Levels Of Hierarchy Research Articles

Missing microbial eukaryotes and misleading meta-omic conclusions.

Meta-omics is commonly used for large-scale analyses of microbial eukaryotes, including species or taxonomic group distribution mapping, gene catalog construction, and inference on the functional roles and activities of microbial eukaryotes in situ. Here, we explore the potential pitfalls of common approaches to taxonomic annotation of protistan meta-omic datasets. We re-analyze three environmental datasets at three levels of taxonomic hierarchy in order to illustrate the crucial importance of database completeness and curation in enabling accurate environmental interpretation. We show that taxonomic membership of sequence clusters estimates community composition more accurately than returning exact sequence labels, and overlap between clusters can address database shortcomings. Clustering approaches can be applied to diverse environments while continuing to exploit the wealth of annotation data collated in databases, and selecting and evaluating these databases is a critical part of correctly annotating protistan taxonomy in environmental datasets. We argue that ongoing curation of genetic resources is crucial in accurately annotating protists in in situ meta-omic datasets. Moreover, we propose that precise taxonomic annotation of meta-omic data is a clustering problem rather than a feasible alignment problem.

Influence of Corporate Culture on the Implementation of Change Initiatives in Kirinyaga County Government, Kenya

This research aimed to examine influence of corporate culture on the implementation of change initiatives in Kirinyaga County Government, Kenya. The investigation used the Kanter Theory of Change Management and Resource-Based Theory. The study adopted a descriptive survey methodology and specifically targeted a cohort of 213 individuals who were engaged in the administrative sector of Kirinyaga County. The sample size was obtained by the use of stratified random sampling, and the appropriate sample size was computed using the Yamane formula. Therefore, the study sample size was 139 respondents The investigating methods used questionnaire. The data were collected by use of questionnaires and examined using version 26.0 of the Statistical Package for the Social Sciences (SPSS) software. The descriptive statistical analysis included the computation of percentages, frequencies, measures of central tendency, and measures of dispersion. The research used inferential statistical analysis, namely correlation and multiple regressions, to ascertain the magnitude and orientation of the relationship between the variables under investigation. The analysis was performed with a 95% confidence level. The findings demonstrated a robust and statistically significant correlation between the corporate culture and the implementation of change initiatives in the Kirinyaga County Government, Kenya. R-Squared is a statistical metric that estimates the proportion of variability in the dependent variable that can be explained by the independent variables. The study revealed that the R-squared value was 0.572, suggesting that the corporate culture, can explain 57.2% of the variability in the implementation of change initiatives in the Kirinyaga County Government, Kenya. The study highlights the need of fostering a more inclusive decision-making process by increasing engagement at lower levels of hierarchy within the firm. The research recommends that county officials should prioritize and improve communication by consistently and transparently explaining the reasons for change initiatives to all employees. The paper proposes conducting a comparative research to examine the effects of different leadership styles and change management tactics used in the county administration.

Data Reconciliation-Based Hierarchical Fusion of Machine Learning Models

In the context of hierarchical system modeling, ensuring constraints between different hierarchy levels are met, so, for instance, ensuring the aggregation constraints are satisfied, is essential. However, modelling and forecasting each element of the hierarchy independently introduce errors. To mitigate this balance error, it is recommended to employ an optimal data reconciliation technique with an emphasis on measurement and modeling errors. In this study, three different machine learning methods for development were investigated. The first method involves no data reconciliation, relying solely on machine learning models built independently at each hierarchical level. The second approach incorporates measurement errors by adjusting the measured data to satisfy each constraint, and the machine learning model is developed based on this dataset. The third method is based on directly fine-tuning the machine learning predictions based on the prediction errors of each model. The three methods were compared using three case studies with different complexities, namely mineral composition estimation with 9 elements, forecasting of retail sales with 14 elements, and waste deposition forecasting with more than 3000 elements. From the results of this study, the conclusion can be drawn that the third method performs the best, and reliable machine learning models can be developed.

Chaxu Climate as a Double-Edged Sword: A Moderated Mediation Model of Organizational Justice, Hierarchy Level on Turnover Intention

Research on chaxu climate, a prevalent organizational phenomenon in China, has captured significant academic attention in recent years. This study aims to explain why some employees who experience chaxu climate in hospitality organizations contemplate leaving, while others do not. We propose a moderated mediation model to examine the double-edged sword impact of chaxu climate on turnover intention. Through one qualitative study and two scenarios-based experimental studies, we reveal that organizational justice plays a crucial role as a contextual cue, influencing employees’ attribution of chaxu climate and subsequent turnover intention. Perceived insider status is identified as a mediator. Furthermore, we establish a second-level moderating effect of the hierarchy level, distinguishing between supervisor and front-line employees. Theoretical and practical implications are discussed accordingly.

Putting the net-zero hierarchy into practice: Build efficiently

Muiris Moynihan summarises available guidance covering the fourth level of the Institution's hierarchy for net-zero design, which encourages engineers to 'build efficiently' by ensuring designs are highly utilised.

High-level visual prediction errors in early visual cortex.

Perception is shaped by both incoming sensory input and expectations derived from our prior knowledge. Numerous studies have shown stronger neural activity for surprising inputs, suggestive of predictive processing. However, it is largely unclear what predictions are made across the cortical hierarchy, and therefore what kind of surprise drives this up-regulation of activity. Here, we leveraged fMRI in human volunteers and deep neural network (DNN) models to arbitrate between 2 hypotheses: prediction errors may signal a local mismatch between input and expectation at each level of the cortical hierarchy, or prediction errors may be computed at higher levels and the resulting surprise signal is broadcast to earlier areas in the cortical hierarchy. Our results align with the latter hypothesis. Prediction errors in both low- and high-level visual cortex responded to high-level, but not low-level, visual surprise. This scaling with high-level surprise in early visual cortex strongly diverged from feedforward tuning. Combined, our results suggest that high-level predictions constrain sensory processing in earlier areas, thereby aiding perceptual inference.

The Oswestry Disability Index is not a good measure for low back pain in adolescents: A Rasch analysis of data from 200 people.

To evaluate the psychometric properties of the modified Oswestry Disability Index in adolescents with low back pain through Rasch analysis. This is a psychometric study. Physical therapy clinics of a large pediatric hospital in Columbus, Ohio (United States). Two hundred adolescent patients with low back pain (112 female, 15.4 ± 1.9 years old). The modified Oswestry Disability Index. The mean modified Oswestry Disability Index score was 17.1 ± 12.1, with scores ranging from 0 to 56 on the 100-point scale. The Rasch person reliability for the Oswestry Disability Index was 0.70, representing minimal acceptable person reliability. The Oswestry Disability Index demonstrated poor item spacing, hierarchy, and targeting of the adolescents' disability level (>1 logit between person mean and item mean). A significant misfit was observed in three of 10 items. There were significant issues with the functioning of responses on all items of the Oswestry Disability Index. Principal component analysis of residuals revealed a ratio of 5.3:1, demonstrating acceptable unidimensionality. Due to multiple psychometric issues, the Oswestry Disability should not be used to assess disability in adolescents with low back pain. The Oswestry Disability Index significantly mistargets adolescent ability and only provides an acceptable assessment of the most disabled adolescents.

Reification of the p factor draws attention away from external causes of psychopathology.

Summarizing specific psychopathology symptoms into higher order factors has a long tradition in mental health science. More recently, the general psychopathology factor (p factor) has gained much interest and currently reflects the highest level of the psychopathology hierarchy. The p factor is modeled from covariance of transdiagnostic psychopathology symptoms. Because such covariance is robust (persons who score higher on symptom X compared to others also tend to score higher on symptom Y), there have been many factor-analytic studies that claim the discovery of-and/or empirical support for-a general psychopathology factor. The reification of the p factor has put person-internal common causes of psychopathology high on the research agenda, while person-external common causes are overlooked. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Relationship between Event Boundary Strength and Pattern Shifts across the Cortical Hierarchy during Naturalistic Movie-viewing.

Our continuous experience is spontaneously segmented by the brain into discrete events. However, the beginning of a new event (an event boundary) is not always sharply identifiable: Phenomenologically, event boundaries vary in salience. How are the response profiles of cortical areas at event boundaries modulated by boundary strength during complex, naturalistic movie-viewing? Do cortical responses scale in a graded manner with boundary strength, or do they merely detect boundaries in a binary fashion? We measured "cortical boundary shifts" as transient changes in multivoxel patterns at event boundaries with different strengths (weak, moderate, and strong), determined by across-participant agreement. Cortical regions with different processing timescales were examined. In auditory areas, which have short timescales, cortical boundary shifts exhibited a clearly graded profile in both group-level and individual-level analyses. In cortical areas with long timescales, including the default mode network, boundary strength modulated pattern shift magnitude at the individual participant level. We also observed a positive relationship between boundary strength and the extent of temporal alignment of boundary shifts across different levels of the cortical hierarchy. In addition, hippocampal activity was highest at event boundaries for which cortical boundary shifts were most aligned across hierarchical levels. Overall, we found that event boundary strength modulated cortical pattern shifts strongly in sensory areas and more weakly in higher-level areas and that stronger boundaries were associated with greater alignment of these shifts across the cortical hierarchy.

Hierarchical meta-porous materials as sound absorbers

The absorption of sound has great significance in many scientific and engineering applications, from room acoustics to noise mitigation. In this context, porous materials have emerged as a viable solution towards high absorption performance and lightweight designs. However, their performance is somewhat limited in the low-frequency regime. Inspired by the concept of recursive patterns over multiple length scales, typical of many natural materials, here, we propose a hierarchical organization of multilayered porous media and investigate their performance in terms of sound absorption. Two types of designs are considered: a hierarchical periodic (HP) and a hierarchical gradient (HG). In both cases, it is found that in some frequency ranges the introduction of multiple levels of hierarchy simultaneously allows for: (i) an increase in the level of absorption compared with the corresponding bulk block of porous material (or to the previous hierarchical levels (HLs)), and (ii) a reduction in the quantity of porous material required. Another advantage of using this approach is on the fabrication, since only one porous material is required. The performances are examined for both normal and oblique incidences, as well as for different values of the static air-flow resistivity. The methodological approach is based on the transfer-matrix method, optimization algorithms such as the metaheuristic greedy randomized adaptive search procedure (GRASP), finite-element calculations and measurements performed in an impedance tube.

Methodical approach to assessing the state of hierarchical systems using a metaheuristic algorithm

The object of the study is hierarchical systems. The subject of the study is the process of assessing the state of hierarchical systems using the advanced antlion algorithm (ALA), an advanced genetic algorithm and evolving artificial neural networks. The problem solved in the study is to increase the efficiency of assessing the state of hierarchical systems, regardless of the system hierarchy level. The originality of the study is that: – the initial setting of ALA is carried out taking into account the type of uncertainty using appropriate correction factors for the degree of awareness of anthill location (priority search directions); – the initial velocity of each ALA is taken into account, which allows determining the priority of search by each ALA in the specified search direction; – the fitness of ALA hunting locations is determined, which reduces the time for assessing the state of the hierarchical system; – the use of the procedure of global restart of the algorithm, which allows the algorithm to go beyond the current optimum and improve the exploration ability of the algorithm, which reduces the time for assessing the state of hierarchical systems; – the possibility of clarifying the choice of an anthill at the hunting stage due to ranking anthills by the level of ant pheromone; – improved ability to select the best ALA in comparison with random selection using an advanced genetic algorithm, which improves the reliability of assessing the state of complex hierarchical systems. The proposed methodical approach provides a 22–25 % increase in the efficiency of assessing the state of hierarchical systems by using additional advanced procedures. The proposed methodical approach should be used to solve the problems of assessing the state of complex hierarchical systems under uncertainty and risks characterized by a high degree of complexity.

Nearest data processing in GPU

Memory wall is known as one of the most critical bottlenecks in processors, rooted in the long memory access delay. With the advent of emerging memory-intensive applications such as image processing, the memory wall problem has become even more critical. Near data processing (NDP) has been introduced as an astonishing solution where instead of moving data from the main memory, instructions are offloaded to the cores integrated with the main memory level. However, in NDP, instructions that are to be offloaded, are statically selected at the compilation time prior to run-time. In addition, NDP ignores the benefit of offloading instructions into the intermediate memory hierarchy levels. We propose Nearest Data Processing (NSDP) which introduces a hierarchical processing approach in GPU. In NSDP, each memory hierarchy level is equipped with processing cores capable of executing instructions. By analyzing the instruction status at run-time, NSDP dynamically decides whether an instruction should be offloaded to the next level of memory hierarchy or be processed at the current level. Depending on the decision, either data is moved upward to the processing core or the instruction is moved downward to the data storage unit. With this approach, the data movement rate has been reduced, on average, by 47 % over the baseline. Consequently, NSDP has been able to improve the system performance, on average, by 37 % and reduce the power consumption, on average, by 18 %.

Performance Analysis of Spin Orbit Torque Magneto-Resistive RAM Caches in 4-core ARM Systems

Spin Orbit Torque Magnetic Random Access Memory (SOT–)MRAM is gaining interest as it eradicates several limitations posed by its predecessor Spin Transfer Torque (STT-)MRAM, yet inherits all its advantages. This work explores in detail, the suitability of SOT–MRAM implemented caches in different levels of memory hierarchy in comparison to conventional SRAM technology, over several performance parameters like area, energy consumption and execution time for an embedded benchmark suite. Our circuit-level analysis shows that SOT–MRAM outperforms SRAM for caches ([Formula: see text] KB), and only lags in area and read-access energy for smaller caches. A typical 512 KB SOT–MRAM cache improves area by 1%, read/write latency by 33/38%, and leakage by over 99% than that of SRAM memory technology. The architecture-level analysis confirms that on average SOT–MRAM is energy efficient by 74% in L1, 97.2% in L2 and 89.3% in both (i.e., L1 + L2) implementations against SRAM, for a 22[Formula: see text]nm technology node. We also estimate that SOT–MRAM only solution offers [Formula: see text]% energy savings and [Formula: see text]% better EDP than Hybrid (L1-SRAM and L2-SOT) memory hierarchy for multi-core ARM processors.

Fast feature- and category-related parafoveal previewing support free visual exploration.

While humans typically saccade every ∼250 ms in natural settings, studies on vision tend to prevent or restrict eye movements. As it takes ∼50 ms to initiate and execute a saccade, this leaves only ∼200 ms to identify the fixated object and select the next saccade goal. How much detail can be derived about parafoveal objects in this short time interval, during which foveal processing and saccade planning both occur? Here, we had male and female human participants freely explore a set of natural images while we recorded magnetoencephalography and eye movements. Using multivariate pattern analysis, we demonstrate that future parafoveal images could be decoded at the feature and category level with peak decoding at ∼110 ms and ∼165 ms respectively, while the decoding of fixated objects at the feature and category level peaked at ∼100 ms and ∼145 ms. The decoding of features and categories was contingent on the objects being saccade goals. In sum, we provide insight on the neuronal mechanism of pre-saccadic attention by demonstrating that feature and category specific information of foveal and parafoveal objects can be extracted in succession within a ∼200 ms intersaccadic interval. These findings rule out strict serial or parallel processing accounts but are consistent with a pipeline mechanism in which foveal and parafoveal objects are processed in parallel but at different levels in the visual hierarchy.Significance Statement We provide neural evidence that future parafoveal saccade goals are processed surprisingly quickly at the feature and the category level before we saccade to them. Specifically, using multivariate pattern analysis applied to magnetoencephalography and eye-tracking data, we found that information about the colour and the category of parafoveal objects emerged at ∼110 ms and ∼165 ms respectively, with the same information about foveal objects emerging ∼100 ms and ∼145 ms. Our findings provide novel insight into the neuronal dynamics of parafoveal previewing during free visual exploration. The dynamics rule out strict serial or parallel processing, but are consistent with a pipelining mechanism in which foveal and parafoveal objects are processed in parallel but at different levels in the visual hierarchy.

In adult dogs is supplementary tryptophan in the diet effective in reducing signs of anxiety?

Question In adult dogs, is dietary supplementation with tryptophan compared with no dietary tryptophan supplement effective in reducing signs of anxiety? Clinical bottom line The category of research question was: Treatment. The number and type of study designs that were critically appraised were: Two studies were found to be considered appropriate in the level of hierarchy of evidence pyramid. One study was a randomised double-blinded, placebo-controlled study and the other was a single-blind, placebo-controlled study. Critical appraisal of the selected papers meeting the inclusion criteria collectively provide zero/weak/moderate/strong evidence in terms of their experimental design and implementation: Moderate. The outcomes reported are summarised as follows… One study found no overall significant influence of tryptophan in the diet as an aid in reducing anxiety and fear-related behaviour in anxious dogs in household conditions. In contrast, the second study reported lower anxiety-related behaviour in dogs by owners but did not find significant differences in cortisol levels based on urine cortisol-to-creatinine ratio. In view of the strength of evidence and the outcomes from the studies the following conclusion is made… Current evidence reveals two studies that have evaluated the effectiveness of tryptophan as a supplement to the diets of anxious canines for pet owners. One study conducted did not find a significant effect or interaction in reducing anxiety-related behaviour whilst the second study reported lower anxiety-related behaviour in dogs by owners but did not find significant differences in cortisol levels based on urine cortisol-to-creatinine ratios. The use of tryptophan as a supplementary component in the diet is thus not enough as a sole treatment to assist in reducing anxiety in anxious adult dogs.

Multimodal transport route selection: An integrated fuzzy hierarchy risk assessment and multiple criteria decision-making approach

Selecting multimodal transport routes is a complex problem due to the presence of various, often conflicting decision-making attributes, such as transportation cost and time, diverse risks with hierarchical relationships both within and across groups, decision-makers’ preferences, and numerous possible options. To address this problem, a novel decision-making approach is proposed, integrating the best-worst method (BWM), fuzzy hierarchy risk assessment (FHRA), and additive ratio assessment (ARAS). This approach decomposes the route decision-making problem into a hierarchy structure and determines the optimal solution through three distinct phases. In the first phase, BWM assigns relative weights to various criteria within the hierarchical structure, ensuring that the decision-makers’ preferences are accurately reflected. In the second phase, FHRA evaluates qualitative risks from the lowest to the highest hierarchy levels. This process involves calculating risk magnitudes at the lowest hierarchy level and progressively recalculating them as they ascend to the highest level of the hierarchy. The multimodal transport cost-model provides transportation cost and time. In the final phase, ARAS synthesizes all the decision-making data to rank all possible multimodal alternative routes based on utility scores. The realistic case study validates the practical applicability and effectiveness of the proposed approach. The findings indicated that the proposed methodology effectively identify the most appropriate multimodal transport route as the best compromise choice. This study contributes new knowledge regarding a new hybrid decision-making framework that combines multiple criteria decision-making methods with fuzzy hierarchy risk analysis, thereby enhancing the design of transportation plans to align with organizational goals and customer requirements.

Competitive interactions shape brain dynamics and computation across species.

Adaptive cognition relies on cooperation across anatomically distributed brain circuits. However, specialised neural systems are also in constant competition for limited processing resources. How does the brain's network architecture enable it to balance these cooperative and competitive tendencies? Here we use computational whole-brain modelling to examine the dynamical and computational relevance of cooperative and competitive interactions in the mammalian connectome. Across human, macaque, and mouse we show that the architecture of the models that most faithfully reproduce brain activity, consistently combines modular cooperative interactions with diffuse, long-range competitive interactions. The model with competitive interactions consistently outperforms the cooperative-only model, with excellent fit to both spatial and dynamical properties of the living brain, which were not explicitly optimised but rather emerge spontaneously. Competitive interactions in the effective connectivity produce greater levels of synergistic information and local-global hierarchy, and lead to superior computational capacity when used for neuromorphic computing. Altogether, this work provides a mechanistic link between network architecture, dynamical properties, and computation in the mammalian brain.

Conjunctive hierarchical secret sharing by finite geometry

AbstractSecret sharing is a general method for distributing sensitive data among the participants of a system such that only a collection of predefined qualified coalitions can recover the secret data. One of the most widely used special cases is threshold secret sharing, where every subset of participants of size above a given number is qualified. In this short note, we propose a general construction for a generalized threshold scheme, called conjunctive hierarchical secret sharing, where the participants are divided into disjoint levels of hierarchy, and there are different thresholds for all levels, all of which must be satisfied by qualified sets. The construction is the first method for arbitrary parameters based on finite geometry arguments and yields an improvement in the size of the underlying finite field in contrast with the existing results using polynomials.

Aligning local governance with SDGs: a study of local government systems in Pakistan

ABSTRACT This study examines the experiences of local government councilors between 2013–2020 in four provinces of Pakistan. The study addresses three key questions: Firstly, it investigates the differences and commonalities in the local government structures across provinces and explores the potential for synchronization to enhance the implementation of Sustainable Development Goals (SDGs) at the local level. Secondly, it examines the main challenges local councilors face in effectively delivering basic services, including water, sanitation, health, education, waste management, and transport, within both rural and urban contexts. Lastly, the study analyses the necessary changes or reforms at the provincial level within the existing local government system in rural and urban areas to ensure the delivery of basic services in alignment with SDGs. The findings reveal that a powerful bureaucracy and political influence at the intermediate to upper levels of the government hierarchy exert significant control over administrative, fiscal, and political.

A survey of keys for the identification of newly described insect genera: recommendations for authors, reviewers, editors, and publishers.

Large numbers of new taxa are described annually and while there is a great need to make them identifiable, there seems little consistency in how this might be facilitated. 427 papers published in 2021 and 2022 were surveyed, which described 587 new insect genera. Only 136 of these papers included keys, and these allowed the identification of 233 of the new genera (31.9% of papers and 39.7% of the new genera). The proportion of papers that included a key varied significantly among insect orders but not among the handful of journals wherein the bulk of the new genera were described. Overall, for 17 key-related variables assessed in a binary fashion (optimal vs suboptimal), the average key had almost six criteria that were scored as being suboptimal. For example, less than one-fifth facilitated retracing and less than 12% had illustrated keys where the images were conveniently located close to the relevant key couplets. Progress towards confirming a putative identification was possible in all papers, through the inclusion of a diagnosis, habitus images, or both. Based upon this analysis, and expanding on previous suggestions for key construction, 23 recommendations are made on how to make an identification key maximally useful for users and I indicate the relative ease with which each could be adhered to. Identification keys should accompany all new taxon descriptions, guidelines for effective key construction should be added to journals' instructions to authors, editors and reviewers should check keys carefully, and publishers should be attentive to the needs of users through, for example, permitting duplication of images to make keys easier to use. Recommendations are likely relevant to all levels in the taxonomic hierarchy for all organisms, despite the data being derived from generic-level keys for insects.