, is attempting to highlight this apparent tactic. Improvements in the supply chain and logistics are desperately needed. There is constant pressure on these sectors to develop and seek out better solutions because almost all businesses rely on them to function efficiently. Blockchain technology, with its decentralized and publicly accessible nature, has provided answers to many of the problems that industry stakeholders are currently considering. The traceability, transparency, and openness of BCT are highlighted in this study as ways in which it benefits the supply chain sector. The top nations with publications about blockchain technology include China, the United States, Singapore, India, and Sweden. The rapid advancement of technology has made it necessary to stay current with the latest developments to stay ahead of the game. The main involvement of this work is a methodical, cyclical assessment of research and reference analysis to improve application and offer a better knowledge of the trajectory of these developments. [ABSTRACT FROM AUTHOR]

In response to the problem of low classification efficiency in existing algorithms that only consider single modal features of music or video, the author proposes a music emotion classification algorithm based on multimodal deep learning. Firstly, a two-dimensional audio Convolutional neural network is designed, which takes the audio Mel spectrum as the input to learn the audio characteristics of music; Secondly, a video neural network was designed to learn the spatiotemporal characteristics of music videos; Adopting multimodal fusion technology, we fused audio and video features and designed a multimodal deep learning classification algorithm to classify music emotions. In response to the lack of labeled music video datasets, the author constructed a diverse music video dataset. Based on this dataset, experiments were conducted to verify the effectiveness of the proposed algorithm, and the impact of different optimizers on the performance of single mode classification models was compared and analyzed. The experimental results show that compared with a single modal sentiment classifier, a multimodal classifier can achieve the best classification performance. [ABSTRACT FROM AUTHOR]

With the advent of Internet information technology, data mining technology has been applied in various fields of China's social and industrial development, and has promoted the quality development of the industry [1]. Nowadays, people are widely influenced by Internet computer technology, and the application of computer technology has been indispensable in life, work and study. At the same time, data mining technology arises from Internet communication technology and is used as an important technical means of operation and development by various industries, especially the application of data mining technology in university software engineering teaching is becoming more and more widespread, but there are still many unavoidable problems that require scholars to pay more attention. The article analyzes and discusses the significance of applying data mining technology in software engineering [2] , and on this basis, the application path of data mining technology in software engineering database design is proposed, including: mining information, mining loopholes, mining execution records and open source. It is hoped that relevant technology users can efficiently apply data mining technology to make software engineering It is more reasonable and efficient, so that software engineering can be further developed. This paper presents the implementation and performance analysis of the teacher management decision-making system based on data mining, and introduces the implementation process of the system in detail. [ABSTRACT FROM AUTHOR]

The images help in the different processes where a visual interpretation of a scene is required, in this sense we find many applications where images are used to analyze, interpret and classify certain objects within the image, there are different types of images generated by different sensors, in this paper describes a method to analyze the behavior of the muscle, mainly of the knee, when performing rehabilitation exercises, coupled with an optical image where you can see the state of the muscle and the location, the method proposed as a super position between optical and thermal images, with the intention of being able to know the state of the optical image and to have the same image with information of the behavior of the temperature, the super position that we propose is to have as a base the optical image and on placing the thermal image, the results that are presented are oriented in proposing a new way of analyzing data with thermal information of the behavior of the muscles, by means of a complex image with optical and thermal information, the method is an aid in the treatment of muscular recovery, with the benefits of being scalable and applicable to other muscles and parts of the human body. [ABSTRACT FROM AUTHOR]

• A mathematical model of submerged floating tunnel(SFT) with elastic boundary and flexible joints under moving load was proposed. • The different cable support stiffness and joint flexural stiffness in SFT was considered in this model. • The factors affecting the maximum displacement and moment response of SFT were discussed. This paper proposed a new theoretical model for analyzing the submerged floating tunnel(SFT) under moving load, whose boundary at two ends was regarded as elastic constraint, and the anchor cable was simplified as the discrete spring support. Besides, the tunnel joint was simulated as a flexural spring to connect two adjacent tube segments. The modes and natural frequencies of the model were solved by transfer matrix method, and the dynamic response of the SFT under moving load was investigated by the modal superposition method, which was verified by comparing with the results obtained by the general FEM software. The influence of boundary conditions, dissimilarity of cable stiffness and joint flexural stiffness on the dynamic response of the SFT was analyzed. The difference of the response between the beam on elastic supports (BOES) and the beam on elastic foundation (BOEF) was compared. The results showed that the boundary conditions had little influence on the vibration response of SFT's intermediate spans, but had great influence on the displacement and bending moment of the side span. Besides, even in the case when the frequency differentials of the BOES and the BOEF were small (1.84%), the displacements of the two models also had significant difference. The disparity of the cable stiffness and joint flexural stiffness had great influence on the distribution of displacement and bending moment of SFT. [ABSTRACT FROM AUTHOR]

Meat qualities of free-range chicken (Xuan-Zhou) (XZ-FRC) are closely associated with slaughter age and directly influence the economic benefits of supplier and consumer's preference. Understanding of the relationship between meat qualities and ages will be of prime important to explore a better slaughter age of XZ-FRC. In this study, the quality traits of breast and thigh muscles from XZ-FRCs at 9 to 14 wk were analyzed to establish a relatively reliable method for selecting a better slaughter age. The results showed that the effects of slaughter ages on color (CIE L *, a * and b * values), shear force, centrifugal loss, and flavor of XZ-FRCs were significant (P < 0.05). There were greater differences in meat qualities, whatever breast or thigh muscles, between same or different ages. Eleven feature indexes used for colligation evaluation of slaughter age were selected by combining the quality characteristics and data analysis. The score of colligation evaluation for XZ-FRCs at 12 wk was higher than that at 9 and 14 wk, suggesting that the 12 wk was an optimal slaughter age. This work would provide a reference method that helps the producers of livestock and poultry to select a better slaughter age. [ABSTRACT FROM AUTHOR]

This paper investigates the knowledge system in Building Information Modeling (BIM) technology in Medical Construction Projects (MCPs). Specifically, it proposes knowledge graph analysis utilizing scient metric techniques and employs VOSviewer and CiteSpace software to analyze the consequence of relevant statistics from the literature concerning BIM medical construction projects (BIM-MCPs). In order to analyze the collaboration network, the analysis of the co-occurrence network, and the analysis of the co-citation network, 83 journal articles were gathered. The results obtained from the research indicate that (a) the intellectual framework of BIM-MCPs can be categorized into four distinct clusters. (b) The research focus in BIM-MCPs has evolved, including model development, frameworks, implementation, system integration, application throughout the entire lifecycle, emergency medical engineering project applications, and intelligent management of medical facilities and smart buildings. (c) Prospective directions for research are delineated in three distinct areas: enhancement of use for BIM Medical Facility Management (BIM-MFM), integration of BIM technology in sustainability assessment, and application of BIM technology in emergency medical engineering and smart medical construction. [ABSTRACT FROM AUTHOR]

Pharmaceuticals are a group of man-made commercial and synthetic products and an important mainstay of modern society. However, their presence in aquatic environments is identified as a potential risk, causing harm to living things even at low concentrations ranging from ng L−1 to μg L−1. Due to their pseudo-persistence, bio-accumulation, and low elimination rate by conventional treatments, the majority of these xenobiotic contaminants and their metabolites are routinely released into the environment by sewage treatment plants and last longer in an aquatic environment. This review aims to provide an extensive report and a current state of knowledge concerning various classes of pharmaceutical compounds (PCs). It also discusses the occurrence, the analytical methods for the determination of PC micro-pollutants, and the remediation methods noted in recent literature focusing on the most recent advances. In this context, this review highlights the statistical data regarding the status of PCs in detection, analysis, and removal methods in aqueous media. In addition, this review provides an overview of the conventional and advanced technologies for the removal of PCs from water sources, offering critical comments, and discussing the challenges related to improving existing technologies for sustainable and eco-friendly management of water resources. It likewise emphasizes the importance of the combination of different treatment methods to reach up to 100% PC removal. Finally, gaps in the literature were found, and recommendations for further research were also cited in accordance with this paper. To the best of our knowledge, this review paper is among the rare works that deal with both: i) the analysis of large varieties of PCs in different water matrices, ii) the removal and identification of target analytes and their by-products generated during the elimination processes, and iii) the possible degradation pathways proposed by different studies. [Display omitted] • Occurrence of PCs in different water matrices are discussed. • Analytical methods for identifying PCs in waters are elucidated critically. • Advanced treatment methods for PCs removal are systematically reviewed. • Performed analytical techniques for by-products identification are described. • Possible mechanistic ways of PCs elimination by AOPs are proposed. [ABSTRACT FROM AUTHOR]

Objective: To investigate the cognitive function and nutritional status of elderly patients with gastric cancer during perioperative period, and to analyze their correlation. Methods: Aged patients undergoing gastric cancer surgery in The Affiliated Cancer Hospital of Shandong First Medical University from March to October 2021 were selected as the subjects of this study. The monitoring data of cognitive function and nutritional status were retrospectively analyzed from 1 to 3 days before surgery, 1 and 3 days after surgery, 7 days after surgery (before discharge) and 30 days after surgery to analyze the correlation between cognitive function and nutritional status in elderly patients with gastric cancer. Results: the incidence of mild cognitive impairment in elderly patients with gastric cancer was 52.43 %, the visual space of the two groups' (mild cognitive impairment) ability of execution, name, attention, language, abstract thinking, delayed memory and cognitive function scores were lower than 1 set of directional force (cognitive function in normal group), statistically significant difference (P < 0.05). The nutritional status of elderly patients with gastric cancer was lower than that of healthy elderly group at the same period (P < 0.05). The scores of visual spatial executive function, name, attention, delayed memory, orientation and total score of cognitive function in elderly gastric cancer patients were positively correlated with nutritional status (P < 0.05). Conclusions: The cognitive function and nutritional status of elderly patients with gastric cancer are both in a low state during treatment and a higher level of cognitive function can help patients maintain a more correct nutritional cognition, and the nutritional status of patients will be relatively better. There is a positive correlation between cognitive function and nutritional status in elderly patients with gastric cancer, which should be paid attention to in the treatment. • This study delves into the correlation between perioperative cognitive function and nutritional status in elderly patients with gastric cancer. • Moreover, their nutritional status is notably lower compared to healthy elderly individuals. • These insights underscore the importance of addressing cognitive and nutritional aspects in the care of elderly gastric cancer patients. [ABSTRACT FROM AUTHOR]

[Display omitted] • Advanced oxidation of NPs can be monitored by turbidity, TOC, COD, TEM and FTIR. • Turbidity and TEM analyses allow determining the conversion of the solids. • COD is an exceptional tool for evaluating the mineralization degree of NPs. • Morphology and particle size can be accurately determined by TEM and AFM. • Carbonyl index serves as a metric to monitor the oxidation progress on NPs surface. There is a growing interest in developing new targeted degradation technologies for the removal of micro- and nanoplastics (NPs) in water, corresponding to increased public concerns regarding their potential negative impacts on urban water systems, and consequently on human life quality. Recently, Advanced Oxidation Processes (AOPs) have been proposed as promising treatment alternatives for effective degradation of NPs in water. However, the selection of appropriate analytical methods for monitoring these oxidation tests remains a challenge. Herein, the feasibility of different characterization strategies for monitoring the evolution of NPs in water upon oxidation tests was systematically studied using polystyrene (PS) NPs of different particle sizes (D 0 = 140, 252, 460, and 909 nm) as model plastic pollutants. To quantify NPs in water, Total Organic Carbon (TOC), Chemical Oxygen Demand (COD) and turbidity measurements were assessed. Moreover, turbidity was correlated to the particle size and PS NPs concentration by developing a response surface. Among the analytical techniques employed to characterize the solid particles, transmission electronic microscopy (TEM) was used to evaluate morphology and particle size. Alternatively, the viability of Dynamic Light Scattering (DLS), Nanoparticle Tracking Analysis (NTA) and Atomic Force Microscopy (AFM) to determine particle size is discussed. Chemical surface modifications were explored by Fourier-Transform Infrared Spectroscopy (FTIR). As a proof of concept, the degradation of PS NPs in water upon photo-Fenton oxidation was investigated at ambient conditions and fully characterized using the mentioned techniques. [ABSTRACT FROM AUTHOR]

The aim of this research is to analyze the impact of mental health on the academic performance of junior and senior students studying in a university setting during the 2022–2023 academic year, in the post-COVID period. The study was conducted in Beijing, China, with the participation of 600 students, including 300 first-year students and 300 fifth-year students. DASS (Depression, Anxiety, and Stress Scale) and PHQ-9 (Patient Health Questionnaire-9) questionnaires were employed to measure mental health. The DASS assesses symptoms of depression, anxiety, and stress, while the PHQ-9 specifically evaluates depression severity. Academic performance was evaluated using a 12-point scale, which incorporated various criteria such as exam scores, coursework performance, and participation in extracurricular activities. The research was conducted across five faculties of the university from 2022 to 2023. In the study, fifth-year students demonstrated a higher level of mental health compared to first-year students, with an average DASS score of 27.1 and 24.2, respectively. Interestingly, despite this, first-year students achieved higher academic performance indicators, with an average score of 8.2 compared to 9.8 in fifth-year students. Correlation analysis revealed significant associations between stress, depression, and anxiety levels with academic performance (stress: r = −0.25, p < 0.001; depression: r = −0.20, p = 0.003; anxiety: r = −0.18, p = 0.008). These findings highlight the critical importance of addressing students' mental well-being, particularly in later academic years. Recommendations include implementing support programs and developing online resources for students. • Fifth-year students exhibited higher levels of mental health compared to first-year students. • Despite better mental health, fifth-year students showed lower academic performance than first-year students. • No significant gender differences in mental health were observed among first-year students • It was indicated a significant association between stress, depression, and anxiety levels with academic performance. • Regression analysis identified mental health as a potential predictor of academic performance. [ABSTRACT FROM AUTHOR]

In this study, the process conditions, physicochemical properties, structural composition and activity of polysaccharides isolated from leechee peel (LPP) by ultrasound-assisted extraction (UAE) with enzyme and alkali solution extraction (ASE) were compared. The results showed that the total sugar content of LPP extracted by UAE accounted for 75.65 %, which was significantly higher than that extracted by alkali solution. The optimum conditions were as follows: extraction temperature of 68.78 ℃, ultrasonic enzymolysis time of 39.68 min, pectinase dosage of 4.03 %, solid–liquid ratio of 1:30 g/mL, and ultrasonic power of 360 W. The antioxidant activities and structure of leechee peel polysaccharide (LPP) prepared under different conditions were compared. It was found that UAE-LPP was an α-type polysaccharide containing 15.83 % uronic acid. Moreover, LPP extracted by UAE showed strong activity in anti-lipid peroxidation and reducing ability. Ultrasound-assisted enzymatic method is an effective means to improve the content and activity of natural plant polysaccharides, and this method has the advantages of short time-consuming, simple process and easy operation, which can greatly improve the utilization rate of polysaccharides and lay a theoretical and scientific basis for the development and utilization of LPP. [ABSTRACT FROM AUTHOR]

• Bird impact tests are performed on simple manufactured composite plates. • An FE impact model is constructed and simulated using the impact test results. • The results of the impact tests show good agreement with the FE model. • This method can increase the pace and decrease the cost of aircraft development. Collisions of aircraft with foreign objects are very common phenomena, so they must be anticipated, and their consequences must be individually checked. Since these incidents can result in severe engine damage and the engine is the aircraft's only thrust-providing system, the effect of bird strikes on engine air intakes and similar systems must be investigated and mitigated with special attention. Principally for tilt-rotor convertible aircraft, this effect is crucial in terms of operational safety, flight mechanics and overall aircraft control. The objective of this work is to demonstrate the durability of the composite air intake for a new tilt-rotor aircraft, which is experimentally verified on components of basic geometric shapes such as plate and cylindrical section. Based on computer-supported simulations, which are correlated with the basic tests, the consequences of the impact on the real air-duct were analysed and potentially dangerous regions were found. Sizing and optimisation was carried out and a design improvement proposal was developed. [ABSTRACT FROM AUTHOR]

The open-source HYPEtools R package aims to address current challenges in hydrological modeling and data analysis by providing a suite of tools for data management, visualization, interpretation, and exploration. Originally developed as a supporting toolbox for the HYPE hydrological model, HYPEtools has since evolved into a standalone package with features ranging from diverse aggregation and evaluation routines to interactive apps and mapping. Specifically, functions are included for data manipulation and conversion, data summarization, plotting and mapping, and interactive data exploration. Two case studies are provided to highlight how HYPEtools can simplify hydrological workflows and analyses both independently of and within HYPE modeling contexts. First, HYPEtools is used for data exploration and mapping of water transfers in South Africa. Then, a case study for the Baltic Sea region of a pan-European HYPE model setup illustrates how HYPEtools can assist model calibration and validation. • HYPEtools is an R package for analysis and visualization of hydrological data. • HYPEtools supports development & calibration of hydrological models such as HYPE. • The package includes interactive features for mapping, plotting, & data exploration. • Interactive apps allow for evaluation of model outputs & data across large domains. • Case studies illustrate how HYPEtools can simplify typical workflows and analyses. [ABSTRACT FROM AUTHOR]

The ongoing problem of inconsistency in the stages of system development encourages the development and elaboration of approaches and methods aimed at eliminating the causes of their occurrence. According to the research community, one of the most promising approaches is to involve users in the development process. Six directions in the implementation of the approach to user involvement in the system development process were formed and analyzed in the article. The analysis of the applicability of such approaches and methods to the task of involving the user in the process of developing an information data model is carried out. The problems of this direction were highlighted. To solve these problems, the sensemaking approach principles which is a development of the methodology for sensemaking proposed by Brenda Dervin, are proposed to use. On the basis of the proposed sensemaking approach, the implementation of the proposed approach is shown with the help of the development of the method, a method for constructing the specification of the information model was developed. The steps of the method are represented by methods that implement the idea of involving the user in the development process by analyzing his work with elements of documentation support. [ABSTRACT FROM AUTHOR]

The use of computers has brought convenience to people's work and life, but also caused network security problems. How to identify network security risks and ensure users' personal privacy has become very important. With the continuous development of the current era, social science and economy have achieved rapid development. The integration of Internet intelligence has also brought efficiency and convenience to people's life. However, under this good development background, there are also some potential security problems in computer networks, it is a great threat to the security of the computer network. It is very important to study the development of computer network security technology. based on this, the article will be the computer network security technology of the influencing factors and preventive measures. this requires us to improve our understanding of network information security technology. This paper aims at how to effectively carry out network security technology and seek scientific prevention strategies, hoping to provide strong support for Internet security. [ABSTRACT FROM AUTHOR]

The worldwide patent database remains one of the least utilized resources in engineering design practice. Analyzing individual patent documents may be particularly challenging. Emerging computational approaches may soon enable us to automatically remodel the information content of patent documents to make them easier to read. For these approaches to be effective in practice, there is an urgent need to understand how design engineers process multimedia documents and how best to present information to them. To this end, we model the cognitive processes involved in patent analysis in terms of two models: the TRACE model of goal-directed document processing, and the Cognitive Theory of Multimedia learning (CTML). These models provide a cognitive basis for understanding the difficulties that designers may face during patent analysis. It also facilitates the identification and testing of information design principles that can be used to remodel patent documents to make them easier to search and comprehend. By testing these principles, our goal is to establish requirements for a new generation of patent analytics tools that are consistent with the ways that engineering designers think. This work introduces a new interdisciplinary research agenda, bringing together research about engineering design, patent analytics, multimedia learning, document search, and information design. [ABSTRACT FROM AUTHOR]

The proper selection of a demand forecasting method is directly linked to the success of supply chain management (SCM). However, today's manufacturing companies are confronted with uncertain and dynamic markets. Consequently, classical statistical methods are not always appropriate for accurate and reliable forecasting. Algorithms of Artificial intelligence (AI) are currently used to improve statistical methods. Existing literature only gives a very general overview of the AI methods used in combination with demand forecasting. This paper provides an analysis of the AI methods published in the last five years (2017-2021). Furthermore, a classification is presented by clustering the AI methods in order to define the trend of the methods applied. Finally, a classification of the different AI methods according to the dimensionality of data, volume of data, and time horizon of the forecast is presented. The goal is to support the selection of the appropriate AI method to optimize demand forecasting. [ABSTRACT FROM AUTHOR]

Global efforts are focused on discussing effective measures for minimizing the impact of COVID-19 on global community. It is clear that the ongoing pandemic of this virus caused an immense threat to public health and economic development. Mathematical models with real data simulations are powerful tools that can identify key factors of pandemic and improve control or mitigation strategies. Compared with integer-order and left-hand side fractional models, two-side fractional models can better capture the state of pandemic spreading. In this paper, two-side fractional models are first proposed to qualitative and quantitative analysis of the COVID-19 pandemic. A basic framework are given for the prediction and analysis of infectious diseases by these types of models. By means of asymptotic stability analysis of disease-free and endemic equilibrium points, basic reproduction number R 0 can be obtained, which is helpful for estimating the severity of an outbreak qualitatively. Sensitivity analysis of R 0 is performed to identify and rank key epidemiological parameters. Based on the real data of the United States, numerical tests reveal that the model with both left-hand side fractional derivative and right-hand side fractional integral terms has a better forecast ability for the epidemic trend in the next ten days. Our extensive computational results also quantitatively reveal that non-pharmaceutical interventions, such as isolation, stay at home, strict control of social distancing, and rapid testing can play an important role in preventing the pandemic of the disease. Thus, the two-side fractional models are proposed in this paper can successfully capture the change rule of COVID-19, which provide a strong tool for understanding and analyzing the trend of the outbreak. [ABSTRACT FROM AUTHOR]

Building integrated photovoltaics (BIPV) typically operate under different conditions compared to standard PV due to non-optimal orientations, poor ventilation, or additional losses in coloured modules. In this work, a test site for BIPV curtain wall façades was constructed at the Technical University of Denmark (DTU) and monitored for a full year. This data is used to analyze various performance aspects, including operating temperatures, power optimizer efficiencies, and DC performance ratios. In addition, parts of the system are modelled in SAM and results of this simulation are used to identify errors. In addition to this analysis, the underlying data set is published for researchers to use in model validation. [ABSTRACT FROM AUTHOR]

Nowadays, there is a continuous increase in global energy demand, due to population growth and economic activities. One of the crucial concerns is that buildings contribute to almost 40% of the world's energy usage and greenhouse gas emissions. Enhancing the building envelope by integrating thermal insulation layers (TILs), phase change materials (PCMs), or a combination of both is a promising method to enhance energy efficiency and reduce the peak energy loads in buildings. The current research utilizes simulations to reduce the heat transfer of external wall construction, with a focus on three distinct strategies. The first strategy focuses on the effect of various materials and the arrangement of TILs, while the second strategy analyzes the consequences of different materials and the positioning of PCMs. The third strategy explores the synergistic effects of integrating TILs with PCMs to enhance the thermal efficiency of the standard residential building models, following the recommendations outlined in the NIST Technical Note 1765. The strategies were tested by evaluating the heating and cooling energy loads of a building in Kansas City throughout the year. The results show that the maximum thermal energy savings that may be achieved by applying the first, second, and third strategies in the building of external walls are 16.4%, 33.02%, and 37.88%, respectively. The corresponding cooling energy savings for the three strategies are as follows: 9.64%, 55.48%, and 26.749%. [ABSTRACT FROM AUTHOR]

Antibiotic resistance is increasingly seen as a future concern, but antibiotics are still commonly used in animals, leading to their accumulation in humans through the food chain and posing health risks. The development of nanomaterials has opened up possibilities for creating new sensing strategies to detect antibiotic residues, resulting in the emergence of innovative nanobiosensors with different benefits like rapidity, simplicity, accuracy, sensitivity, specificity, and precision. Therefore, this comprehensive review provides pertinent and current insights into nanomaterials-based electrochemical/optical sensors for the detection of antibitic residues (ANBr) across milk and dairy products. Here, we first discuss the commonly used ANBs in real products, the significance of ANBr, and also their binding/biological properties. Then, we provide an overview of the role of using different nanomaterials on the development of advanced nanobiosensors like fluorescence-based, colorimetric, surface-enhanced Raman scattering, surface plasmon resonance, and several important electrochemical nanobiosensors relying on different kinds of electrodes. The enhancement of ANB electrochemical behavior for detection is also outlined, along with a concise overview of the utilization of (bio)recognition units. Ultimately, this paper offers a perspective on the future concepts of this research field and commercialized nanomaterial-based sensors to help upgrade the sensing techniques for ANBr in dairy products. [Display omitted] • Food safety, security and quality are significant issues in today's world. • Antibiotics are known as one of the persistent organic pollutants in dairy products. • Using of the bio−/nano-sensors to detect antibiotics has progressed. • Different nanosensors for detection of antibiotics in dairy products are discussed. [ABSTRACT FROM AUTHOR]

Chlordecone is an organochlorine pesticide used from 1972 to 1993 in the French West Indies. Its extensive use and high persistence in soils induced massive contamination of the environment and of the food chain, especially in cattle through contaminated soil ingestion. To ensure suitability for consumption of bovine meat, monitoring plans are set up based on perirenal fat concentrations after slaughtering. In the present study, we have investigated an in-vivo monitoring approach by measuring chlordecone levels in serum samples. For this purpose, a sensitive high-performance liquid-chromatography-tandem mass spectrometry (HPLC-MS/MS) method following a QuEChERS extraction method was successfully optimized and validated, reaching a limit of quantification of 0.05 ng g−1 fresh weight. This method was applied to 121 serum samples collected from bovines originating from contaminated areas of Martinique and Guadeloupe. Chlordecone was detected in 88% of the samples, and quantified in 77% of the samples, with concentrations ranging from 0.05 to 22 ng g−1. Perirenal fat, liver, and muscle were also sampled on the same animals and the measured concentrations of chlordecone were statistically correlated to the levels determined in serum. Mean concentration ratios of 6.5 for fat/serum, 27.5 for liver/serum, and 3.3 for muscle/serum were calculated, meaning that chlordecone was not only distribute in fat (as expected), muscle and liver, but also in serum. Good correlations were found to allow prediction of chlordecone concentrations in muscle based on concentrations measured in serum. This study opens the door to possible pre-control of bovines before slaughter. In cases of probable non-compliance with maximum residue levels (MRLs), farm management could proceed to allow for depuration under controlled conditions. This would have a strong impact on both economic and food safety management measures. [Display omitted] • A QuEChERS protocol was optimized and validated for analyzing chlordecone in bovine serum. • The method was applied to 121 bovine serum samples from the French West Indies. • Serum chlordecone levels were correlated with levels found in fat, muscle and liver. • The ratios allow an in-vivo monitoring and pre-control of bovines before slaughter. • This approach is relevant to assess the contamination level of living farm animals. [ABSTRACT FROM AUTHOR]

Although the implementation of the digital sharing economy provides many positive values, there are still few SMEs that adopt the digital sharing economy. It is inseparable from the readiness of SMEs to adopt the digital sharing economy business model. Therefore, it is necessary to understand the readiness for change in SMEs in helping SMEs adopt the digital sharing economy. The literature review states a need for a strategy and guidelines in implementing the digital sharing economy for SMEs. This study is also supported by other studies in various countries, illustrating that the adoption of the digital sharing economy is not running smoothly. This study aims to understand the current conditions in the field regarding the readiness to change SMEs in adopting the digital sharing economy business model. This preliminary study used a snowballing technique to conduct semi-structured interviews with SME owners in the household consumption sector in Indonesia. The preliminary study's findings include the readiness of SMEs to change in adopting the digital sharing economy business model, elements related to readiness on the digital sharing economy, and the need for guidance in adopting the digital sharing economy. This study concludes that the readiness of SMEs to switch to a digital sharing business model is still limited. Currently, there are no guidelines that direct the implementation of the digital sharing economy business model. Therefore, preparedness and guidelines are needed as a strategic step to help SMEs prepare to switch to a sharing economy business model. [ABSTRACT FROM AUTHOR]

Objective: Knee Image Digital Analysis (KIDA) is standardized radiographic analysis software for measuring osteoarthritis (OA) characteristics. It was validated in mild OA, but used for severe OA as well. The current goal was to evaluate the performance of KIDA in severe OA.Design: Of 103 patients, standardized radiographs were performed before and one and 2 years after treatment for severe OA. All radiographs were evaluated on subchondral bone density, joint space width (JSW), osteophytes, eminence height, and joint angle, twice within years by the same observer. Part of the radiographs were randomly selected for reevaluation twice within 1 month and evaluation by another observer. The intraclass correlation coefficient (ICC), smallest detectable difference (SDD) and coefficient of variation (CV) were calculated; the SDD and CV were compared to those in mild OA. The relation of severity with KIDA parameters and with observer differences was calculated with linear regression.Results: Intra-observer ICCs were higher in the 98 severe radiographs reanalyzed within 1 month (all >0.8) than the 293 reanalyzed within years (all >0.5; most >0.8) and than inter-observer ICCs (all >0.7). SDDs and CVs were smaller when reanalyzed within a month and comparable to those in mild OA. Some parameters showed bias between readings. Severity showed significant relation with osteophytes and JSW parameters, and with the observer variation in these parameters (all P < 0.04).Conclusions: KIDA is a well-performing tool also for severe OA. In order to decrease variability and SDDs, images should be analyzed in a limited time frame and randomized order. [ABSTRACT FROM AUTHOR]

The article discusses the following competencies, should have a specialist in economic security. Today, it is important for an economic security specialist to develop professional skills in the field of transfer pricing risk management. In this regard, analytical competences are of particular relevance. The proposed competencies form the knowledge and skills of future specialists. Among the necessary requirements for specialists are established: the ability to apply analytical knowledge in various fields; identify, suppress and investigate offenses related to the economic sphere; collect, document and analyze information related to economic security. The development of these skills allows, among other things, to strengthen control over the establishment of transfer prices and to make the economic security of the state more stable. [ABSTRACT FROM AUTHOR]

The operation of manufacturing systems is increasingly accompanied by data-driven continuous improvement processes and product traceability is required. Commonly, radio-frequency identification (RFID) technology is applied to track the flow of a uniquely-identifiable workpiece along various stations or waypoints within a factory. Based on an automotive use case, this paper describes how to analyze the resulting traceability data in order to identify several properties of a manufacturing system. The acquired knowledge can support performance evaluation and facilitate model building for material flow simulation as a foundation for digital twins and cyber-physical production systems. [ABSTRACT FROM AUTHOR]

The concern plate's behavior was the growth of the extrema positive tensors of both mean (hydrostatic) and deviatoric moments. Systematic consideration of the limits and growth responses of the mean and deviatoric tensors at the extrema positive sections were calculated. It was found that the use of the shorter dimension for the moment normalization in both directions preserves the directional influence of the dimensions and isolates clearly the hydrostatic phenomenon. Moreover, the presence of non-identical boundary conditions in the plate on two perpendicular directions was found to result in the deviation of the two-way action of behavior. Consequently, a novel understanding of the two-way plate action has been achieved. In such understanding, the pure two-way action accompanies the occurrence of a hydrostatic curvature (or moment) at a plate's extrema positive point. At the later state, the hydrostatic and deviatoric tensor components would be equal. Based on linear and multi-Stage regression analysis, the growth of the normalized hydrostatic moment component (C HMc) with the geometrical aspect has been deemed to follow a linear regression model, as well as the growth of the deviatoric moment component with the geometrical aspect ratio, has been deemed to follow the sigmoidal, Morgan-Mercer-Flodin (MMF) nonlinear regression model. [ABSTRACT FROM AUTHOR]

This paper introduces a new model to identify collective abnormal human behaviors from large pedestrian data in smart cities. To accurately solve the problem, several algorithms have been proposed in this paper. These can be split into two categories. First, algorithms based on data mining and knowledge discovery, which study the different correlation among human behavioral data, and identify the collective abnormal human behavior from knowledge extracted. Secondly, algorithms exploring convolution deep neural networks, which learn different features of historical data to determine the collective abnormal human behaviors. Experiments on an actual human behaviors database have been carried out to demonstrate the usefulness of the proposed algorithms. The results show that the deep learning solution outperforms both data mining as well as the state-of-the-art solutions in terms of runtime and accuracy performance. In particular, for large datasets, the accuracy of the deep learning solution reaches 88%, however other solutions do not exceed 81%. Additionally, the runtime of the deep learning solution is below 50 seconds, whereas other solutions need more than 80 seconds for analyzing the same database. • We formally define the group trajectory outlier detection problem. • We propose several solutions based on data mining and deep learning models to identify outliers. • We evaluate various algorithms using human-behavioral databases to identify the performance of two different models. [ABSTRACT FROM AUTHOR]

The sequence of the DNA contains coding and non-coding regions. The role of the non-coding regions is not known and is hypothesized to maintain the structure of the DNA. This study aimed to investigate the structure of the non-coding sequences in honey bees utilizing bioinformatics. The non-coding sequences of the mtDNA of three honey bee species Apis dorosata , Apis florea , Apis cerana , and ten subspecies of Apis mellifera were investigated. Different techniques were utilized to explore the non-coding regions of these bees including sequence analysis, phylogenetic relationships, enzymatic digestion, and statistical tests. Variations in size and sequences of nucleotides were detected in the studied species and subspecies, but with the same nucleotide abundance (i.e. nucleotides A were more than T and nucleotides G were less than C). The phylogenetic tree based on the non-coding regions was partially similar to the known phylogenetic relationships between these bees. The enzymatic digestion using four restriction enzymes confirmed the results of the phylogenetic relationships. The statistical analysis based on numerical codes for nucleotides showed the absence of significant variations between the studied bees in their sequences in a similar way to results of neutrality tests. This study suggests that the non-coding regions have the same functional role in all the studied bees regardless of the number of nucleotides, and not just to maintain the structure of the DNA. This is approximately the first study to shade lights on the non-coding regions of the mtDNA of honey bees. [ABSTRACT FROM AUTHOR]

This paper concerns the modelling of the simulation structure for the torsional stiffness evaluation of a Formula SAE vehicle frame and presents some insights into the modelling of the problem using a beam-like model together with geometrical simplifications. The insights consist of presenting utilized simplifications and assessing their influence on torsional stiffness. The simulation model, consisting of the vehicle frame, suspension and testing fixture of complex shapes, requires certain simplifications to achieve a sustainable modelling and computational time while getting reasonable results. The influence of simplifications such as a linear isotropic material model for carbon-fibre and geometrical simplifications of the suspension, frame and testing fixture are discussed. The simulation model was developed in such a way to allow the easy change of cross-sections and geometry during the vehicle design process. The simulation model was also experimentally validated. A comparison of the results from the simulation and testing showed an acceptable difference (up to 10%), thus a small influence of the material and geometrical simplifications on the results of torsional stiffness is considered. [ABSTRACT FROM AUTHOR]

Linguistic mitigation is a pragmatic phenomenon that has been profusely treated in the literature, and yet there are few studies dedicated exclusively to offering methodological criteria for its recognition and analysis (but see Albelda, 2010 and Albelda et al., 2014). Consequently, the researcher must infer the methods for its recognition through problems arising during the analysis or through the examples and reflections offered by the authors who have addressed the issue. The purpose of this article is twofold. Firstly, to present some of the main keys for the recognition of mitigation presented by the bibliography, such as the catalogues of mitigation devices, the context, the face and the illocutionary force. Secondly, this text problematizes Villalba's (2018) proposal for the identification of mitigation based on three tests: the absence test, the commutation test, and the solidarity test. In order to do that, we apply this test to a corpus of examples of mitigation extracted from research articles that study this phenomenon. After that, we reflect on the problems the tests or the examples may present to the researcher. The proposal, in conjunction with the traditional criteria, aims to offer strategies in order to facilitate the analysis of mitigation. • Context, facework and illocutionary force are useful to identify mitigation. • A systematic methodology can contribute to homogenize the analysis of mitigation. • Absence test is based on the elimination of the element that can encode mitigation. • Commutation test needs to offer alternative forms to those presented in the utterance. • Mitigation is frequently displayed in one speech act by more than just one device [ABSTRACT FROM AUTHOR]

The novel Coronavirus disease (COVID-19) in Wuhan, China, became a pandemic after its outbreak in January 2020. Countries one after the other are witnessing peak effects of the disease, and they need to learn from the experience of others already affected or peaked countries. Thus, this paper aims to analyse the effect of the COVID-19 pandemic on different countries through COVID-19 cases, resulting in deaths and recoveries. This study analyses quantitatively the lethal effects of the pandemic through the study of infections, deaths, and recoveries on the 13 most-affected COVID-19 countries as of 1 s t June. The daily change in cases, deaths, and recoveries for all the 13 countries were considered. Combined analysis for comparison and separate analysis for the detailed study were both taken for every country. All the graphs were made in RStudio using the R programming language, as it is best for statistical analysis. The casual and ignorant behaviour of people is a major reason for such a large scale spread of the coronavirus. The government of every country should be strict as well as considerate to all sections of people while making policies. There is no room for mistakes, as one wrong decision or one delayed decision can worsen the situation. However, some countries which were once the epicentre of this pandemic are now corona-free, proving that this global threat can be tackled and we should all keep our morale high. The coronavirus disease is not any ordinary viral infection; it has become a pandemic as it has an impact on health, mortality, economy and social well being of the entire world. Qualitative and Quantitative analysis of the statistics related to COVID-19 in different countries is done based on their officials' data. The primary objective of this analysis is to learn about the relationships of various countries in containing the spread of COVID-19 and the various factors such as government policies, the cooperation of people, economy, and tourism. • Analysis of COVID-19 pandemic on 12 most affected countries including China. • Study of the performance curves of various countries in fighting this pandemic. • Analyses the effectiveness of lockdown and to know the further implementations that can be done accordingly. • Learning the right steps to contain the infection by not repeating the mistakes of other countries. [ABSTRACT FROM AUTHOR]

The wire EDM process can be evaluated according to various criteria. In addition to the generated component quality, the cutting rate is a key performance index for the main cut. Process stability is another evaluation factor. It is defined by a resulting constant feed rate during machining and a low probability of occurrence of wire breakages. In several studies, the discharge distribution has been analyzed to predict a wire breakage. Due to the high discharge frequency, the analysis of the electrical signals has been conducted only for very short time periods. A quantitative evaluation of the continuous wire EDM process could therefore not be carried out. By implementing a real time process monitoring system, the detection of single discharges and the allocation to normal and abnormal discharges can be conducted. By using a FPGA (Field Programmable Gate Array), the characteristic discharge parameters can be analyzed in real time. The monitoring system stores only characteristic numbers and thus a continuous process can be recorded without generating a large data volume. [ABSTRACT FROM AUTHOR]

Efficient response to machine failures is crucial for improving machine availability. In practice, the number of alarms occurring during the operation of manufacturing systems could overwhelm even experienced machine operators. Manufacturing execution systems (MES) therefore often allow for alarm data acquisition as well as its filtering and aggregation to reduce the occurring information overload. However, manual configuration of these systems may be cumbersome and time-consuming. Hence, we propose an event-discrete method for temporal alarm data analysis to support the configuration process. [ABSTRACT FROM AUTHOR]

Human workers face major challenges with the increasing complexity in modern production environments. On the shop floor, a wide variety of assisting systems support them in their daily tasks. However, this requires profound knowledge of the underlying tasks and processes to implement highly effective assistive systems which are well accepted by the workforce. This paper describes an approach for analyzing assembly tasks executed by human workers. The analysis combines a manual approach using video and an automatic approach based on sensor data. Further, we use the General Assembly Task Model (GATM) to structure assembly tasks in four phases. This helps us to gain a deeper understanding of manual tasks and it further helps to identify in which situations of an assembly process the workers need assistance and choose the right measure to support them. [ABSTRACT FROM AUTHOR]

Nonlocal differential and integral operators with fractional order and fractal dimension have been recently introduced and appear to be powerful mathematical tools to model complex real world problems that could not be modeled with classical and nonlocal differential and integral operators with single order. To stress further possible application of such operators, we consider in this work an advection-dispersion model, where the velocity is considered to be 1. We consider three cases of the models, when the kernels are power law, exponential decay law and the generalized Mittag-Leffler kernel. For each case, we present a detailed analysis including, numerical solution, stability analysis and error analysis. We present some numerical simulation. [ABSTRACT FROM AUTHOR]

A novel voltammetric method was developed for the sensitive determination of chlorogenic acid (CGA) using a glassy carbon electrode (GCE) modified with niobium nanoparticles (NbNPs) and multiwalled carbon nanotubes (CNTs). The analytical techniques such as energy dispersive X-ray analysis (EDX), scanning electron microscopy (SEM) and X-ray diffraction spectroscopy were used for characterizing electrode material. The proposed voltammetric platform exhibited a highly improved redox couple for CGA. The peak separations (ΔE p) for CGA were 27 mV, 10 mV and 0 mV on the surface of unmodified GCE, CNTs/GCE and NbNPs/CNTs/GCE, respectively. Such a remarkable decrease in the value of ΔE p at NbNPs/CNTs/GCE showed that the electrode process of CGA has been accelerated at the proposed platform. In addition, a potential difference (ΔE p) of 0 V observed at the surface of proposed electrochemical platform was a clear indication of the occurrence of a symmetric voltammogram which could be attributed to a fully surface behavior of CGA. In addition, the current responses of CGA versus concentrations were linear in the range of 2.0 × 10−9 ~ 2.0 × 10−6 M with a detection limit of 8.2 × 10−10 M. Sensitive detection of CGA in samples is of importance for both scientific and therapeutic reasons due to its potential use for the treatment in many diseases. Therefore, the proposed voltammetric method at NbNPs/CNTs/GCE was applied to food samples. [ABSTRACT FROM AUTHOR]

Electrocardiography is the principle of recording the electrical activity of the heart over finite duration of time. It is one of the most practiced methods to detect abnormalities in the heart's electrical, arterial and muscular systems. Electrocardiogram is the voltage versus time graph obtained from the Electrocardiography machine. Automated analysis makes detection of Electrocardiogram findings and consequently determination of pathological abnormalities without human intervention possible. To achieve this goal we process digital Electrocardiograms to quantify the amplitudes and durations of waves and intervals, that is necessary for the automated analysis to find the pathological abnormalities. The proposed method processes Electrocardiograms and localizes the P, Q, R, S and T waves, subsequently it measures the PR, QT and ST intervals and segments. [ABSTRACT FROM AUTHOR]