Time-space Constraints Research Articles

The effect of different cytokinin and auxin source on micropropagation of Kalanchoe blossfeldiana

Micropropagation is widely used for vegetative propagation of many different ornamental and medicinal plant species under aseptic conditions. This technology has widespread practical applications irrespective of seasonal and time-space constraints. The present study aimed to design in vitro propagation using 1 8 different combinations of BAP, NAA, TDZ, and GA3 supplemented with commercial tea sugar at 30 g l-1 for micropropagation using leaf segments close to petiole of Kalanchoe blossfeldiana as explants and to find its efficiency for viable cultures. The results showed clear discrimination between regeneration on TDZ and BAP-supplemented regeneration cultures. Thus, both BAP-NAA and TDZ-NAA combinations were found suitable for micropropagation. the source medium affected the shoot regeneration and the rooting ability, irrespective of the culture of any shoot regeneration medium. These plantlets were rooted in 0.01-0.05 mg l-1 IBA. The study showed successful rooting under in vitro conditions using 1 ½ or 1×MS medium with 0.5 mg L-1 IBA. These rooted plantlets were then transferred to MS medium with 0.2 mg L-1 gibberellic acid and without BAP or with 0.1, 0.2, 0.5 mg l-1 TDZ and 0.1 mg l-1 NAA or without 0.1 mg L-1 NAA for adaptation. The results of the study are of great importance for K. blossfeldiana plant breeding studies and growers.

Kinematics of rift linkage between the Eastern and Ethiopian rifts in the Turkana Depression, Africa

AbstractRift initiation within cold, thick, strong lithosphere and the evolving linkage to form a contiguous plate boundary remains debated in part owing to the lack of time–space constraints on kinematics of basement‐involved faults. Different rift sectors initiate diachronously and may eventually link to produce a jigsaw spatial pattern, as in the East African rift, and along the Atlantic Ocean margins. The space–time distribution of earthquakes illuminates the geometry and kinematics of fault zones within the crystalline crust, as well as areas with pressurized magma bodies. We use seismicity and Global Navigation System Satellites (GNSS) data from the Turkana Rift Array Investigating Lithospheric Structure (TRAILS) project in East Africa and a new digital compilation of faults and eruptive centres to evaluate models for the kinematic linkage of two initially separate rift sectors: the Main Ethiopian Rift (MER) and the Eastern rift (ER). The ca. 300 km wide zone of linkage includes failed basins and linkage zones; seismicity outlines active structures. Models of GNSS data indicate that the ca. 250 km‐wide zone of seismically active en echelon basins north of the Turkana Depression is a zone, or block, of distributed strain with small counterclockwise rotation that serves to connect the Main Ethiopian and Eastern rifts. Its western boundary is poorly defined owing to data gaps in South Sudan. Strain across the northern and southern boundaries of this block, and an ca. 50 km‐wide kink in the southern Turkana rift is accommodated by en echelon normal faults linked by short strike‐slip faults in crystalline basement, and relay ramps at the surface. Short segments of obliquely oriented basement structures facilitate across‐rift linkage of faults, but basement shear zones and Mesozoic rift faults are not actively straining. This configuration has existed for at least 2–5 My without the development of localized shear zones or transform faults, documenting the importance of distributed deformation in continental rift tectonics.

An Ethical Debate in the Philosophy of Information: Is It Possible to Combine or Reconcile Multiple Ethical Theories in a Common Perspective in Artificial Intelligence?

We live in a cyber-universe created by millions of data sets. This universe, where there are almost no time-space constraints, allows people to perform activities in the intercardinal direction in a comfort they could not imagine before. At the same time, this multicultural and global world (infosphere) is a source of ethical challenges. While living in our unique culture, is it possible to share common ethical values in our world, which is becoming more global with each passing day? Or is it getting more and more impossible in this complex cyber universe? This article draws attention to Bynum's Emerging Ethics theory, which carries the umbrella concept that has the potential to unite people around some common values in ethical practices developed for intelligent systems such as artificial intelligence, even if we have different ethical approaches. The main purpose of this article is how, although different approaches they are, theories based on the common nature of human beings can be combined for the same purpose and Thanks to the 'ethical family unity' structure they have created under the ethical umbrella, it is questioned how they will determine the ethical components that can be applied to intelligence systems like AI for the solution of the common problems of the information age.

Triple line-scan camera measurement for efficient and accurate narrow-space 3-D scanning with motion estimation.

Vision-based three-dimensional (3-D) shape measurement plays a crucial role in the inspection of vehicles and trains in the field of transportation. Consequently, the demand for more comprehensive narrow-space inspection has become an inevitable necessity and presents a great challenge to conventional vision methods. We propose the following efficient and accurate narrow-space 3-D scanning method based on triple line-scan cameras. First, the structure of coplanar dual line-scan cameras with a narrow reflector is proposed, making it easy for the optical path to enter a narrow space and obtain 3-D shape information. Efficient in-motion measurement with a large field of view is thereby achieved without the worries that often accompany narrow-space constraints. Secondly, an additional line-scan camera is attached to the coplanar dual cameras and creates a time-space constraint in point cloud stitching direction through the triple line-scan camera structure. With an efficient algorithm template including matching and motion estimation, accurate point cloud stitching is ensured. Lastly, point cloud texture mapping and light source optimization are realized. Our experimental results prove that the method realizes low-distortion in-motion reconstruction in narrow space despite motion variation.

A Novel Reservation and Allocation Approach of Shared Parking Slots considering the Noncritical Aisle Space

Urban areas are experiencing a substantial increase in parking demand, which creates an imbalance due to the limited availability of parking facilities. To address this issue, effective parking management is necessary. Shared parking is an alternative solution that utilizes private plots which are vacant during the day to serve the parking needs of users engaged in nearby activities. While current parking management approaches have achieved high utilization rates for available parking slots, the use of aisle space in parking lots can alleviate oversaturated parking demands, especially during peak periods, for users with relatively short parking durations. This study aims to model the noncritical aisle space with time-space constraints in the shared parking slot allocation problem. We propose a programming model that maximizes the profit of the platform based on a reservation and allocation platform. Numerical experiments demonstrate that utilizing the aisle space can enhance the revenue and enable the platform to accommodate more requests that meet the requirements. Moreover, the presence of aisle parking slots may potentially result in slightly lower turnover rates of normal parking slots.

Probabilistic choice set formation incorporating activity spaces into the context of mode and destination choice modelling

Understanding the constraints that individuals face during their spatial choices is important from a policy perspective. Such constraints, however, are often overlooked in the choice set generation process during model development. In order to address that gap, the current study proposes a probabilistic choice set formation based on Manski's framework assuming that the actual choice set of an individual is latent (unobserved). Though latent class models with heterogeneous choice sets have been used previously in the context of mode and route choice, their application in the context of spatial choices have been hindered due to the inherently large choice sets making the problem computationally intractable. To address this issue, we propose to computationally simplify the problem by utilising the geography-derived notions of Activity Spaces to delineate a range of potential choice sets per individual helping us to capture both issues of spatial awareness and time-space constraints. In order to account for the latent nature of the true choice set, we propose a Latent Class Choice Modelling (LCCM) framework to allocate the individuals probabilistically into the different resulting choice sets, with each class having a different choice set and a different set of parameters. Thus the LCCM is able to capture heterogeneity in the choice sets and in the sensitivities, at the same time. The proposed LCCM framework is empirically tested on joint mode and shopping destination choices captured through a GPS smartphone application. It is compared to a base MNL model estimated on the global choice set, an LCCM capturing heterogeneity only in the sensitivities and a LCCM with latent consideration choice sets, similarly to the proposed model, but with generic parameters across classes. Our proposed specification is able to outperform all of the remaining models, while also providing insights on the factors affecting individuals to be constrained in their location choices across space hinting to cases of spatial cognition, the importance of the home and workplace geography and the individual's socioeconomic status. Such insights can be important for developing more behaviourally realistic models that can be used by planners and policy makers to formulate more effective measures that better relate to the underlying population. Furthermore, the analysis provides insights into the discrepancies that can emerge by accounting for latent consideration sets in willingness-to-pay measures and demand elasticities, which could have significant implications in the effectiveness of policy measures.

Ters Yüz Edilmiş Sınıf Modelinin Türk Müziği Nazariyatı Derslerinde Kullanılmasına Yönelik Bir Durum Çalışması

Bu çalışmada ters yüz edilmiş sınıf (TYES) modelinin Türk Müziği Nazariyatı derslerinde kullanışlılığı araştırılmaktadır. Bu çalışmada TYES uygulanmasına ilişkin hazırlanan on iki haftalık öğretim sürecinde ve bu sürecin sonunda gözlem ve odak grup görüşmeleri yoluyla elde edilen veriler içerik analizine tâbi tutulmuş, temalar ve kategoriler hâlinde yorumlanmıştır. Böylelikle TYES modelinin Türk Müziği Nazariyatı dersinin öğretimine ne kadar uygun olduğu, başarıyı ve öğrenmeyi nasıl ve ne şekilde etkilediği tespit edilmeye çalışılmıştır. Bu çalışmada ters yüz edilmiş sınıf modelinin avantajları erişim kolaylığı, derste uygulama yapılması ve materyaller olarak belirlenmiştir. Bu çalışmadan elde edilen bulgular doğrultusunda öğretim elemanı tarafından hazırlanan hazırlık videolarına ve materyallere öğrencilerin zaman-mekân kısıtı olmaksızın kolaylıkla erişim sağlaması, TYES modelinin en güçlü yanı olarak görülmektedir. Fakat bu süreçte bazı öğrencilerde özdenetim eksikliği olduğu, bazı öğrencilerin odaklanma sorunu yaşadığı, bazı öğrencilerin ise öğrenci-öğretim elemanı ya da öğrenci-bilgisayar arasında etkileşim sorunu yaşadığı ortaya çıkmıştır. Bu çalışmadan elde edilen veriler, Türk Müziği Nazariyatı dersinin TYES modeli ile verilmesinin çoğunlukla tercih edildiğini ve adı geçen derste bu modelin etkili bulunduğunu ortaya koymaktadır.

The impact of online food delivery service on eating-out behavior: a case of Multi-Service Transport Platforms (MSTPs) in Indonesia

Online food delivery services, provided under the multi-service transport platforms such as Grab and Gojek, could significantly change people’s eating-out behavior, which could also change the spatial distribution of restaurants in the long run. This study attempts to empirically identify factors affecting people’s preference on the use of online food delivery services using stated preference (SP) survey data collected with a multi-day smartphone-based travel diary survey in Jakarta, Indonesia. In the survey, we randomly chose observed eating-out trips (i.e., revealed preference (RP)) from a travel diary and asked whether the respondents would like to shift to an online food delivery service in a hypothetical situation in which the delivery cost, delivery time, food cost, and available food types vary across questions. This RP–SP combination allows us to elicit respondents’ preference under the real time–space constraints they had (e.g., he or she must start to work again from 13:00). Our empirical analysis confirms that delivery time and delivery cost are important factors affecting people’s preference. We also discuss the long-term impact of the behavioral changes on the spatial distribution of online food merchants and its policy implications.

가상 인플루언서 ‘로지(Rozy)’: 과연 모든 소비자들에게 어필할 수 있을 것인가?

Influencer marketing has been considered the adaptive strategy in COVID-19 pandemic, largely driven by the widespread use of social media marketing to increase effectively customer base. It is highly resonant and authentic ways to overcome severe time-space constraints of traditional marketing and deliver immediate returns. Influencers-individuals have been becoming vital intermediaries, helping to connect brands with consumers on social media. Especially virtual influencers effectively strengthen cross-functions of social media(aims to generate online leads) and e-commerce(aims to generate clicks). With COVID-19 still raging worldwide, virtual influencers are not subject to any physical restrictions. Furthermore, they are AI generated characters that mimic humans-the features and personalities and controlled entirely by a brand. That is why they remain an important marketing tool. This study aims to develop effective virtual influencer marketing strategy by segmenting consumer markets, which are depending on customers’behavior and attitude toward the virtual influencer, ‘Rozy’. A total of 204 questionaaires were used in this analysis. In the basis on factor analysis of Rozy’s leverage, it can be measured by two factors, namely attractiveness and trustworthiness. Respondents were divided into three groups based on the degree to which Rozy is perceived as attractive or trust. First group is consisted of respondents who evaluate highly Rozy as being a attractive and credible influencer. Second group considers her just as having the quality that attracts people. Last group doesn’t think that Rozy is an alluring and trustworthy influencer. Each group has significantly differences at a degree to the use of social media and continuous usage intention of social media

Utilising activity space concepts to sampling of alternatives for mode and destination choice modelling of discretionary activities

Choice models estimated on datasets with large numbers of alternatives present significant challenges leading to rapidly expanding computational cost, as well as potential behavioural realism issues. Sampling of alternatives has been a well-established method for overcoming the computational limitations, mostly applied to models of residential location. Nonetheless, destination choice models of discretionary activities require a different type of analysis, since the choice can be governed by time–space constraints and familiarity regarding the alternatives. Observing the general areas of travel for a period of days using high resolution GPS tracking can provide important information of the individuals’ whereabouts. The present study, taking advantage of such a dataset, proposes a more behaviourally realistic sampling protocol to reduce the choice set utilising the geography-based concepts of activity spaces. Differential importance sampling rates are applied depending on the individual’s activity space and trip chain making the resulting sampled choice set a function of person-specific spatial awareness and mode-specific time–space constraints. The performance of the sampling protocol developed is assessed using a model estimated with the full choice set and compared with random sampling and several other importance sampling protocols. The modelling outputs suggest that random sampling should be used with care, since it can result in highly biased estimates, but with low standard errors, as well. The proposed approach incorporates both time–space constraints and individual spatial awareness and is able to produce less biased estimates, achieve higher sampling stability and statistical efficiency, while also avoiding overfitting.

Pacesetters in contemporary telework: How smartphones and mediated presence reshape the time–space rhythms of daily work

AbstractWe examine how mobile information and communication technologies (ICTs) and mediated interaction transform daily work activity in contemporary, extended telework. We expand on the concepts of mediated bundles and pacesetters to understand how the rhythms and employee control of work activity change. We draw on in‐depth interviews with 22 teleworkers with varying skills and work tasks. We find that mobile technology not only relaxes the time–space constraints of telework but fosters countering processes of recoupling and fixity. New ICTs shift the relative importance of individually defined and work‐related pacesetters. The rhythm of daily work is increasingly set by horizontal interaction between spatially dispersed coworkers. It is informally regulated through practices of the continuous‐mediated interweaving of workflows and synchronised responsivity in relation to changing work intensity. Highly qualified teleworkers more often signal that they are in control and setting the pace compared to less qualified.

Redefining Human Communication Space Culture and Time in the Epoch of Internet Spatiality

The concepts of time, culture and communication have undergone rapid changes since the advent of the Internet. This text examined the influence of digital spatiality on our social life based on the three concepts. The new media have detached humanity from previous understandings of space, physical setting and culture. Therefore, the arrival of any new media systems advanced the boundaries of time-space and refinement in human manipulation of massages. This paper's thrust was to critically look at the dialectic between spatiality through time, culture, and communication to define how digital spatiality has truncated our conception of reality. The human perceptions of space have shifted from previous primordial physical location and limitations to a world illimitable boundary beyond out of the grasp of the authorities and natural elements. Time, space and culture have undergone rapid changes never experienced in human history. The finding's main conclusion was that advancement of the media brought about by the ubiquitous Internet had impacted heavily has adjusted our perception of living regulated by time-space constraints. The general context was that the Internet had abbreviated our conservative understanding of the concepts, challenging the perceptions of time, space and culture and constricted them as experienced in the epoch of Covid-19 in 2020 when humanity interconnected remotely. KEYWORDS: Digital, Spatiality, Regulated, Time, Conception Culture . DOI: 10.7176/RHSS/11-10-07 Publication date: May 31 st 2021

Time-space constraints to HIV treatment engagement among women who use heroin in Dar es Salaam, Tanzania: A time geography perspective.

Timely initiation and sustained adherence to antiretroviral therapy (ART) are essential to improving the health outcomes of people living with HIV and preventing onward HIV transmission. However, women who use heroin often face challenges to initiating and adhering to ART. In this paper we identify spatial, temporal, and social factors that affect HIV treatment engagement among women who use heroin, drawing from a time geography framework. We conducted in-depth interviews with 30 heroin-using women living with HIV in Dar es Salaam, Tanzania between January and March 2019. We found that unstable housing, high mobility, HIV-related stigma, and unpredictable daily paths due to heroin use and involvement in sex work spatially and temporally constrained women who use heroin from incorporating HIV treatment behaviors into daily routines. Some women, however, were able to overcome these time-space constraints to HIV treatment engagement through social support and social role performance. Time geography, including concepts of time-space constraints and daily paths, is a useful framework for identifying barriers to ART engagement. Structural, relational, and individual interventions aimed at eliminating time-space constraints hold the potential to improve HIV treatment engagement among particularly vulnerable and mobile populations.

Least-squares decomposition with time–space constraint for denoising microseismic data

SUMMARY Microseismic data are usually of low signal-to-noise ratio (SNR), which makes it difficult to utilize the microseismic waveforms for imaging and inversion. We develop a useful denoising algorithm based on a non-stationary least-squares decomposition model to enhance the quality of microseismic signals. The microseismic signals are assumed to be represented by a superposition of several smoothly variable components. We construct a least-squares inverse problem to solve for the the smooth components. We constrain the least-squares inversion via both time and space constraints. The temporal smoothness constraint is applied to ensure the stability when calculating the non-stationary autoregression coefficients. The space-smoothness constraint is applied to extract the spatial correlation among multichannel microseismic traces. The new algorithm is validated via several synthetic and real microseismic data and are proved to be effective. Comparison with the state-of-the-art algorithms demonstrates that the proposed method is more powerful in suppressing random noise of a wide range of levels than its competing methods.

Expression of Concern: Least-squares decomposition with time–space constraint for denoising microseismic data

Expression of Concern: Least-squares decomposition with time–space constraint for denoising microseismic data

An efficient generalised opposition-based multi-objective optimisation method for factory cranes with time-space constraints

In order to improve the performance of large manufacturing enterprises, besides the adoption of new technologies, it is also feasible to efficiently schedule logistics equipment such as cranes, which costs much less since only software changes are involved. In this research, the objectives of minimising total waiting cost and total delay cost are optimised simultaneously when executing crane-delivery tasks in factories. Given the time-space constraints and NP-hard nature of the problem, a generalised opposition-based learning (GOBL) mechanism and two problem-based searching strategies are developed and fused into the multi-objective differential evolution approach, namely GOMODE. The introduction of GOBL mechanism enables the proposed algorithm to search in a more extensive solution space, which improves the population diversity and avoids the premature problem. The performance of the GOMODE has been compared with classical multi-objective optimisation algorithms. The experimental results indicate that the GOMODE achieves a better performance both on solutions' quality and diversity. [Received: 11 December 2018; Accepted: 23 December 2019]

An efficient generalised opposition-based multi-objective optimisation method for factory cranes with time-space constraints

In order to improve the performance of large manufacturing enterprises, besides the adoption of new technologies, it is also feasible to efficiently schedule logistics equipment such as cranes, which costs much less since only software changes are involved. In this research, the objectives of minimising total waiting cost and total delay cost are optimised simultaneously when executing crane-delivery tasks in factories. Given the time-space constraints and NP-hard nature of the problem, a generalised opposition-based learning (GOBL) mechanism and two problem-based searching strategies are developed and fused into the multi-objective differential evolution approach, namely GOMODE. The introduction of GOBL mechanism enables the proposed algorithm to search in a more extensive solution space, which improves the population diversity and avoids the premature problem. The performance of the GOMODE has been compared with classical multi-objective optimisation algorithms. The experimental results indicate that the GOMODE achieves a better performance both on solutions' quality and diversity. [Received: 11 December 2018; Accepted: 23 December 2019]

A Dynamic Discrete Choice Activity-Based Travel Demand Model

This paper presents a dynamic discrete choice model (DDCM) for daily activity–travel planning. A daily activity–travel pattern is constructed from a sequence of decisions of when, where, why, and how to travel. Individuals’ preferences for activity–travel patterns are described by the sum of the utility of all travel and activity episodes in that pattern, but components of the utility functions, such as travel times, may be stochastic. In each decision stage, individuals act as if they maximized the expected utility of the remainder of the day. The DDCM-model presented allows for a detailed treatment of timing decision consistent with other choice dimensions, respects time–space constraints, and enables the inclusion of explicitly modeled uncertainties in, for example, travel time. In a case study, a model for daily planning of activity and travel on workdays is estimated whereby individuals can perform any number of trips that each is a combination of one of 1,240 locations, four modes, and six activities. Simulation results indicate that the model within sample accurately replicates timing decisions, trip lengths, and the distributions of the number of trips, tours, and trips per tour.

A Multivariate Modeling Analysis of Commuters’ Non-Work Activity Allocations in Xiaoshan District of Hangzhou, China

This paper investigates the outdoor non-work activity allocation behaviors of commuters in Xiaoshan District of Hangzhou, China, as well as the underlying relationship among different types of outdoor non-work activities. As per their commute and work schedules, commuters’ outdoor non-work activities are classified into six categories and considered as binary dependent variables for modeling analysis, including from home before work, on commute way from home to work, going home during work, going out (not going home) during work, on commute way from work back home, and from home after work. Independent variables include commute attributes, work schedules, sociodemographic attributes, and built-environmental attributes. A multivariate probit model is developed to explore the effects of explanatory variables and capture correlations among unobserved influential factors. The model estimation results show that daily work time, education years, and traffic zone have substantial impacts on commuters’ non-work activity allocations. As for the underlying relationship among unobserved factors, a positive correlation is found between the outdoor non-work activities on commute way to and from work, indicating a mutually promotive relationship. All other correlations are negative, indicating other types of non-work activities are mutually substitutive. These findings will help to better understand commuters’ behaviors of outdoor activity arrangement subject to the time-space constraint from fixed work schedules, and shed some light on the mechanism of complex work tour formation, so as to guide the development of activity-based travel demand models for commuters.

Least-squares decomposition with time–space constraint for denoising microseismic data

Least-squares decomposition with time–space constraint for denoising microseismic data