Priority Classes Research Articles

Analysis of Consumer Preferences and Satisfaction with Local Chicken Meat in Bandung City Traditional Markets (Survey of Consumers at Cibogo Market, Sukajadi)

ABSTRACT. The demand for local chicken meat in Bandung city has been decreasing over the last five years. Changes in demand for a product can be influenced by consumer preferences and satisfaction. This research was conducted to determine consumer preferences based on the attributes inherent in local chicken meat and the level of consumer satisfaction with local chicken meat. The research location was at Cibogo Market, Sukajadi District, Bandung City. Primary data was obtained from interviews with 97 local chicken meat consumers (as respondents) and secondary data was from the journals and articles research, internet, and related instance. The data analysis method used is Importance Performance Analysis (IPA) to determine preferences and Customer Satisfaction Index (CSI) to determine the level of consumer satisfaction. The results of the research showed that consumer preferences for local chicken meat at Cibogo Market are based on attributes that are included in the main priority class, namely meat tenderness, age, and weight of local chicken. The result of CSI calculation was 71.61% which means that consumers are satisfied. In conclusion, the performance of attributes that are a priority for consumers need to be improved in terms to increase consumer satisfaction to a very satisfied level.

Priority-based band sharing for secondary users in cognitive radio networks

To improve wireless spectrum utilization, cognitive radio networks (CRNs) allow secondary users (SUs) opportunistic access to the frequency bands owned by primary users (PUs), when such PUs are inactive. A fully distributed CRN architecture is quite favorable, as it does not require any centralized scheduling, nor does it require coordination between SUs via a common control channel. This can reduce cost and avoid single point-of-failure issues. However, uncoordinated access to the spectrum by competing SUs, if not done judiciously, can cause excessive collisions and limit performance, which can be detrimental to the usefulness of CRNs, especially if some SUs have urgent traffic to send. Hence, we present a novel protocol to support traffic with multiple classes of priority within a fully distributed CRN. In such protocol, competing SUs attempt to access the spectrum autonomously, but rationally, based on their priority level, opting to transmit their packets when it is beneficial to both the SU itself and also to the overall system performance. Simulations confirm that the proposed protocol achieves excellent overall network performance, while ensuring that higher priority packets experience higher throughput and smaller delay compared to lower priority packets.

Prioritizing management actions for invasive non-native plants through expert-based knowledge and species distribution models

Given the high number of non-native plants that are being introduced worldwide and the time required to process formal pest risk analyses, a framework for the prioritization of management actions is urgently required. We therefore propose a framework for a replicable and standardized prioritization for management actions (eradication, control and monitoring) of invasive non-native plants, combining expert knowledge, current and future climatic suitability estimated by species distribution models (SDMs), clustering and ordination techniques. Based on expert consultation and using Italy as case study, invasive non-native plant species were selected and three categories of management actions were identified: eradication, control and containment, and monitoring. Finally, two further classes of priorities were proposed for each of the management actions: “high” and “low” priority. Overall, SDMs highlighted a high and very high suitability for Continental and Mediterranean bioregions for most invasive plants. Cluster analysis revealed three distinct clusters with varying levels of suitability for the Italian bioregions. Cluster 1 exhibited a higher suitability across all Italian bioregions, whereas non-native plants grouped in Cluster 2 predominantly featured high suitability in Mediterranean areas. Finally, Cluster 3 showed the lowest suitability values. Two ordination analysis highlighted the variability in bioclimatic suitability for each non-native plant within each cluster, as well as their current distribution pattern. Lastly, a third ordination, integrating bioclimatic suitability and spatial patterns, has allowed the differentiation of management actions for each non-native plant at both national and bioregional scales. Specifically, seven non-native plants were earmarked for eradication action, six for monitoring action, while the remaining species were deemed suitable for control and containment. Our results and the methodology proposed meet the demand for replicable new early warning tools; that is to predict the location of new outbreaks, to establish priorities for eradication, control and containment, and to monitor invasive non-native species.

Prioritizing Trail Bridge Construction Projects in Nepal Using Linear Discriminant Analysis

The country of Nepal desperately requires trail bridges to foster rural connectivity and socio-economic progress especially in the remote areas. However, despite their significance, political meddling among other socio-economic factors has continuously influenced bridge construction priorities resulting in a non-uniform distribution that does not always match key indicators like population density, disadvantaged population or number fatalities due to river crossing. This report identifies Linear Discriminant Analysis (LDA) as an objective and fair approach for prioritizing bridge projects. This research will provide a structured way of ranking these bridges with reduced political influence and consequently decreasing social disparities through focusing on such criteria as benefiting homes, total population, disadvantaged population, time saved, type of rivers and risk factor. Trail bridges were in history demanded by Nepal since 1950s in a bid to receive international aid and community-based construction. The participation of the communities in the planning, designing and maintenance of these bridges has increased over time thus promoting sustainability and ownership. However, there are still challenges that remain to ensure equitable distribution of bridge projects that adequately address the needs of the most marginalized groups. Through LDA application, this research demonstrates how priority classes can be effectively separated to objectively determine areas with urgent need for bridge constructions by decision makers. From the analysis it is clear that bridges can be ranked by using socio-economic or geographical data that is quantifiable in order to promote fair infrastructure development. The nexus between traditional knowledge and modern analysis techniques toward sustainable equitable infrastructural development in Nepal cannot be overemphasized by the findings from this study. This. therefore, strengthens rural community resilience while also ensuring resources are distributed based on objective requirements but not political or socio-economic influences.

CareConnect: Improving Patient Appointment Scheduling using Markov Decision Process with K-Means Algorithms

Abstract: In the healthcare sector, the number of patients has increased by a significant amount in the last decade which has resulted in overcrowding of hospitals and clinics. Using machine learning (ML) in this domain has proven very useful for both doctors and patients. This research mainly focuses on designing an algorithm for appointment scheduling for doctor’s which helps them prioritize patients based on their medical conditions and history which will give them an idea of which patient needs attention on a priority basis. The study implements various machine learning algorithms such as K-means clustering, which clusters patients into three classes of priority, Markov’s Decision Process (MDP) is used to define the problem and Q-Learning is used to solve it. Using these techniques the research aims to develop a system which automatically books an appointment with a doctor based on inputs given to certain questions related to their medical conditions.

Service-level computation in time-varying queueing system with priorities: Application to physician staffing in the emergency department

For many service systems, waiting time is a critical performance indicator of customer experience. This study addresses service-level computation techniques in a time-varying mixed-preemptive priorities queueing system. This queueing system has two distinguishing characteristics. First, it has time-varying arrival rates and service capacities. Second, and more importantly, there are multiple classes of customers, and both preemptive and non-preemptive priorities are considered in this system. Combining these two characteristics in a queueing system makes it difficult to compute the service level for each customer class. We derive the exact computation methods for pointwise and piecewise service levels. Furthermore, we propose closed-form expressions to approximate the service levels. Lastly, we show how to apply these service-level computation techniques to a real-life physician staffing problem in the emergency department, in which the service levels of each class of patients are considered as constraints. The computational tests are performed using data generated based on real data from a large hospital. The results show that our staffing solutions are better than the hospital’s current staffing solution, guaranteeing patients’ service levels and reducing physicians’ working times.

A pathway to urban resilience: investigating green open space priority areas to mitigate the urban heat island phenomenon in Samarinda City, Indonesia

PurposeThe potential for massive economic growth exists in Samarinda City due to the intensification of activities in built-up areas. This suggests the potential for increased urban disease in the relocation of Indonesia’s new capital city to a location adjacent to Samarinda. One of the most striking impacts is the urban heat island (UHI). The increase in this phenomenon can be addressed effectively and efficiently through the provision and arrangement of appropriate vegetation-based actions. Therefore, this study aims to identify priority areas of green open space (GOS) based on UHI levels. In addition, this study also aims to present alternative mitigation measures to reduce the risk of disasters due to UHI.Design/methodology/approachA mixed-method approach was used in this research, involving an initial land surface temperature analysis to identify the UHI class. This analysis was complemented by quantitative spatial analyses, such as scoring, overlay and intersect methods, to determine the priority level class and the typology of GOS priority. A qualitative analysis was also conducted through data triangulation or comparison methods, such as examining existing land use, GOS priority maps and spatial plan policies.FindingsThe findings show that the total UHI area in Samarinda City was 6,936.4 ha in 2019 and is divided into three classifications. In Class 1, the UHI area is very dominant, reaching 87% of the total area. Meanwhile, the main results identified two priority classes of GOS in Samarinda, namely, the medium and high categories with an area of 960.43 ha and 113.57 ha, respectively. The results also showed that there were 17 typologies associated with five alternative mitigation measures: green industry, greening parking lots, improving urban green infrastructure and buildings, urban greening and mining restoration.Research limitations/implicationsSpecific to assessing UHI, image data were available only in medium spatial resolution, leading to a consequence of detailed accuracy. In addition, since the determination of mitigation considered local policies, the method should be used in other locations requiring adjustments to existing regulations, specifically those related to spatial planning.Originality/valueThis study makes a significant contribution to the understanding of the UHI phenomenon in Indonesia, especially in the urban areas of Kalimantan Island. In addition, the study presents new insights into alternative mitigation actions to reduce the risk of UHI. Innovatively, this study introduces a typology of regions associated with appropriate alternative mitigation actions, making it an important achievement for the first time in the context of this study.

Recognizing high-priority disinfection byproducts based on experimental and predicted endocrine disrupting data: Virtual screening and in vitro study

So far, about 130 disinfection by-products (DBPs) and several DBPs-groups have had their potential endocrine-disrupting effects tested on some endocrine endpoints. However, it is still not clear which specific DBPs, DBPs-groups/subgroups may be the most toxic substances or groups/subgroups for any given endocrine endpoint. In this study, we attempt to address this issue. First, a list of relevant DBPs was updated, and 1187 DBPs belonging to 4 main-groups (aliphatic, aromatic, alicyclic, heterocyclic) and 84 subgroups were described. Then, the high-priority endocrine endpoints, DBPs-groups/subgroups, and specific DBPs were determined from 18 endpoints, 4 main-groups, 84 subgroups, and 1187 specific DBPs by a virtual-screening method. The results demonstrate that most of DBPs could not disturb the endocrine endpoints in question because the proportion of active compounds associated with the endocrine endpoints ranged from 0 (human thyroid receptor beta) to 32% (human transthyretin (hTTR)). All the endpoints with a proportion of active compounds greater than 10% belonged to the thyroid system, highlighting that the potential disrupting effects of DBPs on the thyroid system should be given more attention. The aromatic and alicyclic DBPs may have higher priority than that of aliphatic and heterocyclic DBPs by considering the activity rate and potential for disrupting effects. There were 2 (halophenols and estrogen DBPs), 12, and 24 subgroups that belonged to high, moderate, and low priority classes, respectively. For individual DBPs, there were 23 (2%), 193 (16%), and 971 (82%) DBPs belonging to the high, moderate, and low priority groups, respectively. Lastly, the hTTR binding affinity of 4 DBPs was determined by an in vitro assay and all the tested DBPs exhibited dose-dependent binding potency with hTTR, which was consistent with the predicted result. Thus, more efforts should be performed to reveal the potential endocrine disruption of those high research-priority main-groups, subgroups, and individual DBPs.

Utilizing statistical and MCDM techniques in indexing morphometric parameters towards improved watershed management in the Nandhour-Kalish drainage system

ABSTRACT Morphometric analysis plays a crucial role in the effective management of the surface as well as subsurface water resources. In the present study, ASTER DEM and Survey of India toposheets were utilized to characterize various morphological aspects of the Nandhour-Kalish watershed in Uttarakhand, India. In total, 12 sub-watersheds were determined using the SWAT 2012 in ArcGIS v10.4.1 and were coded as SW1–SW12. For each sub-watershed, 17 morphometric indices were estimated using Principal Component Analysis to determine the most influential indices or criteria of morphometric parameters in the context of the hydro-sedimentological response. The relative influence of identified criteria parameters was assessed using the Criteria Importance for Intercriteria Correlation (CRITIC) method. The developed sub-watersheds were then prioritized towards the prompt implementation of soil and water conservation measures. The results indicate that three sub-watersheds (SW2, SW3, and SW4) were categorized under a very high class of priority, while three (SW1, SW5, and SW6) were indexed under a high priority class. The study is an attempt to ease out the morphometry-based prioritization, which otherwise is relatively complex due to the availability of a large number of input parameters and ambiguity concerning parameter selection for the process of prioritization.

Assessing groundwater quality in semi-arid conditions: a geographic information systems-integrated approach using water quality index

Meeting freshwater demands in water-scarce regions is imperative, given challenges like hydric stress, urbanization, aridity, and population growth. This study focuses on the Tebessa aquifer system, a critical water source facing escalating demands. A primary objective is to identify actionable management strategies to improve groundwater quality dynamics in semi-arid regions. The methodology involves a comprehensive assessment of groundwater health using the Water Quality Index (WQI) approach. This method integrates diverse parameters influenced by intensive agricultural and industrial activities. Additionally, spatial relationship analysis, facilitated by Geographic Information System (GIS) technology, is employed to gain insights into complex hydrogeological processes. The multi-parameter strategy implemented results in water quality maps that highlight various ions, revealing spatial disparities and assigning quality priority classes from 'Good' to 'Poor' across the groundwater. The research, conducted in the semi-arid Tebessa-Morsott Plain, encompasses field and laboratory investigations to delineate hydrochemical traits, including alkalinity, salinity, and heightened ion concentrations. The results of the study contribute to scientific understanding by uncovering interrelationships and spatial intricacies, offering valuable insights for water resource governance. The effectiveness of the methodology, incorporating WQI and GIS, is demonstrated in the comprehensive evaluation of groundwater quality. Ultimately, this research provides a foundation for informed decision-making and sustainable management of groundwater resources in semi-arid regions.

Reliability analysis of intelligent manufacturing systems based on improved FMEA combined with machine learning

Along with the booming of intelligent manufacturing, the reliability management of intelligent manufacturing systems appears to be becoming more significant. Failure mode and effects analysis (FMEA) is a prospective reliability management instrument extensively utilized to manage failure modes of systems, products, processes, and services in various industries. However, the conventional FMEA method has been criticized for its inherent limitations. Machine learning can handle large amounts of data and has merits in reliability analysis and prediction, which can help in failure mode classification and risk management under limited resources. Therefore, this paper devises a method for complex systems based on an improved FMEA model combined with machine learning and applies it to the reliability management of intelligent manufacturing systems. First, the structured network of failure modes is constructed based on the knowledge graph for intelligent manufacturing systems. Then, the grey relation analysis (GRA) is applied to determine the risk prioritization of failure modes. Hereafter, the k-means algorithm in unsupervised machine learning is employed to cluster failure modes into priority classes. Finally, a case study and further comparative analysis are implemented. The results demonstrate that failure modes in system security, production quality, and information integration are high-risk and require more resources for prevention. In addition, recommendations for risk prevention and monitoring of intelligent manufacturing systems were given based on the clustering results. In comparison to the conventional FMEA method, the proposed method can more precisely capture the coupling relationship between the failure modes compared with. This research provides significant support for the reliability and risk management of complex systems such as intelligent manufacturing systems.

Analyzing the age of information in prioritized status update systems under an interruption-based hybrid discipline

Motivated by real-life applications, a special research-work interest has been recently directed towards the prioritized status update systems, which prioritize the update streams according to their timeliness constraints. The preferential service treatment between priority classes is commonly based on classical disciplines, preemption and non-preemption. However, both disciplines fail to give an even satisfaction between all classes. In our work, an interruption-based hybrid preemptive/non-preemptive discipline is proposed under a single-buffer system modeled as an M/M/1/2 priority queueing system. Each class being served (resp. buffered) can be preempted unless its recorded number of service preemptions reaches the predetermined in-service (resp. in-waiting) threshold. All thresholds between classes are the controlling parameters of the whole system’s performance. Using the stochastic hybrid system approach, the age of information (AoI) performance metric is analyzed in terms of its statistical average along with the higher-order moments, considering a general number of priority classes. Closed-form results are also obtained for some special cases, giving analytical insights about the AoI stability in heavy loading conditions. The average AoI and its dispersion are numerically investigated for the case of a three-class network. The significance of the proposed model is manifested in achieving a compromise satisfaction between all priority classes by a thorough adjustment of its threshold parameters. Two approaches are proposed to clarify the adjustment of these parameters. It turned out that the proposed hybrid discipline compensates for the limited buffer resource, achieving more promising performance with low design complexity and low cost. Moreover, the proposed scheme can operate under a wider span of the total offered load, through which the whole network satisfaction can be optimized under some legitimate constraints on the age-sensitive classes.

Assessing flood susceptibility in the Godavari River Basin, Bhadrachalam Region, Sothern India: A GIS-AHP Multi-Criteria Approach

Floods, as recurrent natural disasters, exert significant impacts on both the environment and human settlements. This study focuses on evaluating the flood susceptibility of the Bhadradri Kothagudem district in Telangana, situated within the Godavari River Basin. The research integrates Geographic Information System (GIS) and the Analytic Hierarchy Process (AHP) in a multi-criteria approach to analyze and map flood susceptibility. Landsat-8 data, digital elevation model data and rainfall serve as inputs for assessing the flood susceptibility. The study considers various topographical features such as elevation, slope, roughness, contours and aspect, along with factors like land use land cover, flow accumulation, stream direction, stream network, drainage density, flow length, distance from the river, soil, normalized difference vegetation index and topographic wetness index. These variables are rescaled on a scale of one to five and combined to generate a comprehensive flood susceptibility map of the Kothagudem district using GIS. The AHP is implemented through GIS, assigning weightages on a scale of one to five based on the priority of spatial classes within thematic maps. The flood susceptibility map is produced on a scale of five, designating scale class five as of very high susceptibility and scale class one as low. Scale classes two, three and four represent intermediate levels of susceptibility. The resulting flood susceptibility maps offer valuable insights for disaster preparedness, risk mitigation and land-use planning in the Kothagudem district. The integration of GIS and AHP provides a robust methodology for assessing and visualizing flood susceptibility, enabling informed decision-making for resilient and sustainable development in flood-prone regions.

Enhancing information freshness in multi-class mobile edge computing systems using a hybrid discipline

Timely status updating in mobile edge computing (MEC) systems has recently gained the utmost interest in internet of things (IoT) networks, where status updates may need higher computations to be interpreted. Moreover, in real-life situations, the status update streams may also be of different priority classes according to their importance and timeliness constraints. The classical disciplines used for priority service differentiation, preemptive and non-preemptive disciplines, pose a dilemma of information freshness dissatisfaction for the whole priority network. This work proposes a hybrid preemptive/non-preemptive discipline under an M/M/1/2 priority queueing model to regulate the priority-based contention of the status update streams in MEC systems. For this hybrid discipline, a probabilistic discretionary rule for preemption is deployed to govern the server and buffer access independently, introducing distinct probability parameters to control the system performance. The stochastic hybrid system approach is utilized to analyze the average age of information (AoI) along with its higher moments for any number of classes. Then, a numerical study on a three-class network is conducted by evaluating the average AoI performance and the corresponding dispersion. The numerical observations underpin the significance of the hybrid-discipline parameters in ensuring the reliability of the whole priority network. Hence, four different approaches are introduced to demonstrate the setting of these parameters. Under these approaches, some outstanding features are manifested: exploiting the buffering resources efficiently, conserving the aggregate sensing power, and optimizing the whole network satisfaction. For this last feature, a near-optimal low-complex heuristic method is proposed.

Influence of pre-processing methods on the automatic priority prediction of native-language end-users’ maintenance requests through machine learning methods

Feedback and requests by occupants are relevant sources of data to improve building management, and building maintenance. Indeed, most predictable faults can be directly identified by occupants and communicated to facility managers through communications written in the end-users’ native language. In this sense, natural language processing methods can support the request identification and attribution process if they are robust enough to extract useful information from these unstructured textual sources. Machine learning (ML) can support assessing and managing these data, especially in the case of many simultaneous communications. In this field, the application of pre-processing and ML methods to English-written databases has been widely provided, while efforts in other native languages are still limited, impacting the real applicability. Moreover, the performance of combinations of methods for pre-processing, ML and classification classes attribution, has been limitedly investigated while comparing different languages. To fill this gap, this work hence explores the performance of automatic priority assignment of maintenance end-users’ requests depending on the combined influence of: (a) different natural language pre-processing methods, (b) several supervised ML algorithms, (c) two priority classification rules (2-class versus 4-class), (d) the database language (i.e. the original database written in Italian, the native end-users’ language; a translated database version in English, as standard reference). Analyses are performed on a database of about 12000 maintenance requests written in Italian concerning a stock of 23 buildings open to the public. A random sample of the sentences is supervised and labelled by 20 expert annotators following the best-worst method to attribute a priority score. Labelled sentences are then pre-processed using four different approaches to progressively reduce the number of unique words (potential predictors). Five different consolidated ML methods are applied, and comparisons involve accuracy, precision, recall and F1-score for each combination of pre-processing action, ML method and the number of priority classes. Results show that, within each ML algorithm, different pre-processing methods limitedly impact the final accuracy and average F1-score. In both Italian and English conditions, the best performance is obtained by NN, LR, SVM methods, while NB generally fails, and by considering the 2-class priority classification scale. In this sense, results confirm that facility managers can be effectively supported by ML methods for preliminary priority assessments in building maintenance processes, even when the requests database is written in end-users’ native language.

Spatial Conservation Prioritization of Persian Squirrel Based on Habitat Suitability and Climate-Induced Forest Mortality Risk

The Zagros forests in Iran are currently experiencing an exacerbation of climate-induced mortality, placing the Persian squirrel, a keystone species reliant on these ecosystems, in jeopardy. Addressing this imminent threat, our research employed a spatial prioritization methodology, integrating assessments of habitat suitability and mortality risk. Utilizing a weighted ensemble approach, incorporating the strengths of diverse models and expert rules, we discerned that approximately 62% of surveyed forests are at risk, with 7% classified as high risk and 17% as very high risk. Notably, 83% of the forests exhibited varying degrees of habitat suitability, with 11% and 12% demonstrating high and very high suitability, respectively. Employing a conservation prioritization framework, we systematically categorized habitats into priority classes, with 8%, 17%, 29%, and 46% assigned to very high, high, moderate, and low conservation priority classes, respectively. Significantly, areas classified as very high priority demand immediate restoration efforts due to ongoing mortality, while other priority classes underscore the importance of protection and prevention in unaffected habitats. Acknowledging the irreversible nature of current climatic conditions in the Zagros forests, our strategic emphasis aligns with conservation triage principles, prioritizing the preservation of intact habitats yet to succumb to irreversible mortality.

Deterministic Scheduling Algorithm Based on Proportional Conflict and Deadline in Industrial Wireless Networks

<p>Deterministic scheduling technology is of great significance for the real-time and deterministic transmission of industrial wireless network data. In view of the fact that the industrial wireless network data stream itself has priority classification attribute, this paper, based on multi-channel time-division multiple access (TDMA) technology, analyzes link conflict delay and channel contention delay caused by high priority data stream to low priority data stream, and performs scheduling preprocessing on the network, so as to eliminate the network with unreasonable parameters, and feedback to the network manager. For preprocessed networks, the scheduling algorithm prioritizes the allocation of time slots and channel resources for links of high priority data streams, while for data streams belonging to the same priority class, a scheduling scheme based on Maximum Proportional Conflict and Deadline First (MPC-D) is proposed. Under the premise of meeting schedulability conditions, time slots and channel resources are allocated sequentially according to the proportional conflict deadline values of each link, from largest to smallest. The experimental results show that the proposed scheduling algorithm can achieve a high network scheduling success rate.</p> <p> </p>

Minimizing total completion time with machine-dependent priority lists

We consider a natural, yet challenging variant of the parallel machine scheduling problem in which each machine imposes a preferential order over the jobs and schedules the jobs accordingly once assigned to it. We study the problem of minimizing the total completion time, distinguishing between identical and unrelated machines, machine-dependent and identical priority lists, or a constant number of different priority classes. Additionally, we consider the setting in which the priority list on a machine must satisfy longest processing time first. We resolve the computational complexity of the problem and provide a clear distinction between problems that are polynomial time solvable and APX-hard.

Compostos identificados em extratos vegetais aplicados na agricultura e no tratamento de sementes

ABSTRACT: Plant extracts effects have been regarded and proven in researches under different applications in agriculture, including seed treatment. The plant extracts effectiveness is attributed to the presence of certain classes of compounds; therefore, studies that aim to identify and quantify these composites, which are present in plant extracts used in agriculture, are important, as well as the seed treatment. This research aimed to understand and describe through a systematic review, what were the main carried approaches, classes and identified compounds in studies with plant extracts to different applications in agriculture and the seed treatment. The period 2015 to 2020 was the one that exposed the higher research publication indexes, considering the theme under analysis, plant extracts in agriculture, seeking to identify or quantify the presence of metabolic composites, indicating a growing interest in this theme. Such studies pursued identifying and/or quantifying the compounds that are present in the plant extracts. The phenolic compounds constitute the priority class of metabolites to different functions, effects and applications in agriculture, mainly in seed treatment. The terpenes present a substantial potential as bioinsecticides to agriculture. Plant species that are rich in phenolic and terpenes compounds are a significant source of alternative control in the protection of the productive system.

Morphometric parameters based prioritization of watersheds for soil erosion risk in Upper Jhelum Sub-catchment, India.

Soil erosion is the inherent and destructive threat affecting agricultural production and livelihood of million mouths. The increased frequency of floods and land use/land cover changes has made Upper Jhelum Sub-catchment susceptible to soil erosion risk. Morphometric based watershed prioritization for soil erosion risk may help in sustainable management of natural resources. Thus, this paper endeavors to prioritize watersheds of Upper Jhelum Sub-catchment in India based on morphometric parameters for soil erosion risk using geospatial techniques. Weights to the morphometric parameters were assigned through a multi-criteria decision method. The watersheds in the Sub-catchment have been categorized into low, medium, high and very high priority classes based on prioritization ranks that were determined by computing the compound value for the soil erosion risk, based on prioritization ranks obtained through compound value for the soil erosion risk. The results revealed 1E1D3 and 1E1D8 watersheds accorded very high priority. The watersheds namely IE1D2 and IEID4 were found under high priority. Medium priority for soil erosion risk was determined in IEID5 and IED7 watersheds while 1E1D1 and IE1D6 watersheds were identified for low priority. The study calls for implementing soil conservation practices in the Sub-catchment. The Sub-catchment can be made less hazardous for the soil erosion risk by implementing contour farming, building check dams, terrace farming, afforestation and limiting large scale overgrazing. The findings of this study may offer valuable insights for stakeholders for conservation of soil resource. The approach utilized in the study may be linked with soil loss estimation for effective conservation of natural resources in further future studies.