Search Results (29)

Identifying when and which adolescents are at increased risk of developing problematic social media use (PSMU) is critical for effective prevention and early intervention. Previous research has examined risk and protective factors using theory-driven (confirmatory-explanatory) approaches, such as regression models. However, few studies have simultaneously considered personal, peer, and parent characteristics to assess their relative contributions, and none have explored how these factors are structurally interrelated using data-driven (inductive–exploratory) approaches. To address these gaps, this study combines logistic regression and psychological network analysis to examine which personal, parent, and peer factors are most relevant in identifying at-risk/problematic social media use among adolescents. Using three waves of data analyzed cross-sectionally from N = 2441 secondary school students, adolescents were classified as normative (0–1 symptoms) or at-risk/problematic (2+ symptoms) users based on the Social Media Disorder Scale. Logistic regression showed that fear of missing out, impulsivity, depressive symptoms, intensity of meeting with friends, and reactive parental rules uniquely predicted at-risk/problematic use. Psychological network analysis identified self-esteem, attention problems, impulsivity, depressive symptoms, and life satisfaction as central, highly interconnected nodes. These findings show that theory- and data-driven approaches illuminate different aspects of PSMU risk, and that network analysis can generate novel hypotheses about underlying processes. Full article

Background: Dietary intake plays a crucial role in health research, yet existing methods for its measurement can lead to participant burden, lengthy recording, and human errors, and do not account for age-specific variations. Libro is a real-time diet-tracking mobile-based app offering flexible features. An automated food recording program within Libro was customized for young people vulnerable to eating misbehaviour. This preliminary study assessed its relative validity using a self-administered 24 h recall method as the reference method. Methods: The relative validity of Libro was tested by adopting a cross-over design that recorded food intake over a period of 3 non-consecutive weekdays and 1 weekend day with both methods. The participants were recruited online through a mental health research charity, and this study was conducted fully online. The primary outcome was the concordance of total energy intake between the two methods, with secondary outcomes focusing on the intake of protein, carbohydrates, fats, free sugars, fibre, and trans-fatty acids. Test–retest validity was assessed per method with the intraclass correlation coefficient; a Bland–Altman plot and t-test were performed to test agreement at the group level; correlation coefficient and cross-classification were performed to assess agreement at the individual level. Results: Forty-seven participants were included in the final analysis. The average intraclass correlation coefficient for energy intake measured by Libro over four days was 0.85 (95% CI: 0.76–0.91). Compared to Intake24, the average energy intake recorded using Libro was significantly lower (mean difference: −554 Kcal, 95% CI: −804.1 to −305.6 Kcal, p < 0.001), potentially driven by the reduced reporting of foods rich in free sugars. The correlation coefficient for average energy intake measured by Libro vs. Intake24 was 0.32 (95% CI: 0.03, 0.55), with only 27.7% of subjects classified in the same quartile with both methods (κ = 0.31, 95% CI: −0.03, 0.55). Concordance varied across specific dietary component measures. Conclusions: While Libro had good test–retest reliability if adopting a multiple administration method, it underreported energy and other aspects of dietary intake, along with poor classification performance compared to Intake24 in a population vulnerable to eating misbehaviour. We suggest that future studies improve user experience to increase compliance and data accuracy. Full article

Due to the deficiency of radio spectrum resources caused by the progress in technology, cognitive radio networks (CRNs) have made significant progress. CRNs have two types of users, namely, primary users (PUs) and secondary users (SUs). Considering that PUs have a higher priority and diversified data transmission requirements, this study divides PUs into two levels, namely, PU1s with a higher priority and PU2s with a lower priority. On the other hand, the occurrence of failures is inevitable in CRNs, which affects the data transmission of users. In this paper, combined with an adjustable PU packets transmission rate mechanism, a communication failure and repair mechanism with classified PUs based on the single-channel CRNs is proposed, and different preemption principles are set according to different system states. A queueing model is established and analyzed with a Markov chain, the performance index expressions that need targeted research are listed, numerical experiments are conducted, and the system performance change trends are obtained. The comparison experiment shows that the proposed communication failure and repair mechanism with classified PUs can improve the throughput of PU1 packets and reduce the blocking rate of PU1 packets compared with the conventional communication failure and repair mechanisms with unclassified PUs. Full article

In cognitive radio networks, channel assembling allows secondary users (SUs) to expand network capacity and improve spectrum utilization. Scheduling strategies only based on heterogeneous service classification cannot guarantee the delivery priority of vital elastic services in special scenarios such as emergency rescue. Therefore, a priority scheduling strategy with reserved queue (Ps-rq) is proposed in this work. A static factor is defined to classify SUs into elastic services and real-time services based on message type, while a dynamic factor is defined to differentiate high-priority elastic services based on information validity, message correlation and message size. The high-priority users in the interrupted elastic services are placed in the reserved queue to ensure its services. Accordingly, the scheduling algorithm and the dynamic channel access process is presented. A continuous-time Markov chain analysis is conducted and all possible transition states, trigger events, transition rates and transition conditions of the system starting from a general state are derived. Furthermore, evaluation indexes of system performance are obtained. Study cases and simulation results prove that the proposed strategy can enhance network capacity, reduce blocking probability and forced termination probability for secondary users, and notably enhance the performance of high-priority elastic services. In addition, we analyze the characteristics of Ps-rq through a comprehensive comparison with four other schemes. The experiment proves that the Ps-rq strategy can effectively improve the service quality of the vital elastic services on the basis of providing fair scheduling. Full article

The main objective of this manuscript is to collect, classify, and compile all available data about secondary mineral sources of REEs in the South-Eastern Europe (SEE). The material is generated from the extracting and processing sector, that might be possibly transformed in the business process becoming an important raw material for another industry. The management inventory guide will strengthen communication and dissemination efforts and simultaneously contribute to Europe’s self-sufficiency and support transitioning to green and digital technology. Identification of the knowledge gaps associated with secondary sources of REEs in SEE will contribute to connections between all partners being involved at the beginning, during the lifetime of products and at the end of the life cycle, represented with deposit owners, technology developers and potential processors, producers, and potential users. At the investigated area it was found 1835 individual landfills, most of them belonging to waste rocks. The total quantity of all material in SRM is about 3.2 billion tons on an area of about 100 km². The largest 95 individual landfills were selected as potential prospective landfills, containing about 1600 million tons of material. The estimated total potential of REEs (ΣREE) is more than 200 Kt. The largest quantities are found in landfills for coal fly ash and Cu flotation, which correspond to more than 80% of the ΣREE. Most of the promising sites are located in Serbia and North Macedonia. It has been calculated that the valorisation potential and perspectivity of REE₂O₃ is about 32.5 billion USD (prices from December 2022). According to the average concentrations of REEs, the most prospective are the red mud dams but their total volume is limited compared to massive amounts of coal fly ash landfills. The REEs content in all type of investigated materials, especially in coal fly ash in North Macedonia is twice as high as in other countries. Full article

Settlements situated downstream of hills and dams are invariably at risk of floods. Constant land use/land cover changes in the Vishwamitri Watershed and long-term climate variability have made Vadodara City more susceptible to river flooding in recent times. In the past, the local government authorities have only tried to solve this issue within the administrative boundary of the Vadodara City. This study demonstrates the importance of watershed scale investigation over administrative scale. The study presents a review of the current flooding and environmental degradation challenges that are affecting the Vishwamitri Watershed. For the analysis, the entire watershed was classified into two parts: (1) the upper watershed and (2) city limits. The data-deficient upper watershed was studied with the aid of site visits and secondary sources. The floodplain within the city limits was simulated for the 2005 peak discharge event (805 cumec) using Hydrological Engineering Centre—River Analysis System (HEC-RAS, 5.0.7.) software and HEC-GeoRAS (10.2.) (a set of procedures, tools, and utilities for processing geospatial data in ArcGIS using a graphical user interface). The simulated water surface elevation from HEC-RAS was validated with the in situ data available within the city limits. The generated floodplain extent map was used for conducting a primary flood impact survey and analysing the inundation-affected zones. Various aspects of the flood plain, like land use, land cover, built form, affected demography, river system, natural environment and habitat, were analysed during this survey. For the mitigation of floods and other maladies associated with the watershed, this probing calls for the restoration of the river ecosystem back to its original state to the maximum possible extent. Nature-based solutions were found to be the remedy for most of the issues pertaining to the floodplain. The study can potentially help concerned stakeholders of any flood-prone urban settlement to envision the issue of flooding with the whole river system and watershed in mind. This will discourage the usual approach of interventions limited to administrative boundaries. Full article

In the field of eHealth, several works have proposed telemonitoring systems focused on patients with chronic kidney disease (CKD) undergoing peritoneal dialysis (PD) treatment. Nevertheless, no secondary study presents a comparative analysis of these works regarding the technology readiness level (TRL) framework. The TRL scale goes from 1 to 9, with 1 being the lowest level of readiness and 9 being the highest. This paper analyzes works that propose telemonitoring systems focused on patients with CKD undergoing PD treatment to determine their TRL. We also analyzed the requirements and parameters that the systems of the selected works provide to the users to perform telemonitoring of the patient’s treatment undergoing PD. Fourteen works were relevant to the present study. Of these works, eight were classified within TRL 9, two were categorized within TRL 7, three were identified within TRL 6, and one within TRL 4. The works reported with the highest TRL partially cover the requirements for appropriate telemonitoring of patients based on the specialized literature; in addition, those works are focused on the treatment of patients in the automated peritoneal dialysis (APD) modality, which limits the care of patients undergoing the continuous ambulatory peritoneal dialysis (CAPD) modality. Full article

Decision support systems based on machine learning models should be able to help users identify opportunities and threats. Popular model-agnostic explanation models can identify factors that support various predictions, answering questions such as “What factors affect sales?” or “Why did sales decline?”, but do not highlight what a person should or could do to get a more desirable outcome. Counterfactual explanation approaches address intervention, and some even consider feasibility, but none consider their suitability for real-time applications, such as question answering. Here, we address this gap by introducing a novel model-agnostic method that provides specific, feasible changes that would impact the outcomes of a complex Black Box AI model for a given instance and assess its real-world utility by measuring its real-time performance and ability to find achievable changes. The method uses the instance of concern to generate high-precision explanations and then applies a secondary method to find achievable minimally-contrastive counterfactual explanations (AMCC) while limiting the search to modifications that satisfy domain-specific constraints. Using a widely recognized dataset, we evaluated the classification task to ascertain the frequency and time required to identify successful counterfactuals. For a 90% accurate classifier, our algorithm identified AMCC explanations in 47% of cases (38 of 81), with an average discovery time of 80 ms. These findings verify the algorithm’s efficiency in swiftly producing AMCC explanations, suitable for real-time systems. The AMCC method enhances the transparency of Black Box AI models, aiding individuals in evaluating remedial strategies or assessing potential outcomes. Full article

The concept of radiological sustainability has been reviewed using the possibility of the reutilization of NORMs regarding the 2050 sustainable development concepts. This study is reviewing the capability of re-production of NORM residues based on their properties and the applicable treatment before reutilization. Linking the waste producers and construction materials industry as mineral end-users could contribute to the creation of industrial symbiosis and result in waste reduction and preserving natural resources on one hand, and CO₂ reducing and energy saving on the other hand. According to EU-BSS (European Basic Safety Standards Directive), the characterization of NORMs as a secondary raw material for use as construction materials is necessary, however as an additive or secondary material their radiological behavior and material properties are quite important regardless of their origin. This is to say that the reutilization of NORM residues provides a better financial and environmental solution while reducing possible radiological effects on humans. NORM residue or waste could become a high material resource for the cement industry. In this regard, the assessment of radium equivalents (Raeq) and external and internal indexes (I-indexes) are useful tools to classify NORM residues before their inclusion in building products. This assessment and/or indexes reflect the risk of external exposure much better than the specific activity concentration of Ra-226, Th-232 and K-40. Equally, building material properties such as density and thickness should be taken into consideration when designing building materials that contain NORM residue. Furthermore, mixtures or additives of NORM residues are proven to result in the reduction of activity concentration in the raw residue with other raw materials, and this offers flexible reuse options depending on the final product. By integrating radiological considerations into sustainable development initiatives, we can work towards a safer, healthier and more sustainable future. Full article

The implementation of bike-sharing systems (BSSs) is expected to lead to modifications in the travel habits of transport users, one of which is the choice of travel mode. Therefore, this research focuses on the identification of factors influencing the shift of private car users to BSSs based on stated preference survey data from the city of Alexandroupolis, Greece. A binary logit model is employed for this purpose. The estimation results indicate the impacts of gender, income, travel time, travel cost and safety-related aspects on the mode shift, through which behavioural insights are derived. For example, car users are found to be twice as sensitive to the cost of BSSs than to that of car. Similarly, they are highly sensitive to BSS travel time. Based on the behavioural findings, policy measures are suggested under the following categories: (i) finance, (ii) regulation, (iii) infrastructure, (iv) campaigns and (v) customer targeting. In addition, a secondary objective of this research is to obtain insights from the comparison of the specified logit model with a machine learning approach, as the latter is slowly gaining prominence in the field of transport. For the comparison, a random forest classifier is also developed. This comparison shows a coherence between the two approaches, although a discrepancy in the feature importance for gender and travel time is observed. A deeper exploration of this discrepancy highlights the hurdles that often occur when using mathematically more powerful models, such as the random forest classifier. Full article

A number of mobile health apps related to coronavirus infectious disease 2019 (COVID-19) have been developed, but research into app content analytics for effective surveillance and management is still in its preliminary stages. The present study aimed to identify the purpose and functions of the currently available COVID-19 apps using content analysis. The secondary aim was to propose directions for the future development of apps that aid infectious disease surveillance and control with a focus on enhancing the app content and quality. Prior to conducting an app search in the App Store and the Google Play Store, we reviewed previous studies on COVID-19 apps found in Google Scholar and PubMed to examine the main purposes of the apps. Using the five selected keywords based on the review, we searched the two app stores to retrieve eligible COVID-19 apps including those already addressed in the reviewed literature. We conducted descriptive and content analyses of the selected apps. We classified the purpose types of the COVID-19 apps into the following five categories: Information provision, tracking, monitoring, mental health management, and engagement. We identified 890 apps from the review articles and the app stores: 47 apps met the selection criteria and were included in the content analysis. Among the selected apps, iOS apps outnumbered Android apps, 27 apps were government-developed, and most of the apps were created in the United States. The most common function for the iOS apps (63.6%) and Android apps (62.5%) was to provide COVID-19-related knowledge. The most common function among the tracking apps was to notify users of contact with infected people by the iOS apps (40.9%) and Android apps (37.5%). About 29.5% of the iOS apps and 25.0% of the Android apps were used to record symptoms and self-diagnose. Significantly fewer apps targeted mental health management and engagement. Six iOS apps (6/44, 13.6%) and four Android apps (4/24, 16.7%) provided behavioral guidelines about the pandemic. Two iOS apps (2/44, 4.5%) and two Android apps (2/24, 8.3%) featured communication functions. The present content analysis revealed that most of the apps provided unilateral information and contact tracing or location tracking. Several apps malfunctioned. Future research and development of COVID-19 apps or apps for other emerging infectious diseases should address the quality and functional improvements, which should begin with continuous monitoring and actions to mitigate any technical errors. Full article

Despite the large quantities of secondary materials flowing within the built environment, their actual volume and respective waste management processes are not accurately known and recorded. Consequently, various sustainability and material efficiency policies are not supported by accurate data and information-reporting associated with secondary materials’ availability and sourcing. Many recent studies have shown that the integration of digital technologies such as city information management (CIM), building information modeling (BIM), and blockchain have the potential to enhance construction waste management (CWM) by classifying recycled materials and creating value from waste. However, there is insufficient guidance to address the challenges during the process of CWM. Therefore, the research reported in this paper aims to develop a blockchain-enhanced construction waste information management conceptual framework (BeCW). This paper is the first attempt to apply the strengths of integrated information-management modeling with blockchain to optimize the process of CWM, which includes a WasteChain for providing a unified and trustworthy credit system for evaluating construction-waste-recyclability to stakeholders. This is enabled through the use of blockchain and self-executing smart contracts to clarify the responsibility and ownership of the relevant stakeholders. As a result, this study provides a unified and explicit framework for referencing which quantifies the value-contribution of stakeholders to waste-recovery and the optimization of secondary construction materials for reuse and recycling. It also addresses the issue of sustainable CWM through information exchange at four levels: user, application, service, and infrastructure data levels. Full article

The education sector is recognised as an ideal platform to promote good nutrition and decision making around food and eating. Examining adolescents in this setting is important because of the unique features of adolescence compared to younger childhood. This systematic review and meta-analysis examine interventions in secondary schools that provide a routine meal service and the impact on adolescents’ food behaviours, health and dining experience in this setting. The review was guided by Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Checklist and Cochrane Handbook recommendations. Studies published in English searched in four databases and a hand search yielded 42 interventions in 35 studies. Risk of bias was assessed independently by two reviewers. Interventions were classified using the NOURISHING framework, and their impact analysed using meta-analysis, vote-counting synthesis or narrative summary. The meta-analysis showed an improvement in students selecting vegetables (odds ratio (OR): 1.39; 1.12 to 1.23; p = 0.002), fruit serves selected (mean difference (MD): 0.09; 0.09 to 0.09; p < 0.001) and consumed (MD: 0.10; 0.04 to 0.15; p < 0.001), and vegetable serves consumed (MD: 0.06; 0.01 to 0.10; p = 0.024). Vote-counting showed a positive impact for most interventions that measured selection (15 of 25; 41% to 77%; p = 0.002) and consumption (14 of 24; 39% to 76%; p = 0.013) of a meal component. Interventions that integrate improving menu quality, assess palatability, accessibility of healthier options, and student engagement can enhance success. These results should be interpreted with caution as most studies were not methodologically strong and at higher risk of bias. There is a need for higher quality pragmatic trials, strategies to build and measure sustained change, and evaluation of end-user attitudes and perceptions towards intervention components and implementation for greater insight into intervention success and future directions (PROSPERO registration: CRD42020167133). Full article

Spectrum sensing is an important function in radio frequency spectrum management and cognitive radio networks. Spectrum sensing is used by one wireless system (e.g., a secondary user) to detect the presence of a wireless service with higher priority (e.g., a primary user) with which it has to coexist in the radio frequency spectrum. If the wireless signal is detected, the second user system releases the given frequency to maintain the principle of not interfering. This paper proposes a machine learning implementation of spectrum sensing using the entropy measure as a feature vector. In the training phase, the information about the activity of the wireless service with higher priority is gathered, and the model is formed. In the classification phase, the wireless system compares the current sensing report to the created model to calculate the posterior probability and classify the sensing report into either the presence or absence of wireless service with higher priority. This paper proposes the novel application of the Fluctuation Dispersion Entropy (FDE) measure recently introduced in the research community as a feature vector to build the model and implement the classification. An improved implementation of the FDE (IFDE) is used to enhance the robustness to noise. IFDE is further enhanced with an adaptive method (AIFDE) to automatically select the hyper-parameter introduced in IFDE. Then, this paper combines the machine learning approach with the entropy measure approach, which are both recent developments in spectrum sensing research. The approach is compared to similar approaches in literature and the classical energy detection method using a generated radar signal data set with different conditions of SNR(dB) and fading conditions. The results show that the proposed approach is able to outperform the approaches from literature based on other entropy measures or the Energy Detector (ED) in a consistent way across different levels of SNR and fading conditions. Full article

Background: We evaluated the impact of prior statin use on successful reperfusion and stroke outcomes after endovascular treatment (EVT). Method: Using consecutive multicenter databases, we enrolled acute ischemic stroke patients receiving EVT between 2015 and 2021. Patients were classified into prior statin users and no prior statin users after a review of premorbid medications. The primary outcome measure was successful reperfusion defined as modified TICI grade 2b or 3 after EVT. Secondary outcome measures were early neurologic deterioration (END) and a 3-month modified Rankin Scale (mRS) score of 0 to 2. Results: Among 385 patients receiving EVT, 74 (19.2%) were prior statin users, who had a significantly higher successful reperfusion rate compared with no prior statin users (94.6% versus 78.8%, p = 0.002). Successful reperfusion and END occurrence were improved according to statin intensity with a dose–response relationship. In multivariate analysis, prior statin was associated with successful reperfusion after EVT (adjusted odds ratio (95% confidence interval) 5.31 (1.67–16.86)). In addition, prior statin was associated with a lower occurrence of END and good functional status. Conclusion: Our study showed that prior statin use before ischemic stroke might improve successful reperfusion and stroke outcomes after EVT. Full article