Potential Constraints Research Articles

Approaching the Conformal Limit of Quark Matter with Different Chemical Potentials

We study in detail the influence of different chemical potentials (baryon, electric charge, strange, and neutrino) on how and how fast a free gas of quarks in the zero-temperature limit reaches the conformal limit. We discuss the influence of non-zero masses, the inclusion of leptons, and different constraints, such as charge neutrality, zero-net strangeness, and fixed lepton fraction. We also investigate for the first time how the symmetry energy of the system under some of these conditions approaches the conformal limit. We find that the inclusion of all quark masses (even the light ones) can produce different results depending on the chemical potential values or constraints assumed. A positive or negative deviation of 10% from the pressure of free massless quarks with the same chemical potential was found to take place as low as μB=77 to as high as 48,897 MeV. This illustrates the fact that the “free” or conformal limit is not a unique description. Finally, we briefly discuss what kind of corrections are expected from perturbative QCD as one goes away from the conformal limit.

Gauged Q-ball dark matter through a cosmological first-order phase transition

As a new type of dynamical dark matter mechanism, we discuss the stability of the gauged Q-ball dark matter and its production mechanism through a cosmological first-order phase transition. This work delves into the study of gauged Q-ball dark matter generated during the cosmic phase transition. We demonstrate detailed discussions on the stability of gauged Q-balls to rigorously constrain their charge and mass ranges. Additionally, employing analytic approximations and the mapping method, we provide qualitative insights into gauged Q-balls. We establish an upper limit on the gauge coupling constant and give the relic density of stable gauged Q-ball dark matter formed during a first-order phase transition. Furthermore, we discuss potential observational signatures or constraints of gauged Q-ball dark matter, including astronomical observations and gravitational wave signals.

Survival costs of reproduction are independent of energy costs in a seabird, the pelagic cormorant.

Life-history theory predicts that investment in reproduction should decrease survival (the 'cost of reproduction'). It is often assumed that energy allocation drives such trade-offs, with limited energy available for both reproduction and survival. However, the underlying mechanisms remain poorly understood, maybe because survival costs of reproduction are only apparent when resources are limited. Here, we took advantage of a natural experiment created by fluctuating environmental conditions to compare energy expenditure of a seabird, the pelagic cormorant (Phalacrocorax pelagicus), between contrasting population-scale scenarios of survival costs of reproduction. We used multi-state capture-recapture modelling across 16 years to identify which breeding seasons induced high survival costs (survival ratebreeders < survival ratenon/failed breeders) and we concomitantly estimated energy expenditure of chick-rearing males using time-energy budget models across 4 years. Daily energy expenditure (DEE) of chick-rearing pelagic cormorants varied significantly among years. However, survival costs of reproduction were observed in only 1 year, and contrary to our expectations, variation in DEE was not associated with population-level survival costs. Similarly, at the individual level, DEE in 1 year did not predict the probability of being observed again at the colony in following years (apparent survival). Finally, DEE was independent of brood size and brood age, but older individuals tended to expend less energy than younger ones. Given the lack of an apparent energetic 'cost of reproduction', lower DEE in older birds could be due to improved efficiency rather than avoidance of costs in old birds. Although future studies should account for potential sex-specific energetic constraints by including data on female energy expenditure, we conclude that a direct link between the rate of energy expenditure during breeding and subsequent survival is unlikely in this system.

MODEL BARU PENGEMBANGAN KARYAWAN ADMINISTRATIF DI PERGURUAN TINGGI SWASTA: INTEGRASI TEKNOLOGI DAN KOMPETENSI SOFT SKILLS

This research aims to explore the influence of Technology Integration and Soft Skills Competence on the Development of Administrative Staff in Private Higher Education Institutions in Bandung. In the modern era marked by technological advancements and the complexity of job demands, this study is relevant for examining how the integration of technology and the development of soft skills competence can affect administrative staff in higher education institutions. The research methodology adopts a quantitative approach with a Total Population of 414 employees from 138 institutions, and a sample of 203 employees selected through Area Probability Sampling. Data collection is conducted using a questionnaire, and data analysis is performed utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that both Technology Integration and Soft Skills Competency have a positive and significant influence on the development of administrative staff, with the Mediating Effect of Development Model Effectiveness playing a pivotal role. Limitations of the study include potential constraints on generalization within the context of private higher education institutions in Bandung. The research contributes by providing strategic recommendations to enhance technology integration, develop soft skills competencies, and design effective development models for administrative human resources. These findings are expected to serve as a foundation for efforts to improve the performance and development of administrative staff in higher education institutions.

Robust and effective ab initio molecular dynamics simulations on the GPU cloud infrastructure using the Schrödinger Materials Science Suite

Ab initio Born-Oppenheimer molecular dynamics (AIMD) is a valuable method for simulating physico-chemical processes of complex systems, including reactive systems, and for training machine learning models and force fields. Speed and stability issues on traditional hardware preclude routine AIMD simulations for larger systems and longer timescales. We postulate that any practically useful AIMD simulation must generate a trajectory of a minimum 1000 MD steps a day on a moderate cloud resource. In this work, we implement a computing workflow that enables routine calculations at this throughput and demonstrate results for several non-trivial atomistic dynamical systems. In particular, we have employed the GPU implementation of the Quantum ESPRESSO code which we will show increases AIMD productivity compared to the CPU version. In order to take advantage of transient servers (which are more cost and energy effective compared to the stable servers), we have implemented automatic restart/continuation of the AIMD runs within the Schrödinger Materials Science Suite. Finally, to reduce simulation size and thus reduce compute time when modeling surfaces, we have implemented a wall potential constraint. Our benchmarks using several reactive systems (lithium anode surface/solvent interface, hydrogen diffusion in an iron grain boundary) show a significant speed up when running on a GPU-enabled transient server using our updated implementation.

Does macro-prudential regulation lead to low interest rate? An empirical analysis based on the transnational panel model

Based on a simple theoretical analysis, this paper constructs a transnational panel model based on the macroeconomic variable data of 131 countries from 2007 to 2020 for empirical analysis, so as to systematically explore the potential impact of macro-prudential policy regulation on the evolution of interest rates. It is found that the nominal interest rate of developed economies is closer to the lower bound of interest rate than that of underdeveloped economies, and the implementation of macro-prudential policies in the world generally shows a tightening trend, and the tightening degree of macro-prudential policies in developed economies is higher. Macro-prudential regulation has significantly exacerbated the evolution of global low interest rates, but the interest rate evolution of different types of economies is heterogeneous under the influence of macro-prudential regulation, especially in developed economies. Based on these insights, the paper proposes several policy recommendations: firstly, strengthen the precision of the implementation of macro-prudential policies to avoid “one-size-fits-all” policy implementation; secondly, refine macro-prudential policy regulation to be more scientific and prevent policy overshoots that could inadvertently harm the real economy; thirdly, attach great importance to the potential constraints of the low interest rate environment, and use diversified means to get rid of the lower bound of the nominal interest rate.

Unleashing the Power of Artificial Intelligence and Automation in Public Administration

The study explores the use of automation and artificial intelligence (AI) in public administration and its potential benefits and constraints for government organizations. It discusses how automation and AI can revolutionize public administration by improving productivity, effectiveness, and service quality. The study provides an overview of automation and AI technologies, highlighting their strengths and weaknesses. It identifies key areas where automation and AI can be integrated into public administration, including policymaking, service delivery, decision-making, and citizen engagement. The advantages of using automation and AI in public administration are examined, such as data-driven policy decisions, streamlined administrative procedures, and enhanced service delivery through AI-powered chatbots and virtual assistants. Ethical considerations, privacy concerns, and job displacement are also addressed, proposing methods for ensuring ethical and equitable application of automation and AI. The study includes case studies from different countries that demonstrate successful applications of automation and AI, showcasing increased citizen involvement, transparency, and improved government services. It concludes by emphasizing the importance of strong leadership, collaboration between governmental organizations and technical experts, and ongoing research and development to maximize benefits and minimize risks. The study aims to provide insights and recommendations for policymakers, administrators, and researchers, contributing to the knowledge base on AI and automation in public administration.

Seismic and Potential Field Constraints on Upper Crustal Architecture of Inner Bering Shelf, Offshore Southwestern Alaska

Southwestern Alaska encompasses a group of fault-bounded tectonostratigraphic terranes that were accreted to North America during the Mesozoic and Paleogene. To characterize the offshore extension of these terranes and several significant faults identified onshore, we reprocessed three intersecting multichannel deep seismic reflection profiles totaling ∼750 line-km that were shot by the R/V Ewing across part of the inner Bering continental shelf in 1994. Since the uppermost seismic section is often contaminated by high amplitude water layer multiples from the hard and shallow seafloor, the migrated reflection images are supplemented with high-resolution P wave velocity models derived by traveltime tomography of the recorded first-arrivals to depths of up to 2000 m. Additionally, other geophysical datasets such as seismicity, well logs, high resolution satellite-altimetry gravity, air-borne magnetics, ship-board gravity and magnetics, are also incorporated into an integrated regional interpretation. We delineate the offshore extension of the major mapped geological elements, including the Togiak fault (TGF), East Kulukak fault (EKF), Chilchitna fault (CF), Lake Clark fault (LCF), Togiak terrane (TT), Goodnews terrane (GT), Peninsular terrane (PT), Northern Kahiltna flysch (NKF) and Southern Kahiltna flysch (SKF) deposits, and the regional suture zone. The geophysical evidence from this study suggest that the major faults and terrane boundaries of southwestern Alaska, excluding the LCF, not only extend beneath the Bering shelf offshore but also appear to rotate, forming a trend parallel to the inactive Beringian margin. The LCF extends offshore but likely terminates in the northeastern part of the Bristol Bay basin. Additionally, the constraints from seismicity data indicates that while the major faults in southwestern Alaska exihibit some activity onshore, they remain dormant in the offshore region. These new findings will contribute to a better understanding of terrane accretion processes and existing fault models of southwestern Alaska.

Can carbon emissions trading policy promote product bargaining power increases for high-carbon enterprises? Evidence from China.

Carbon emissions trading policies play a crucial role in facilitating the transition to high-end products within high-carbon enterprises. Nevertheless, current empirical analyses of the carbon emissions trading market exhibit a lack of precision and are susceptible to bias in their findings. Limited research has been conducted on the influence of product quality as a potential constraint on the impact of carbon trading on product bargaining power. This study presents a double-difference model utilizing data on emission-control enterprises in China's carbon market to examine the influence of the carbon emissions trading mechanism on the bargaining power of high-carbon products. Empirical analysis is conducted using financial data from listed companies in China spanning the years 2010 to 2020. The findings indicate that the implementation of carbon emissions trading policies has a dampening impact on the product bargaining power of high-carbon enterprises. Moreover, carbon emissions trading policies have heterogeneous effects on the product bargaining power of high-carbon firms with different life cycles, with mature high-carbon firms receiving a boost and declining high-carbon firms receiving a dampening effect. Mechanism test finds that the incomplete transmission effect of cost shocks resulting from carbon emissions trading policies has negatively affect the product bargaining power of high-carbon enterprises. Further research finds that product quality is a key factor in determining the effect of the carbon emissions trading policy, and that the impact of the carbon emissions trading policy on the bargaining power of products of high-carbon firms takes on a "U" shape due to product quality. Once the product quality exceeds the bottleneck value of 0.5956, the policy significantly increases the bargaining power of products. The study confirms that the establishment of carbon markets can effectively increase the bargaining power of superior products. These results offer a comprehensive theoretical and practical foundation for nations to advance the development of carbon markets and facilitate the achievement of sustainable development by high-carbon enterprises.

Maker music education: Towards a post-digital, participatory and empowering music education

This theoretical paper aims to delineate a music-specific interpretation of a concept known as maker education by addressing substantial challenges within educational technologies, particularly in music education. Challenges include the potential constraints on musical agency, limitations to specific music genres and sounds and the environmental impact of increased music technology use in classrooms. In response, the maker education concept is introduced, adapted and refined as a pathway towards a post-digital, participatory and empowering music education. Employing a Design-Based Research approach, design principles for ‘maker music education’ are delineated, and the subsequent steps in the research process are outlined.

Nonuniform response of vegetation phenology to daytime and nighttime warming in urban areas

The phenological response of vegetation to changing climates requires exploration across environments, including urban areas with elevated daytime and nighttime temperatures (TD and TN) and artificial lights. Here, we analyzed multiple remotely-sensed datasets (2012–2021) from and around a highly-urbanized area, Seoul, in South Korea. While the urban fraction and artificial lights largely contributed to advancing the start-of-season (SOS) and delaying the end-of-season (EOS) (30.7–42.1%), SOS changes were more driven by TD (15.7%), whereas EOS changes were more attributed to TN (21.8%). A higher response of the SOS to TD was found in more urbanized areas with higher artificial lights, while the sensitivity of the EOS to TN increased in brighter areas during nighttime. These findings indicate that vegetation’s response to rising temperatures will become dynamic, given the potential constraints by light and other attributes, calling for further assessments of its implication for terrestrial ecosystems and carbon cycle.

Moth resonant mechanics are tuned to wingbeat frequency and energetic demands.

An insect's wingbeat frequency is a critical determinant of its flight performance and varies by multiple orders of magnitude across Insecta. Despite potential energetic benefits for an insect that matches its wingbeat frequency to its resonant frequency, recent work has shown that moths may operate off their resonant peak. We hypothesized that across species, wingbeat frequency scales with resonance frequency to maintain favourable energetics, but with an offset in species that use frequency modulation as a means of flight control. The moth superfamily Bombycoidea is ideal for testing this hypothesis because their wingbeat frequencies vary across species by an order of magnitude, despite similar morphology and actuation. We used materials testing, high-speed videography and a model of resonant aerodynamics to determine how components of an insect's flight apparatus (stiffness, wing inertia, muscle strain and aerodynamics) vary with wingbeat frequency. We find that the resonant frequency of a moth correlates with wingbeat frequency, but resonance curve shape (described by the Weis-Fogh number) and peak location vary within the clade in a way that corresponds to frequency-dependent biomechanical demands. Our results demonstrate that a suite of adaptations in muscle, exoskeleton and wing drive variation in resonant mechanics, reflecting potential constraints on matching wingbeat and resonant frequencies.

What explains exit in Indian manufacturing industries?

PurposeThe purpose of this paper is to investigate the likelihood of firm exit, focusing on firm- and sector-specific factors and other potential constraints that may lead to exit.Design/methodology/approachThe authors address the main research question by using hazard-cox and probit models on plant level data for the period 1998–1999 to 2012–2013, drawn from the Annual Survey of Industries collected by the Central Statistical Organisation.FindingsThe authors find that probability of exit reduces with improved firm performance. Urban firms, proprietary firms and smaller firms are more likely to exit as compared with their respective counterparts. The findings are robust to alternate measures of performance, alternate specifications and different methods.Originality/valueStudies of entry and exit rates at a point in time are useful in examining the turnover of establishments. But to understand the establishment survival, the authors must also examine the probability of firm exit and the possible determinants that aid exit. There are institutional factors that prevent easy exit of firms from an industry. It would be worthwhile to see how the exit rate will be impacted if these barriers ceased to exist. In this study, the authors construct a model of exit, which would help us to predict firm exit.

The inhibitory effect of a recent distractor: singleton vs. multiple distractors

In the complex interplay between sensory and cognitive processes, the brain must sift through a flood of sensory data to pinpoint relevant signals. This selective mechanism is crucial for the effective control of behaviour, by allowing organisms to focus on important tasks and blocking out distractions. The Inhibition of a Recent Distractor (IRD) Task examines this selection process by exploring how inhibiting distractors influences subsequent eye movements towards an object in the visual environment. In a series of experiments, research by Crawford et al. (2005a) demonstrated a delayed response to a target appearing at the location that was previously occupied by a distractor, demonstrating a legacy inhibition exerted by the distractor on the spatial location of the upcoming target. This study aimed to replicate this effect and to investigate any potential constraints when multiple distractors are presented. Exploring whether the effect is observed in more ecologically relevant scenarios with multiple distractors is crucial for assessing the extent to which it can be applied to a broader range of environments. Experiment 1 successfully replicated the effect, showing a significant IRD effect only with a single distractor. Experiments 2–5 explored a number of possible explanations for this phenomenon.

ChatGPT and Higher Education: A Pathway to Unprecedented Progress

ChatGPT is an extensively prominent chatbot that operates on artificial intelligence (AI), garnering significant recognition in diverse fields such as education, healthcare, and Business. This study examines the potential benefits, difficulties, and consequences of using ChatGPT in research, application, and policy. This article analyzes the prevailing patterns and obstacles in ChatGPT, along with the prospective implementations of ChatGPT in the domains of education, healthcare, and Business. The study aims to conduct a thorough assessment examining the efficacy of ChatGPT in healthcare, education, research, and practice. Furthermore, this analysis aims to ascertain potential constraints or obstacles that may emerge during its execution. This study also examines the importance of ChatGPT as a groundbreaking advancement in AI, highlighting its potential to augment the abilities of individuals in various sectors, such as education, healthcare, and business, thereby enabling the realization of unprecedented accomplishments. The exact extent of limitations demonstrated by the current language model remains uncertain, and the capabilities of ChatGPT have raised significant worries inside academic institutions regarding the potential increase in instances of plagiarism. This study provides recommendations for politicians, educators, and healthcare practitioners. The main objective is to address and minimize the potential hazards of implementing ChatGPT while promoting its ethical and responsible utilization.

Perceptions and experiences of UK‐based mothers of autistic daughters in relation to the potential affordances and constraints of an autism diagnosis

AbstractWhilst there is a growing body of research about autistic girls, much less is known about the experience of mothering an autistic girl, and the potential impact of the diagnosis. This study qualitatively explored the diagnostic journey of 12 mothers in the UK to identify the meanings attributed to their daughter's diagnosis. A thematic analysis was applied with themes examined through the lens of the CMM LUUUUTT model to further explore the stories lived and told by the mothers. Eighteen themes linked to perceived affordances, perceived constraints/constraints of perception, experienced affordances and experienced constraints. Participants highlighted the impact of autism myths and stereotypes which influenced identification, referral, diagnosis and ongoing support for the girls. Myths and stereotypes told about autism also played a significant role in the mothers lived experience of the diagnosis. An affordance of diagnosis was a new understanding about their daughters' needs, which led to new parenting styles, letting go of blame and resisting perceived societal ‘oughtisms’ about how parenting should be. Whilst the diagnosis was seen as relationally transformative, challenges were described in accessing emotional or educational support for their daughter's post‐assessment.

Deep learning-based auditory attention decoding in listeners with hearing impairment * *This paper’s data analysis is based on a Master’s Thesis work [].

Objective. This study develops a deep learning (DL) method for fast auditory attention decoding (AAD) using electroencephalography (EEG) from listeners with hearing impairment (HI). It addresses three classification tasks: differentiating noise from speech-in-noise, classifying the direction of attended speech (left vs. right) and identifying the activation status of hearing aid noise reduction algorithms (OFF vs. ON). These tasks contribute to our understanding of how hearing technology influences auditory processing in the hearing-impaired population. Approach. Deep convolutional neural network (DCNN) models were designed for each task. Two training strategies were employed to clarify the impact of data splitting on AAD tasks: inter-trial, where the testing set used classification windows from trials that the training set had not seen, and intra-trial, where the testing set used unseen classification windows from trials where other segments were seen during training. The models were evaluated on EEG data from 31 participants with HI, listening to competing talkers amidst background noise. Main results. Using 1 s classification windows, DCNN models achieve accuracy (ACC) of 69.8%, 73.3% and 82.9% and area-under-curve (AUC) of 77.2%, 80.6% and 92.1% for the three tasks respectively on inter-trial strategy. In the intra-trial strategy, they achieved ACC of 87.9%, 80.1% and 97.5%, along with AUC of 94.6%, 89.1%, and 99.8%. Our DCNN models show good performance on short 1 s EEG samples, making them suitable for real-world applications. Conclusion: Our DCNN models successfully addressed three tasks with short 1 s EEG windows from participants with HI, showcasing their potential. While the inter-trial strategy demonstrated promise for assessing AAD, the intra-trial approach yielded inflated results, underscoring the important role of proper data splitting in EEG-based AAD tasks. Significance. Our findings showcase the promising potential of EEG-based tools for assessing auditory attention in clinical contexts and advancing hearing technology, while also promoting further exploration of alternative DL architectures and their potential constraints.

Comparative analysis of hydrogen production pathways for emethanol synthesis to decarbonise industry

Methane pyrolysis and water electrolysis offer alternative hydrogen pathways to methane reforming that utilise renewable power and avoid generating carbon dioxide (CO2). On a scope 1 and 2 basis, both technologies have the potential to generate carbon neutral emethanol fuel when combined with biogenic CO2. However, pyrolysis requires significantly less energy and has a lower capital expenditure (CAPEX). Being a more nascent technology, it also has upside potential for cost reductions and valorisation of the solid carbon by-product can yield lower hydrogen costs than the incumbent technology, however, limited demand from existing markets is a potential constraint. Industry decarbonisation is constrained due to a lack of available renewables and hydrogen infrastructure. Pyrolysis offers a potential cost-effective use of the available renewable assets in the early stages while the carbon by-product is not a constraint and until sufficient renewable infrastructure is in place to support water electrolysis at a cost-effective scale.

Artificial intelligence and human integration: a conceptual exploration of its influence on work processes and workplace learning

ABSTRACT This paper examines the influence of Artificial Intelligence (AI) on job and organizational processes, drawing on sociotechnical systems (STS) theory. It discusses the emergence of AI and its implications for HRD on individual, group, and organizational learning. AI has changed the trajectory of work processes and outcomes, paving the way for the future of work. An AI-human integration process model is proposed to reinforce its influence on job design in response to AI applications in the workplace. Learning is experienced between humans and between humans and AI. AI’s cognitive intelligence is capable of learning from each other through a process of deep learning. Humans, too, can learn from AI to modify their response towards working with AI-enabled applications. The model illustrates the relationships between individuals, work processes, and sociotechnical systems through multilevel interactions. Of relevance to HRD are two critical processes, namely learning and innovating, that facilitate the continuation of AI-human integration, enabling employees to embrace AI as an integral part of their work life. The paper offers several implications for HRD research and practice, recognizing opportunities for workplace learning and the potential constraints and risks arising from AI adoption that are applicable globally and internationally.

Cluster cosmology redux: a compact representation for the halo mass function

ABSTRACT Groups and clusters of galaxies imprint coherent, arcminute-scale features across the spectrophotometric sky, especially optical-IR clusters of galaxies, spectral distortions in the cosmic microwave background, and extended sources of X-ray emission. The space–time density of the host dark matter halo population – the halo mass function (HMF) – is a common theoretical basis for modelling such observable features. We explore a compact representation – a dual-quadratic (DQ-HMF) form – that features readily interpretable parameters representing polynomial expansions of the space–time number density surface, first in terms of log-mass, then in redshift. The DQ-HMF form fits Mira-Titan N-body emulator expectations for halo masses $10^{13.7-14.5} \, h^{-1}\, {\rm M}_\odot$ over redshifts, 0.1 &amp;lt; z &amp;lt; 1.5 to within $\sim \! 5~{{\ \rm per\ cent}}$. We provide best-fitting parameters for a Planck 2018 cosmology and demonstrate model self-similarity in the Ωm−$\, \sigma _8$ plane. Convolving with a minimal mass–observable relation (MOR) yields closed-form expressions for counts, mean mass, and mass variance of cluster samples characterized by an observable property. Performing information-matrix forecasts of potential parameter constraints from existing and future surveys, we demonstrate the potential for percent-level constraints on model parameters by an LSST-like optical cluster survey of 300 000 clusters and a richness–mass variance of 0.32. Even better constraints could potentially be achieved by a survey with one-tenth the sample size but with a reduced selection property variance of 0.12. Potential benefits and extensions to the basic MOR parametrization are discussed.