Previous studies have consistently demonstrated that positive attitudes toward aging are associated with better psychological well-being and cognitive performance among older adults. Building upon these findings, the present study focused on memory improvement as a direct indicator of cognitive benefit derived from more positive self-perceptions of aging. Specifically, we examined whether an implicit social comparison manipulation could enhance older adults’ memory performance by altering their attitudes toward aging. A total of 161 community-dwelling older adults (M = 66.88 years) were randomly assigned to one of five conditions: Better-self (downward comparison), Worse-self (upward comparison), Equal-good, Equal-bad, and Control. In four experimental conditions, an adopted directed-thinking task was used to activate attitudes toward one’s own and peers’ aging in different combinations, implicitly triggering upward or downward social comparisons. Attitude toward own aging (ATOA), attitude toward peers’ aging (ATPA), self-superiority (ATOA–ATPA), and memory performance were assessed before and after the manipulation. Results showed that significant changes in self-superiority were found only under the two contrast conditions. Specifically, self-superiority increased in the Better-self group and decreased in the Worse-self group. Moreover, the Better-self group demonstrated greater memory gains than the Control and Worse-self groups. These findings suggest that implicit downward comparison can serve as an effective, non-defensive strategy to strengthen older adults’ self-perceptions of aging and to produce short-term improvements in memory. The study extends prior research on social comparison in old age by linking its psychological and cognitive effects within a single experimental framework.
Csikszentmihalyi’s psychological flow and self-directed learning have a well-researched and direct connection. Lacking is an investigation of this relationship across the lifespan—the aim of this review. A search of seven primary databases and one supplementary database (searched eight different ways) with the keywords “self-directed learning, lifespan, psychological flow”—for English-language empirical research studies in peer-reviewed publications—provides this assessment of recent publications with high recall and high precision. The hypothesis is that distinct topics are recognizable, concerning the relationships among self-directed learning, lifespan, and psychological flow, regarding how self-directed learning promotes psychological flow throughout the lifespan. As a quasi-scoping review, the standardized PRISMA-ScR is the methodology. The supplementary database search, without Boolean functions, and yielding the highest returns, produced the five results included. Corroborating the hypothesis, three Csikszentmihalyi-inspired topics synthesize the results: (1) feeling better in the moment, (2) body and mind are in harmony, and (3) improving the quality of life. Based on the synthesis, the level of meaning the learner ascribes to their work determines the relationship among the three keywords. The conclusion is that the relevance of flow to self-directed learning throughout the lifespan depends on learner engagement in supporting their work-related purpose and meaning regarding the learning material.
Under the concept of “Rights of Nature”, the governance of land-based marine pollution in China faces unprecedented opportunities and challenges. Traditional governance paradigms are predominantly anthropocentric, treating the ocean as a resource to be utilized. From this perspective, governance measures for the prevention and control of land-based marine pollution primarily rely on administrative management and end-of-pipe treatments. Within this context, “Rights of Nature” provide a new pathway for marine ecological protection. However, promoting a shift in land-based marine pollution governance from the traditional anthropocentric view to an eco-centrism under the “Rights of Nature” concept is by no means an instantaneous process, and it must proceed gradually and systematically. Currently, China’s institutional framework for preventing and controlling land-based marine pollution remains dominated by the anthropocentric paradigm. Furthermore, it encounters multiple difficulties across many key areas, including the legal system, law enforcement mechanisms, relief mechanisms, and public participation. Issues such as poor coordination within the legal framework, fragmented law enforcement, lagging legislation related to ecological restoration, and insufficient public participation significantly constrain the effectiveness of land-based marine pollution governance. Given the fundamental differences between anthropocentrism and “Rights of Nature”, directly introducing this concept would likely have a substantial impact on China’s existing legal framework. Therefore, at the current stage, China could first incorporate the proposition from the “Rights of Nature” concept that nature possesses “intrinsic value independent of human use or perception”. This involves weakening the perception of the ocean as a mere appendage to human activities, recognizing and respecting the unique value of the ocean as a living entity and ecosystem at a conceptual level, and gradually forming a set of nature-friendly governance paradigms for land-based marine pollution that respect the intrinsic value of nature. This approach can ultimately drive transformative practices in China’s land-based marine pollution governance.
Hybrid-Based Abrasive Flow Finishing (HAFF) represents a significant evolution in precision manufacturing, particularly in addressing the inherent limitations of traditional finishing techniques when dealing with complex geometries and challenging materials. HAFF achieves remarkable precision in managing particle motion by blending state-of-the-art energy inputs and mechanical reinforcements, including sonic vibrations, electromagnetic influences, and beam-guided supports, which accelerate the pace of material extraction and elevate the overall finish of surfaces. This paper comprehensively reviews various HAFF approaches, including energy-assisted methods (e.g., electrochemical, ultrasonic, and laser), force-assisted techniques (e.g., magnetic, hydrodynamic, and vibration), and hybrid energy-force integrated systems. Recent advancements, such as cryogenic-assisted, rotational-assisted, and magnetorheological-assisted AFF, are also discussed in this review. Recent studies from 2023 to 2025 highlight improvements in material removal rates of up to 80% and reductions in surface roughness of over 90% across various HAFF variants, underscoring the timeliness of these developments. Incorporating diverse power sources and mechanical aids into HAFF allows for exact oversight of particle interactions, speeding up the removal of excess material, refining the exterior finish, and broadening its utility across detailed designs and tough-to-process substances. Despite significant progress, challenges persist in scaling HAFF processes for industrial applications, improving cost efficiency, and implementing effective real-time monitoring systems. The future trajectory of HAFF research will focus on the development of innovative abrasive media, advanced automation technologies, artificial Intelligence techniques, and sustainable manufacturing practices. This study examines all existing HAFF technology solutions and evaluates product applications for aerospace, automotive, medical equipment, and micro-manufactured devices. The discussion highlights the industries that require more advanced technological investigations.
The increasing demand for sustainable and cost-efficient construction highlights the need to minimize material consumption in civil engineering structures without compromising safety or performance. This study investigates the optimization of steel purlin cross-sections in metal buildings as a means to enhance structural efficiency and environmental sustainability. Finite Element Analysis (FEA) and the Solid Isotropic Material with Penalization (SIMP) method were employed to identify optimal material distributions and evaluate the effects of varying cross-section geometries. Both rectangular and IPE purlin sections were analyzed under realistic loading conditions to compare stress, deformation, and weight performance before and after optimization. The results demonstrate that substantial reductions in material mass, up to approximately 25–30%, can be achieved while maintaining nearly identical stress and displacement responses. These findings confirm that structural optimization effectively reduces both construction costs and environmental impact. The study concludes by recommending the adoption of topology and cross-section optimization techniques in the design of steel structures, particularly in public projects, to promote resource efficiency and sustainable construction practices.
Rural women often start enterprises in sectors that are vital for long-term rural sustainability, but these organizations run the risk of not being properly recognized by public rural development support systems. In this paper, we ask whether existing business support measures meet the needs of rural women entrepreneurs, and if not, what can be improved? Our data consists of recorded interviews with twenty women entrepreneurs from the rural regions of southern Sweden. We asked how they perceive the business support that is provided, used, and needed. We found a gendered mismatch between the forms of public support provided and the support needed by women entrepreneurs in rural areas. The analysis reveals that current business support initiatives often overlook social, cultural, and environmental innovations and enterprises that do not prioritise economic growth as their primary objective, despite their importance for rural viability and development. We argue for a shift towards valuing alternative growth models, broadening eligibility criteria, and simplifying access to funding. As key players in this context, public funds should support long-term sustainability. By embracing the proposed changes, the business support system can be better aligned with the realities of rural entrepreneurship, contributing more meaningfully to rural development and gender equality.
Absent pulmonary valve syndrome (APVS) is a rare cardiac malformation that is almost always associated with a Fallot-type ventricular septal defect (VSD). More rarely, it can occur with an intact ventricular septum or muscular VSD. The limited number of observations reported in the medical literature affects the quality of prenatal counselling given to the families concerned. We report 3 new cases of APVS without Fallot-type VSD, with 1 case associated with a muscular VSD, and have carried out a review of the literature on this rare malformation. Two of the fetuses had hydrops fetalis and one of these two had intra-uterine death. A 16p13.11 microduplication transmitted by the father was found in one fetus whose post-natal evolution was favorable following surgical ligation of an aneurysmal ductus arteriosus. A newborn with hydrops fetalis had a favorable outcome after spontaneous closure of the ductus arteriosus on the third day of life. Unlike Fallot-type APVS, non-Fallot type APVS is characterized antenatally by the constant presence of a large ductus arteriosus, the absence of aneurysmal pulmonary branches, a high frequency of chromosomal anomalies, but the absence of 22q11 micro deletion. After birth, early closure of the ductus may be indicated in cases of significant heart failure.
Traditional Value Engineering (VE) has long focused on optimizing the function-to-cost ratio but faces limitations in digitalized industrial contexts. Conventional VE lacks integration with advanced technologies, empirical validation in smart environments, and alignment with sustainability and circular economy objectives. The emergence of Industry 4.0—driven by cyber-physical systems, IoT, big data analytics, digital twins, and artificial intelligence—has transformed industrial ecosystems, necessitating a redefinition of VE practices. This study employs a systematic literature review and structured gap analysis to examine the evolution, applications, and challenges of VE across manufacturing, construction, supply chain, and service sectors. The analysis identifies three key deficiencies in conventional VE: (i) absence of integrated digital frameworks, (ii) limited empirical validation in smart environments, and (iii) weak incorporation of sustainability and circular economy principles. To address these gaps, Value Engineering 4.0 (VE 4.0) is proposed as a function-driven, data-intelligent, and human-centric methodology. It is structured around a six-component strategic framework: (1) digital foundations for technological readiness and organizational alignment; (2) smart VE processes leveraging AI, IoT, and advanced analytics for predictive, connected decision-making; (3) an enhanced Job Plan integrating AR/VR, NLP, and blockchain for improved speed, accuracy, and lifecycle alignment; (4) a phased implementation roadmap; (5) real-time DMAIC integration for continuous optimization; and (6) enablers covering leadership, skills, infrastructure, and cybersecurity. VE 4.0 provides both a research agenda and a practical roadmap, enabling organizations to innovate, enhance resilience, and achieve sustainable competitiveness in Industry 4.0 ecosystems.
A brief critical analysis of kinetic models is presented, particularly the quadratic model (QM), highlighting their strengths and weaknesses. A generalized quadratic model (GQM) is proposed that can accommodate the experimental observation that the degradation rate is non-zero in the limit of zero substrate concentration. The limits of this model are outlined by comparison with a more extended kinetic scheme.
Unmanned aerial vehicles (UAVs) have increased in popularity for several diverse applications over the past few years. Parking, especially in crowded parking lots, can be very time-consuming, as a driver must manually search for vacant spaces among many occupied ones. In this work, reinforcement learning—a category of machine learning in which an agent receives inputs from the environment while outputting actions in order to maximize reward—was utilized in tandem with AirSim, a drone simulator developed by Microsoft, to automate a virtual UAV’s movement. A convolutional neural network (CNN) was then utilized to detect both vacant and filled parking spots, which achieved 98% recall and 93% accuracy. Unreal Engine was used to create a custom environment that resembled a parking lot, and the virtual drone was trained using a Deep Q-Network (DQN). The DQN achieved a mean reward of 394.5 in training and 460.4 in evaluation. A pre-trained CNN integrated with the DQN enables the real-time classification of vacant/occupied parking spaces from drone imagery. Results validate the effectiveness of combining reinforcement learning navigation with CNN image classification, demonstrating deployment-ready performance for real-world congested parking applications.