Search Results (98)

Urban mobility is increasingly affected by air pollution and traffic congestion caused by conventional private vehicles, as well as by insufficient flexibility of public transport. Micromobility devices (MMDs) can mitigate these and other negative impacts on quality of life due to their distinctive characteristics, the significance of which is investigated in this research. To address these challenges facing the modern city, a system of 15 hierarchically unstructured criteria influencing the deployment of MMDs in urban areas was established. The relative weights of these criteria were calculated based on the assessments of 16 experts and the criterion weights were determined using four multi-criteria decision-making (MCDM) methods: ARTIW-L (Average Rank Transformation into Weight—Linear), ARTIW-N (Average Rank Transformation into Weight—Non-Linear), DPW (Direct Percentage Weight), and AHP (Analytic Hierarchy Process). The results indicate that the expert judgments are consistent, as Kendall’s coefficient of concordance 0.406 is 3.8 times greater than the minimum value of 0.106 (at a significance level 0.05 and 14 degrees of freedom). In addition, the consistency ratios (C.R.) calculated from the AHP pairwise comparison matrices were below 0.1. The demonstrated consistency of the expert judgements and the compatibility of all matrices justify adopting the average of the relative weights obtained using the four MCDM methods as the final solution. According to the experts, the most important criteria for MMD deployment are travel safety (0.1336), travel duration (0.1302), the influence of infrastructure quality on comfort (0.0841), impact on health (0.0805), and the cost of purchasing an MMD (0.0713), while the remaining criteria are of lower significance. Based on the research results it is expected that the identified micromobility implementation measures will be useful for decision-makers and urban development planners. Full article

Although higher technical efficiency is theoretically expected to enhance farm sustainability, empirical evidence in livestock systems remains ambiguous. This study investigates the interplay between technical efficiency and sustainability using data from 72 farms in Tokat, Türkiye, selected via stratified random sampling. Technical efficiency was calculated using Data Envelopment Analysis (DEA), while a multidimensional Sustainability Index was constructed using the analytic hierarchy process (AHP) for weighting dimensions. Determinants of inefficiency were estimated using a Tobit model. Results revealed an average technical efficiency of 0.717 and a Composite Sustainability Index of 0.41, classifying the sector as “moderate” but fragile. Crucially, the Kruskal–Wallis test showed no statistically significant difference in sustainability scores across efficiency groups (p > 0.05). This finding confirms a “structural disconnect,” demonstrating that high technical efficiency does not guarantee sustainability because of systemic bottlenecks such as dysfunctional organizations and infrastructure deficits. Furthermore, Tobit results showed that non-farm income and internet access were positively associated with technical efficiency, whereas indebtedness was negatively associated. Consequently, achieving lasting sustainability requires a shift from simple productivity support to structural modernization policies, including the integration of sustainability-oriented criteria such as institutional strengthening, environmental management, and financial capacity into existing support schemes. Full article

Background: Parenting is increasingly recognized as a highly demanding and stressful role that, in the absence of sufficient resources, may lead to parental burnout (PB). This risk may be particularly pronounced in the Russian context, where limited access to childcare for children under three and reduced extended family support coincide with strong social expectations of intensive parenting. Although PB and maternal depression frequently co-occur, it remains unclear whether PB exerts a unique influence on child development, especially during toddlerhood. The present study examined the association between PB and behavioral problems in children aged 1.5 to 4 years while controlling for maternal depression assessed both during the first year postpartum and concurrently with PB. Methods: Using a longitudinal design, maternal mental health was assessed within the first 12 months postpartum (Stage 1) and again at follow-up (Stage 2), on average 2.24 years later, in 419 Russian mother–child dyads. Mothers completed measures of postpartum depression (PPD) (Edinburgh Postnatal Depression Scale), current depressive symptoms (Beck Depression Inventory-II), and PB (Parental Burnout Inventory). Child emotional and behavioral problems were assessed at Stage 2 using the Russian version of the Child Behavior Checklist (CBCL/1½–5). Results: Mothers of children with borderline/clinically significant internalizing, externalizing, and total problems had significantly higher PB, PPD, and present maternal depressive symptoms, although the effect sizes were small. PB was strongly associated with all domains of child behavioral problems, also after correction for both postpartum and present depressive symptoms, as well as for other important covariates. Higher maternal PB symptoms further increased the odds of children having borderline/clinically significant internalizing and externalizing problems, although those effects were not independent of maternal depression. In turn, neither postpartum nor present maternal depressive symptoms were associated with any of the child behavioral problems domains. Conclusions: PB represents a distinct and clinically relevant risk factor for emotional and behavioral problems in toddlers, beyond the effects of maternal postpartum or present depression, in a context characterized by high caregiving demands and limited institutional support. These findings highlight an urgent need for programs aimed at identifying and supporting families in which parents experience high levels of exhaustion, regardless of whether they meet the criteria for other diagnosable mental health disorders. Addressing PB during toddlerhood may be critical for protecting both parental well-being and early child development. Full article

This paper proposes a wavefront-based method for detecting and locating single-line-to-ground faults in distribution lines using only the transient waveform recorded at one line terminal. The measured current is transformed into a time–frequency representation by the S-transform, and a low-rank structure is extracted by truncated singular value decomposition to suppress broadband noise. On this basis, a hysteresis-type energy envelope is constructed to determine the onset of the fault surge front. To distinguish the genuine fault echo—the main reflection associated with the fault location—from branch echoes and terminal ringing, three complementary criteria are combined: a generalized likelihood ratio test on the time–frequency energy, a dual-pulse interval matching based on the expected round-trip time between the terminal and the fault, and a multi-band consistency check over low-, medium-, and high-frequency components. Numerical experiments under different fault locations and signal-to-noise ratios show that the proposed method improves the average echo recognition rate by about 3.5% compared with conventional single-criterion detectors, while maintaining accurate wavefront-onset estimation with MHz-class sampling (1–5 MHz) that is readily available in practical on-line travelling-wave recorders, rather than relying on ultra-high sampling (e.g., tens of MHz and above). The method therefore offers a physically interpretable and practically feasible tool for fault-echo detection in overhead distribution feeders. Full article

Source plasma centers sustain hematology therapeutics by safeguarding testing, traceability, and cold-chain integrity before fractionation. Despite regulatory requirements (21 CFR 606/640; EU Directive 2005/62/EC), published pre-operational validation frameworks demonstrating deviation-readiness before first collections remain sparse. We conducted a simulation-based pre-operational validation of an electronic quality management system (eQMS) with an Incident → Deviation → Corrective Action and Preventive Action (CAPA) pathway at a new source plasma center, performing 20 chairside mock runs, 3 freezer-alarm drills, and a document-control stress test. Primary endpoints were anomaly rate, alarm-response time relative to a 15 min service-level agreement (SLA), and deviation-closure SLA compliance. Analyses were descriptive and designed to demonstrate system functionality, not long-term process stability. Minor anomalies occurred in 6/20 mock runs (30.0%; 95% CI 11.9–54.3); no major/critical events were observed (0/20; 95% CI 0–16.8). Deviation-closure SLAs were met in 6/6 tests (100%; 95% CI 54.1–100). Alarm-response times averaged 7.0 min (SD 1.0; range 6–8 min; 95% CI 4.5–9.5), and all drills met the 15 min vendor SLA, illustrating a preliminary readiness margin (Cpu ≈ 2.7) rather than a statistically stable capability estimate. Simulation-based pre-operational validation produced inspection-ready documentation and quantitative acceptance criteria aligned to U.S./EU expectations, supporting reproducible multi-site deployment. By protecting cold-chain integrity and traceability before first collections, the validated QMS helps preserve supply reliability for plasma-derived therapeutics central to hematology care and establishes the measurement infrastructure for post-operational performance validation. Full article

Efficient clustering and cluster head (CH) selection are the critical parameters of wireless sensor networks (WSNs) for their prolonged network lifetime. However, the performances of the traditional clustering algorithms like LEACH and HEED are not satisfactory when they are implemented on a dense WSN due to their unbalanced load distribution and high contention nature. In the traditional methods, the cluster heads are selected with respect to the residual energy criteria, and often create a circular cluster shape boundary with a uniform node distribution. This causes the cluster heads to become overloaded in the high-density regions and the unutilized cluster heads gather in the sparse regions. Therefore, frequent cluster head changes occur, which is not suitable for a real-time dynamic environment. In order to avoid these issues, this proposed work develops a density-aware adaptive clustering (DAAC) protocol for optimizing the CH selection and cluster formation in a dense wireless sensor network. The residual energy information, together with the local node density and link quality, is utilized as a single cluster head detection metric in this work. The local node density information assists the proposed work to estimate the sparse and dense area in the network that results in frequent cluster head congestion. DAAC is also included with a minimum inter-CH distance constraint for CH crowding, and a multi-factor cost function is used for making the clusters by inviting the nodes by their distance and an expected transmission energy. DAAC triggers re-clustering in a dynamic manner when it finds a response in the CH energy depletion or a significant change in the load density. Unlike the traditional circular cluster boundaries, DAAC utilizes dynamic Voronoi cells (VCs) for making an interference-aware coverage in the network. This makes dense WSNs operate efficiently, by providing a hierarchical extension, on making secondary CHs in an extremely dense scenario. The proposed model is implemented in MATLAB simulation, to determine and compare its efficiency over the traditional algorithms such as LEACH and HEED, which shows a satisfactory network lifetime improvement of 20.53% and 32.51%, an average increase in packet delivery ratio by 8.14% and 25.68%, and an enhancement in total throughput packet by 140.15% and 883.51%, respectively. Full article

Introduction: Palliative needs assessment and referral to advanced palliative care resources are fundamental aspects of complex chronic patients’ care. Primary care Nurse Case Managers play a key role in the care of these patients. Objective: We aimed to describe the experiences of primary care Nurse Case Managers in palliative care needs identification and complex chronic patients’ referral to advanced palliative care resources. Method: This is a qualitative descriptive study with a phenomenological approach. Semi-structured online interviews were conducted with primary care Nurse Case Managers. A thematic analysis was performed using ATLAS.ti software. Results: 20 nurses participated, 16 of whom were women, with a mean age of 52.3 years and an average of 15.9 years of experience in primary care. Regarding “Palliative care Needs Assessment”, four sub-themes have been identified: “What do you understand?”, “How do you assess?”, “Difficulties” and “Alternatives” to current palliative care needs assessment. For the “Palliative Care Referral” theme four sub-themes have been identified: “Criteria”, “Tools”, “Difficulties” and “Alternatives” for referral. Discussion: Palliative needs are identified in patients with incurable diseases when there are no curative treatment options and when quality of life must be prioritized. Symptoms, general condition, progression, and comorbidity are assessed. Open interviews and home visits are essential for assessing the social and family context and the home resources available. Barriers identified include the conspiracy of silence, limited training in non-oncological palliative care, and a lack of staff and caregiver’s understanding of illness situation. The presence of difficult symptoms and a limited life expectancy were identified as key criteria for referral to palliative care. The physician’s assessment, the family’s request, and consultation with specialized teams play a key role in prognosis. Barriers include late referrals, lack of a palliative background, inequity in access to resources, and low visibility of the palliative care needs of non-cancer patients. Conclusions: Significant challenges remain in identifying palliative needs and referral to specialized resources, highlighting the need to optimize resources, strengthen professional training, and improve coordination between levels of care to ensure quality palliative care. Full article

Portfolio optimization is a cornerstone of modern financial decision-making, traditionally based on the mean–variance model introduced by Markowitz. However, this framework relies on restrictive assumptions—such as normally distributed returns and symmetric risk preferences—that often fail in real-world markets, particularly in volatile and non-Gaussian environments such as cryptocurrencies. To address these limitations, this paper proposes a novel multi-objective model that combines expected return maximization, mean absolute deviation (MAD) minimization, and entropy-based diversification into a unified optimization structure: the Mean–Deviation–Entropy (MDE) model. The MAD metric offers a robust alternative to variance by capturing the average magnitude of deviations from the mean without inflating extreme values, while entropy serves as an information-theoretic proxy for portfolio diversification and uncertainty. Three entropy formulations are considered—Shannon entropy, Tsallis entropy, and cumulative residual Sharma–Taneja–Mittal entropy (CR-STME)—to explore different notions of uncertainty and structural diversity. The MDE model is formulated as a tri-objective optimization problem and solved via scalarization techniques, enabling flexible trade-offs between return, deviation, and entropy. The framework is empirically tested on a cryptocurrency portfolio composed of Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB), using daily data over a 12-month period. The empirical setting reflects a high-volatility, high-skewness regime, ideal for testing entropy-driven diversification. Comparative outcomes reveal that entropy-integrated models yield more robust weightings, particularly when tail risk and regime shifts are present. Comparative results against classical mean–variance and mean–MAD models indicate that the MDE model achieves improved diversification, enhanced allocation stability, and greater resilience to volatility clustering and tail risk. This study contributes to the literature on robust portfolio optimization by integrating entropy as a formal objective within a scalarized multi-criteria framework. The proposed approach offers promising applications in sustainable investing, algorithmic asset allocation, and decentralized finance, especially under high-uncertainty market conditions. Full article

Background: The growing complexity of last-mile logistics (LML) in urban environments has created an urgent need for sustainable, efficient, and stakeholder-inclusive solutions. This study addresses these challenges by exploring a holistic, human-centered approach to evaluating LML strategies, recognizing the diverse expectations of logistics service providers, delivery personnel, customers, and local authorities. Methods: To capture both subjective and objective factors influencing decision-making, the study employs a Multi-Criteria Decision-Making (MCDM) framework that integrates the Fuzzy Analytic Hierarchy Process (FAHP) and Evaluation based on Distance from Average Solution (EDAS). Evaluation criteria encompass operational efficiency, environmental impact, social acceptance, and technological feasibility. Results: Six LML solutions were assessed and ranked using this approach. The results indicate that the cargo bike (A₂) emerged as the most favorable alternative, while electric freight vehicles (A₅) ranked lowest. These findings reflect significant trade-offs between stakeholder priorities and the varying performance of different delivery strategies. Conclusions: The proposed methodology offers practical guidance for designing balanced and socially responsible urban logistics systems. By emphasizing inclusivity in decision-making, this approach supports the development of LML solutions that are not only operationally effective but also environmentally sustainable and broadly accepted by stakeholders. Full article

The development of digital technology has changed various aspects of life, including in the financial sector. One of the innovations that has received a significant amount of attention is the PayLater service. Generation Z, as a generation born in the digital era, has a unique consumption pattern. Members of Generation Z tend to look for financial solutions that are fast, practical, and accessible through technology. This study aims to provide guidance for Generation Z (age 20–28 years) in choosing the PayLater application that best suits their needs and financial situation. Using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, this study evaluates the effectiveness of several popular PayLater applications. Data were collected through an online questionnaire aimed at potential users who already had a monthly income. The criteria used in the assessment include the average transaction value, difficulty in paying installments, data security and privacy, ease of application access, and interest rates. The results of the analysis show that Shopee PayLater has the highest preference score, making it the best choice for Generation Z. This research is expected to contribute to improving financial literacy and helping Generation Z to make better decisions regarding financial services in the digital era. Full article

An asymptotic stability velocity tracking controller is designed to enable the autonomous maneuvering flight of unmanned helicopters. Firstly, taking the UH-60A without pilots as the research object, a high-efficient rotor aerodynamic modeling is developed, which incorporates a free-wake vortex method with the flap response of blades. The consummate flight dynamic model is complemented by wind tunnel-validated fuselage/tail rotor load regressions. Secondly, a linear state–space equation is derived via the small perturbation linearization method based on the flight dynamic model within the body coordinate system. A decoupled model is formulated based on the linear state–space equation by employing the implicit model approach. Subsequently, a system of ordinary differential equations is constructed, which is related to the deviation between actual velocity and its expected value, along with higher-order derivatives of this discrepancy. The I&I (immersion and invariance) theory is then employed to facilitate the design of a non-cascade control loop. Finally, the response of desired velocity in longitudinal channel is simulated with step signal to compare the control effect with a PID (proportional–integral–derivative) controller. By adjusting the coefficients, the response progress of the PID controller is similar to the effect of adaptive controller with I&I theory. However, there is no obvious overshoot in the process with I&I adaptive controller, and the average response amplitude accounts for 16.69% of the random white noise, which is 7.38% of the oscillation level under the PID controller. The parameter tuning complexity when employing I&I theory is significantly lower than that of the PID controller, which is evaluated by mathematical derivations and simulations. Meanwhile, the sidestep and pirouette maneuvers are simulated and analyzed to examine the controller in accordance with the performance criteria outlined in the ADS-33E-REF standards. The simulation results demonstrate that the speed expectation-oriented asymptotic stability control can achieve a fast response. Both sidestep and pirouette maneuvers can satisfy the desired performance requirements stipulated by ADS-33E-REF. Full article

With the advance of digital transformation, the assessment of the environmental impact of digital tools and technologies grows more relevant. Considering the inflated expectations of environmental responsibility in higher education, this study analyses how websites of Polish universities conform to sustainable web design criteria. The sustainability audit employed a methodology encompassing carbon emissions measurement, technical website analysis, and SEO evaluation. The author analysed 63 websites of public universities in Poland using seven independent audit tools, including an original AI Custom GPT agent preconfigured in the ChatGPT ecosystem. The results revealed a substantial differentiation in CO₂ emissions and website optimisation, with an average EcoImpact Score of 66.41/100. Nearly every fourth website exhibited a significant carbon footprint and excessive component sizes, which indicates poor asset optimisation and energy-intensive design techniques. The measurements exposed considerable variability in emission intensities and resource intensity among the university websites, suggesting the need for standardised digital sustainability practices. Regulations on the carbon footprint of public institutions’ websites and mobile applications could become vital strategic components for digital climate neutrality. Promoting green hosting, “Green SEO” practices, and sustainability audits could help mitigate the environmental impact of digital technologies and advance sustainable design standards for the public sector. The proposed auditing methodology can effectively support the institutional transition towards sustainable management of digital infrastructure by integrating technical, sustainability, and organisational aspects. Full article

This study conducts a comparative evaluation of Turkiye’s carbon emission profile from both sectoral and global perspectives. Utilizing 2022 data from 76 countries, it applies two widely recognized multi-criteria decision-making (MCDM) methods: MEREC, for determining objective weights of criteria, and PROMETHEE II, for ranking countries based on these criteria. All data used in the analysis were obtained from the World Bank, a globally recognized and credible statistical source. The study evaluates seven criteria, including carbon emissions from the energy, transport, industry, and residential sectors, along with GDP-related indicators. The results indicate that Turkiye’s carbon emissions, particularly from industry, transport, and energy, are substantially higher than the global average. Moreover, countries with higher levels of industrialization generally rank lower in environmental performance, highlighting a direct relationship between industrial activity and increased carbon emissions. According to PROMETHEE II rankings, Turkiye falls into the lower-middle tier among the assessed countries. In light of these findings, the study suggests that Turkiye should implement targeted, sector-specific policy measures to reduce emissions. The research aims to provide policymakers with a structured, data-driven framework that aligns with the country’s broader sustainable development goals. MEREC was selected for its ability to produce unbiased criterion weights, while PROMETHEE II was chosen for its capacity to deliver clear and meaningful comparative rankings, making both methods highly suitable for evaluating environmental performance. This study also offers a broader analysis of how selected countries compare in terms of their carbon emissions. As carbon emissions remain one of the most pressing environmental challenges in the context of global warming and climate change, ranking countries based on emission levels serves both to support scientific inquiry and to increase international awareness. By relying on recent 2022 data, the study offers a timely snapshot of the global carbon emission landscape. Alongside its contribution to public awareness, the findings are expected to support policymakers in developing effective environmental strategies. Ultimately, this research contributes to the academic literature and lays a foundation for more sustainable environmental policy development. Full article

Smart home devices and home automation systems, which control features such as lights, blinds, heaters, door locks, cameras, and speakers, have become increasingly popular and can be found in homes worldwide. Central to these systems are smart home hubs, which serve as the primary control units, allowing users to manage connected devices from anywhere in the world. While this feature is convenient, it also makes smart home hubs attractive targets for cyberattacks. Unfortunately, the average user lacks substantial cybersecurity knowledge, making the security of these systems crucial. This is particularly important as smart home systems are expected to safeguard users’ privacy and security within their homes. This paper synthesizes eight prevalent cybersecurity challenges associated with smart home hubs through a systematic literature review. The review process involved identifying relevant keywords, searching, and screening 713 papers in multiple rounds to arrive at a final selection of 16 papers, which were then summarized and synthesized. This process included research from Scopus published between January 2019 and November 2024 and excluded papers on prototypes or individual features. The study is limited by scarce academic sources on open-source smart home hubs, strict selection criteria, rapid technological changes, and some subjectivity in study inclusion. The security of extensible smart home hubs is a complex and evolving issue. This review provides a foundation for understanding the key challenges and potential solutions, which is useful for future research and development to secure this increasingly important part of our everyday homes. Full article

Hand gestures serve as a fundamental means of communication, and extensive research has been conducted to develop automated recognition systems. These systems are expected to improve human/computer interaction, particularly in environments where verbal communication is limited. A key challenge in these systems is the classification of non-target actions, as everyday movements are often not included in the training set, but resembling target gestures can lead to misclassification. Unlike previous studies that primarily focused on target action recognition, this study explicitly addresses the unseen non-target classification problem through an experiment to distinguish target and non-target activities based on movement characteristics. This study examines the ability of deep learning models to generalize classification criteria beyond predefined training sets. The proposed method was validated with arm movement data from 20 target group participants and 11 non-target group participants, achieving an average F1-score of 84.23%, with a non-target classification score of 73.23%. Furthermore, we confirmed that data augmentation and incorporating a loss factor significantly improved the recognition of unseen non-target gestures. The results suggest that improving classification performance on untrained, non-target movements will enhance the applicability of gesture recognition systems in real-world environments. This is particularly relevant for wearable devices, assistive technologies, and human/computer interaction systems. Full article