Time Demands Research Articles

Living and Dying in Shadow Times

Normative time frameworks obscure the ontological ambiguities of lived time, particularly in relation to illness and death. These forms of ambiguous lived time constitute what we call ‘shadow time’ – the unnoticed and the unknowable in our dominant temporal order. We examine what we call ‘diagnostic time’, ‘prognostic time’, ‘terminal time’ and ‘mourning time’ as shades of shadow time that elude the ordering of time that orders life. A critical stance to temporality is necessary, we argue, in order to recognise the regulatory imperatives of normative time, to mark the slippages between the demands of normative time and how time is often lived (out) and, importantly, to do justice to those living in the complicated ontological ambiguities of lived shadow time – where we see the intricate folding of life and death

341 Impact of genomic selection for growth on feet and leg structure in Angus cattle

Abstract Historically fitness and production traits have shown an antagonistic relationship at the genetic level. Genetic selection mainly focused on growth traits (GT) might have influenced feet and leg structure traits (FT; recently added trait), causing changes over time. We aimed to examine genetic parameter changes in FT and GT over time in Angus cattle. Currently, genetic parameters are estimated using Variance Components Estimation (VCE), which has limitations in handling large datasets and is time demanding. To address these limitations, we explored the use of predictivity-based formulas to estimate heritability and genetic correlations in GT. The analysis was conducted using the blupf90+ family of programs. To estimate variance components (VCE), sampled data (2011-2022) from the American Angus population included 311K GT records [birth (BW), weaning weight (WW), and post-weaning gain (PWG)], ~70K FT records [foot claw set (CS) and foot angle (FA)], and 142K genotypes. FT are taken on the worst foot of the animal and can be scored multiple times throughout the life of the animal, but in this study, we did not include repeated records. The dataset was split into 5-yr intervals. Since FT ranged from 5 to 9, and 5 is ideal, a negative correlation is desired between FT and GT. The correlation FA-PWG, changed in an undesirable way over time, as it changed from -0.10 to 0.02 in the last interval. The correlation FA-WW is becoming more favorable over time, shifting from 0.05 to -0.15. The GT selection process shifted the correlations between CS and GT in the desired direction, changing from positive (~0.1) to almost zero in the more recent two intervals. To test the predictivity-based formulas, Genomic Estimated Breeding Values (GEBV) were estimated using GT data from 6.3M animals born from 2000 to 2022, and 900K genotyped animals. The data were divided into four slices, with a 2-yr validation period. Over time, heritabilities remained constant: ~0.3 for BW and WW, and ~0.2 for PWG. Correlations between GT also remained constant, approximately at 0.45 for BW-WW, 0.30 for BW-PWG, and 0.75 for WW-PWG. Although the formulas show promising results for heritabilities, interpreting the outcomes of genetic correlation estimation remains unclear, needing further tests. While certain correlations between FT and GT suggest a positive impact of genetic selection, some undesirable outcomes are realized. Probably due to the early FT genetic selection implementation (2019). This emphasizes the need for a multi-trait genomic selection approach to mitigate genetic antagonism. Adopting predictivity-based formulas reduces the time for genetic parameter estimation and allows the use of complete data instead of sampled data, as currently done in VCE.

Pengukuran Beban Kerja Mental Mahasiswa Pekerja Fakultas Ekonomi Dan Bisnis Universitas Muhammadiyah Gresik Menggunakan Metode Nasa-TLX

Background – Mental workload is the difference between the workload demands of a task and capacity. Mental workload is a human evaluation or assessment of the limits of attentional load while carrying out tasks optimally, which is influenced by the workload demands of a task and the worker's capacity to handle the task, especially for students who carry out work activities while studying. Therefore, it is necessary to examine how much mental workload is actually borne by student workers so that the negative impacts it causes can be minimized. Aim – To measure the mental workload of student workers at the Faculty of Economics and Business, Muhammadiyah University of Gresik using the NASA-TLX method Design / methodology / approach – The research approach used is Quantitative Descriptive. Quantitative data was obtained from the results of questionnaire responses from student workers at the Faculty of Economics and Business, Muhammadiyah University of Gresik regarding the level of mental workload using the NASA-TLX method. Findings – High mental workload can have a negative impact on a person's mental health, especially for students who work while studying. Mental workload is defined as the difference between task demands and an individual's capacity to handle the task, focusing more on the mental aspect than the physical. Students who work while studying often experience fatigue, lack of time to rest and socialize, and experience sleep disorders and stress which can reduce their academic and work performance. Therefore, it is important to measure and analyze the level of mental workload experienced by student workers in order to reduce its negative impact. The method used to measure mental workload is NASA-TLX (Task Load Index). Conclusion – Student workers at the Faculty of Economics and Business, Muhammadiyah University of Gresik experience a high mental workload, with the time demand indicator (Temporal Demand/TD) having the highest weight and the level of effort (Effort/EF) getting the highest score. The time requirement (TD) indicator most dominantly influences their mental workload, while the level of frustration (FR) has the lowest influence. Overall, their average mental workload score was in the high category, namely 77.64. Research implication – This research is aimed at students who are studying while working, with the aim of measuring the level of mental load that objects experience. The load in question is a high mental workload, with indicators of time requirements (Temporal Demand/TD), level of effort (Effort/EF) and indicators of time requirements (TD), as well as levels of frustration (Frustration/FR). Limitations – The limitations of this research only include the mental workload variable on the research object (student workers)

A profit driven optimal scheduling of virtual power plants for peak load demand in competitive electricity markets with machine learning based forecasted generations

The operation of generating resources connected to VPPs during peak demand is crucial for determining the economic sustainability of participants in a competitive electricity market and minimizing the need for expensive peaking power plants. This paper proposes a profit driven strategy for scheduling of resources associated with VPP during peak load demand in competitive electricity market followed by different machine learning approaches for predicting the pattern of generation in DAM. The MILP has been developed by using GAMS integrated with MATLAB. The Intra-VPPs consisting TGs and DERs have been designed by using IEEE 33 bus distribution system. The performance of this system aligns with cases which investigate the interplay of EM dynamics, DERs contribution and scheduling of TGs, in order to optimize the operation of VPPs and assured the profits along with generating power amount for given time and load demand. Additionally, the generation pattern for future load in DAM operations has been forecasted using different neural network fitting models for enhancing the system's efficiency. The proposed work reduced the operating cost, nodal prices of the system followed by considerable improved profit for designed VPPs, in which more than 50 % profit driven by DERs itself of the whole system.

Clinical Reasoning Skills Among Second-Phase Medical Students in West Bengal, India: An Exploratory Study.

Introduction Proper application of clinical reasoning skills is essential to reduce diagnostic and management errors. Explicit inclusion of training and assessment of clinical reasoning skills is the demand of time. The study intended to measure the clinical reasoning skills of second-phase undergraduate students in a medical college inWest Bengal, India, and its distribution across several individual variables. Methods The clinical reasoning skills of 105 undergraduate medical students were assessed in a cross-sectional exploratory study using key feature questions (KFQs) with the partial credit scoring system. Six case vignettes aligned to the core competencies in the subject of pharmacology, pathology, and microbiology were designed and validated by the subject material experts for this purpose. The responses of the participants were collected through Google Forms (Google, Mountain View, CA) after obtaining written informed consent. The scores obtained in all KFQs were added and expressed in percentage of the maximum attainable score. Results The mean (±SD) clinical reasoning score of the participants was 42.5 (±12.6). Only 29.6% of respondents scored ≥ 50. Students with higher subjective economic status (p-value = 0.01) and perceived autonomy (p-value < 0.001) were more likely to have higher clinical reasoning scores. The marks obtained in previous summative examinations were significantly correlated with clinical reasoning scores. Conclusion Average score < 50.0 and inability to score ≥ 50.0 by more than two-thirds of the participants reflected the deficit in the clinical reasoning skills of second-phase MBBSstudents. The association of clinical reasoning skills with economic status, autonomy, and previous academic performances needs further exploration.

Optimization and modeling of process parameters for nutrient recovery from sewage wastewater

ABSTRACT Nitrogen (N) and phosphorus (P) contamination in wastewater pose significant environmental challenges. Recovering these elements as struvite not only mitigates environmental pollution but also aligns with sustainable development goals by recycling valuable resources. This research hypothesizes that optimized recovery methods can enhance the efficiency and effectiveness of struvite crystallization, addressing existing challenges in conventional techniques. To achieve optimal removal and recovery of N and P from sewage, a response surface model was employed. This model allowed for the identification of optimal process conditions and the elucidation of interactions among various components. Key variables impacting struvite recovery were identified using the Plackett–Burman design, while the central composite design was used for further optimization. The study determined the optimized parameters for phosphate recovery to be an Mg:P ratio of 1:2, pH of 10.5, additive concentration of 350 ppm, and a precipitation time of 30 min. Thermogravimetric analysis indicated that the residual amounts were below 50%. Additionally, the size and surface morphology of the final product were influenced by the process parameters, particularly the Mg:P ratio and pH. An inexpensive, quick, and efficient method to recover struvite fertilizer with a minimum demand of time and chemicals is established toward SDG 2 and 6.

Consumer preference on packed tender coconut water: Conjoint analysis approach

The study examined consumer preferences towards packed tender coconut water, which has witnessed higher demand in recent times. This is mainly due to its potential health benefits and being an ideal alternative to energy drinks and other carbonated beverages. For this study, primary data were collected from 150 consumers in Coimbatore using a well-structured questionnaire. Expending conjoint analysis, it was found that brand name was recorded as a highly preferred attribute, followed by quality parameters and price range. Therefore, creating awareness about the health benefits using various promotional tools and increasing its availability by establishing tender coconut water kiosks would raise its consumption. Further, due to the emergence of digital marketing adoptions, firms could focus on its availability through online platforms, which could increase consumption. Therefore, processing firms, in addition to traditional channels, should focus on adopting digital marketing channels with innovative products that have natural taste and good quality. Keywords: Brand, consumer buying behaviour, online platforms, processed foods, quality. JEL Codes: L66, M31.

Optimization of Microgrid fed from Hybrid Energy Sources using the Swarm Intelligence

To combat the widening gap between demand and supply, it is crucial to include renewableenergy sources as a Distributed Generation newline (DG). Despite their usefulness in reducing the gap, REsources place a heavy pressure on generation scheduling, particularly wind power. Utilities are compelledto use wind curtailment due to fluctuations in wind energy output, which reduces the incentive to switch torenewable sources of power. Demand Side Management (DSM) is one approach to this problem, since itadjusts demand in real time to match generation while the grid is operating. Conventional DemandResponse (DR) systems were created to minimise consumption during peak hours by arranging userloads in exchange for incentives. The Smart Grid (SG) concept provides a high-tech means ofcommunication for keeping tabs on and managing the power grid. This motivated the creation of asophisticated DR technique known as Demand Dispatch (DD), in which consumption is timesynchronizedwith electricity production. In order to deal with the fluctuations in wind power production,this thesis proposes a DD method. The entities involved in DD and the way in which data is sharedbetween them are laid out in an operational framework. In order to find and evaluate all of the studiesthat have been published on a certain topic, researchers often undertake systematic literature reviews.

How to better communicate the likely effectiveness of proposed training: Interpreting meta-analytic results for (and with) an applied audience

ABSTRACT Whether to develop and/or conduct training can represent a significant decision for organizations due to associated financial, personnel, and time demands. Various stakeholders in that decision will want to know what to expect from such training, including its likely effectiveness. Informing such stakeholders of the magnitude of relevant previous results – from a single study or, better, from studies aggregated via a meta-analysis – can help their decisions to be as informed as possible. But explanations of the size of training effects as found in the literature are not always phrased in a way easy for an applied audience to understand. This brief article clarifies the ‘percent improvement due to training’ interpretation of d, and extends it to differing rates of pre-training success. This in turn suggests that customer involvement in estimating appropriate base-line success rates could make interpretations of the degree of training effectiveness more organizationally specific and hence more useful.

Pre-implementation planning for a sepsis intervention in a large learning health system: a qualitative study

BackgroundSepsis survivors experience high morbidity and mortality. Though recommended best practices have been established to address the transition and early post hospital needs and promote recovery for sepsis survivors, few patients receive recommended post-sepsis care. Our team developed the Sepsis Transition and Recovery (STAR) program, a multicomponent transition intervention that leverages virtually-connected nurses to coordinate the application of evidence-based recommendations for post-sepsis care with additional clinical support from hospitalist and primary care physicians. In this paper, we present findings from a qualitative pre-implementation study, guided by the Consolidated Framework for Implementation Research (CFIR), of factors to inform successful STAR implementation at a large learning health system prior to effectiveness testing as part of a Type I Hybrid trial.MethodsWe conducted semi-structured qualitative interviews (n = 16) with 8 administrative leaders and 8 clinicians. Interviews were transcribed and analyzed in ATLAS.ti using a combination deductive/inductive strategy based on CFIR domains and constructs and the Constant Comparison Method.ResultsSix facilitators and five implementation barriers were identified spanning all five CFIR domains (Intervention Characteristics, Outer Setting, Inner Setting, Characteristics of Individuals and Process). Facilitators of STAR included alignment with health system goals, fostering stakeholder engagement, sharing STAR outcomes data, good communication between STAR navigators and patient care teams/PCPs, clinician promotion of STAR with patients, and good rapport and effective communication between STAR navigators and patients, caregivers, and family members. Barriers of STAR included competing demands for staff time and resources, insufficient communication and education of STAR’s value and effectiveness, underlying informational and technology gaps among patients, lack of patient access to community resources, and patient distrust of the program and/or health care.ConclusionsCFIR proved to be a robust framework for examining facilitators and barriers for pre-implementation planning of post-sepsis care programs within diverse hospital and community settings in a large LHS. Conducting a structured pre-implementation evaluation helps researchers design with implementation in mind prior to effectiveness studies and should be considered a key component of Type I hybrid trials when feasible.Trial registrationClinicaltrials.gov, NCT04495946. Registered August 3, 2020.

Developing two-dimensional indicators of transport demand and supply to promote sustainable transportation equity

Inadequate supply of transport infrastructure is often seen as a barrier to a sustainable future for cities globally. Such barriers often perpetuate significant inequalities in who can and who cannot benefit from sustainable transport opportunities, and as a result there is momentum for transformative urban planning to promote sustainable transportation equity. This study introduces a new set of two-dimensional indicators, merging elements of supply and demand, to identify barriers and imbalances in sustainable transport equity. The accessibility indicators, which are generated for bus, rail, and cycle infrastructure, consider the proximity of administrative areas to good quality transport infrastructure, as well as mode-specific demand, to clearly identify areas where the supply of infrastructure is inadequate to support local populations. We present a policy case study for Liverpool City Region, which demonstrates how these indicators can be used in an analytical framework to support transformative urban planning in long-term. In particular, the indicators reveal policy priority areas where demand for sustainable transport is greater than supply, as well as neighbourhoods where multiple transport inequalities are intersecting spatially, highlighting the need for specific types of infrastructure investment to promote sustainable transport equity (e.g. more frequent services, additional cycle paths). Our framework lays the foundations for improved decision-making in urban systems, through development of mode-specific sustainable transport indicators at small area levels, which harmonise elements of supply and demand for the first time.

Analisis Pembelajaran Berdiferensiasi pada Mata Pelajaran Aqidah Akhlak di MAS Simatorkis Kecamatan Rao Selatan

This study is prompted by challenges in implementing differentiated learning, including the time and preparation demands on teachers, reduced student engagement without effective communication ofthe approach, insufficient teacher training in designing and managing differentiated learning, and resource constraints, such as limited facilities for Aqidah Akhlak-aligned materials. The focus is on class X students, and the research employs a qualitative approach, collecting data through observation, interviews with school officials and teachers, and documentation at MAS Simatorkis, South Rao District. Results indicate successful differentiation in Aqidah Akhlak learning, fostering increased student participation, active engagement, and application of religious values in daily life. Key findings stress the importance of diverse learning methods, relevant materials, and tailored attention to individual student needs, considering varying levels of understanding.

Gestión de factores de riesgo psicosocial: estudio de caso en una mediana empresa salvadoreña

Psychosocial risk factors are anything in the design or management of work that increases the risk of work-related stress. Their assessment is legally required in Salvadoran companies, nevertheless, the legislation itself lacks a specific methodology to comply with such requirements. In 2020, the Spanish National Institute of Health and Safety at Work (INSST) published a methodology to assist Micro and Small Enterprises in managing these factors that can also be used for larger companies. This article presents a case study in a medium-sized advertising Salvadoran company, where this methodology was applied in positions of its production department. The information required for its application was obtained through consultation with workers along with the review of their job descriptors and direct observation of work. The results indicate that all the jobs analyzed have psychosocial factors in adverse conditions. The most predominant ones were associated with autonomy, work pace, time and emotional demands, all with a medium level of importance. It was identified that taking on orders with short delivery deadlines modified the planning and extension of working hours, contributing to the situation observed. Based on this information, various measures were suggested, such as conducting capability and workload studies along with improving planning visibility. Given that the methodology facilitated the management of psychosocial factors, was easy to use and had a participatory approach, the experience of this research suggests that it can be an alternative for other micro, small and medium-sized Salvadoran companies to respond to regulatory requirements, ensuring the welfare of workers. Realidad Empresarial No. 18, 2024: 76-95.

ExpoNano: A Strategy Based on Hyper-Cross-Linked Polymers Achieves Urinary Exposome Assessment for Biomonitoring.

Urinary analysis of exogenous and endogenous molecules constitutes an efficient, noninvasive approach to evaluate human health status. However, the exposome characterization of urinary molecules remains extremely challenging with current techniques. Herein, we develop an ExpoNano strategy based on hyper-cross-linked polymers (HCPs) to achieve ultrahigh-throughput measurement of exo/endogenous molecules in urine. The strategy includes a simple trapping-detrapping procedure (15 min) with HCPs in enzymatically treated urine, followed by mass spectrometer determination. Molecules that can be determined by ExpoNano have a wide range of molecular weight (75-837 Da) and Log Kow (octanol-water partition coefficient; -9.86 to 10.56). The HCPs can be repeatedly used five times without decreasing the trapping efficiency. Application of ExpoNano in a biomonitoring study revealed a total of 63 environmental chemicals detected in >50% of the urine pools collected from Chinese adults living in 13 cities, with a median concentration of 0.026-47 ng/mL, while nontargeted analysis detected an additional 243 exogenous molecules. Targeted and nontargeted analysis also detected 926 endogenous molecules in pooled urine. Collectively, the ExpoNano strategy demonstrates unique advantages over traditional urine analysis approaches, including a wide range of analytes, satisfactory trapping efficiency, high simplicity and reusability, and extremely reduced time demand and financial cost.

ЗАМАНБАП БИЛИМ БЕРҮҮДӨГҮ ИНТЕРАКТИВДҮҮ УСУЛДАР ЖАНА ИНСТРУМЕНТТЕР

In the current era of computer technology, the use of digital educational resources plays a key role in the education system. On the one hand, this is an increase in the level of computer knowledge of teachers and students, and on the other hand, the demand of time and the need of society for a modern education system. The purpose of the article is to give some recommendations on improving the quality of education, the development of distance education in the higher education system using interactive methods and tools based on digital education. The article discusses the use of digital educational technologies for the innovative development of the university, the remote organization of the educational process, interactive teaching methods at the university. Fulfilment of the tasks set by the state for the sphere of higher education often depends on the competence of the teacher. The profession of a teacher is honourable, but very difficult.

Automation and Augmentation: Artificial Intelligence, Robots, and Work

This article reviews the literature that examines the potential, limitations, and consequences of robots and artificial intelligence (AI) in automation and augmentation across various disciplines. It presents key observations and suggestions from the literature review. Firstly, displacement effects from task automation continue to persist. However, one should not assume an unequivocally increasing efficacy of technology in automation or augmentation, especially given the declining productivity growth in high-income countries and some large emerging economies in recent decades. Jobs less likely to be negatively impacted are those that require diverse tasks, physical dexterity, tacit knowledge, or flexibility, or are protected by professional or trade associations. Despite countervailing effects, without policy intervention, automation and augmentation could widen inequality between social groups, labor and capital, and firms. Secondly, AI's promise in task automation and labor augmentation is mixed. AI tools can cause harm, and dissatisfaction and disengagement often arise from their opaqueness, errors, disregard for critical contexts, lack of tacit knowledge, and lack of domain expertise, as well as their demand for extra labor time and resources. The inadequate autonomy to override AI-based assessments further frustrates users who have to use these AI tools at work. Finally, the article calls for sociological research to specify conditions and mechanisms that ameliorate adverse consequences and enhance labor augmentation by embedding the study of automation and augmentation in concrete social and political contexts at multiple levels.

Numerical Method for Predicting Transient Aerodynamic Heating in Hemispherical Domes

In this research, a streamlined numerical approach designed for the quick estimation of temperature profiles across the finite thickness of a hemispherical dome subjected to aerodynamic heating is introduced. Hemispherical domes, with their advantageous aerodynamic, structural, and optical properties, are frequently utilized in the front sections of objects traveling at supersonic velocities, including missiles or vehicles. The proposed method relies on one-dimensional analyses of fluid dynamics and flow characteristics to approximate the local heat flux across the exterior surface of the dome. By calculating these local heat flux values, it is also possible to predict the temperature variations within the thickness of the dome by employing the finite difference technique, to solve the heat conduction equation in spherical coordinates. This process is iterated over successive time intervals, to simulate the entire flight duration. Unlike traditional Computational Fluid Dynamics (CFD) simulations, the proposed strategy offers the benefits of significantly lower computational time and resource demands. The primary objective of this work is to provide an efficient numerical tool for evaluating aerodynamic heating impact and temperature gradients on hemispherical domes under specific conditions. The effectiveness of the proposed method will be validated by comparing the temperature profiles derived for a standard flight scenario against those obtained from 2-D axisymmetric transient CFD simulations performed using ANSYS-Fluent 2022 R2.

Ligand-selective turn-off sensing, harvesting and post-adsorptive use of Dy(III) and Yb(III) by intrinsically fluorescent flower-shaped Gum Acacia-grafted hydrogels

Rare earth metals (REMs), such as Dysprosium (Dy) and Ytterbium (Yb), have experienced unprecedented demand in recent times due to their applications in high-end technologies. REMs are found only in select geographic locations placing tremendous economic constraints on their use. In this work, we have developed Gum Acacia-grafted hydrogels (GmAc-FluoroTerPs) that are capable of selective detection and capture of Dy and Yb. The intrinsically blue fluorescent polymer hydrogel GmAc-FluoroTerP has been optimized for Dy(III) and Yb(III) specific quenching, enabling limit of detection of the REMs at 0.13 nM and 60.8 pM, respectively. A comprehensive structural characterization of the fluorescent hydrogel has been performed via NMR, FTIR, XPS, EPR, TGA, XRD, TEM, SEM, EDX, TCSPC, and DLS. In addition to an in situ generated fluorophore, GmAc-FluoroTerP displays a distinctive aggregation induced emission enhancement in mixed solvents. The complexation of Dy(III)/Yb(III) with GmAc-FluoroTerP hydrogel has been characterized by XPS, TCSPC, and logic gate analyses, and the adsorptive capacity for Dy(III) and Yb(III) are found to be best reported till date as 125.57 mg g−1 and 102.27 mg g−1, respectively. Desorption at acidic pH allows recovery of the REMs. We also report semiconducting behaviour of the native fluorescent hydrogel, that is enhanced upon adsorptive capture of Dy(III) and Yb(III), with calculated band gaps at 1.37, 0.77, and 0.49 eV, respectively. The convergent sensing, capture, and reuse of Dy(III) and Yb(III) presented in this work promises a hitherto unreported template for application on other REMs.

NeuroQuMan: quantum neural network-based consumer reaction time demand response predictive management

NeuroQuMan: quantum neural network-based consumer reaction time demand response predictive management

Citrus leaf disease detection through deep learning approach

The majority of people in the world directly or indirectly depend on agriculture. Plant diseases are a significant threat to agricultural production and food security. Due to its high nutritional value, citrus fruit is one of the most abundant fruits in the world. However, different diseases are responsible for degraded citrus production as well as financial losses to the farmers. Traditionally, visual observation by experts has been attended to diagnose plant diseases. Usually, plant leaf disease recognition methods mainly rely on expert experiences to manually extract the colour, composition, and other features of diseased leaf images. Black spot, greening, canker, and melanoses are four common citrus leaf diseases. Rapid and accurate diagnosis of these diseases is a demand of time. Deep learning is a promising solution to these problems. There are different types of deep learning architecture like ImageNet, GoogleNet, VGG16, ResNet50, and InceptionV3, which show promising results in different object detection. Though most of these benchmark models give almost similar accuracy. However, this paper uses two deep learning models to find the better ones for the detection of citrus leaf disease detection. Hence, InceptionV3 outperforms VGG16 in terms of accuracy.