Scalable Resource Research Articles

A modularized parallel distributed High–Performance computing framework for simulating seasonal frost dynamics in Canadian croplands

The winter/spring season in cold climate regions has been recognized as a critical period for cropland nutrient loss and greenhouse gas emissions and has been predicted to be vulnerable to climate change. Agricultural system models serve as essential tools in anticipating potential outcomes. The RZ-SHAW model, combining the Simultaneous Heat and Water Model (SHAW) with the Root Zone Water Quality Model 2 (RZWQM2), comprehensively describes snow and soil freezing dynamics. Showing promising results in simulating winter frost dynamics it, however, remains computationally intensive. High-performance computing has been recognized as a promising approach to reducing simulation runtimes. A modularized parallel distributed high-performance computing (HPC) framework (RS-DPCF) with both multi-threading (MT) and multi-processing (MP) capability was developed for RZ–SHAW calibration and simulation. Two case studies demonstrated that the developed RS-DPCF program could significantly reduce RZ–SHAW calibration time, allow improved computation resource scalability, and perform field-scale simulations of overwintering conditions for multiple croplands across Canada. For the RZ–SHAW model, MT provided a parallel-computing efficiency superior to that of MP.

Dynamic Matching and Spatial Optimization of Land Use and Resource-Environment Constraints in Typical Regions of the Yellow River Basin in China

Accurately identifying the matching relationships between territorial space evolution and the resources and environment carrying capacity will directly guide the sustainable use of territorial space. Based on the evaluation of the territorial space dynamics of the lower Yellow River, this paper evaluates the suitability of territorial space development by focusing on ecological protection, agricultural development, and urban construction. Specifically, the resources and environment carrying capacity is estimated by identifying and mediating potential conflicts in the development of territorial space. The matching relationship between the evolution of territorial space and the resources and environment carrying capacity is identified using the matching degree model. The results demonstrated that: (1) Between 2000 and 2020, the agricultural space of the lower Yellow River was relatively stable, while the ecological space was generally shrinking, and the urban space continued to increase; (2) The characteristics of suitability for the agricultural development and urban construction of the lower Yellow River are characterized by landform and land-sea differentiation. The carrying scale of resources and the environment is based on agricultural space and is increasing yearly, followed by ecological space, which is gradually decreasing, and urban space, which first increased and then decreased; (3) Between 2000 and 2020, the matching index of the ecological and agricultural space evolution and the resource and environmental carrying capacity in the lower Yellow River exhibited a downward trend, while the regional difference increased. Furthermore, the matching index of urban space and the resources and environment carrying capacity indicated an upward trend, while the regional difference decreased.

Child neurodevelopment and the quality of ecological environments for children in the Amazon

The purpose was to analyse the relationship between the quality of the ecological environment and the neurodevelopment of children in an Early Childhood Education Unit in the Amazon. Seventy children from two to five years participated, assessed by the Denver-II neurodevelopment screening test, in three times in the year. The Home Environment Resources Scale, the Family Poverty Index, the Neighbourhood Quality Instrument, and the ECERS-R Scale were applied in two times. The results agreed with the hypothesis that there would be a longitudinal increase in the percentage of children with normal score according to Denver-II and in the environmental quality scores. The findings showed that children belonging to families with a higher poverty rate and poorer environmental quality obtained a higher percentage of delay in neurodevelopment. The explanatory-model showed that the most influential variables in suspect a delay were poverty index, few joint activities with parents, and the low interaction among the neighbours and, at school. However, family poverty is a multidimensional risk factor that cuts across ecological contexts.

Managerial Resourcefulness and Leadership in Small Firms During the COVID-19 Pandemic

This study explores the influence of managerial resourcefulness (affective, intellectual and action-oriented competencies) of the owner-manager of small firms on employee outcomes, including the perception of leadership effectiveness and satisfaction with the leader. Owner-manager of small firms ( n = 49) completed a questionnaire with a managerial resourcefulness scale, and their employees ( n = 193) completed a questionnaire with the scales of perception of leadership effectiveness and satisfaction with the leader. Hierarchical linear modelling analyses were used to test the study hypotheses. The results indicated that affective and intellectual competencies are significantly and positively associated with both employee perception of leadership effectiveness and satisfaction with the leader. However, action-oriented competence is significantly and positively associated with employees’ perception of leadership effectiveness but is not related to satisfaction with leaders. The results of this study imply that management resourcefulness is a useful foundation to examine a variety of critical organizational and human outcomes.

Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data Meshes

The last decade of database research has led to the prevalence of specialized systems for different workloads. Consequently, organizations often rely on a combination of specialized systems, organized in a Data Mesh. Data meshes present significant challenges for system administrators, including picking the right system for each workload, moving data between systems, maintaining consistency, and correctly configuring each system. Many non-expert end users (e.g., data analysts or app developers) either cannot solve their business problems, or suffer from sub-optimal performance or cost due to this complexity. We envision BRAD, a cloud system that automatically integrates and manages data and systems into an instance-optimized data mesh, allowing users to efficiently store and query data under a unified data model (i.e., relational tables) without knowledge of underlying system details. With machine learning, BRAD automatically deduces the strengths and weaknesses of each engine through a combination of offline training and online probing. Then, BRAD uses these insights to route queries to the most suitable (combination of) system(s) for efficient execution. Furthermore, BRAD automates configuration tuning, resource scaling, and data migration across component systems, and makes recommendations for more impactful decisions, such as adding or removing systems. As such, BRAD exemplifies a new class of systems that utilize machine learning and the cloud to make complex data processing more accessible to end users, raising numerous new problems in database systems, machine learning, and the cloud.

Large-scale and cost-effective production of recombinant human serum albumin (rHSA) in transgenic Bombyx mori cocoons

HSA is considered a versatile natural cargo carrier with multiple bio-functions and applications. However, insufficient supply of HSA has limited widespread use. Although various recombinant expression systems had been applied to produce the rHSA to overcome the limited resource, cost-effective and large scale production of rHSA remains a challenge. Herein, we provide a strategy for the large-scale and cost-effective production of rHSA in cocoons of transgenic silkworms, achieving a final 13.54 ± 1.34 g/kg of rHSA yield in cocoons. rHSA was efficiently synthesized and stable over the long-term in the cocoons at room temperature. Artificial control of silk crystal structure during silk spinning significantly facilitated rHSA extraction and purification, with 99.69 ± 0.33 % purity and a productivity of 8.06 ± 0.17 g rHSA from 1 kg cocoons. The rHSA had the same secondary structure to natural HSA, along with effective drug binding capacity, biocompatibility, and bio-safe. The rHSA was successfully evaluated as a potential substitute in serum-free cell culture. These findings suggest the silkworm bioreactor is promising for large-scale and cost-effective production of high quality rHSA to meet the increased worldwide demand.

A Conceptual Model Study of Tourism Resource Sharing in the Digital Economy

The concept of “sharing” and the development of the digital economy have driven the transformation of resource allocation in the tourism industry to resource sharing. The article defines resource sharing in the tourism industry from a comprehensive perspective according to the systematic analysis of the concept of resource sharing and the background of the digital economy, which is an economic behavior of integrating, opening and sharing resources among members under the guidance of the government and relying on modern information technology such as the internet, with the sharing of usage rights as the main feature. Based on the dual inspiration of MICK resource classification theory and digital economy theory, the article constructs a conceptual model and measurement scale of resource sharing in the tourism industry from four dimensions: knowledge resource sharing, material resource sharing, information resource sharing and capital resource sharing. The panel data of 31 provinces (municipalities and autonomous regions) from 2016 to 2020 were analyzed mathematically and statistically. The analysis results showed that the weight of knowledge resources is the highest. Moreover, the eastern region had the highest level of resource sharing and the western region had the lowest level of resource sharing. Furthermore, the level of resource sharing in the past five years showed a trend of year-on-year improvement. Based on the scores, countermeasures are proposed in terms of enhancing the importance of information technology and strengthening basic support according to local conditions, which provide a reference for cities to improve the level of resource sharing.

Exploring the use of tutorial recordings for beginner distance learners of Chinese

Abstract Previous research into listening to recorded tutorial interaction highlights the potential of tutorial recordings as a scalable resource in online contexts. Dialogue between tutor and learner or between peers may mediate the understanding not just of direct participants, but also of listeners, and listening to interactive recordings may facilitate deep learning. In language learning, both direct and indirect interactions have been shown to contribute to language development. This is relevant for online learning design, especially so in contexts where no tutorials are provided, and students are confronted with a challenging language such as Chinese. This paper reports on a small-scale project which explored potential learning benefits of working with recordings of online tutorials in a non-accredited, self-directed course for beginner learners of Chinese through surveys and stimulated recall interviews. Our analysis of the data shows that viewers focused on different visual and auditory elements of the recordings; they participated vicariously in interactions between a teacher and students, and they reported benefits for language learning and motivation. Findings suggest that these perceived benefits and preferences are linked to linguistic competence in comparison with direct attenders. The study aims to feed into a larger research project including more languages and accredited courses at the next stage.

Translation and preliminary validation of the Brazilian family resources scale in a sample of parents of children with congenital Zika virus syndrome.

Family-centered rehabilitative care optimizes outcomes for children with significant developmental disabilities. Family-centered services involve assessing family resources that promote positive developmental outcomes for children. Little is known regarding family resources in the context of caring for a child with developmental disabilities in Brazil due to an absence of validated measures. This study describes the translation and cultural adaptation of the Family Resource Scale and explored the measurement quality of the resulting measure (the Brazilian-Family Resource Scale, or B-FRS). A rigorous serial translation process that emphasized linguistic accuracy as well as cultural adaptation was utilized. The resulting 27-item B-FRS was theoretically related and reflected the contextual intent of the original measure. A four-factor scoring approach yielded acceptable internal consistency estimates for the subscales and total scale score. Overall, low levels of family resources were reported by caregivers of children with Congenital Zika Syndrome. Low family resources were associated with parental depressive and stress-related symptoms. Confirmatory factor analysis of the B-FRS in a larger sample is recommended. Practitioners in Brazil should broadly consider family needs and resources to provide family-centered care that is effective for the child and engages the family in a way that highlights their strengths and promotes positive developmental trajectories.

Research on risk assessment model of subjective and objective integration in manual handling work

AbstractThe main hazards in the process of manual handling work are triggered by human factors and ergonomics. This study is intended to implement a valid approach to quantitatively evaluate the risk level during manual handling work. A risk assessment model for manual handling workers was proposed based on subjective and objective correlation. A simulation experiment of manual handling process was developed and an ergonomics evaluation method was carried out. The 33‐point human joint model of BlazePose neural network and the Rapid Upper Limb Analysis (RULA) method were utilized to determine the risk posture and risk index of the manual handling workers. This study brought together hemodynamic parameters and the score of Borg Rating of Perceived Exertion (RPE) scale and Physical Resources Scale (PRS) to obtain the final comprehensive index of work risk. The results showed that the risk indexes from the three stages of the experiment obtained by RULA method were 3, 4, and 7. And the scores of comprehensive indexes obtained by the risk assessment model were 1.841, 1.900, and 1.987, suggesting that the evaluation model based on subjective and objective correlation had the same ability to determine the risk levels of different handling tasks. Therefore, the risk assessment model proposed in this study verified the effectiveness of the comprehensive evaluation index integrating hemodynamic parameters and subjective evaluation scores.

Factors related to family’s ability to care for schizophrenic patients

Schizophrenic patients in family members makes the family helpless and unable to care for them. Patient, family, social environment, and health service factors can affect the family's ability to care for schizophrenic patients. The aims of this study are to analyze factor predictors of the family's ability to care for schizophrenic patients. This study used a correlational study design. The sample of this study is 135 schizophrenic families in the outpatient clinic of Dr. Amino Gondohutomo Regional Psychiatric Hospital with purposive sampling. The instruments in this study are the questionnaire which developed and modified from the experience caregiving instrument (ECI), inner resource scale (SAS-I), mental health inventory (MHI), Berkeley Expressivity Questionnaire, Barthel Index, caregiving tasks in caring for an adult with mental illness scale (CTiCAMIS). Data were analyzed using a multiple regression test. The result shows the family's ability to care for schizophrenic patients predicted by the variables of family resources (p-value=0.019) and social support (p-value=0.0.32). Family resources as the strongest predictor of the family's ability to care for schizophrenic patients (B=0.242). It is necessary to develop integrated family empowerment interventions that increase and activate the family's inner resources.

Strategic management of the agroindustrial network of first transformation in La Pampa, Argentina

ABSTRACT The objective of this work is to measure and analyze the management of resources and strategic capacities of the agroindustrial organizations of the first transformation of the province of La Pampa, Argentina. The strategic management of the network, measured in aggregate terms, can be classified as intermediate. The most positive aspects are maintaining the image of the company, the importance of price, and the strategic value that know-how has for businesses. As factors with performance to improve, there are the lack of conventions and agreements with scientific and technical organizations that promote innovation processes and the nonuse of a command board for the strategic and operational management of the business. The flour, dairy, and balanced food complexes were the ones that presented the best strategic management performance. The strategic management of first-transformation agroindustries is directly related to capacities (management training and seniority in the business) and resources (scale).

Emergency Department-Based HIV Testing Services and Self-Testing Programs: A Qualitative Study of Healthcare Providers and Patients in Kenya

Introduction:Young people in Sub-Saharan Africa, especially males, have been insufficiently engaged in HIV Testing Services (HTS). In Kenya, these persons are often treated in emergency departments (EDs) for injuries, a healthcare interaction where HTS including HIV self-testing (HIVST) could be leveraged. There is, however, limited data from stakeholders on ED-HTS which impedes programmatic advancement.Method:A qualitative study was completed to understand facilitators and challenges for ED-HTS and HIVST delivery in Kenya (12/2021-03/2022). Data were collected via 28 in-depth patient interviews (14 males and 14 females) who had been treated in the Kenyatta National Hospital (KNH) ED and through seven focus-group discussions conducted with 49 ED healthcare personnel (nurses, doctors, HIV testing counselors, and administrators). Transcripts were double-coded and thematically analyzed with Dedoose™ software using a parallel inductive and deductive approach to capture both a priori and emergent themes.Results:Patients and providers viewed ED-HTS as a beneficial provision that was facilitated by engaged staff, education, perceived high HIV risk, and confidentiality. However, ED-HTS was limited by burdens on staff time and material resources, lacking systems integration, and patient illness severity. Facilitators of ED-HIVST delivery were perceived to have greater autonomy and confidentiality as well as lower health resource utilization. Challenges for ED-HIVST identified included patients’ concerns about HIVST accuracy and psychological stress, as well as providers’ concerns for loss to follow up and inability to complete confirmatory testing.Conclusion:ED stakeholders are receptive to HTS and HIVST provisions. This data provides insight into the patient, provider, and systems aspects that can be leveraged in ED-based HTS to enhance program impacts via intervention functions in the forms of education, care integration, resource scaling, and solidified post-self-testing follow-up mechanisms.

Enhancing fault tolerance and scalability in multi-region Kafka clusters for high-demand cloud platforms

This study examines strategies for enhancing fault tolerance and scalability in multi-region Kafka clusters, essential for supporting high-demand cloud environments. As cloud-based applications expand globally, achieving seamless data streaming across regions requires advanced configurations in Apache Kafka. This paper provides a thorough analysis of key approaches, including replication strategies, dynamic resource management, and real-time monitoring techniques tailored for multi-region deployments. Through a comprehensive literature review and real-world case studies, the study identifies critical challenges in managing latency, data consistency, and resilience within distributed Kafka clusters. Findings reveal that fault tolerance can be significantly improved through hybrid replication models that balance latency and data integrity, while advanced partitioning and load balancing techniques optimize Kafka’s scalability under fluctuating demands. The integration of container orchestration tools such as Kubernetes has also proven effective in automating resource scaling and failover across distributed environments. Furthermore, the paper highlights future research directions, including edge computing integration, predictive scaling, and enhanced security protocols to address evolving data privacy requirements. In conclusion, while multi-region Kafka deployments offer robust solutions for distributed data streaming, achieving optimal performance and resilience requires a combination of adaptive replication, proactive resource management, and secure, compliant data flows. Future research should focus on refining edge-compatible solutions and regulatory-compliant frameworks to sustain Kafka’s role in global, real-time data processing.

Scalable Data Processing Platform for Earth Observation Data Repositories

Earth observation (EO) satellite data is essential to environmental monitoring. At a national and regional level, the open data cubes harness the power of satellite data by providing application programming interfaces and services to the end-users. The volume and the complexity of satellite observations are increasing, demanding novel approaches for data storing, managing, and processing. High-performance computing (HPC) and cloud platforms may improve Big EO data processing performance. However, it is necessary to consider several vital aspects for efficient and flexible EO data processing, such as the interoperability from cloud-HPC and EO data repositories, automatic provisioning and scaling of cloud-HPC resources, cost-effectiveness, support of new EO data formats and open-source packages, or linkage with data cube platforms. The article proposes a scalable EO data processing platform interoperable from cloud-HPC and EO data repositories. The platform enables linking any data repository supporting web coverage service or SpatioTemporal Asset Catalog Application Programming Interfaces (STAC-API), and any cloud or HPC resource supporting scheduling system API for providing access to the cluster backends.

Framework for Performance Enhancement of MPI based Application on Cloud

Cloud technology is a major revolution that has happened in the computing era that has changed the way applications and resources are used. Elasticity is the key characteristic of the cloud, wherein the required number of resources are provided as a service with a pay-as-you-go principle. This reduces the huge cost involved in buying, installing, and maintaining the resources. Cloud computing, with its highly scalable resources, can be a good platform for High-Performance Computing (HPC). HPC application performance highly depends on the quantity of the resources, which makes the cloud a suitable candidate. But the HPC community is not very happy with cloud technology, and most of the users still think cloud technology is not suitable for HPC applications. But virtualization technology, which is the foundation of the cloud, degrades the performance of applications in the urge to improve utilization. The hypervisor layer and resource sharing by the virtual machines (VMs) hosted on the same node are the main reasons for performance degradation in the cloud. The majority of the HPC applications belong to the message passing (MPI) category, and for these applications, communication cost is the major stakeholder in deciding performance. If these applications are hosted on the cloud, it leads to further performance degradation as the process on a VM communicates through the virtual network interface, which in turn shares the network interface of the host machine with other VMs. MPI-based applications hosted on MPPs work in a bandwidth shared environment as multiple processes communicate over the same network. But in the cloud, as the number of VMs increases, per node bandwidth availability per communication reduces. To address the above issues, we have built a framework to enhance the performance of MPI-based HPC applications on VMs by considering proper VM placement strategy and resource reservation policies, with knowledge of resource availability and process communication patterns. A VM placement strategy for dynamic clustering of VMs with high priority for shared memory-based communication is proposed and tested. Results show that with a medium number of processes, there is an improvement of around 70% with our placement strategy for high data communicating processes. If there are fewer processes, and the single physical node can hold all the VMs, then the performance improvement is up to 500%.

MVMS: RNN based Pro-Active Resource Scaling in Cloud Environment

Cloud computing offers various services to its users, ranging from infrastructure, and system development environment, to software as a service over the internet. Having such promising services available over the internet consistently, it has become an ever-demanding facility. As a reliable services provider, a cloud service provider (CSP) needs to deliver its services seamlessly to users and is also required to optimally utilize the resources. Optimal resource utilization eliminates over and under-provisioning and improves the availability of cloud services. Therefore, it is a great need to have a model allowing CSP to systematize its resources to cater to customers' demands. Such a model should be computationally light and quick enough to produce effective results. In this work, a simple yet effective neural network-based resource prediction model named MVMS is proposed, which enables a CSP to predict the customer's resource demand in advance. The results show that compared to GRU, the proposed Multi-Variate Multi-Step (MVMS) model predicts the resources accurately. Thus, CSP can schedule the resources precisely and process real-time requests of users. Experiments on the bitbrains dataset indicate that the proposed MVMS resource prediction model is quick and accurate, with lower RMSE and MAE values.

Large-Scale Docking in the Cloud

Molecular docking is a pragmatic approach to exploit protein structures for new ligand discovery, but the growing size of available chemical space is increasingly challenging to screen on in-house computer clusters. We have therefore developed AWS-DOCK, a protocol for running UCSF DOCK in the AWS cloud. Our approach leverages the low cost and scalability of cloud resources combined with a low-molecule-cost docking engine to screen billions of molecules efficiently. We benchmarked our system by screening 50 million HAC 22 molecules against the DRD4 receptor with an average CPU time of around 1 s per molecule. We saw up to 3-fold variations in cost between AWS availability zones. Docking 4.5 billion lead-like molecules, a 7 week calculation on our 1000-core lab cluster, runs in about a week depending on accessible CPUs, in AWS for around $25,000, less than the cost of two new nodes. The cloud docking protocol is described in easy-to-follow steps and may be sufficiently general to be used for other docking programs. All the tools to enable AWS-DOCK are available free to everyone, while DOCK 3.8 is free for academic research.

Adaptive Cloud Bundle Provisioning and Multi-Workflow Scheduling via Coalition Reinforcement Learning

The efficient cloud resource provisioning for the execution of complex workflow applications has always been one of the important research issues. Most of the existing approaches focus on the resource provisioning of single-type virtual machine (VM) instances for the single or multiple workflows, while few consider the situation of provisioning multi-type VM instances simultaneously. As a result, the executing performance of complex workflows degrades. Different from the existing work, this paper proposes an adaptive cloud bundle provisioning and multi-workflow scheduling model to dynamically perform both the horizontal and vertical cloud resource scaling on multi-type VM instances for the execution of complex workflows. Among the model, a depth-first-search coalition reinforcement learning (DFSCRL) provisioning policy is presented to realize the resource scaling, which integrates the physical machine (PM) coalition formation with the Q-learning algorithm, then dynamically generates an optimal multi-type VM instance bundle from the PM coalition, and finally provisions these instances to the concurrent execution of multiple workflows. The theoretical proofs and various experiments with the multifaceted metrics demonstrate that the performance of the proposed algorithms is superior to that of the state-of-the-art relevant policies.

Psychometric Evaluation of the Grit Psychological Resources Scale (GPRS).

Psychological resources have been shown to play a prominent role in buffering against stress and are associated with various positive constructs, including grit, defined as having the disposition to pursue long-term goals with both passion and perseverance. The objective of this study was to validate a new scale developed to measure the psychological resources of grit. A quantitative research study was conducted online with an international sample of 277 adults from a range of professional backgrounds. The psychometric properties of the 20-item Grit Psychological Resources Scale (GPRS) were evaluated by performing tests of reliability and validity. Reliability tests provided evidence of high internal consistency (α = 0.91) and test-retest reliability (r = 0.75). Demographic variables did not significantly predict scores or influence survey completion. Face, content and convergent validity provided additional psychometric support for the GPRS with this sample. Confirmatory factor analysis results supported a second-order model with four sub-scales. The first-order factors loaded highly onto the second-order factor, with correlations ranging from 0.80 to 0.97. The GPRS showed satisfactory psychometric properties, indicating that the scale is a reliable and valid instrument for measuring the psychological resources of grit. This scale can be used to identify more targeted developmental approaches for personal and professional growth. Further, the tool enables information to be gathered on changes pre- and post-improvement initiatives to assess their effectiveness in training and continuing education.