Personal Information Research Articles

Contributions to the study of the Trichoniscidae (Isopoda, Oniscidea). I.I. analysis of the morphological characters

Abstract. – Following an introductory presentation of the problems raised by the examination of the morphological characters according to the phylogenetic systematics, a detailed analysis of the morphological characters of the family Trichoniscidae within the context of the characters of the representatives of the suborder Oniscidea, is achieved, based on the author’s personal data and an examination of the literature. Starting from this analysis we argue once more the author’s conclusions concerning controversial issues: the monophyly of the terrestrial isopods; the dichotomy of the suborder Oniscidea; the dichotomy of the infraorder Ligiamorpha; the dichotomy of the monophylum Orthogonopoda

Social Humanoid Robot dalam Perspektif Syariat dan Etika

Social humanoids robots are robots designed to interact with people in a manner resembling interaction betweenn humans, with the appearance and manners resembling that of man. This study looking at the use of social humanoids robots from the perspektive of islam and ethics, to ensure that this technology used by way in accordance with religious and moral. The perspektive of islam the use of these robots acceptable for used in the right people, as in the helath sector, education, and public services. Islam teaches that everything is benefecial to mankind and not break acceptable. But is also stressed in creating an image or effigy that imitates life, although robots used for the purpose of education or medical and not treated as living creatures acceptable. It is also vital to keep interaction in islamic culuture and attitude with robot. Make sure that privacy and dignity of the individual are not violated. Ethically the use of social humanoid robots should consider the social impact. Justice and human well being as a whole, this technology should help relieve human work without causing mass unemployment, maintains physical and user data, and don’t overplace human interactions with other human beings. Robot must be designed and programmed to treat humans with respect, not to manipulate emotional or unethical human decisions.

"A clinical case of ossification of soft tissues of the plantar surface in the projection of the calcaneus."

Introduction:Foci of pathological ossification that occur outside the bones of the skeleton- ossifications can complicate the course of severe trauma, including with central nervous system damage or be a symptom of a genetic or systemic disease of the skeleton. Cases of ossifications in adults, less often in children, are described in the literature. Description of the observation:An 8-year-old patient with an uncomplicated comorbid status, without anamnestic data of a previous injury, was hospitalized for acute walking function disorder and a local picture of an acute inflammatory process. During the examination, ossification of the soft tissues of the plantar surface in the projection of the calcaneus was detected and removed. Conclusion:The authors present a clinical case of the detection of ossification of soft tissues of the plantar surface in the projection of the calcaneus in a pediatric patient, the clinical picture required differential diagnosis with acute inflammatory disease of the calcaneus and surrounding tissues. Keywords:ossification, childhood, plantar surface of the calcaneus Patient consent. The patient's legal representative gave informed voluntary written consent to the publication of personal medical information in an impersonal form in the journal of Pediatric Surgery.

Perspectives of Clients and Health Care Professionals on the Opportunities for Digital Health Interventions in Cerebrovascular Disease Care: Qualitative Descriptive Study.

Cerebrovascular diseases (CVDs) are a major and potentially increasing burden to public health. Digital health interventions (DHIs) could support access to and provision of high-quality health care (eg, outcomes, safety, and satisfaction), but the design and development of digital solutions and technologies lack the assessment of user needs. Research is needed to identify opportunities to address health system challenges and improve CVD care with primary users of services as the key informants of everyday requirements. This study aims to identify opportunities for DHIs from clients' and health care professionals' perspectives to address health system challenges and improve CVD care. This study used a qualitative, descriptive approach. Semistructured, in-person interviews were conducted with 22 clients and 26 health care professionals in a single tertiary-level hospital in Finland between August 2021 and March 2022. The data were analyzed using a deductive and inductive content analysis. Identified opportunities for DHIs in CVD care were organized according to clients, health care professionals, and data services and classified into 14 main categories and 27 generic categories, with 126 subcategories of requirements. DHIs for clients could support the long-term management of health and life changes brought on by CVD. They could provide access to personal health data and offer health information, support, and communication possibilities for clients and their caregivers. Health care professionals would benefit from access to relevant patient data, along with systems and tools that support competence and decision-making. Intersectoral and professional collaboration could be promoted with digital platforms and care pathways. DHIs for data services could enhance care planning and coordination with novel predictive data and interoperable systems for data exchange. The combined study of client and health care professional perspectives identified several opportunities and requirements for DHIs that related to the information, availability, quality, acceptability, utilization, efficiency, and accountability challenges of health systems. These findings provide valuable social insights into digital transformation and the emerging design, development, and use of user-centered technologies and applications to address challenges and improve CVD care and health care.

An examination of the perception of future time among university students from the perspective of demographic variables

This study aimed to examine the future time perspective of students in the faculty of sports sciences and the factors influencing this perception in relation to various variables. The population of the study consists of university students, while the sample includes a total of 266 students (128 males, 48.1%, and 138 females, 51.9%) studying at the Faculty of Sports Sciences at Kütahya Dumlupınar University. The data collection instruments include a personal information form developed by the researcher, as well as the Future Time Perspective Scale (FTPS), developed by Husman and Shell (1996) and adapted to Turkish culture with validity and reliability studies conducted by Avcı and Erden (2009). In this study, a non-experimental causal-comparative model, one of the quantitative research methods, was employed to identify the current situation. The data were analyzed using the SPSS program. Descriptive statistics such as means, standard deviations, and percentages were included in the analysis. Skewness and kurtosis values were examined, revealing a normal distribution, and Independent Samples T-Test and MANOVA were used to compare the differences. According to the findings, no statistically significant differences were found regarding students' gender, participation in recreational sports, and academic success (p>0.05). However, significant differences were found in favor of students in upper classes based on the class variable (p<0.05). These findings indicate that students become more focused on career goals as they approach graduation, which creates differences in their future time perspectives.

Navigating The Duality: Privacy And Security Concerns In Knowledge Management Systems (KMS)

This study delves into the intricate relationship between privacy and security within Knowledge Management Systems (KMS). It identifies and analyzes distinct employee perceptions of these intertwined issues within a company's KMS environment. By exploring these perspectives, the authors aim to dispel the confusion surrounding privacy and security in KMS settings. The research employed an explanatory sequential mixed method design. First, a questionnaire survey was distributed directly to KM staff across three companies. This was followed by semi-structured interviews with four KM staff members. The findings reveal a high level of employee awareness regarding the importance of KMS and, consequently, the significance of personal information privacy and security. The study further distinguishes between privacy and security concerns within KMS. Privacy concerns, differentiated across three dimensions: confidentiality, trust, and behavior, are primarily viewed from the organizational layer. Security aspects, on the other hand, are seen as aligned with the ICT layer, governed by legal frameworks and KMS architecture.

Predicting Counselor Candidates’ Altruism in Türkiye: The Role of Wellness, Mindfulness, and Gender

This study aimed to assess how well the wellness, mindfulness, and gender of counselor candidates could predict their level of altruism. A total of 357 senior students from Guidance and Psychological Counseling programs across 18 universities in various Turkish cities participated. The data were collected using the Personal Information Form, Altruism Scale, Well-Star Scale, and Mindfulness Scale. The results of the multiple linear regression analysis indicated that these variables collectively explained 24% of the variance in altruism. Wellness and gender emerged as significant predictors of altruism among the candidates, with wellness being the strongest predictor. Notably, wellness was positively associated with altruism, and female candidates scored higher in altruism than their male counterparts. However, mindfulness did not significantly influence the prediction of altruism. The study’s results were examined in the context of the literature, and recommendations for counselor educators and researchers were provided based on these findings.

Optimized Multi-Level Security for Content Contribution and Retrieval in Online Social Networks using a Content Visualization Mechanism

Online social networks have become an integral part of modern communication, providing platforms for users to share personal information, media, and opinions. However, these platforms face significant challenges in preserving user privacy while ensuring efficient data retrieval and maintaining data integrity. Existing privacy preservation methods, such as PPK-MEANS, CFCAF, and CLDPP, are limited in their ability to handle the growing complexity and scale of user data, often leading to inefficiencies such as high Content Retrieval Time (CRT), increased Information Loss (IL), and compromised data accuracy. These inefficiencies are crucial to address, as they can degrade the user experience by causing delays, compromising data integrity, and limiting system scalability. High CRT frustrates users, while increased IL reduces data accuracy, undermining trust and system reliability. The primary issue addressed in this study is the need for an advanced privacy-preserving mechanism that can provide multilevel security while maintaining optimal system performance. To overcome these limitations, the Layered Secure Online Collaborative Verification (LSOCV) algorithm is proposed, designed to offer a scalable solution with tiered privacy controls based on user requirements. LSOCV enhances Privacy Retrieval Accuracy (PRA), significantly reduces CRT, and minimizes IL. The experimental results show that LSOCV achieved a PRA of 91.97%, reduced CRT to 7ms, and decreased IL by up to 8% for 500KB files, outperforming existing approaches. This method provides robust privacy protection and efficient data handling on social networks, with the potential for future application in big data environments, such as Hadoop, to ensure scalable, secure, and efficient privacy-preserving solutions.

"Scientists are People too": Biology Students Relate More to Scientists When They are Humanized in Course Materials.

Featuring scientists in classroom materials provides opportunities for students to relate to scientists as role models and see themselves in science. However, it is unclear what information students find most relatable when encountering scientists throughout their education. In this study, we manipulated the amount and type of information provided about scientists featured in biology courses. Within the context of activities focused on a scientist's research study and data, we provided students with either no personal information about the scientist (Control treatment), pictures of the scientist (Visual treatment), or pictures and humanizing details about the scientist (Humanizing treatment). We asked students to describe how they related to the featured scientist, and qualitatively coded responses. Results showed that students related to the scientist's 1) professional research interests (e.g., research topic, science as a career) and 2) personal information (e.g., life experiences, hobbies, personality characteristics, race/ethnicity, gender, and socioeconomic status). In addition, we observed differences in how students related to scientists across our treatments. Students were twice as likely to relate to featured scientists, and related in a greater variety of ways, when course materials included personal, humanizing information. We discuss implications for curriculum development and call for intentionality in how we present scientists throughout biology education.

Improving university students awareness of cookies used by web technologies

Web technologies are developing rapidly and although the advantages provided by these technologies are quite numerous, they also bring with them problems related to the security of personal data. Especially, the fact that personal data is obtained by using cookies on websites requires web users to be made aware of this issue. While cookies have important advantages in terms of providing fast and easy access to websites, they also have the ability to record and process what web users read, location information, IP addresses, user device information, e-mail addresses, usernames and passwords. It is not legal to record this data without the explicit consent of the person. However, this situation is generally unknown to users or users who have incomplete information. The aim of this study is to raise awareness of university students who frequently use web technologies about cookies so that they can ensure their personal data security with their own efforts. In line with this purpose, a mixed method study was implemented. 25 university students participated in this study. Participants were determined using the convenience sampling method. Online interactive training consisting of 12 lesson hours was organized for the participants. Since quantitative and qualitative data from the participants were collected simultaneously and support each other, a concurrent mixed method model was applied. Quantitative data were collected with awareness pre-test and post-test, and qualitative data were collected with the interview form. Test scores obtained by a single group at different times were analyzed with the Paired Samples t-Test. Qualitative data were also analyzed by content analysis method. As a result of the study, it was revealed that the participants' awareness levels about cookies increased significantly. In addition, the participants' knowledge about risks, advantages and working principles of cookies increased.

Privacy during pandemics: Attitudes to public use of personal data

In this paper we investigate people’s attitudes to privacy and sharing of personal data when used to help society combat a contagious disease, such as COVID-19. Through a two-wave survey, we investigate the role of personal characteristics, and the effect of information, in shaping privacy attitudes. By conducting the survey in Norway and Sweden, which adopted very different strategies to handle the COVID-19 pandemic, we analyze potential differences in privacy attitudes due to policy changes. We find that privacy concern is negatively correlated with allowing public use of personal data. Trust in the entity collecting data and collectivist preferences are positively correlated with this type of data usage. Providing more information about the public benefit of sharing personal data makes respondents more positive to the use of their data, while providing additional information about the costs associated with data sharing does not change attitudes. The analysis suggests that stating a clear purpose and benefit for the data collection makes respondents more positive about sharing. Despite very different policy approaches, we do not find any major differences in privacy attitudes between Norway and Sweden. Findings are also similar between the two survey waves, suggesting a minor role for contextual changes.

Recognition in Personal Data: Data Warping, Recognition Concessions, and Social Justice

Data-related harm and injustice are commonly viewed through instrumental, procedural, distributional, or representational theories of social justice. These theories do not account for the social injustice that occurs through the lack of recognition of individuals when data are first conceptualized. We explore the recognition of individuals in data conceptualization by drawing from information systems (IS) literature on data artifacts that acknowledges the fact that data are comprised of semantics and formats. Guided by recognition theory, we studied a project to expand sexual orientation and gender identity (SOGI) data collection at a public, U.S.-based LGBTQ+ welcoming university. We found that while the actors involved worked towards recognizing in SOGI data the fact that identities are layered, non-binary, plural, and fluid, the data themselves still misrecognized individuals due to data warping. We argue that data warping occurs because of recognition concessions between social recognition through data semantics and systems recognition through data formats. Such concessions are both necessary for some recognition but accessory to misrecognition. Our findings have implications for recognition theory, data justice, and information systems research, as well as for personal data in practice.

The corporate cultivation of digital resignation in policymaking: How weak US regulations enable data trafficking to China

Studies of digital resignation focus on the idea of the corporate cultivation of digital resignation among users, an area of intense importance when examining user data sharing with corporations. To best appreciate the implications of digital resignation in a transnational context, it is important to consider not just resignation by users, but by policymakers. Weak digital policymaking in the US context enables continued digital resignation by users. It also allows for data trafficking, or government directed movement of user data across borders without clear user consent. This paper compares digital policymaking using three cases of national, regional, and sector-based digital policymaking. The commentary argues that while US policymakers often cast concerns on Chinese influence in the US, such arguments obscure the systematic resignation of US policymakers to a weak and ineffectual domestic digital oversight system. Examining digital resignation through a national government lens enables a more complete view of the transnational implications of this important concept.

Secure fuzzy retrieval protocol for multiple datasets

With the diversification of data sources and the massive growth of datasets, data retrieval has become increasingly complex and time-consuming. In the traditional retrieval method, if a user wants to query multiple datasets, the general approach is to retrieve them one by one in order, which may lead to duplication of work and waste of resources. Private set intersection is a specific issue in secure multi-party computation. It allows several participants, each holding different sets, to jointly calculate the intersection of their sets without revealing any information other than the intersection. This method is naturally suitable for data fusion. In this work, we propose a secure fuzzy retrieval protocol for multiple datasets. First, we use private set intersection technology to fuse multiple datasets. Then, we perform secure retrieval based on this fused dataset, effectively avoiding the waste of resources caused by separate retrievals, thereby maximizing resource efficiency. It is worth mentioning that the protocol proposed in this paper can also be used for fuzzy retrieval to improve the user’s search experience. More importantly, the protocol can maximize privacy protection during the retrieval process, including strict protection of sensitive information such as retrieval keywords, ensuring that user data and query intentions will not be leaked during the entire retrieval process. Finally, we provide a rigorous security proof and demonstrate the effectiveness of the protocol through simulation experiments.

Adherence to melanoma screening and surveillance skin check schedules tailored to personal risk.

Population-wide skin cancer screening is not currently recommended in most countries. Instead, most clinical guidelines incorporate risk-based recommendations for skin checks, despite limited evidence around implementation and adherence to recommendations in practice. We aimed to determine adherence to personal risk-tailored melanoma skin check schedules and explore reasons influencing adherence. Patients (with/without a previous melanoma) attending tertiary dermatology clinics at the Melanoma Institute Australia, Sydney, Australia, were invited to complete a melanoma risk assessment questionnaire via iPad and provided with personal risk information alongside a risk-tailored skin check schedule. Data were collected from the risk tool, clinician-recorded data on schedule deviations, and appointment booking system. Post-consultation, we conducted semi-structured interviews with patients and clinic staff. We used a convergent segregated mixed methods approach for analysis. Interviews were audio recorded, transcribed and data were analysed thematically. Participant data were analysed from clinic records (n = 247) and interviews (n = 29 patients, 11 staff). Overall, there was 62% adherence to risk-tailored skin check schedules. In cases of non-adherence, skin checks tended to occur more frequently than recommended. Decisions to deviate were similarly influenced by patients (44%) and clinicians (56%). Themes driving non-adherence among patients included anxiety and wanting autonomy around decision-making, and among clinicians included concerns around specific lesions and risk estimate accuracy. There was moderate adherence to a clinical service program of personal risk-tailored skin check recommendations. Further adherence may be gained by incorporating strategies to identify and assist patients with high levels of anxiety and supporting clinicians to communicate risk-based recommendations with patients.

Concept of a human-centered electronic health record

Abstract This paper examines the challenges and opportunities associated with the implementation of Electronic Health Records (EHR) globally and, specifically, in Germany. While EHRs promise improved accessibility and comprehensibility of patient data, healthcare professionals face challenges such as resource constraints and the need for user-friendly interfaces. The focus is on the design of the graphical user interface (GUI) to enhance accessibility for both healthcare professionals and patients. The proposal suggests a three-dimensional digital human model as the basis for organizing and retrieving medical documents, integrating visual and chronological aspects. Additionally, the paper explores the potential benefits of incorporating personal health data, addressing data access separation concerns for users and promoting interdisciplinary information exchange. The concept-study concludes by highlighting the importance of a well-designed user interface in facilitating effective utilization of EHR systems.

Vaccination coverage of persons using hospital outpatient mental health services at Heraklion, Crete, Greece: A cross-sectional study during pandemic.

Persons with mental health disorders are vulnerable, with demanding care needs. This cross-sectional study aimed to report on their vaccination coverage against COVID-19 and common vaccine-preventable diseases. The study was conducted from September to November 2022 at the psychiatric outpatient settings of Venizeleion General Hospital of Heraklion, Crete, Greece, and the Mental Health Center of Heraklion. Information was collected via personal medical interview and prescription data. The study population included 361 participants, with a mean age of 49.8±14.3 years, 59.0% women. The most common diagnoses were recurrent depressive disorder (24.1%), schizophrenia (22.7%), anxiety disorder (21.3%), and bipolar disorder (15.7%). Vaccination coverage against seasonal influenza for 2020, 2021, and 2022 was 43.2%, 39.8%, and 40.7%, respectively. Pneumococcal vaccination included the conjugate (28.8%) and polysaccharide (7.7%) vaccines. Vaccination against Tetanus, Diphtheria, Pertussis (TDP) accounted for 11.0%, Hepatitis B for 5.8%, and Herpes Zoster for 27.1%. COVID-19 vaccination, with at least one booster dose, reached a high 73.6%. Patients with severe mental illness, 139 out of 361, were less likely to have contracted COVID-19 (50/139, 35.9%) than those without (108/222, 46.6%), (p=0.018). Coverage with polysaccharide vaccine, TDP, and Hepatitis B, was very low. Patients annually vaccinated for influenza in the last three consecutive years were most likely to be fully vaccinated for pneumococcal disease and for COVID-19 (with at least one booster dose). Future research and clinical practice should focus on identifying patients at risk of not receiving preventive services, such as vaccines.

Research of Federated Learning Application Methods and Social Responsibility

Federated learning is a multi-party distributed machine learning system that allows each participant to complete the training task without their data out of the locality. The real meaning of the realization of data availability is invisible to protect personal privacy and data security. Although some institutions need to obtain their data's benefit by sharing their own data sets, the potential trust risks lead to the data not being better utilized cooperatively. In addition,regulatory requirements on data computing and sharing are different in various countries. These contribute to enormous challenges in the actual application of federated learning. In this research, we make a comprehensive review of the application patterns of federated learning in different fields. Then, we illustrate the social responsibilities of federated learning from four dimensions, including compliance application, system security mechanism, the trust mechanism, and ethical security. Finally, based on the current characteristics and regulatory requirements of federated learning, we discuss the future research directions for federated learning.

Privacy-preserving data mining optimization for big data analytics using deep reinforcement learning

The rapid growth of big data analytics has heightened concerns about data privacy, necessitating the development of advanced privacy-preserving techniques. This research addresses the challenge of optimizing privacy-preserving data mining (PPDM) for big data analytics through the innovative application of deep reinforcement learning (DRL). We propose a novel framework that integrates DRL to dynamically balance privacy and utility, ensuring robust data protection while maintaining analytical accuracy. The framework employs a reinforcement learning agent to adaptively select optimal privacy-preserving strategies based on the evolving data environment and user requirements, while ensuring compliance with the latest security and privacy standards such as ISO/IEC 27001:2023. Experimental results demonstrate significant improvements in both privacy protection and data utility, surpassing traditional PPDM methods. Our findings highlight the potential of DRL in enhancing privacy-preserving mechanisms, offering a scalable and efficient solution for secure big data analytics.

Sum rate optimization in STAR-RIS assisted multiuser massive MIMO-OFDM VLC systems

Visible Light Communication (VLC) offers a promising solution for future networks, leveraging existing lighting infrastructure in indoor environments. However, VLC requires a direct line of sight to function which can be limiting. Reconfigurable Intelligent Surface (RIS) is a new technology that can bend light and radio waves, addressing this limitation in VLC. RIS come in three types: passive which reflects signals, active that boosts and reflects signals, and Simultaneous Transmission and Reflection (STAR)-RIS which can both reflect and transmit signals simultaneously. STAR-RIS offers the most control over the signal. In this paper, we propose a new multi-user VLC system with a massive Multiple-Input, Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) architecture, leveraging STAR-RIS to optimize data rates and improve coverage, particularly in non-line-of-sight scenarios. We formulate a system model and solve a convex optimization problem to determine the optimal transmission and reflection coefficients for STAR-RIS elements, aiming to maximize the total sum rate for all users. By employing maximum ratio transmission precoding, we minimize interference among users and demonstrate significant performance gains. Simulation results show that our proposed energy splitting-based STAR-RIS configuration outperforms traditional mode selection and time switching approaches with fixed or random coefficients, yielding substantial improvements in data rates and user experience. This study offers the first detailed exploration of STAR-RIS in VLC systems, highlighting its potential for future high-speed, multi-user communication networks. Our findings set the stage for further research into optimizing VLC systems using STAR-RIS, particularly in complex environments with non-line-of-sight users and massive MIMO-OFDM configurations.