bluuebunny/crossref_metadata_2025 huggingface dataset:
Le potentiel de l’approche cartographique et de la réflexion spatiale dans l’analyse des finances locales : les juridictions financières mobilisent des méthodes et outils innovants
Authors: Bailly Xavier , Pezziardi André
Published: October 2021

L’approche géographique, qui s’appuie sur le traitement et le recours à des données spatiales, est un puissant vecteur d’analyse et de valorisation des informations sur la gestion des collectivités territoriales.

Toutefois le potentiel de la réflexion spatiale est encore largement sous exploité par les administrations dans leurs analyses des finances locales. Les juridictions financières ont néanmoins su mobiliser des méthodes et créer des outils innovants pour compléter leurs instructions.

Cette approche, largement mobilisée pour le Rapport public thématique de la Cour des comptes sur les villes moyennes (octobre 2020) pourrait être séminale dans le cadre de la nouvelle stratégie JF2025, dont une des orientations est de multiplier les évaluations des politiques publiques.
L’indissociabilité du pouvoir fiscal local et des impôts à base foncière
Authors: Nazon Romaric
Published: October 2021

Le démantèlement de la fiscalité directe locale assure une spécialisation fiscale des impôts à base foncière et donc du pouvoir fiscal local à l’échelon du bloc communal. Désormais se pose la question de la protection et de l’utilité de ces impôts propres.
Caleb, Son of Jephunneh - As an Example of Fidelity to God. An Exegetical Study of Sir 46:7-10
Authors: Pudełko Judyta
Published: October 2021

Caleb is a secondary character in several episodes of the Exodus as well as the conquest of the Promised Land (Numbers 13–14; Josh 14:6-14). Praise of the Ancestors (Syr 44–49) contains a presentation of the biblical history of ancient Israel as the history of the covenant and a place of God's intervention. Sirach presents, according to his own criteria, the most relevant and positive characters of this history. Caleb in Sirach’s description (46:7-10) is a faithful scout, who, together with Joshua, in the face of the rebellion of the Israelites, bears witness to an exemplary trust in God and encourages the Israelites to follow his lead. His courage and faithfulness are rewarded by receiving an inheritance of the piece of land in the Promised Land, which then becomes the property of his offspring. In the brief account of Sirach, Caleb has become a timeless example of the wise man – a sage who lives according to the word of God and encourages others to take up this challenge.
Investigation of the influence of the geodynamic position of coal-bearing dumps on their endogenous fire hazard
Authors: Batugin Andrian , Kobylkin Aleksandr , Musina Valerija
Published: October 2021

The paper investigates the hypothesis according to which one of the factors influencing the spontaneous combustion of coal-bearing dumps is its geodynamic position, i.e. its location in the geodynamically dangerous zone (GDZ) at the boundary of the Earth crust blocks. This hypothesis is put forward on the basis of scientific ideas about the block structure of the Earth crust and the available statistical data on the location of burning dumps and is studied using computer modeling. A dump located in the area of Eastern Donbass was chosen as the object of research. The simulation results show that the penetration of air into the dump body from the mine through the GDZ, which crosses the mining zone, is possible at an excess pressure of 1000 Pa created by the main ventilation fans. The fire source appearance in the dump body causes an increase in the temperature of the dump mass and becomes a kind of trigger that "turns on" the aerodynamic connection between the dump and the environment, carried out through the GDZ. It is concluded that the establishment of an aerodynamic connection between the mine workings and the dump through the GDZ can be an important factor contributing to the endogenous fire hazard of coal-bearing dumps. The simulation results can be used in the development of projects for monitoring coal-bearing dumps and measures to combat their spontaneous combustion.
The effect of rheopheresis treatment on the cytokine profile in diabetic foot syndrome with hyperviscosity in the aspect of clinical changes: A preliminary study
Authors: Gal Kristof , Veres Katalin , Halmi Sándor , Bozoki-Beke Krisztina , Fekete Klara , Homoki Judit , Remenyik Judit , Barath Barbara , Varga Adam , Nemeth Norbert , Soltesz Pal
Published: October 2021

BACKGROUND: Rheopheresis is a selective extracorporal double cascade filtration treatment, which can extract high molecular weight proteins being responsible for hyperviscosity. As the whole blood and plasma viscosity decrease microcirculation improves. OBJECTIVE: In this preliminary study we aimed to analyze additional beneficial effects of rheopheresis treatment with changes of pro-inflammantory cytokine levels in diabetic foot syndrome patients. METHODS: Two rheopheresis treatments were performed for 6 patients with diabetic foot ulcer and/or neuropathy on consecutive days. Before and after the treatments whole blood and plasma viscosity, as well as IL-6, IL-8, and TNF-alpha serum levels were determined, and complex angiological and ENG examinations were performed. RESULTS: Rheopheresis decreased the whole blood and plasma viscosity, and the serum levels of IL-6, IL-8, and TNF-alpha were markedly reduced. The life quality of the patients improved, the ulcers healed, the pain decreased. Daily dose of analgesics decreased in the follow-up period (6 months). The ENG showed improving amplitude and/or normalizing conduction speed. CONCLUSION: Application of rheopheresis in patients with diabetic foot syndrome has a beneficial effect, providing favorable rheological condition, normalizing cytokine profile and reducing the sensorineural symptoms.
In Vivo Microdialysis in Mice Captures Changes in Alzheimer’s Disease Cerebrospinal Fluid Biomarkers Consistent with Developing Pathology
Authors: Bjorkli Christiana , Louet Claire , Flo Trude Helen , Hemler Mary , Sandvig Axel , Sandvig Ioanna
Published: October 2021

Background: Preclinical models of Alzheimer’s disease (AD) can provide valuable insights into the onset and progression of the disease, such as changes in concentrations of amyloid-β (Aβ) and tau in cerebrospinal fluid (CSF). However, such models are currently underutilized due to limited advancement in techniques that allow for longitudinal CSF monitoring. Objective: An elegant way to understand the biochemical environment in the diseased brain is intracerebral microdialysis, a method that has until now been limited to short-term observations, or snapshots, of the brain microenvironment. Here we draw upon patient-based findings to characterize CSF biomarkers in a commonly used preclinical mouse model for AD. Methods: Our modified push-pull microdialysis method was first validated ex vivo with human CSF samples, and then in vivo in an AD mouse model, permitting assessment of dynamic changes of CSF Aβ and tau and allowing for better translational understanding of CSF biomarkers. Results: We demonstrate that CSF biomarker changes in preclinical models capture what is observed in the brain; with a decrease in CSF Aβ observed when plaques are deposited, and an increase in CSF tau once tau pathology is present in the brain parenchyma. We found that a high molecular weight cut-off membrane allowed for simultaneous sampling of Aβ and tau, comparable to CSF collection by lumbar puncture in patients. Conclusion: Our approach can further advance AD and other neurodegenerative research by following evolving neuropathology along the disease cascade via consecutive sampling from the same animal and can additionally be used to administer pharmaceutical compounds and assess their efficacy.
Gradual GRAM and secure computation for RAM programs1
Authors: Hazay Carmit , Lilintal Mor
Published: October 2021

Despite the fact that the majority of applications encountered in practice today are captured more efficiently by RAM programs, the area of secure two-party computation (2PC) has seen tremendous improvement mostly for Boolean circuits. One of the most studied objects in this domain is garbled circuits. Analogously, garbled RAM (GRAM) provide similar security guarantees for RAM programs with applications to constant round 2PC. In this work we consider the notion of gradual GRAM which requires no memory garbling algorithm. Our approach provides several qualitative advantages over prior works due to the conceptual similarity to the analogue garbling mechanism for Boolean circuits. We next revisit the GRAM construction from (In STOC (2015) 449–458) and improve it in two orthogonal aspects: match it directly with tree-based ORAMs and explore its consistency with gradual ORAM.
Adaptive Knn-based algorithm for network selection in next-generation networks
Authors: Bendaoud Fayssal
Published: October 2021

Nowadays, mobile users are equipped with multi-mode terminals allowing them to connect to different radio access technologies like WLAN, 3G (HSPA and HSPA+), and Long term evolution (LTE) each at a time. In this context, the challenge of the next-generation networks is to achieve the Always Best Connected (ABC) concept. To this end, solving the problem of selecting the most suitable radio access technology (RAT) from the list of available RAT is at the heart of the next-generation systems. The decision process is called access network selection and it depends on several parameters, such as quality of service, mobility, cost of each RAT, energy consumption, battery life, etc. Several methods and approaches have been proposed to solve the network selection problem with the fundamental objective which is to offer the best QoS to the users and to maximize the usability of the networks without affecting the users’ experience. In this paper, we propose an adaptive KNN (K nearest neighbour) based algorithm to solve the network selection problem, the proposed solution has a low computation complexity with a high level of veracity is compared with the well-known MADM methods.
Path planning in an unknown environment based on deep reinforcement learning with prior knowledge
Authors: Lou Ping , Xu Kun , Jiang Xuemei , Xiao Zheng , Yan Junwei
Published: October 2021

Path planning in an unknown environment is a basic task for mobile robots to complete tasks. As a typical deep reinforcement learning, deep Q-network (DQN) algorithm has gained wide popularity in path planning tasks due to its self-learning and adaptability to complex environment. However, most of path planning algorithms based on DQN spend plenty of time for model training and the learned model policy depends only on the information observed by sensors. It will cause poor generalization capability for the new task and time waste for model retraining. Therefore, a new deep reinforcement learning method combining DQN with prior knowledge is proposed to reduce training time and enhance generalization capability. In this method, a fuzzy logic controller is designed to avoid the obstacles and help the robot avoid blind exploration for reducing the training time. A target-driven approach is used to address the lack of generalization, in which the learned policy depends on the fusion of observed information and target information. Extensive experiments show that the proposed algorithm converges faster than DQN algorithm in path planning tasks and the target can be reached without retraining when the path planning task changes.
Combination of improved Harris’s hawk optimization with fuzzy to improve clustering in wireless sensor network
Authors: Nivedhitha V. , Thirumurugan P. , Gopi Saminathan A. , Eswaramoorthy V.
Published: October 2021

A Wireless Sensor Network (WSN) is divided into groups of sensor nodes for efficient transmission of data from the point of measuring to sink. By performing clustering, the network remains energy-efficient and stable. An intelligent mechanism is needed to cluster the sensors and find an organizer node, the cluster head. The organizer node assembles data from its constituent nodes called member nodes, finds an optimal route to the sink of the network, and transfers the same. The nomination of cluster head is crucial since energy utilization is a major challenge of sensor nodes deployed over a hostile environment. In this paper, a fuzzy-based Improved Harris’s Hawk Optimization Algorithm (IHHO) is proposed to select an able cluster head for data communication. The fuzzy inference model ponders balance energy, distance from self to sink node, and vicinity of nodes from cluster head as input factors and decides if a candidate node is eligible for becoming a cluster head. The IHHO tunes the logic into an energy-efficient network with less complexity and more ease. The novelty of the paper lies in applying the hawk-pack technique based on fuzzy rules. Simulations show that the combination of Fuzzy based IHHO reduces the death of nodes through which network lifetime is enhanced.
A new approach to solving target coverage problem in wireless sensor networks using an effective hybrid genetic algorithm and tabu search
Authors: Ajam Leila , Nodehi Ali , Mohamadi Hosein
Published: October 2021

Literature in recent years has introduced several studies conducted to solve the target coverage problem in wireless sensor networks (WSNs). Sensors are conventionally assumed as devices with only a single power level. However, real applications may involve sensors with multiple power levels (i.e., multiple sensing ranges each of which possesses a unique power consumption). Consequently, one of the key problems in WSNs is how to provide a full coverage on all targets distributed in a network containing sensors with multiple power levels and simultaneously prolong the network lifetime as much as possible. This problem is known as Maximum Network Lifetime With Adjustable Ranges (MNLAR) and its NP-completeness has been already proved. To solve this problem, we proposed an efficient hybrid algorithm containing Genetic Algorithm (GA) and Tabu Search (TS) aiming at constructing cover sets that consist of sensors with appropriate sensing ranges to provide a desirable coverage for all the targets in the network. In our hybrid model, GA as a robust global searching algorithm is used for exploration purposes, while TS with its already-proved local searching ability is utilized for exploitation purposes. As a result, the proposed algorithm is capable of creating a balance between intensification and diversification. To solve the MNLR problem in an efficient way, the proposed model was also enriched with an effective encoding method, genetic operators, and neighboring structure. In the present paper, different experiments were performed for the purpose of evaluating how the proposed algorithm performs the tasks defined. The results clearly confirmed the superiority of the proposed algorithm over the greedy-based algorithm and learning automata-based algorithm in terms of extending the network lifetime. Moreover, it was found that the use of multiple power levels altogether caused the extension of the network lifetime.
Multi-granularity belief interval-valued soft set
Authors: Wang Xiaomin , Liu Yang , Zhou Rui
Published: October 2021

A new model named multi-granularity belief interval-valued soft set is introduced in this paper. Some basic properties about it are presented and illustrated. The improved concepts of the soft belief value and soft belief degree are proposed, which provided an easier and better compared horizontally and vertically method among the different objects and different parameters. An algorithm for decision-making problems on multi-granularity belief interval-valued soft set is put forward and its validity is proved by the application of an example. Moreover, the newly proposed algorithm is compared with existing method to indicate its extensive application.
Unsupervised labelling of remote sensing images based on force field clustering
Authors: Wang Zhenggang , Jin Jin
Published: October 2021

Remote sensing image segmentation provides technical support for decision making in many areas of environmental resource management. But, the quality of the remote sensing images obtained from different channels can vary considerably, and manually labeling a mass amount of image data is too expensive and inefficiently. In this paper, we propose a point density force field clustering (PDFC) process. According to the spectral information from different ground objects, remote sensing superpixel points are divided into core and edge data points. The differences in the densities of core data points are used to form the local peak. The center of the initial cluster can be determined by the weighted density and position of the local peak. An iterative nebular clustering process is used to obtain the result, and a proposed new objective function is used to optimize the model parameters automatically to obtain the global optimal clustering solution. The proposed algorithm can cluster the area of different ground objects in remote sensing images automatically, and these categories are then labeled by humans simply.
Fuzzy modeling and prediction of combustion layers′ temperature for power plant
Authors: Yang Guotian , Wang Xiaowei , Wang Yingnan
Published: October 2021

This paper develops a fuzzy modeling strategy to study the temperature of different combustion layers in a power plant. First, a new infrared temperature measurement system is developed to measure three layers (bottom, middle and upper) temperature on both sides of the boiler. Then, a fuzzy clustering modeling algorithm is designed based on entropy to determine the structure of the fuzzy model and the corresponding fuzzy memberships of local models. The effect of modeling mismatches are overcome by the use of online identification of parameters. Simulation results show that the effectiveness of the proposed method can be achieved for a 660 MW power plant.
Modeling of relative collision risk based on the ships group situation
Authors: Wang Xiaoyuan , Zhang Lulu , Wang Gang , Wang Quanzheng , He Guowen
Published: October 2021

The collision risk of ships is a fuzzy concept, which is the measurement of the likelihood of a collision between ships. Most of existed studies on the risk of multi-ship collision are based on the assessment of two-ship collision risk, and collision risk between the target ship and each interfering ship is calculated respectively, to determine the key avoidance ship. This method is far from the actual situation and has some defects. In open waters, it is of certain reference value when there are fewer ships, but in busy waters, it cannot well represent the risk degree of the target ship, since it lacks the assessment of the overall risk of the perceived area of the target ship. Based on analysis of complexity of ships group situation, the concept of relative domain was put forward and the model was constructed. On this basis, the relative collision risk was proposed, and the corresponding model was obtained, so as to realize risk assessment. Through the combination of real ship and simulation experiments, the variation trend, stability and sensitivity of the model were verified. The results showed that risk degree of the environment of ships in open and busy waters could be well assessed, and good references for decision-making process of ships collision avoidance could be provided.
Deep Vigilante: A deep learning network for real-world crime detection
Authors: Jan Atif , Khan Gul Muhammad
Published: October 2021

Identification/recognition of assault, fighting, shooting, and vandalism from video sequence using deep 2D and 3D convolutional neural networks (CNNs) is explored in this paper. Recent wave of extensive unrestricted urbanization has not only uplifted the standard of living, but has also threatened the safety of a common man leading to an extraordinary rise in crime rate. Although Closed-circuit television (CCTV) footage provides a monitoring framework, yet, it’s useless without an auto volume crime detection system. The system proposed in this work is an effort to eradicate volume crimes through accurate detection in real-time. Firstly, a fine-grained annotated dataset including instance and activity information has been developed for real-world volume crimes. Secondly, a comparison between 3D CNN and 2D CNN network has been presented to identify the malicious event from the video sequence. This is carried out to explore the significance of spatial and temporal information present in the video for event recognition. It has been observed that 2D CNN even with lesser parameters achieved a promising classification accuracy of 91.2%and Area under the curve (AUC) of 95.2%on four classes. The system also reduces false alarm rate in comparison to state-of-the-art approaches.
An automatic brain tumor segmentation using modified inception module based U-Net model
Authors: Sambath Kumar K. , Rajendran A.
Published: October 2021

Manual segmentation of brain tumor is not only a tedious task that may bring human mistakes. An automatic segmentation gives results faster, and it extends the survival rate with an earlier treatment plan. So, an automatic brain tumor segmentation model, modified inception module based U-Net (IMU-Net) proposed. It takes Magnetic resonance (MR) images from the BRATS 2017 training dataset with four modalities (FLAIR, T1, T1ce, and T2). The concatenation of two series 3×3 kernels, one 5×5, and one 1×1 convolution kernels are utilized to extract the whole tumor (WT), core tumor (CT), and enhance tumor (ET). The modified inception module (IM) collects all the relevant features and provides better segmentation results. The proposed deep learning model contains 40 convolution layers and utilizes intensity normalization and data augmentation operation for further improvement. It achieved the mean dice similarity coefficient (DSC) of 0.90, 0.77, 0.74, and the mean Intersection over Union (IOU) of 0.79, 0.70, 0.70 for WT, CT, and ET during the evaluation.
RMGCS: Real-time multimodal garbage classification system for recyclability
Authors: Su Nan , Lin Zhishuo , You Wenlong , Zheng Nan , Ma Kun
Published: October 2021

Management of garbage classification is a general term for a series of activities to sort, store and transport garbage into public resources according to certain regulations or standards. Current garbage classification systems have several drawbacks, such as inability to identify multiple garbage categories, and high dependence on the surrounding environment. To address these issues, this paper has proposed the Real Time Multi-Modal Garbage classification System (abbreviated as RMGCS). It consists of two sub systems: an indoor garbage classification applet (abbreviated as IGCA) and an outdoor garbage classification system (abbreviated as OGCS). IGCA provides users with three methods of garbage classification, and OGCS provides users with outdoor real-time multi-target garbage classification and can dynamically update the recognition model. RMGCS achieves real-time, accurate, and multimodal classification. Finally, the experiments with RMGCS show that our approaches are effective and efficient.
System cost minimisation in hybrid energy storage systems connected to microgrids: A comparative approach
Authors: Jeyaprakash P. , Agees Kumar C. , Ravi A.
Published: October 2021

Electricity is the most critical facility for humans. All traditional energy supplies are rapidly depleting. As a result, the energy resources are moved from traditional to non-conventional. In this research, mixture of two energy tools, namely wind and solar energy are used. Using a Hybrid Energy Storage System (HESS), continuous power can be provided. Electricity can be produced at a cost that is affordable. The integration of solar and wind in a hybrid system cause an increase in the system’s stability, which is the key benefit of this research. The system’s power transmission efficiency and reliability can be greatly enhanced by integrating these two intermittent sources. When one of the energy source is unavailable or inadequate to meet load demands, the other energy source will supply the power. The major contribution in this research is that, the proposed bidirectional single-inductor multiple-port (BSIMP) converter significantly lowers the component count, smaller circuit size and lower cost, allowing HESS to be integrated into DC microgrid. Minimum number of components are used for the same number of ESs in HESS in the proposed BSIMP converter. The hybridization of battery and supercapacitor (SC) for storage purpose is more cost effective, as compared to the battery energy storage system, thus improving the battery stress and hence used for large scale grid energy storage. SC’s are accepted as backup and found very useful in delivering high power, not possible with batteries. The use of SC in addition to batteries can be one solution for achieving the low life cycle economy. The Single Objective Adaptive Firefly Algorithm (SOAFA) is introduced for optimising the Proportional-Integral (PI) controller parameters. The system cost is reduced by about 32%, with the constraints on wind turbine swept area, PV area, total battery and SC capacity with the proposed optimisation algorithm.
Resolution-intensity optimisation on quasielastic neutron scattering spectrometers
Authors: Zorn Reiner
Published: October 2021

In quasieleastic neutron scattering spectrometers one usually faces a trade-off between energy resolution and counting statistics. If the resolution is improved the intensity at the detectors reduces and vice versa. It is not immediately clear how to weigh both factors against each other. In this paper it is proposed to use the maximum time obtainable by Fourier transform of the spectra as the quantity to be optimised. It is shown that this leads to a well-defined criterion for the choice of the resolution.
Antioxidant potential of diet – Association between dietary antioxidant index and odds of coronary heart disease: A case-control study
Authors: Vahid Farhad , Nasiri Zahra , Abbasnezhad Amir , Moghadam Ezatollah Fazeli
Published: October 2021

BACKGROUND: Oxidative stress and chronic inflammation are among the leading causes of coronary heart disease (CHD). Studies investigated the relationship between dietary antioxidants and the risk/odds of CHD, and contradictory results have been reported. Dietary antioxidant index (DAI) is a novel and reliable nutritional tool that examines the diet’s overall antioxidant capacity. Its validity was examined using serum total antioxidant capacity and malondialdehyde. OBJECTIVE: This study aimed to investigate the relationship between DAI score and odds of CHD. METHODS: In this incidence case-control study, 320 individuals with a definitive diagnosis of CHD and 320 participants without CHD or related risk factors attending the same hospitals/polyclinics were selected as the case and control groups. We estimated the DAI by summing up six standardized intakes of major dietary antioxidants, including manganese, vitamin E, A, C, selenium, and zinc. RESULTS: Modeling DAI categorized according to the median (–0.38), in multi-adjusted model showed a significant protective association with the odd of CHD (OR = 0.72; 95%CI:0.51–0.99, p-value = 0.05). Also, modeling DAI as a continuous variable in multi-adjusted models (OR = 0.94;95%CI:0.90–0.95; p-value = 0.01) showed significant results. CONCLUSION: Using the DAI to investigate the relationship between dietary antioxidants and CHD can show more realistic results than a single study of antioxidants.
Compositional changes in human gut microbiota reveal a putative role of intestinal mycobiota in metabolic and biological decline during aging
Authors: Al Bataineh Mohammad Tahseen , Alzaatreh Ayman , Hajjo Rima , Banimfreg Bayan Hassan , Dash Nihar Ranjan
Published: October 2021

BACKGROUND:

Age-related alterations in the composition and function of gut microbiota may influence human health and disease mechanisms. However, connections between compositional changes in gut bacterial and fungal communities, and their role in the aging process, remain poorly understood.

OBJECTIVE:

Compare the gut microbiota and mycobiota composition in different age groups and evaluate the functionality.

METHODS:

In this study, we performed 16S rRNA and ITS2 gene-based microbial profiling analysis and shotgun metagenomics using the NextSeq platform.

RESULTS:

We observed a shift in compositional changes of human gut microbiota with age. Older individuals revealed a significantly different gut microbiota profile compared to younger individuals. For example, gut microbiota composition of the older individuals showed increase in genera Bacteroides, Blautia, Ruminococcaceae, and Escherichia coli. Additionally, older individuals had significant reduction in fungi belonging to saccharomyces cerevisiae and candida albicans in comparison to their younger counterparts. Moreover, metagenomics functional profiling analysis using shotgun metagenomics sequencing data showed substantial differences in the enrichment of 48 pathways between the young and older age groups. Metabolic pathways such as amino acid biosynthesis, carbohydrate metabolism, cell structure biosynthesis and vitamin biosynthesis were declined in the older age group, in comparison with the younger individuals.

CONCLUSIONS:

The identified differences provide a new insight to enrich our understanding of age-related changes in gut microbiota, their metabolic capabilities, and potential impact on health and disease conditions.
The wellbeing of elderly people during the COVID-19 pandemic: A narrative review
Authors: Gayatri Maria
Published: October 2021

BACKGROUND:

Quarantine and social distancing are important to avoid spreading the coronavirus. Being active, healthy and happy is a central part of managing daily challenges.

OBJECTIVE:

The objective of the study was to improve understanding regarding the wellbeing of the elderly during COVID-19 pandemic.

METHODS:

For the literature review, keywords such as wellbeing and COVID-19 were searched for associations with terms such as elderly, ageing and old people. Only research published in peer-reviewed journals and written in English was considered in this study. The studies included in the analysis were only those published between the start of the pandemic and April 2021.

RESULTS:

From a total of 75 searches, eight studies fit the inclusion criteria. Elderly people are the population group most vulnerable to COVID-19, which threatens their lives and wellbeing. Technology such as artificial intelligence can maintain the wellbeing of the elderly. Lockdown, the lack of social interaction and the limited access to medications and health facilities can worsen the physical and mental health and the psychological wellbeing of the elderly. Conclusion: It is important to highlight elderly care and mitigation programmes related to physical activities and cognitive psychology to prevent mental and functional decline in older people. Further studies should focus on their quality of life and the wellbeing.
Autonomous Systems and Artificial Intelligence – Hype or Prerequisite for P5 Medicine?
Authors: Blobel Bernd , Ruotsalainen Pekka , Brochhausen Mathias
Published: October 2021

For meeting the challenge of aging, multi-diseased societies, cost containment, workforce development and consumerism by improved care quality and patient safety as well as more effective and efficient care processes, health and social care systems around the globe undergo an organizational, methodological and technological transformation towards personalized, preventive, predictive, participative precision medicine (P5 medicine). This paper addresses chances, challenges and risks of specific disruptive methodologies and technologies for the transformation of health and social care systems, especially focusing on the deployment of intelligent and autonomous systems.
Predictive Modeling of COVID and non-COVID Pneumonia Trajectories
Authors: Mramorov Nikita , Derevitskii Ilya , Kovalchuk Sergei
Published: October 2021

Today pneumonia is one of the main problems of all countries around the world. This disease can lead to early disability, serious complications, and severe cases of high probabilities of lethal outcomes. A big part of cases of pneumonia are complications of COVID-19 disease. This type of pneumonia differs from ordinary pneumonia in symptoms, clinical course, and severity of complications. For optimal treatment of disease, humans need to study specific features of providing 19 pneumonia in comparison with well-studied ordinary pneumonia. In this article, the authors propose a new approach to identifying these specific features. This method is based on creating dynamic disease models for COVID and non-COVID pneumonia based on Bayesian Network design and Hidden Markov Model architecture and their comparison. We build models using real hospital data. We created a model for automatically identifying the type of pneumonia (COVID-19 or ordinary pneumonia) without special COVID tests. And we created dynamic models for simulation future development of both types of pneumonia. All created models showed high quality. Therefore, they can be used as part of decision support systems for medical specialists who work with pneumonia patients.
Understanding the Gap Between Information Models and Realism-Based Ontologies Using the Generic Component Model
Authors: Brochhausen Mathias , Bost Sarah J. , Singh Nitya , Brochhausen Christoph , Blobel Bernd
Published: October 2021

The wide-spread use of Common Data Models and information models in biomedical informatics encourages assumptions that those models could provide the entirety of what is needed for knowledge representation purposes. Based on the lack of computable semantics in frequently used Common Data Models, there appears to be a gap between knowledge representation requirements and these models. In this use-case oriented approach, we explore how a system-theoretic, architecture-centric, ontology-based methodology can help to better understand this gap. We show how using the Generic Component Model helps to analyze the data management system in a way that allows accounting for data management procedures inside the system and knowledge representation of the real world at the same time.
Gait Analysis Platform for Measuring Surgery Recovery
Authors: Codina Marc , Navarrete Manuel , Rezaee Ashkan , Castells-Rufas David , Torrelles Maria Jesús , Burkard Stefan , Arndt Holger , Drevet Sabine , Boudissa Medhi , Tonetti Jerome , Marque Isabelle , Moreau-Gaudry Alexandre , Castillejo Armand , Carrabina Jordi
Published: October 2021

Gait analysis has evolved significantly during last years due to the great development of the Medical Internet of Things (MIoT) platforms that allow an easy integration of sensors (inertial, magnetic and pressure in our case) to the complex analytics required to compute, not only relevant parameters, but also meaningful indexes. In this paper, we extend a previous development based on a fully wireless pair of insoles by implementing an updated version with more reliable and user-friendly devices, smartphone app and web front-end and back-end. We also extend previous work focused on fall analysis (with the corresponding fall risk index or FRI) with the proposal of a new surgery recovery index (SRI) to account for the individual speed recovery speed that can be measured either at clinical facilities or at home in a telemedicine environment or while doing daily life activities. This new index can be personalized for different types of surgeries that affect gait such as hip, knee, etc. This paper presents the case of hip recovery and is built on top of the clinical standard SPPB test and allows obtaining quantitative parameters directly from the sensors.
A Mobile App to Improve Patient Management in Emergency Departments: Caregiver Needs Analysis, Design and Early Technology Acceptance Assessment
Authors: Ehrler Frédéric , Tuor Carlotta , Rey Robin , Siebert Johan N.
Published: October 2021

Emergency care is very complex in that it requires patient-centered care in a coordinated manner among multiple providers in a highly distractible, unpredictable and stressful environment. Sharing information efficiently between providers in this context is difficult. Connecting emergency providers with each other through a digital communication channel could improve the efficiency of information sharing and emergency care. This study describes the development process of PIMPmyHospital, a mobile app dedicated to emergency department physicians and nurses to collaboratively manage their patients. We relied on a user-centered design process involving caregivers from a pediatric emergency department. The process started with semi-structured interviews that informed the specifications of the app, followed by an iterative design and development approach. The resulting prototype was evaluated by end-users using the perceived usefulness dimension of the technology acceptance model questionnaire. Early user engagement during the design and development of a dedicated mobile app must be taken into account to improve its perceived usefulness and future adoption.
Assessment of mHealth Solutions Applied to Fall Detection for the Elderly
Authors: de Oliveira Fellipe Soares , da Silva Camila Carvalho , Pinheiro Talita Santos , Yokoi Larissa Mayumi , dos Santos Pablo Deoclecia , Tanaka Harki , Simões Priscyla Waleska
Published: October 2021

Mobile Health has been increasingly present in healthcare due to the wide availability of applications for smartphones, however, robust assessment methods must be considered, seeking to provide evidence for clinical practice and mHealth solutions. This research presents the assessment of applications aimed at detecting and preventing falls for the elderly, available for Android and IOS, through the Mobile App Rating Scale. Based on the results presented, it can be concluded that the fall detection and prevention applications for the elderly available for Android and IOS showed good quality after rigorous evaluation.
Factors Predicting Sexual Risk Behaviors of Adolescents in North-Eastern Thailand
Authors: Butdabut Athiwat , Homchampa Pissamai
Published: October 2021

The number of young people who have had sex at an early age increases in proportion, it concerns unsafe sexual behaviors, teenage pregnancy, HIV aids and sexually transmitted infections (STIs). This study examines the health behaviors and factors predicting sexual risk behaviors pertaining to teenage pregnancy among adolescents in Thailand. Adolescents consulted the reproductive health center about problems with the same gender. The factors of adolescent reproductive behaviors were significantly associated with age, education level, and the perception of peer norms. Receiving social support from media information also significantly correlated with those behaviors. The results recommend that to prevent premature pregnancy, adolescents should protect themselves. Parents should take the issue of social media use by their teenagers very seriously.
Detection of COVID-19 from Chest CT Images Using CNN with MLP Hybrid Model
Authors: Rajasekar Sakthi Jaya Sundar , Narayanan Vasumathi , Perumal Varalakshmi
Published: October 2021

COVID-19 when left undetected can lead to a hazardous infection spread, leading to an unfortunate loss of life. It’s of utmost importance to diagnose COVID-19 in Infected patients at the earliest, to avoid further complications. RT-PCR, the gold standard method is routinely used for the diagnosis of COVID-19 infection. Yet, this method comes along with few limitations such as its time-consuming nature, a scarcity of trained manpower, sophisticated laboratory equipment and the possibility of false positive and negative results. Physicians and global health care centers use CT scan as an alternate for the diagnosis of COVID-19. But this process of detection too, might demand more manual work, effort and time. Thus, automating the detection of COVID-19 using an intelligent system has been a recent research topic, in the view of pandemic. This will also help in saving the physician’s time for carrying out further treatment. In this paper, a hybrid learning model has been proposed to identify the COVID-19 infection using CT scan images. The Convolutional Neural Network (CNN) was used for feature extraction and Multilayer Perceptron was used for classification. This hybrid learning model’s results were also compared with traditional CNN and MLP models in terms of Accuracy, F1-Score, Precision and Recall. This Hybrid CNN-MLP model showed an Accuracy of 94.89% when compared with CNN and MLP giving 86.95% and 80.77% respectively.
A secondary analysis to develop a scale for measuring unemployed workers’ experiences of Australian employment services
Authors: O’Halloran David , Thomacos Nikos , Casey Simone , Farnworth Louise
Published: October 2021

BACKGROUND: Research consistently shows that Australian employment services are failing those they are intended to serve. Based on findings in other human service areas, a valid and reliable instrument to measure unemployed workers’ experiences may provide an opportunity for improvement in this sector. OBJECTIVE: To establish a basis for developing a suitable rating scale. METHODS: An exploratory factor analysis combined with qualitative cross check for face validity of an existing large survey of Australian unemployed workers. RESULTS: Six factors appear to be important elements of service delivery: (F1) useful and competent, (F2) client-centred, (F3) receptive to feedback, (F4) trustworthy, (F5) fair, and (F6) friendly. CONCLUSIONS: While each of these factors have been either described explicitly or referred to implicitly in previous studies, this study is the first to attempt to combine these factors and is a precursor to establishing a valid and reliable rating scale for use by unemployed workers in evaluating their employment service providers. At a time when Australia is exploring new approaches to employment services, such a scale using a robust set of factors may allow for the improvement of employment services and thus be held accountable to a significant stakeholder group whom they aim to serve –unemployed workers.
Understanding uptake of prevention and mitigation of COVID-19 at work actions in Iran
Authors: Gharibi Vahid , Cousins Rosanna , Jahangiri Mehdi , Bargar Mina
Published: October 2021

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has required organizations to make changes to ways of working to prevent and mitigate COVID-19 in employees. OBJECTIVE: To assess the workplace response to COVID-19 in Iran. METHODS: 255 organizations completed a two-part survey. Part 1 collected data describing the business; part 2 comprised the International Labor Organization (ILO) 30-item Prevention and Mitigation of COVID-19 at Work Actions Checklist. A four-point Likert scale was used to score each item according to whether preventative measures had been actioned. RESULTS: We found a dichotomy of commitment to managing COVID-19 at work. 42.5%of organizations had fully implemented the ILO recommended preventative actions, and 45.6%workplaces had not implemented any. Large organizations had significantly more preventative actions than SMEs; the healthcare sector had significantly better COVID-19 mitigation measures in place than construction projects; and organizations with a health and safety management system had significantly more prevention actions in place. CONCLUSIONS: ILO provided a good framework to support risk assessment of COVID-19, however only half the organizations were undertaking the necessary biological hazard control actions to prevent COVID-19 at work. There remains a need to understand the inaction of organizations who do not risk assess despite being in a pandemic.
Employee well-being in the COVID-19 pandemic: The moderating role of teleworking during the first lockdown in the province of Quebec, Canada
Authors: Parent-Lamarche Annick , Boulet Maude
Published: October 2021

BACKGROUND: The COVID-19 pandemic has had a profound effect on all aspects of society, including mental health. Many employees have had to pivot suddenly to teleworking to prevent the virus from spreading. While teleworking may have some negative consequences, it may also represent a human resources practice that may improve employee well-being. OBJECTIVE: The study main objective was to determine if teleworking played a moderating role in the relation between potential stressors and employee well-being during the first months of the COVID-19 pandemic when working from home. This was based on the theory of conservation of resources. METHODS: Multivariate regression analyses were conducted with Stata 13 software to determine the contribution of potential stressors on employee well-being, as well as the moderating role of teleworking on a sample of 480 Canadian employees. Data were collected once for white and blue collar from both public (67.08%) and private (32.92%) business sectors. RESULTS: Results indicated that work-life imbalances, workload, and marital tension were associated with lower levels of well-being. On the other hand, teleworking and household income were associated with higher levels of well-being. Teleworking also moderated the differences in well-being between the public and private sectors. Teleworking in the public sector seems to increase employee well-being. Conversely, working on-site in the public sector seems to decrease well-being. CONCLUSIONS: Employers need to proactively address work-life imbalances, workloads, and teleworking to maintain employee well-being. Specific recommendations are offered to ensure that teleworking remains positive for employee well-being both during a pandemic and afterward.
“Can you hear me now?” Video conference coping strategies and experience during COVID-19 and beyond
Authors: Johns Hannah , Burrows Emma L. , Rethnam Venesha , Kramer Sharon , Bernhardt Julie
Published: October 2021

BACKGROUND: During COVID-19 the workforce quickly adapted to using existing video-conferencing tools in order to work from home. OBJECTIVE: To explore use and experience of remote video-facilitated work practices in response to COVID-19: termed ‘COVID Response Zoom-style Interactions (CRAZI)’. METHODS: A cross sectional study via a 66-item online survey for health and medical research and education sector workers. The survey included 8 sections: 1) pre-COVID video-conferencing meeting habits, 2) CRAZI meeting habits, 3) socialising, 4) CRAZI fashion, 5) behaviour standards, 6) family life, 7) future work, 8) participant demographics. Main outcomes were pre-COVID to CRAZI differences in frequency, length and type of video-based meetings, and video-conferencing experience. RESULTS: 202 participants, mostly Australian (median age 36–45, IQR 26–55 years) completed the survey. Women-to-men ratio was 3 : 1, 44.6%had children. COVID-19 changed video-conferencing frequency and maximum meeting size. Most participants found CRAZI meetings tiring and hard. Casual clothes dominated dress code (71.1%), pets were commonly seen. “Can you hear me now” was a commonly heard phrase. Good and bad behaviour were described, with formal codes of CRAZI conduct missing (58.7%) or unknown (21.9%). 76.6%of participants observed a child interrupting a CRAZI meeting, parents were mostly female. Despite challenges, most participants (76.6%) favoured video-conferencing post-pandemic, but preference for continuing to work from home varied. CONCLUSIONS: CRAZI work, while tiring, has fostered different work-practices that may continue beyond the pandemic. Working from home with children adds joy for others, but complexity for workers. Pets may help owners and co-workers cope with the pandemic.