Suitable Resources Research Articles

Social implementation and intervention with estimated morbidity of heat-related illnesses from weather data: A case study from Nagoya City, Japan

The estimation of heat-related illness cases is a key factor in proposing and implementing suitable intervention strategies and healthcare resource management. This paper proposes new frameworks to estimate the number of patients with heat-related illnesses by administrative wards in Nagoya City using 2014–2019 data. The proposed frameworks are based on the derivation of estimation formulae and machine learning. The daily residual estimation error in the 16 wards was less than one person with both the frameworks. The daily working time average ambient temperature may yield a better correlation than the daily average temperature or daily highest temperature with the number of patients transported by an ambulance from outdoor sites. The results also indicate that patients transported from indoor sites are influenced by earlier ambient conditions over approximately 50 days. In contrast, those transported from outdoor sites are influenced by a relatively short period (20 days), which may correspond to heat adaptation. The frameworks provide a better understanding of the different factors that would lead to an accurate prediction of the number of cases of heat-related patients from weather forecasts. These findings would lead to efficient ambulance allocation as well as public awareness on hot days to suppress heat-related morbidity.

An Efficient Technique for Virtual Machine Clustering and Communications Using Task-Based Scheduling in Cloud Computing

Cloud computing models use virtual machine (VM) clusters for protecting resources from failure with backup capability. Cloud user tasks are scheduled by selecting suitable resources for executing the task in the VM cluster. Existing VM clustering processes suffer from issues like preconfiguration, downtime, complex backup process, and disaster management. VM infrastructure provides the high availability resources with dynamic and on-demand configuration. The proposed methodology supports VM clustering process to place and allocate VM based on the requesting task size with bandwidth level to enhance the efficiency and availability. The proposed clustering process is classified as preclustering and postclustering based on the migration. Task and bandwidth classification process classifies tasks with adequate bandwidth for execution in a VM cluster. The mapping of bandwidth to VM is done based on the availability of the VM in the cluster. The VM clustering process uses different performance parameters like lifetime of VM, utilization of VM, bucket size, and task execution time. The main objective of the proposed VM clustering is that it maps the task with suitable VM with bandwidth for achieving high availability and reliability. It reduces task execution and allocated time when compared to existing algorithms.

Upper-secondary teachers’ perceptions of the Matura exam in mathematics

This paper reports a study on mathematics teachers’ perceptions of the Matura mathematics exam in Croatia. The study focuses on the suitability of mathematics school textbooks for students’ preparation for the exams, the complexity of the tasks in the exams, the grading and scoring of the exams, and teachers’ level of satisfaction with student achievement. The study used a convenience sampling method. It was conducted through a questionnaire administered to 308 upper secondary mathematics teachers. The findings showed that teachers do not perceive school textbooks as suitable resources to prepare for the higher level exam. Furthermore, the teachers believe that the test length is not appropriate i.e., the time given to students for the higher level exam is insufficient. On average they are satisfied with their students’ results, but are undecided about the criteria and scoring of the Matura. Vocational school teachers showed more dissatisfaction with the requirements and outcomes of the Matura exam compared to grammar school teachers. The results of this empirical study can be taken as a good starting point for re-assessing the requirements of the Matura exam in mathematics.

A potential flavor seasoning from aquaculture by-products: An example of Takifugu obscurus

There is a growing interest in the value utilization of fish by-products. In this study, an advisable method of Takifugu obscurus by-products hydrolysates (F0) developed into flavor seasoning was investigated. The F0 were separated into two peptide fractions, >3000 Da (F1) and 300–3000 Da (F2), and performed Maillard reaction, then flavor characteristics of the Maillard reaction products were comprehensively analyzed. The results showed that the Maillard reaction largely improved flavor profiles of the hydrolysates i.e., enhancing umami taste, volatile aroma, and overall acceptance, while reducing bitterness and fishy smell. Compared with F0 and F1, F2 as the suitable resource of flavor precursors, had the optimal flavor attributes after Maillard reaction. This paper provides a new idea of comprehensive utilization of by-products of pufferfish and guides the preparation of Maillard peptide-based seasoning from fish by-products.

From gene banks to farmer’s fields: using genomic selection to identify donors for a breeding program in rice to close the yield gap on smallholder farms

Key messageDespite phenotyping the training set under unfavorable conditions on smallholder farms in Madagascar, we were able to successfully apply genomic prediction to select donors among gene bank accessions.Poor soil fertility and low fertilizer application rates are main reasons for the large yield gap observed for rice produced in sub-Saharan Africa. Traditional varieties that are preserved in gene banks were shown to possess traits and alleles that would improve the performance of modern variety under such low-input conditions. How to accelerate the utilization of gene bank resources in crop improvement is an unresolved question and here our objective was to test whether genomic prediction could aid in the selection of promising donors. A subset of the 3,024 sequenced accessions from the IRRI rice gene bank was phenotyped for yield and agronomic traits for two years in unfertilized farmers’ fields in Madagascar, and based on these data, a genomic prediction model was developed. This model was applied to predict the performance of the entire set of 3024 accessions, and the top predicted performers were sent to Madagascar for confirmatory trials. The prediction accuracies ranged from 0.10 to 0.30 for grain yield, from 0.25 to 0.63 for straw biomass, to 0.71 for heading date. Two accessions have subsequently been utilized as donors in rice breeding programs in Madagascar. Despite having conducted phenotypic evaluations under challenging conditions on smallholder farms, our results are encouraging as the prediction accuracy realized in on-farm experiments was in the range of accuracies achieved in on-station studies. Thus, we could provide clear empirical evidence on the value of genomic selection in identifying suitable genetic resources for crop improvement, if genotypic data are available.

Content-aware convolutional neural networks

Convolutional Neural Networks (CNNs) have achieved great success due to the powerful feature learning ability of convolution layers. Specifically, the standard convolution traverses the input images/features using a sliding window scheme to extract features. However, not all the windows contribute equally to the prediction results of CNNs. In practice, the convolutional operation on some of the windows (e.g., smooth windows that contain very similar pixels) can be very redundant and may introduce noises into the computation. Such redundancy may not only deteriorate the performance but also incur the unnecessary computational cost. Thus, it is important to reduce the computational redundancy of convolution to improve the performance. To this end, we propose a Content-aware Convolution (CAC) that automatically detects the smooth windows and applies a 1 ×1 convolutional kernel to replace the original large kernel. In this sense, we are able to effectively avoid the redundant computation on similar pixels. By replacing the standard convolution in CNNs with our CAC, the resultant models yield significantly better performance and lower computational cost than the baseline models with the standard convolution. More critically, we are able to dynamically allocate suitable computation resources according to the data smoothness of different images, making it possible for content-aware computation. Extensive experiments on various computer vision tasks demonstrate the superiority of our method over existing methods.

Dynamic Content Enabled Microservice for Business Applications in Distributed Cloudlet Cloud Network

This study introduces Mob-Cloud, a mobility aware adaptiveoffloading system that incorporates a mobile device as a thick client, ad-hoc networking, cloudlet DC, and remote cloud to improve the performance and availability of microservices services. These cloudlet cloud has emerged as a popular model for bringingthe benefits of cloud computing to the proximity of mobile devices. the microservices preliminary goal is to improve service availability as well as performance and mobility features. The impact of dynamic changes in mobile content (e.g., network status, bandwidth, latency, and location) on the task offloading model is observed by proposing a mobility aware adaptive task offloading algorithm aware microservices, which makes a task offloading decision at runtime on selecting optimal wireless network channels and suitable offloading resources. The decision problem, which is well-known as an NP-hard issue, is the subject of this work. However, for the entire proposed microservices system has the following phases: I adaptive offloading decision based on real-time information, (ii) workflow task scheduling phase, (iii) mobility model phase to motivate end-users to invoke cloud services seamlessly while roaming, and (iv) faulttolerant phase to deal with failure (either network or node). We conduct real-world experiments on the built instruments to assess the online algorithm's overall performance. Compared to baseline task offloading solutions, the evaluation findings show that online algorithm incorporates dynamic adjustments on offloading decision during run-timeand achieves a massive reduction in overall response time with better service availability.

Discourse on Methods of Teaching Islamic Studies in Nigerian Senior Secondary Schools


 
 
 In a teaching process, method of teaching is of great importance as it may be difficult for students to have a comprehensive understanding of topics under discussion in the absence of appropriate methods. To this end, this paper is a qualitative research or a position paper. It adopted theoretical approach to make an introspection of methods of teaching considered suitable to teach Islamic Studies in Nigerian senior secondary schools. The fact is that effective teaching of the subject requires various methods with suitable instructional resources as the case may be to accomplish the set objectives. The study revealed that method to adopt for a particular lesson depends largely on the topic, objectives and characteristics of the learners. More so, skills required of an Islamic Studies teachers are discussed. Similarly, some of the facts analysed in the study include curriculum and plan for teaching the Qur'anic, Hadith, Tadhib (Moral Education based on the Qur'an and Sunnah), Tawheed (Theology), 'Ibadah (Worship of Allah), the Mu'amalah (Human Relation), and the Tarikh (Historical Development of Islam) aspects of Islamic Studies are analysed in this study. Based on the reviewed theories, it was recommended that Islamic Studies teachers should be professionally trained and provided with opportunity to update their professional capability in line with modern teaching methodology.
 
 

Estimating the Resource Demand in Power-Aware Clusters by Regressing a Linearly Dependent Relation

Large-scale clusters are often built with over-provisioned service resources, so as to satisfy the huge demand raised by enormous users in cloud environments. By estimating the resource demand of workloads, an on-demand resource provisioning method can be realized in these clusters, thus improving the energy efficiency. However, to guarantee Quality of Service (QoS), the resource demand of workload should be accurately estimated so as to provide suitable resources. Many statistical approaches estimate actual resource demand based on some workload features. But the relations between actual resource demand and workload features are generally obscure, and it's a big challenge to gain an accurate estimation under an obscure relation. In this paper, by considering a cluster as a queueing system, we construct a linearly dependent relation between resource demand and multiple feature combinations. The linearly dependent relation is inconstant due to its variable coefficients. Then, to ascertain specific relations which match actual situations, we design a Basic Linear regression (BL) algorithm. BL can obtain the optimal values for these coefficients, thus determining the inconstant relation to specific ones. Finally, we propose a Constructed Linear regression (CL) approach to estimate actual resource demands. CL forms a two-layer neural network by using several processes of BL as the neurons. To evaluate CL, we realize an On-Demand Resource Provisioning (ODRP) method in a typical power-aware cluster. Several evaluation metrics are proposed for conducting extensive experiments. The experimental results show that CL is effective to make accurate estimations.

High-quality pectin from cantaloupe waste: eco-friendly extraction process, optimization, characterization and bioactivity measurements.

The rind from cantaloupe is an agricultural waste of cantaloupe industrial processing. The current study tried to (i) evaluate the potential use of cantaloupe rind as a pectin source, (ii) optimize the factors of microwave-assisted extraction process using Box-Behnken design, and (iii) characterize the isolated pectin using various physicochemical, structural, functional and bioactivity properties. Four variables of the extraction process were successfully optimized at a microwave power of 700 W, irradiation time of 112 s, pH value of 1.50 and liquid to solid (LS) value of 30 mL g-1 , with a yield of 181.4g kg-1 . The analysis indicated a high-methylated galacturonic acid-rich (703.4g kg-1 ) sample with an average molecular weight of 390.475 kDa. Also, the isolated pectin showed considerable functionality and antioxidant ability. The main functional groups, structural characteristics and crystallinity of samples were comparatively studied using Fourier transform infrared, nuclear magnetic resonance and X-ray diffraction spectroscopies. In comparison to commercial citrus pectin, isolated pectin showed a significantly higher value for most of the functional analysis such as oil holding capacity, emulsifying capacity, emulsion stability, DPPH• and ABTS•+ scavenging activity, and reducing power assay. In other analyses the isolated sample was close to the commercial one, indicating that cantaloupe rinds should be considered as a suitable additional resource for pectin production. © 2021 Society of Chemical Industry.

A Multicriteria Decision-Making Model for the Selection of Suitable Renewable Energy Sources

With the expansion of its industrial and manufacturing sectors, with the goal of positioning Vietnam as the world’s new production hub, Vietnam is forecast to face a surge in energy demand. Today, the main source of energy of Vietnam is fossil fuels, which are not environmentally friendly and are rapidly depleting. The speed of extraction and consumption of fossil fuels is too fast, causing them to become increasingly scarce and gradually depleted. Renewable energy options, such as solar, wind, hydro electrical, and biomass, can be considered as sustainable alternatives to fossil fuels. However, to ensure the effectiveness of renewable energy development initiatives, technological, economic, and environmental must be taken in consideration when choosing a suitable renewable energy resource. In this research, the authors present a multi-criteria decision-making model (MCDM) implementing the grey analytic hierarchy process (G-AHP) method and the weighted aggregates sum product assessment (WASPAS) method for the selection of optimal renewable energy sources for the energy sector of Vietnam. The results of the proposed model have determined that solar energy is the optimal source of renewable energy with a performance score of 0.8822, followed by wind (0.8766), biomass (0.8488), and solid waste energy (0.8135) based on the calculations of the aforementioned methods.

Rapid realist review of the role of community pharmacy in the public health response to COVID-19

IntroductionCommunity pharmacists and their teams have remained accessible to the public providing essential services despite immense pressures during the COVID-19 pandemic. They have successfully expanded the influenza vaccination programme and...

Application of digital image processing technology in online education under COVID-19 epidemic

In the current epidemic situation, appropriate learning resources are the prerequisite and basis for effective online education. Current online education, teacher teaching, and student learning lack a sense of immersion and participation. The reason is that the technology of online education is lagging. The thesis introduces augmented reality technology to enhance the immersion and interactivity of online education; introduces the technology based on Hadhoop to store image resources and improve the speed of reading image resources; the use of random forest algorithm for image recognition digital information Mining to recommend the most suitable online resources. Image processing technology can develop appropriate interactions and functions according to different scenarios to enhance the immersion and participation of online teaching, comprehensively improve the quality of teaching and learning, and provide technical support for the improvement of online learning.

A new mathematical model for determining optimal workforce planning of pilots in an airline company

This study aims to model a workforce-planning problem of pilot roles which include captain and first officer in an airline company and to make an efficient plan having maximal utilization of minimum workforce requirements. To tackle this problem, a mixed integer programming based a new mathematical model is proposed. The model considers different conditions such as employing pilots with different skill types, resignations, retirements, holidays of pilots, transitions between different skills regarding needs of the demands during the planning horizon. The application of the proposed approach is investigated using a case study with real-world data from an airline company in Turkey. The results show that a company can use transitions instead of new employment and this is a more suitable medium-term production and human resource planning decision.

Assessment of Tomato Peels Suitable for Producing Biomethane within the Context of Circular Economy: A GIS-Based Model Analysis

Biomass is seen as one of the most dominant future renewable energy sources. In detail, agro-industrial by-products represent a cheap, renewable, and abundant feedstock useful for several new products, including biochemical, biomaterials, and above all biogas, which are taking on an ever-increasing role in Italy. In this context, the tomato chain was analysed aiming at estimating the amount of processed tomato and the related waste production as a new suitable resource for producing biofuel as a new frontier within the context of a circular economy. Due the importance of the tomato industry, this research aims at filling gaps in the knowledge of the production and yield of the by-products that are useful as biomass for energy use in those territorial areas where the biomethane sector is still developing. This aim could be relevant for planning the sustainable development of the biomethane sector by reducing both soil consumption for dedicated energy crops and GHG emissions coming from the biomass logistic supply. The achieved results show the localization of territorial areas highly characterized by this kind of biomass. Therefore, it would be desirable that the future policies of development in the biomethane sector consider the availability and the distribution of these suitable biomasses within the territory.

A GIS-Based Model to Assess the Potential of Wastewater Treatment Plants for Enhancing Bioenergy Production within the Context of the Water–Energy Nexus

The necessity of developing renewable energy sources has contributed to increasing interest in developing the anaerobic digestion for producing biomethane since it both provides green energy and reduces disposal treatment. In this regard, to assure efficient water utilization by finding alternative water sources, sewage sludge collected from the wastewater treatment plant (WWTP) was recently investigated because it could represent a suitable resource for producing biomethane within the context of a circular economy. Therefore, this study aims at improving the current knowledge on the feasibility of biomethane production from sewage sludge by optimizing the logistic-supplying phase. In this regard, a GIS-based model was developed and applied to the Emilia-Romagna region to consider the existing networks of WWTPs and biogas systems to valorize sewage sludge for bioenergy production and minimizing environmental impact. The results of the GIS analyses allowed to localize the highest productive territorial areas and highlighted where sewage sludges are abundantly located and could be better exploited within agricultural biogas plants. Finally, the achieved results could help plan suitable policy interventions that are centered on biomass supply and outputs diversification, governance, and social participation, since the regulatory framework could play a crucial role in planning the reuse of these wastes for developing a more sustainable biomethane sector in line with the green economy goals.

In vitro and in vivo evaluation of some tree leaves as forage sources in the diet of Baluchi male lambs

This study aimed to evaluate the potential use of some tree leaves including almond (Prunus amygdalus L.), pomegranate (Punica granatum L.), apple (Malus domestica Borkh.), pistachio (Pistacia vera L.), and walnut (Juglans regia L.) compared to whole plant alfalfa (Medicago sativa L.) in sheep feeding through in vitro experiments in order to screening of the resources and then selecting the most suitable candidate resource for in vivo experiment. Chemical and mineral composition, in vitro gas production, and fermentation parameters of leaves were also determined. The results indicated that the crude protein (CP) ranged from 12.12 % for almond leaves (AL) to 16.77 % for alfalfa, on dry matter (DM) basis. The highest and lowest neutral detergent fiber (NDF) content was observed in alfalfa (48.50 %) and pomegranate leaves (26.45 %), respectively (P < 0.0001). The range of metabolisable energy (ME) differs from 6.76 for pomegranate leaves to 10.50 MJ/kg DM for AL. Alfalfa and AL exhibited the lowest (9.53 g kg DM) and the highest calcium (44.15 g/kg DM) concentration, respectively (P < 0.0001). The highest true organic matter (TOMD) and dry matter digestibility (TDMD) (88.44 and 87.88 %, respectively) was also obtained in AL (P < 0.0001). It was found that AL had the highest potential gas production, and total volatile fatty acids (VFA) values of 61.05 ml/200 mg DM, and 48.33 mmol/L, respectively (P < 0.0001). In in vivo experiment, the effects of partial substitution of dietary forage with AL at 0, 5.8, 11.5, and 23 % of diet DM on growth performance, nutrient digestibility, ruminal fermentation, and blood metabolites were investigated using thirty-two Baluchi male lambs [28 ± 1.5 kg body weight (BW)] in a completely randomized design for 90 days. Compared to the control group, dry matter intake (DMI, P = 0.02), average daily gain (ADG, P = 0.05), DM (P = 0.0004), organic matter (OM, P = 0.003) and NDF digestibility (P = 0.0005), total VFA (P = 0.02), and plasma total antioxidant capacity (TAC, P = 0.0005) increased when high level of almond (HAL) was added to the diet. Overall, it is revealed that AL had a high nutritional value and could be used up to 23 % of diet DM in the diet of Baluchi male lambs as an inexpensive alternative forage without negative effects on performance and blood metabolites.

The Effects of Climate on the Hypogean Migration of Cave Salamanders, Eurycea lucifuga (Rafinesque, 1822)

Terrestrial migrations of salamanders are influenced by many factors, particularly temperature and moisture availability. Although photoperiod is generally an important migratory cue, it has not been specifically considered for salamanders. We examine the importance of the above abiotic factors at the macro- and microclimatic scales in the subterranean migration of a population of Cave Salamanders (Eurycea lucifuga) in Kentucky. This species exhibited a cyclic, seasonal migratory pattern between the twilight and dark zones in a cave. Our regression analysis of macroclimate indicated this migration was influenced largely by photoperiod, but also highly correlated with temperature and relative humidity; principle components analysis confirmed the importance of similar microclimate variables (illumination, temperature, and substrate wetness), explaining 88% of the variability in the data. These environmental variables fluctuate little in the dark zone of caves and thus likely do not provide cues on seasonal conditions to salamanders. However, these cues do vary in the twilight zone and may inform salamanders of epigean seasonal conditions. This information is critical to these cave-dwelling salamanders, particularly for finding suitable food resources.

The UCR Minicore: a resource for cowpea research and breeding

AbstractIncorporation of new sources of genetic diversity into plant breeding programs is crucial for continuing to improve yield and quality, as well as tolerance to abiotic and biotic stresses. A minicore (the “University of California, Riverside (UCR) Minicore”) composed of 368 worldwide accessions of cultivated cowpea has been assembled, having been derived from the UCR cowpea collection. High‐density genotyping with 51,128 SNPs followed by principal component and genetic assignment analyses identified six subpopulations in the UCR Minicore, mainly differentiated by cultivar group and geographic origin. All six subpopulations were present to some extent in West African material, suggesting that West Africa is a center of diversity for cultivated cowpea. Additionally, population structure analyses supported two routes of introduction of cowpea into the U.S.: (1) from Spain to the southwest U.S. through Northern Mexico and (2) from Africa to the southeast U.S. via the Caribbean. Genome‐wide association studies (GWAS) narrowed several traits to regions containing strong candidate genes. For example, orthologs of the Arabidopsis FLOWERING LOCUS T lie within a major QTL for flowering time. In summary, this diverse, yet compact cowpea collection constitutes a suitable resource to identify loci controlling complex traits, consequently providing markers to assist with breeding to improve this crop of high relevance to global food and nutritional security.

Determination of Suitable Resource Discovery Tool and Methodology for High-Volume Internet of Things (IoT)

This paper discusses several issues supporting a knowledge-based methodology for discovery of high-volume IoT resources in the simulator NS-3 environment. We found the concept was developed in previous researches, especially based on widely accepted concepts of Q-Learning discovery model. The model is validated using samples from emulations of tested data in the NS-3 simulator. Proper simulation in NS-3 based on the different modules such as checkpoint and restore was used to model and analyse the data. The main feasibility checkpointing concept of simulations in the NS-3 processes were using Distributed Multi-Threaded Checkpointing (DMTCP) to run on a single machine and Message Passing Interface (MPI) under distributed machine to speed up the NS-3 model initialization and execution. As the chosen model to be implemented in this analysis, the Q-learning algorithm proposal offers a possible solution for addressing evolving IoT environments and configurations. Q-learning is one of the successful techniques available for the exploration of IoT nodes, but context-based problems have already been established and simplified as issues of dedicated server management, IoT object data acquisition issues, and unique application requirements. The findings empirically support the validation of the Q-Learning model improvement for high-volume IoT resource discovery cases. The study will contribute to the new model development by providing new insights on the conceptualization and validation of knowledge-based methodology based on widely accepted techniques and approaches.