User's Situation Research Articles

Health Information System Users in Public Health Facilities: A Descriptive Analytics

Health Information Systems (HIS) are vital in making or developing the policies of health programs in the Philippines. The HIS is broadly defined as a system that integrates data collection, processing, reporting, and use of the information necessary for improving health service effectiveness and efficiency through better management at all levels of health services. This study is conducted to visualize the current situation of the health information system users in public health facilities in the Province of Bataan. It endeavors to answer on how the health facilities in the Province are described based on their report platform, the number of personnel, trained and untrained per facility, and the needs of the facilities when it comes to the training of the encoders. The researchers used the descriptive method specifically the Dashboarding, Analysis, and Reporting or DAR method for this study. This study focused on the data gathered from the Health Information Systems Assessment Tool. The assessment was conducted with 24 different health facilities in the Province of Bataan. The study revealed that 14 out of 24 (58%) of the Rural Health Units (RHUs) in the Province of Bataan used paper-based reporting while 10 out of 24 (42%) of the RHUs used a health information system. Twenty one out of 48 encoders (44%) are untrained while 27 out of 48 (56%) are trained. Capability training in each health information system used is proposed. In conclusion, the Province of Bataan is supporting the implementation of the use of health information systems (HIS) by designating encoders for every public health facility.

Intrapersonal Communication in the Perspective of WeChat’s Circle of Friends Self-presentation Analysis

In the mobile Internet era, social media as a medium based on virtual platforms has dramatically changed the form of people's social interaction. the Circle of Friends, as an essential social module in the most used instant messaging software, carries the function of users' self-presentation and communication and creates an online social platform for WeChat users that is not only in line with the actual social relations but also relies on the virtual platform. The expression of the "self" relies on the regulation of the "guest me" to the real reflection of the "master me", and the online socialization platform provided by WeChat's Friend Circle encourages users to express their own emotions, and the same time, it also encourages users to express their emotions. The online social platform provided by the WeChat Friendship Circle encourages users to express their own emotions and provides users with social feedback with personal cognitive significance due to its social interaction method that is both dependent on the virtual network environment and rooted in real social relationships. Through the comparison of different sharing contents and personal expression forms, users can obtain more accurate psychological expectations of social feedback and a form of self-presentation that is more in line with their personal psychological needs, to realize their needs for a good social experience and the regulation and development of self-expression within the social media platform. This paper takes the social media context provided by WeChat's Circle of Friends as the entry point, starts from the perspective of intra-personal communication, disassembles the process of self-perception and presentation, the two comparative cognitive methods of "vertical" and "horizontal" are used to categorize the process of WeChat users' self-recognition on the platform of the Circle of Friends and the regulation of self-expression in the online environment, and to discuss the situation of users' self-representation in the online social environment of the Circle of Friends.

Improving Recommendation for Effective Personalization in Context-Aware Data Using Novel Neural Network

The digital technologies that run based on users’ content provide a platform for users to help air their opinions on various aspects of a particular subject or product. The recommendation agents play a crucial role in personalizing the needs of individual users. Therefore, it is essential to improve the user experience. The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites. In Context-Aware Recommender Systems (CARS), several influential and contextual variables are identified to provide an effective recommendation. A substantial trade-off is applied in context to achieve the proper accuracy and coverage required for a collaborative recommendation. The CARS will generate more recommendations utilizing adapting them to a certain contextual situation of users. However, the key issue is how contextual information is used to create good and intelligent recommender systems. This paper proposes an Artificial Neural Network (ANN) to achieve contextual recommendations based on user-generated reviews. The ability of ANNs to learn events and make decisions based on similar events makes it effective for personalized recommendations in CARS. Thus, the most appropriate contexts in which a user should choose an item or service are achieved. This work converts every label set into a Multi-Label Classification (MLC) problem to enhance recommendations. Experimental results show that the proposed ANN performs better in the Binary Relevance (BR) Instance-Based Classifier, the BR Decision Tree, and the Multi-label SVM for Trip Advisor and LDOS-CoMoDa Dataset. Furthermore, the accuracy of the proposed ANN achieves better results by 1.1% to 6.1% compared to other existing methods.

<i>c</i>-SNE: Deep Cross-modal Retrieval based on Subjective Information using Stochastic Neighbor Embedding

Cross-modal information retrieval based on subjective information aims to enable flexible media retrieval services, such as allowing users to specify, for example, an image to search audio clips. The resulting audio clips should have an impression similar to the specified image. Existing methods focus on building cross-media cross-modal relationships using objective information (such as the standard caption). However, such a relation can be built only between the pieces of media that are originally related, which limits the flexibility of cross-modal media retrieval. This research leverages subjective information in the media clips for similarity calculation to achieve more flexibility. We propose a novel cross-modal stochastic neighbor embedding technique called c-SNE. c-SNE can extract features of subjective information from pieces of media and map them in the common embedding space. It is a learning technique to bridge the heterogeneous gap between the modal distributions using label-weighted SNE. It allows users to find the media that share the same subjective information with a query medium. Our experimental results on the benchmark datasets demonstrate that the proposed method effectively performs in cross-modal distribution alignment and retrieval. Furthermore, our user study with ten users with 600 data points confirmed that c-SNE outperforms three related methods in the actual usage situation from the users' perspective.

Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

With the popularity of 5G and the rapid development of mobile terminals, an endless stream of short video software exists. Browsing short-form mobile video in fragmented time has become the mainstream of user’s life. Hence, designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements. Nevertheless, the explosive growth of data leads to the low efficiency of the algorithm, which fails to distill users’ points of interest on one hand effectively. On the other hand, integrating user preferences and the content of items urgently intensify the requirements for platform recommendation. In this paper, we propose a collaborative filtering algorithm, integrating time context information and user context, which pours attention into expanding and discovering user interest. In the first place, we introduce the temporal context information into the typical collaborative filtering algorithm, and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos. There remains one more point. We also introduce the user situation into the traditional collaborative filtering recommendation algorithm, considering the context information of users in the generation recommendation stage, and weight the recommended short-form videos of candidates. At last, a diverse approach is used to generate a Top-K recommendation list for users. And through a case study, we illustrate the accuracy and diversity of the proposed method.

Folklore of the Polish-Ukrainian Borderland in the Studies of Mikhał Łesiów (On the Occasion of the 95th Anniversary of Birthday)

The article is based on a review of the published works of Ukrainian folklore by the Polish scholar Mikhał Łesiów (1928–2016), who is known primarily as a Slavic studier and dialectologist. He is the author of scientific works in the history and culture of language. Studies dedicated to folklore, inter-ethnic contacts, folklore aspects of linguistic interaction on the Polish-Ukrainian borderland occupy a significant place in his scientific heritage. He is also the author of the studies representing folklore materials from the Lublin region and Podlasie. In the 1950s M. Łesiów, while collecting dialect material, has recorded casually oral literature from the inhabitants of the Podillian village of Stara Huta in Buszacki County. These people have been relocated to the territory of the Western Voivodeships of Poland to the villages of the Szczecin Voivodeship (now it is the West Pomeranian Voivodeship) in the 1945–1946. Collected factual material has been published in a number of the researcher’s studies. Cohabitation over the centuries has influenced the folklore of both ethnoi. Ukrainian oral literature has become an integral part of the spiritual culture of Poles, who have incorporated a number of systemic features of the local dialects and Ukrainian folklore into their speech. Characterizing the situation of folklore usage on the Polish-Ukrainian borderland, the scholar has concluded that the works of oral literature continue partially to function among the representatives with a conscious belonging to both Polish and Ukrainian people. These works can have Polish, Ukrainian and even mixed linguistic form. Regardless the language in which folklore texts are performed, they have become the property and common ownership of the borderland cultural space. The existence of linguistically mixed folklore texts is one of the main external distinguishing features determining the specific identity of the culture of the Polish-Ukrainian borderland. Folklore bilingualism is a consequence of borrowing, as well as the fact of two-way translation of works, where certain sentences, phrases or separate words have been preserved in the original state.

Interprecedent Semiotic Space, or When the Cannons are Heard, the Muses aren’t Silent

The paper focuses on the description of chrematonyms as precedent names of Russian arms domain, developing a special semiotic space in the digital media news discourse and social networks represented in various code formats, readers and participants of which share the same axiological values as the authors of messages. The study aims to analyze the new precedent concepts, which form the new cells in the semantic network and the directions of linguistic cognitive channels generation on the Internet platform for communication. On the basis of the illustrative examples, verbalizing the Russian arms names, that is “Geran” (“Geranium”), “Bal” (“Ball”), “Topol” (“Сottonwood”), “Tishina” (“Silence”), “Burya” (“Tempest”), “Buratino” and “Cheburashka”, the issues of interprecedent links generation and rooting in the intertextual aspect are covered for the first time The description of the specific cases of the new precedent names usage is based on the complex application of such linguistic methods as metaphorical modeling, intertextual, contextual, structural, semantic and discursive analysis. It has been established that chrematonyms as secondary nominations are formed in two ways: they can be semantically derived from common names on the basis of metaphoricaland metonymic meanings, associative links or appear as a result of transonymization from other precedent names. The chrematonyms denoting weapons are of a hybrid type, which combines the features of proper names, nicknames and terms. Textual examples demonstrate pragmatic characteristics of chrematonyms to perform referential, emotive, persuasive, euphemistic, axiological, culturally differentiating and consolidating, and a number of other functions. The models of multiple metaphorical realizations and allusions to literary heritage and modern texts are characterized. The new precedent names and parodies, related to the situations of the Russian weapons usage, are analyzed from the point of view of their linguistic creativity. The main directions and trends of the development of precedent phenomena interactions, the ways of current intertextual domains extention are considered, recent examples of linguistic creativity on precedent resources production are described and the generation of new semantic memory cells and arcs of connections between them (in modern terms) in the Russian semiotic space is demonstrated.

Employment Survey of Wheelchair Users and Influential Factors Identified in a Social Enterprise in Hong Kong

Wheelchair users often face both physical and psychological barriers to employment due to restrictions on body movement and inadequate infrastructure in the environment. Over the years, the Hong Kong government and authorities have gradually improved barrier-free access in establishments and major transport service. Employment support has been offered in terms of job pairing, subsidies for employers, and training programs while regulation is not adopted. Individual voices of wheelchair users were usually conveyed through local community groups working to serve their needs. The collection of quantitative data on the employment situation of wheelchair users could provide evidence for representatives to address their concerns during discussions with government committees. While environmental obstructions are removable, personal barriers and motivating factors are less visible and endogenous. This collaboration project with a social enterprise has collected employment data of their members who are wheelchair users and explored factors associated with their employment prospects.

Analysis of the usage of post-exposure prophylaxis and related factors among men who have sex with men

A survey was conduct to analyze the usage situation of post-exposure prophylaxis(PEP) and related factors among men who have sex with men(MSM) in 6 cities of Shandong Province. Total of 2 620 subjects, the use ratio was 2.98% (78/2 620). Compared with age≤24 years,monthly income<5 000 yuan,non-commercial sex, non-DU,non-STD,role for being insert in the anal intercourse,MSM was more likely to use PEP with age≥45 years(OR=3.87, 95%CI:1.12-13.36),monthly income≥5 000 yuan(OR=1.87, 95%CI:1.07-3.28),commercial sex (OR=3.13, 95%CI:1.56-6.28), drug users (DUs) (OR=4.63, 95%CI:2.51-8.52),STD patient (OR=2.35,95%CI:1.05-5.27),the mixed sex role group(OR=2.25,95%CI:1.10-4.62).

Improving User Environment Detection Using Context-Aware Multi-Task Deep Learning in Mobile Networks

Cognition of user behavior can make future mobile networks more intelligent and flexible. Knowledge about users’ habits can be used to personalize services and intelligently manage network resources. However, inferring this key information with a low-cost signaling implementation, and avoiding constant user interaction, is crucial for Mobile Network Operators (MNOs). With this motivation, this paper investigates the detection of the real-life mobile user environment using context-aware detection via multi-task learning (MTL). We propose models that are able to automatically detect up to eight distinct real-life user environments. We also improve the detection accuracy with the assistance of the mobility state profiling task. We associate both environment and mobility tasks because they correspond to the main attributes of user behavior and, additionally, both of them are correlated. Using MTL, the task of detecting environment corresponds to simultaneously answering the questions: “how and where mobile user consumes mobile services?”. We build models using real-life radio data which is already available in network. This data has been massively gathered from multiple diversified situations of mobile users. Simulation results support our claim to detect several environment classes in network infrastructure with improved UED accuracy.

Performance Analysis of Wireless Powered Cooperative NOMA-Based CDRT IoT Networks

This article investigates the outage performance of a wireless powered cooperative nonorthogonal multiple access-based coordinated direct and relay transmission (CDRT) Internet-of-Things (IoT) network with simultaneous wireless information and power transfer capable IoT master node. A method to remedy the high outage situation of a near user by exploiting information from earlier communication phase is proposed and analyzed. The competency of this method and the closed form of the outage probability for the users under practical variations, such as imperfect successive interference cancellation and different incentive transmission power levels for the IoT master node, are derived analytically and confirmed via simulation. In addition to that, our asymptotic analysis shows that it is not mandatory to enhance the outage performance of the near user in the final phase of CDRT by using many antennas in the high signal-to-noise-ratio regime. Moreover, a modified particle swarm optimization algorithm is introduced to solve a joint power splitting, time splitting, and power allocation for the proposed system. Numerical results from our analysis exemplify significant tradeoff between near user performance and the quality of service of the master node’s client.

Identification Technology of Relationship Between User Meter and Transformer Based on Spatial-Temporal Correlation of Data

Due to the inability to accurately gain the changes of transformer archives, there are often problems such as confusion and loss of control of the station area archives in the electricity consumption information collection system, which seriously affects the work of line loss management and fault operation and maintenance, and it is difficult to achieve refined management. Existing manual identification methods are time-consuming and labor-intensive, while the dedicated device identification method is costly and requires a device-independent identification technology. Aiming at the above problems, a data analysis method based on temporal and spatial correlation is studied to analyze the temporal and spatial correlation of the voltage data of the power collection equipment. On this basis, use the classification method in data mining to divide collection equipment of each station area into one category, and calculate the probability that the acquisition equipment to be identified belongs to each category, to determine the belonging relationship of its station area. By simulating the user situation of the real station area, the simulation verifies the effectiveness of the intelligent station area identification method based on the spatial and temporal correlation of data, which can not only ensure the accuracy and efficiency of station area identification, but also does not depend on equipment and labor, and can meet the application requirements.

“Deep-Dive analysis of the latest Lithium-Ion battery safety testing standards and regulations in Germany and China”

Lithium-Ion batteries contribute a key technology for electric vehicles. Their production is being accelerated worldwide as electric vehicles become increasingly popular – not only because of the tremendous challenges of climate change. Currently, the homologation of Lithium-Ion batteries is subject to different regulations and authorities in different markets, provoking high efforts for battery testing in general. This comprehensive comparison of the latest updated Chinese regulation GB 38031–2020 with German as well as UN and ECE standards and regulations aims at exploring potentials for their harmonization. For this purpose, testing methods in the corresponding standards and regulations are systematically categorized, matched and evaluated. It is found that the testing methods for vibration, crush, altitude and pressure, thermal propagation and water immersion safety show the greatest differences. In corresponding deep dives, those testing methods are examined and challenged with regard to realistic vehicle usage situations and suggestions for further improvements are being derived. Finally, the authors discuss possible harmonization approaches, taking into account upcoming battery technological developments.

Is the Rural Population Caught in the Whirlwind of the Digital Divide?

Technology represents a benchmark ally for today’s rural world and is a sine qua non-condition for achieving sustainable development. Indeed, today the arrival of digitization and information and communication tools makes life easier for the inhabitants of the rural world in general and for those who work in agriculture. However, not everyone has and knows how to use these technologies. There are very visible differences between the rural world and the urban one in the accessibility and use of technology, especially among vulnerable people (unemployed, elderly, women, etc.), causing a digital divide that reflects the great discrimination suffered by the rural world, full of stereotypes and very traditional role assignments. The objective of this study is to evaluate the differences in terms of access and use of technology. For this reason, the results of a survey carried out on the Spanish rural population have been analyzed with the structural equations tool “PLS-SEM”. They show digital gaps, as well as a disturbance between the different gaps and the socioeconomic situation of users, which imposes the need to take immediate measures to reduce and fight against this type of inequality.

Estimating the Impact of Influenza Vaccination on Acute and ICU Hospital Bed Usage in an Influenza Season under Endemic COVID-19 in the US.

In 2021-2022, influenza vaccine coverage in the US dropped below pre-COVID-19 pandemic levels. Cocirculation of COVID-19 and influenza could place a substantial burden on hospital utilization in future seasons, particularly given the reduced exposure to influenza during the pandemic. We used a dynamic susceptible-exposed-infected-recovered model to simulate influenza transmission with varying influenza vaccine coverage against a background of COVID-19 circulation, in order to estimate acute and ICU hospital bed occupancy for both diseases. We evaluated two vaccine scenarios: egg-based quadrivalent influenza vaccine (QIVe) for all age groups or cell-based QIV (QIVc) for 0.5-64 year-olds with adjuvanted QIV (aQIV) for ≥65 year-olds. ICU bed availability was more limiting than general hospital bed availability, with a vaccine coverage of ≥70% required to avoid negatively impacting ICU bed availability in a high-incidence influenza season. The timing of disease peaks was a key factor together with vaccine coverage, with a difference of ≥50 days needed between peak influenza and COVID-19 bed usage together with 65% influenza vaccine coverage to avoid negative impacts. QIVc + aQIV resulted in lower bed occupancy which, while not substantial, may be critical in very high hospital resource usage situations. In a situation with co-circulating influenza and COVID-19, proactive vaccination planning could help to avert overwhelming healthcare systems in upcoming influenza seasons.

Fuzzy based energy proficient secure clustered routing (FEPSRC) for IOT-MWSN

IoT-Mobile Wireless Sensor Networks (IMWSNs) are being employed in a variety of simulators to visually demonstrate the exposure, energy usage situation, and expected life duration of Internet of Things (IoT) mobile sensors. The majority of academics have projected and expanded routing procedures in order to extend the network’s life cycle. In IMWSNs, clustering is the most important process for improving energy efficiency. In cluster approaches, each IoT sensor node provides the acquired data to the cluster-head of their own cluster. The cluster-head embraces the conscientiousness of gathering prepared information and directing it to the arranged network’s basestation. A fuzzy based energy proficient secure clustered routing (FEPSRC) is proposed in this research effort, which takes the residue energy, remoteness from the basestation, and compactness of IoT sensor nodes in its locality as input to the Fuzzy-Inference-System. For cluster-head selection, an eligibility ratio is calculated for each IoT sensor node. This protocol guarantees energy harmonizing by electing the preeminent IoT sensor node for the position of cluster-head, velocity of IoT sensor nodes are estimated and also provides best path for routing. The simulation consequence illustrates that projected fuzzy based energy proficient secure clustered routing condensed entire power expenditure, diminishes E-to-E delay, amplifies packet deliverance percentage and accomplishes maximal network life span.

Weibo Depression Posts Detection by Natural Language Processing

The goal of this paper is to detect depression based on the posts on social media. The dataset combined both tweet dataset and Weibo dataset scraped from the social media. To classify emotions, researchers have been using traditional models such as Bagging, Support Vector Machines, Decision tree, Multinomial Naïve Bayesian and K-nearest neighbor. In this paper, K- nearest neighbor is chosen based on the better precision in result. The main challenge is Chinese context translation and complexity of the context of the post. Finally, an UI page is designed to complete the mission to input a Weibo ID and output of the depression classification. Our approach achieves relatively higher quality results compared to the previous models in literature, while combining depression detection with the real-time social media posts. With this system, we demonstrate the practicability of our project by predicting the depression situation of Internet users through the model, to bring help to the depressed people or people with potential depression tendencies in society.

Affective feature knowledge interaction for empathetic conversation generation

ABSTRACT A popular chatbot can generate natural and human-like responses, and the crucial technology is the ability to understand and appreciate the emotions and demands expressed from the perspective of the user. However, some empathetic dialogue generation models only specialise in commonsense and neglect emotion, which can only get a one-sided understanding of the user's situation and makes the model unable to express emotion better. In this paper, we propose a novel affective feature knowledge interactive model named AFKI, to enhance response generation performance, which enriches conversation history to obtain emotional interactive context by leveraging fine-grained emotional features and commonsense knowledge. Furthermore, we utilise an emotional interactive context encoder to learn higher-level affective interaction information and distill the emotional state feature to guide the empathetic response generation. The emotional features are to well capture the subtle differences of the user's emotional expression, and the commonsense knowledge improves the representation of affective information on generated responses. Extensive experiments on the empathetic conversation task demonstrate that our model generates multiple responses with higher emotion accuracy and stronger empathetic ability compared with baseline model approaches for empathetic response generation.

True specs for dimmed and colour tuned LED luminaires

Colour tunable and dimmable LED lamps and LED luminaires are used increasingly in both domestic and professional lighting sector. The documentation of energy and light quality performance are often very limited for only for a single setting of multiple possible settings. Fifteen LED lamps and eight LED luminaires that are dimmable and tuneable white have been selected for demonstration of the variations in these parameters and possible errors in lighting design simulations. These have been tested in over twenty modes of operation including dimming from 100% to 25%, and correlated colour temperature (CCT) settings from 2200K to 6500K. The inherent standby power of these colour tuneable devices is measured, and the overall efficacy is evaluated in normal usage situations. In average for all tested LED lamps and luminaires 49% and 33% of the energy consumption is used in standby mode for 1h and 2h on time pr. day, respectively. The extensive set of test results show that there can be very large variations in e.g. the luminous flux, luminous efficacy and stroboscopic visibility measure (SVM), while others are very consistent over the modes of operation. The luminous flux shows variations of down to 22% of the maximum and rated value, and these lowest values occurs at the lowest CCT. The best performing has over 90% at all CCT settings. For the luminous efficacy most test results show a maximum value at 4000K or at the maximum CCT, and variations down to 20-50% of the maximum efficacy. The SVM is shown to increase to values over 1 and up to 2.4 at low dimming levels or low light output, demonstrating that stroboscopic effect will be visible for the majority of people and hence a problem in such settings. This is seen for approx. half the tested while the others have SVM < 0.4 for all settings and will not be visible over all settings. These results demonstrate the need for testing over the possible modes of operation to be able to correctly design and simulate a lighting installation using dimmable and colour tunable LED lamps and luminaires. The needed new dataformats and adoption of these from test laboratories to lighting software, in order for lighting designers and energy engineers to be able to make use of the extensive test results, are discussed.

The Impossible, the Unlikely, and the Probable Nudges: A Classification for the Design of Your Next Nudge

Nudging provides a way to gently influence people to change behavior towards a desired goal, e.g., by moving towards a healthier or more environmentally friendly lifestyle. Personalized and context-aware digital nudging (named smart nudging) can be a powerful tool for efficient nudging by tailoring nudges to the current situation of each individual user. However, designing smart nudges is challenging, as different users may need different supports to improve their behavior. Determining the next nudge for a specific user must be done based on the user’s current situation, abilities, and potential for improvement. In this paper, we focus on the challenge of designing the next nudge by presenting a novel classification of nudges that distinguishes between (i) nudges that are impossible for the user to follow, (ii) nudges that are unlikely to be followed, and (iii) probable nudges that the user can follow. The classification is tailored to individual users based on user profiles, current situations, and knowledge of previous behaviors. This paper describes steps in the nudge design process and a novel set of principles for designing smart nudges.