Path Diversity Research Articles

Review of AI Based Career Counselling

In the domain of technology-driven educational advancements, artificial intelligence (AI) has become pivotal in enriching student experiences. This abstract gives into the rising need for effective career counseling and proposes the development for a specialized AI-based software application to address this need. Recognizing the universal role of technology in students’ lives, leveraging AI for personalized career guidance becomes necessity. The envisioned desktop application aims to redefine traditional career counseling methods by using AI algorithms' advanced capabilities. It seeks to provide personized recommendations to individuals or students navigating diverse career paths by evaluating academic performance, skills, and interests. Targeting three main user groups students, career counselors, and educational institutions the application offers a user-friendly interface for students to input details and preferences, resulting in a personalized career roadmap. Career counselors benefit from AI-generated analytics, entitle them to offer more informed guidance. This abstract covers the transformative potential of AI in enhancing career counseling services and facilitating informed career decisions

Exploring paths underpinning the implementation of municipal waste sorting: Evidence from China

Urban residents' waste sorting plays a pivotal role in addressing the pressing challenge of the “waste siege” in China and is also closely associated with the quality of urban governance. We draw on the Technology–Organization–Environment framework to identify six key variables affecting municipal waste sorting in China: digital technology innovation, attention allocation, publicity and education, enforcement intensity, economic development level, and volunteer resources. Using the fuzzy set Qualitative Comparative Analysis (fsQCA) method to analyze the effect of conditions on municipal waste sorting, the results show the following: 1) A single condition is insufficient to ensure high levels of municipal waste sorting. We propose that the “multiple concurrency” of technological, organizational, and environmental conditions creates diverse paths for promoting municipal waste sorting. 2) We then identify three paths for waste-sorting promotion, which all require technological, organizational, and environmental conditions. In addition, the two paths obtained for low levels of municipal waste sorting are not the opposite of those obtained for high levels. By observing the above five paths of waste sorting, we deduced that digital technology innovation is indispensable in driving waste sorting, and 3) Our cross-case comparisons show that there are effective paths to waste-sorting governance that work for cities regardless of their location or piloting. In some cases, high levels of attention allocation and high levels of publicity and education are substitutes for each other. We recommend that local governments refer to their own unique characteristics and resource endowments when tailoring waste-sorting strategies.

Icograda: The International Council of Graphic Design Associations 1963-2013

Abstract This article follows the International Council of Graphic Design Associations throughout its first 50 years of activities. Initially shaped from Western European perspectives on graphic design, which included the pursuit of universal standards and favoritism towards designers from certain countries, the Council's quest for recognition led its change towards a more inclusive and diverse path. This article concludes by acknowledging the changes the Council went through throughout the years and the nature of its work, along with remarks about the potential contributions of its archive to graphic design history, with valuable documentation on its membership from 1963 to 2003.

The unseen invaders: Tracking phylogeographic dynamics and genetic diversity of cryptic Pomacea canaliculata and P.maculata (Golden apple snails) across Taiwan.

The cryptic invasion of golden apple snails (Pomacea canaliculata and P. maculata) in Taiwan has caused significant ecological and economical damage over the last few decades, however, their management remains difficult due to inadequate taxonomic identification, complex phylogeny, and limited population genetic information. We aim to understand the current distribution, putative population of origin, genetic diversity, and potential path of cryptic invasion of Pomacea canaliculata and P. maculata across Taiwan to aid in improved mitigation approaches. The present investigation conducted a nationwide survey with 254 samples collected from 41 locations in 14 counties or cities across Taiwan. We identified P. canaliculata and P. maculata based on mitochondrial COI and compared their genetic diversity across Taiwan, as well as other introduced and native countries (based on publicly available COI data) to understand the possible paths of invasion to Taiwan. Based on mitochondrial COI barcoding, sympatric and heterogeneous distributions of invasive P. canaliculata and P. maculata were noted. Our haplotype analysis and mismatch distribution results suggested multiple introductions of P. canaliculata in Taiwan was likely originated directly from Argentina, whereas P. maculata was probably introduced from a single, or a few, introduction event(s) from Argentina and Brazil. Our population genetic data further demonstrated a higher haplotype and genetic diversity for P. canaliculata and P. maculata in Taiwan compared to other introduced regions. Based on our current understanding, the establishment of P. canaliculata and P. maculata is alarming and widespread beyond geopolitical borders, requiring a concerted and expedited national and international invasive species mitigation program.

Protection of Handicraft Skills from the Perspective of Folklore Studies

Handicraft skills and material culture are a research perspective of folklore and a major landscape of various ethnic customs in China. The long history, vast territory, and clever and wise Chinese people have formed a colorful and diverse folk handicraft technique. Under the diverse paths of design participation in handicraft innovation, handicrafts have undergone semantic, functional, and situational transformations in contemporary times, presenting an ecosystem of alienation and diversification. This article takes the ceramic craftsmanship of Yaozhou kiln as an example to explore how to improve the construction of national cultural soft power by protecting local traditional handicrafts.

Exploring Vocational High School EFL Teacher’s Role in the Problem-based Learning: Emancipated Curriculum Context in Indonesia

The shift to a PBL-oriented approach necessitates a reevaluation of the roles and identities of teachers. In the context of vocational high school English classrooms, where there may be diverse student backgrounds, aspirations, and career paths, exploring how English teachers adapt their roles as facilitators, mentors, and content experts can provide crucial insights. Additionally, understanding the factors that influence teachers' willingness and ability to embrace these roles within the context of an emancipated curriculum remains largely unexplored. Thus, this research aims to discover teachers’ role in implementing problem-based learning in the Emancipated Curriculum. This qualitative case study employed an interview to one English honorarium teacher as the main instrument, which covered the questions regarding teacher’s understanding and practice of the teacher’s role in problem-based learning. Through thematic analysis, the study revealed that, in general, the teacher’s role is in line with the previous related study, theory, and the goals of the Emancipated Curriculum in which the role of the teacher is: as the facilitator, evaluator, and guide. The findings provide practical implications for educational policymakers, school administrators, teacher educators, and English teachers seeking to enhance student engagement, motivation, and skill development in vocational education settings, specifically the findings allow teachers to learn and understand the teacher’s role in the classroom to support students in achieving problem-based learning objectives.

Impacts of NEP 2020 on Vocational Education and Emerging role of counsellors

The National Education Policy (NEP) 2020 in India has brought about significant reforms aimed at transforming the educational landscape, particularly in the realm of vocational education. This paper provides an in-depth analysis of the impact of NEP 2020 on vocational education and the emerging roles of counsellors within this context. NEP 2020 emphasizes the integration of vocational education into mainstream curriculum from the secondary level onwards, aiming to equip students with practical skills and job-oriented training. This shift necessitates a revaluation of the role of counsellors, who are now tasked with guiding students towards diverse career paths and facilitating their transition into the workforce. Additionally, NEP 2020 underscores the importance of providing holistic support to students, including psychometric assessments, career guidance, and skill development. The paper examines the challenges and opportunities presented by NEP 2020 in implementing vocational education initiatives and highlights the crucial role of counsellors in supporting students' career aspirations and personal development. Furthermore, it discusses the need for counsellor training programs to adapt to the changing educational landscape and equip counsellors with the skills and knowledge required to effectively fulfil their emerging roles. Overall, this paper provides valuable insights into the transformative potential of NEP 2020 in shaping vocational education and the evolving responsibilities of counsellors in guiding students towards success in the dynamic job market of the 21st century.

Tracing the Diverse Paths of One-Carbon Metabolism in Cancer and Beyond.

One-carbon (1C) metabolism is a network of biochemical reactions distributed across organelles that delivers folate-activated 1C units to support macromolecule synthesis, methylation, and reductive homeostasis. Fluxes through these pathways are up-regulated in highly proliferative cancer cells, and anti-folates, which target enzymes within the 1C pathway, have long been used in the treatment of cancer. In this work, we review fundamental aspects of 1C metabolism and place it in context with other biosynthetic and redox pathways, such that 1C metabolism acts to bridge pathways across compartments. We further discuss the importance of stable-isotope-tracing techniques combined with mass spectrometry analysis to study 1C metabolism and conclude by highlighting therapeutic approaches that could exploit cancer cells' dependency on 1C metabolism.

A tree-recursive partitioned multicast mechanism for NoC-based deep neural network accelerator

In chip multiprocessor systems (CMPs), Network on Chip (NoC) has been widely used due to its advantages of favorable reusability, high reliability, and low power consumption. Recently, using NoC platforms to accelerate deep neural networks (DNNs) has become a new trend. This design can enable the intermediate computation results of DNNs to be transmitted within the chip, reducing the number of accesses to off-chip memory. However, a large amount of one-to-many traffic in the DNN accelerator will occupy the system bandwidth, which will significantly reduce the performance of the NoC platform dominated by one-to-one traffic. To address this issue, we propose a tree-based recursive partitioning multicast scheme (TRPM), which increases the path diversity and improves the system bandwidth. We also design a single-cycle per-hop router architecture, which effectively enhances the transmission efficiency of multicast packets. Detailed simulation results show that compared with the latest tree-based multicast algorithm for DNN accelerators, our scheme reduces the number of routed packets by 35%, the classification latency by 13.5% and the average packet latency by 14.5% on average.

Impact of Static Urban Traffic Flow-Based Traffic Weighted Multi-Maps Routing Strategies on Pollutant Emissions

Addressing urban traffic congestion is a pressing issue requiring efficient solutions that need to be analyzed regarding travel time and pollutant emissions. The traffic weighted multi-maps (TWM) method has been proposed as an efficient mechanism for congestion mitigation that enables differential traffic routing and path diversity by strategically distributing different network views (maps) to the drivers. Previous works have focused on TWM generation by creating optimal edge weights, but the complexity exponentially increases with the network size and traffic group diversity. This work describes how congestion and emissions can be addressed using TWM maps based on the k-shortest paths for the traffic flows (instead of individuals) that are optimally assigned and distributed to the components of the traffic flow. The map allocation strategies optimal TWM (OTV), optimal TWM per path flow with linear constraints (LCTV), and its variant unconstrained optimal TWM per path flow (UCTV) are described. They use maps generated from the k-shortest paths of the traffic flows (kSP-TWM). The heuristic solution obtained is compared with the theoretical static traffic assignment estimation baseline with different configurations, regarding congestion reduction, total travel time enhancement, and pollutant emissions. Experiments are developed using a synthetic traffic grid network scenario with a mesoscopic simulation. They show that the solution provided is adequate for its proximity to the theoretical equilibrium solutions and can generate minimum emissions patterns. The presented solution opens new possibilities for further congestion and pollutant management studies and seamless integration with existing traffic management frameworks.

Pay structure and firm technological innovation: comparative research based on three pay gaps

While previous research has extensively explored the correlation between pay gaps and firm innovation, the comprehensive investigation of various pay gaps within a unified framework remains an understudied domain. We advance the understanding of the intricate relationship between pay gaps and firm innovation by examining the tournament effect and social comparison effect. Through empirical analysis spanning the period from 2009 to 2019 of Chinese listed companies, our findings reveal a potential inverted U-shaped curve in the impact of all pay gaps on firm innovation. Specifically, the effects of internal pay gap and management pay gap exhibit the left half of an inverted U-shaped curve, while the external pay gap demonstrates a complete inverted U shape. Additionally, utilizing fsQCA, we unveil that small firms can stimulate innovation through management pay incentives, internal tournaments, or employee tournaments. Conversely, large firms can pursue diverse paths, including management equity incentives, strategic emphasis on low pay for firm growth, or a harmonious combination of management pay and equity incentives. The intricate interplay between pay gaps and firm innovation is contingent upon industry and firm characteristics. Consequently, our study underscores the importance of meticulously designing pay structures that align with strategic goals and unique attributes.

High-efficiency electromagnetic wave absorption of lightweight Nb2O5/CNTs/polyimide with excellent thermal insulation and compression resistance integration

With the widespread application of electronic devices, multifunctional electromagnetic wave (EMW) absorbing materials suitable for complex high-temperature and high-pressure environments, have attracted tremendous research attention. Herein, multifunctional polyimide (PI) composites doped with fluffy Nb2O5/CNTs microspheres (NBCP) were prepared by combining the ultrasonic spraying process and freeze-drying technique. Introduction of fillers results in the formation of diverse conduction paths and heterogeneous interfaces, which enhance the dielectric loss. Furthermore, the multi-porous structure of the PI matrix induces multiple reflection scattering events and improves the impedance matching. Such a multi-loss mechanism endows NBCP with an excellent EMW absorption performance. At 15 vol% filling, the minimum reflection loss is −41.66 dB, the effective absorption bandwidth is 2.63 GHz, and the radar cross-section is reduced by 85%. Furthermore, NBCP exhibits outstanding mechanical properties and a thermal insulation of over 100 °C in thermal environment at 150 °C. The proposed multifunctional NBCP is thus an efficient EMW absorber that offers significant potential for applications in harsh environments.

Combined effect mechanism of linear loading and disturbance with different amplitudes on sandstone

To comprehend the stress state and response characteristics of materials under complex conditions, researchers have decomposed stress states into fundamental paths and investigated diverse path combinations. To ensure comparability, four identical samples were carefully selected from a pool of 100 samples using ultrasonic tests based on the wave speed and waveform characteristics. These samples underwent specially designed stress paths to analyze the combined effects of linear loading and perturbation. Our result analysis centred on the perturbation amplitude and stress levels during composite action, revealing intricate relationships between the stress levels, strain, and nonlinear/linear energy evolution under complex stress paths. Simultaneously, 3D surface fractures were precisely reconstructed using the YOLOv5 and FAST feature point detection algorithms, elucidating the evolving patterns of the fractures. As a result of our study, the rotation trend of the main fracture was validated by integrating mechanics and P-wave reflection rules. Notably, our experimental results closely aligned with the theoretical predictions, showing the reliability of our study. These findings can significantly contribute to guiding safety protocols in the field of underground engineering.

Generalization of Deep-Learning Models for Classification of Local Distance Earthquakes and Explosions across Various Geologic Settings

Abstract Although accurately classifying signals from earthquakes and explosions at local distance (<250 km) remains an important task for seismic network operations, the growing volume of available seismic data presents a challenge for analysts using traditional source discrimination techniques. In recent years, deep-learning models have proven effective at discriminating between low-magnitude earthquakes and explosions measured at local distances, but it is not clear how well these models are capable of generalizing across different geological settings. To address the issue of generalization between regions, we train deep-learning models (convolutional neural networks [CNNs]) on time–frequency representations (scalograms) of three-component earthquake and explosion signals from eight different regions in the continental United States. We explore scenarios where models are trained on data from all regions, individual regions, or all but one region. We find that although CNN models trained on individual regions do not necessarily generalize well across different settings, models trained on multiple regions that include diverse path coverage generalize to new regions, with station-level accuracy of up to 90% or more for data sets from unseen regions. In general, CNN-based discrimination models significantly outperform models based on uncorrected P/S ratio (measured in the 10–18 Hz frequency band), even when CNN models are tested on data from entirely unseen regions.

Global change alters coastal plankton food webs by promoting the microbial loop: An inverse modelling and network analysis approach on a mesocosm experiment

Marine organisms are currently, and will continue to be, exposed to the simultaneous effects of multiple environmental changes. Plankton organisms form the base of pelagic marine food webs and are particularly sensitive to ecosystem changes. Thus, warming, acidification, and changes in dissolved nutrient concentrations have the potential to alter these assemblages, with consequences for the entire ecosystem. Despite the growing number of studies addressing the potential influence of multiple drivers on plankton, global change may also cause less obvious alterations to the networks of interactions among species. Using inverse analyses applied to data collected during a mesocosm experiment, we aimed to compare the ecological functioning of coastal plankton assemblages and the interactions within their food web under different global change scenarios. The experimental treatments were based on the RCP 6.0 and 8.5 scenarios developed by the IPCC, which were extended (ERCP) to integrate the future predicted changes in coastal water nutrient concentrations. Overall, we identified that the functioning of the plankton food web was rather similar in the Ambient and ERCP 6.0 scenarios, but substantially altered in the ERCP 8.5 scenario. Using food web modelling and ecological network analysis, we identified that global change strengthens the microbial loop, with a decrease of energy transfer efficiency to higher trophic levels. Microzooplankton responded as well by an increased degree of herbivory in their diet and represented, compared to mesozooplankton, by far the main top-down pressure on primary producers. We also observed that the organisation of the food web and its capacity to recycle carbon was higher under the ERCP 8.5 scenario, but flow diversity and carbon path length were significantly reduced, illustrating an increased food web stability at the expense of diversity. Here, we provide evidence that if global change goes beyond the ERCP 6.0 scenario, coastal ecosystem functioning will be subjected to dramatic changes.

Anatomizing online collaborative inquiry using directional epistemic network analysis and trajectory tracking

AbstractAccurate assessment and effective feedback are crucial for cultivating learners' abilities of collaborative problem‐solving and critical thinking in online inquiry‐based discussions. Based on quantitative content analysis (QCA), there has been a methodological evolvement from descriptive statistics to sequential mining and to network analysis for mining coded discourse data. Epistemic network analysis (ENA) has recently gained increasing recognition for modelling and visualizing the temporal characteristics of online discussions. However, due to methodological restraints, some valuable information regarding online discussion dynamics remains unexplained, including the directionality of connections between theoretical indicators and the trajectory of thinking development. Guided by the community of inquiry (CoI) model, this study extended generic ENA by incorporating directional connections and stanza‐based trajectory tracking. By examining the proposed extensions with discussion data of an online learning course, this study first verified that the extensions are comparable with QCA, indicating acceptable assessment validity. Then, the directional ENA revealed that two‐way connections between CoI indicators could vary over time and across groups, reflecting different discussion strategies. Furthermore, trajectory tracking effectively detected and visualized the fine‐grained progression of thinking. At the end, we summarize several research and practical implications of the ENA extensions for assessing the learning process.Practitioner notesWhat is already known about this topic Assessment and feedback are crucial for cultivating collaborative problem‐solving and critical thinking in online inquiry‐based discussions. Cognitive presence is an important construct describing the progression of thinking in online inquiry‐based discussions. Epistemic network analysis is gaining increasing recognition for modelling the temporal characteristics of online inquiries. What this paper adds Directional connections between discourses can reflect different online discussion strategies of groups and individuals. A pair of connected discourses coded with the community of inquiry model can have different meanings depending on their temporal order. A trajectory tracking approach can uncover the fine‐grained progression of thinking in online inquiry‐based discussions. Implications for practice and/or policy Besides the occurrences of individual discourses, examining the meanings of directional co‐occurrences of discourses in online discussions is worthwhile. Groups and individuals can employ different discussion strategies and follow diverse paths to thought development. Developmental assessment is crucial for understanding how participants achieve specific outcomes and providing adaptive feedback.

Uncovering heterogeneity in mental health changes among first-year medical students

ABSTRACT Introduction The initial year of medical school is linked to a decline in mental health. To assess mental health comprehensively, the dual-factor model posits the consideration of both psychopathology (e.g., depression) and positive mental health (e.g., well-being). Previous mental health research among medical students has primarily examined these two factors independently. This study uses the dual-factor approach for a deeper understanding of mental health changes during the first year of medical school. Methods Students from eight German medical schools (N = 450) were surveyed three times (T0 = entering medical school, T1 = end of the first semester, T2 = end of the second semester) regarding depression (PHQ-9), well-being (subscale of FAHW-12), and general life satisfaction (German Single-Item Scale L1). Latent profile analysis was used to identify distinct mental health groups based on their combinations of psychopathology and positive mental health. We then analysed trajectories descriptively by examining the longitudinal stability and dynamics of mental health group membership during the first year of medical school. Results We identified five mental health groups: (1) complete mental health, (2) moderately mentally healthy, (3) symptomatic but content, (4) vulnerable, and (5) troubled. The examination of change trajectories unveiled diverse paths pointing towards both recovery and deterioration. In comparison to the other groups, students belonging to the complete mental health group exhibited greater stability and a higher potential to recover after initial deteriorations in the first semester. Conclusions Our study uncovers distinct mental health trajectories in the first year of medical school, emphasizing the crucial role of initial mental health status. Our findings stress the diverse nature of mental health changes in medical students, underscoring the need for tailored prevention strategies. The implications for research and practice are discussed.

Reliable edge-to-core optical networks: An optimal algorithm for maximal path diversity

With the emergence of IoT applications, 5G, and edge computing, network resource allocation has shifted toward the edge, bringing services closer to the end users. These applications often require communication with the core network for purposes that include cloud storage, compute offloading, 5G-and-Beyond transport communication between centralized unit (CU), distributed unit (DU) and core network, centralized network monitoring and management, etc. As the number of these services increases, efficient and reliable connectivity between the edge and core networks is of the essence. Wavelength Division Multiplexing (WDM) is a well-suited technology for transferring large amounts of data by simultaneously transmitting several wavelength-multiplexed data streams over each single fiber optics link. WDM is the technology of choice in mid-haul and long-haul transmission networks, including edge-to-core networks, to offer increased transport capacity.Optical networks are prone to failures of components such as network fiber links, sites, and transmission ports. A single network element failure alone can cause significant traffic loss due to the disruption of many active data flows. Thus, fault-tolerant and reliable network designs remain a priority. The architecture called “dual-hub and dual-spoke” is often used in metro area networks (MANs). A dual-hub, or in general a multi-hub network, consists of a set of designated destination nodes (hubs) in which the data traffic from all other nodes (the peripherals) should be directed to the hubs. Multiple hubs offer redundant connectivity to and from the core or wide area network (WAN) through geographical diversity. The routing of the connections (also known as lightpaths) between the peripheral node and the hubs has to be carefully computed to maximize path diversity across the edge-to-core network. This means that whenever possible the established redundant lightpaths must not contain a common Shared Risk Link Group (SRLG).An algorithm is proposed to compute the most reliable set of SRLG disjoint shortest paths from any peripheral to all hubs. The proposed algorithm can also be used to evaluate the overall edge-to-core network reliability quantified through a newly introduced figure of merit.

Metapopulation networks unlock the effects of landscape fragmentation on agricultural pests and natural predators

In agricultural landscapes, patches containing diverse species can serve as nodes, and species diffusion among these patches can serve as edges, forming metapopulation networks. However, the impact of landscape fragmentation on these networks and consequences for keystone crop species remain unclear. Here, we investigated thrips (pests) and lacewings (natural predators) in mango orchards and surrounding habitats across 15 landscapes. Using a machine learning neural network, we built metapopulation networks for thrips and lacewings based on the relationship between patch properties and species abundance. We evaluated these networks with metrics like clustering coefficient, strength, and path length. Furthermore, we analyzed the effects of landscape fragmentation, as indicated by edge density and landscape Shannon diversity, on network structures and the subsequent effect on species abundance using piecewise structural equation modeling. The results show that the neural network effectively constructed the metapopulation network, accurately predicting lacewing and thrips abundance in various patches with an accuracy rate exceeding 80%. Positive correlations were observed between edge density and network strength for both species. However, a positive relationship between landscape Shannon diversity and network path length was identified only for lacewings. Edge density had a greater impact on lacewings and thrips compared to landscape Shannon diversity, with a negative effect on lacewings and a positive effect on thrips. Edge density affected lacewing abundance both directly and indirectly through network strength. However, no network structure mediated the effect of landscape fragmentation on thrips abundance. Our conclusion highlights that the effects of landscape fragmentation on species are primarily driven by edge effects. These effects were clarified by the metapopulation network structure, which varied depending on the species. The findings provide new perspectives on metapopulation networks to unlock the landscape fragmentation effects.

Assessing Hydropower Potential under Shared Socioeconomic Pathways Scenarios Using Integrated Assessment Modelling

The world is facing a global sustainability crisis affecting environmental systems and society. Addressing these issues requires a multi-dimensional approach that can integrate energy, water, and environment Systems, as well as provide scientific policy advice. In this study, an updated version of an Integrated Assessment Model (IAM) was used, together with new data compatible with Shared Socioeconomic Pathways (SSPs) projections, to significantly improve the work developed before. SSP climate data (temperature, precipitation, and total radiative forcing) and socioeconomic data (population and GDP) were loaded into the IAM, together with different scenario parameters. By analyzing varying socioeconomic scenarios, mitigation efforts, and adaptation strategies, this study assesses their impact on primary energy demand and, consequently, their impact on hydropower potential production. Our results show diverse energy paths, strongly dependent on the future scenario. Energy demand could increase up to 160%; however, several projections foresee a decline in hydropower production to minus 46% due to both climate change and socioeconomic transformation. Our findings highlight the importance of considering a range of potential future scenarios in energy planning and policy development. The varied outcomes across the considered scenarios emphasize the need for flexibility in strategies to accommodate for uncertainties and address the challenges posed by divergent trajectories in hydropower use and renewable energy shares.