Ward's Hierarchical Clustering Research Articles

Innovations in daily commuting - what types of solutions are competitive? European case study.

Innovations drive dynamic changes in daily commuting especially in functional urban areas (FUAs), where transport systems are developed. Innovative solutions are being tested in FUAs, and they not only pose an alternative to conventional transport systems, but can also revolutionize daily commuting in cities. The main aim was to classify the innovation drivers identified in literature and to determine their impact on travel behaviour (TB) in FUAs with a use of expert survey. The following research hypothesis was examined: the level of innovation utilization in daily commuting and its impact on TB is similar in countries with similar levels of economic development. The survey results were processed to (1) create a ranking of innovations in TB in European OECD countries and (2) group countries based on similarity in prioritizing innovations influencing TB, using Ward hierarchical clustering method. As a result, innovative factors were comprehensively identified and classified in six categories.

Corporate Debt Dynamics: Sectoral Clustering Analysis Using NACE Classification in Slovakia

Research background: Many authors are currently exploring the impact of the industry on the financial structure of enterprises since there are statistically significant differences across various sectors, exposing the industry as a critical factor influencing corporate indebtedness. Clusters of sectors with homogeneous patterns of indebtedness and comparable levels of debt in various economic conditions may be determined, and, therefore, firms and their debt levels should be systematically examined and evaluated. Purpose of the article: The main aim of this paper is to evaluate the level of debt in the Slovak environment on a sample of 19,480 firms from various sectors and to identify the relationships among them to identify sectors with homogeneous patterns of indebtedness and, consequently, comprehend which sectors are the most stable and independent. Methods: Because NACE classification provides a framework for gathering and presenting statistical data based on many economic sectors, the number of input data was reduced based on cluster analysis. Using Ward's hierarchical clustering method using squared Euclidean distance, selected indebtedness ratios were used to define the sectors with comparable debt levels. To determine if there were statistically significant differences between the calculated debt ratios related to the economic sector, the Kruskal-Wallis test was performed. Subsequently, as the results indicated significant differences across various indebtedness ratios, a post hoc analysis was performed. Findings & Value added: A group of NACE activities in which enterprises are sufficiently comparable that it is appropriate to evaluate their debt levels using the chosen indebtedness ratios are considered to be in sectors C, F, G and H, which included to tertiary sector, while the sectors K, R and S may also be grouped in one cluster, while form the secondary sector. The key relevance of our findings is the benchmarking of selected sectors about indebtedness, which may be used to further examine their growth in each of the V4 nations, which is an essential area for the evolution of the European economy as a whole. Studies considering a relatively significant amount of capital structure determinants may be beneficial to owners and managers, regulators, and financial institutions since debt policy affects firm performance, value, and survival.

Assessing learning gains of pharmacy students in communications, ways of thinking, and intercultural skills through self-assessment

Background and purposeIntercultural and Global Health Issues was developed to address learning outcomes in communication, ways of thinking, intercultural personal skills, and intercultural knowledge. The aim of this study was to assess learning gains of pharmacy students through self-assessment. Educational activity and settingCourse design, learning outcomes, objectives, and activities were created to meet the expected educational outcomes. A revised rubric was created from the American Association of Colleges and Universities (AACU) Valid Assessment of Learning in Undergraduate Education rubrics on Intercultural knowledge, Information literacy, and Creative thinking. Students completed self-assessments at the beginning and end of the course. Ward hierarchical clustering, paired sample t-tests, and independent t-tests analyzed multidimensional data in two clusters. FindingsCluster 1 (C1) students reported pre-course capstone performances for cultural self-awareness, problem solving, and access and use of information ethically and legally. Post-course scores for C1 students statistically increased for all AACU domains reaching capstone performances for intercultural competence, creative thinking, and information literacy. Cluster 2 (C2) students reported capstone performance levels for all AACU domains from the beginning to the end of the course. All students reported achievement of self-efficacy, creative thinking, and cultural competency at the end of the course. There was no statistically significant difference in course learning outcome scores for C1 and C2 students. SummaryStudents achieved embedded learning outcomes of ways of thinking, communication, interpersonal skills, and intercultural knowledge as demonstrated from self-assessments. Course activities aided students' demonstration of self-efficacy, creative thinking, and intercultural knowledge.

Assessing the security status and future scenarios of the Mediterranean region through the water-energy-food nexus

Over the past decades, the Mediterranean region has faced significant challenges due to the impacts of climate change and ongoing conflicts. This study proposes an assessment of the region's security status and potential future scenarios through the lens of the water-energy-food nexus, utilising indicators that align with the Sustainable Development Goals (SDGs). These indicators include agricultural yields, value-added, and land variations, water and sanitation services, income and inequality, use of renewable energy, carbon footprints, and political stability. To evaluate the situation, this analysis applies Ward's hierarchical clustering algorithm to group countries based on these indicators, examining the average terms and the years 2006 and 2015 for comparative analysis. Additionally, an exponential smoothing algorithm forecasts future trends and generates clusters for the years 2030 and 2050. By computing an index of convergence for each cluster and indicator, this contribution identifies areas of particular interest from a security perspective. The findings of this analysis reveal a growing polarisation within the Mediterranean region, with European countries and Israel forming one distinct group, and African and Eastern countries (excluding Israel) forming another. Notably, recurring disparities exist in variables such as agricultural land, political stability, violence, income per capita, and agricultural value added. Conversely, certain variables, including the Gini coefficient, prevalence of overweight population, and access to drinking water services, show signs of convergence. These results shed light on potential areas of both conflict and cooperation in the Mediterranean region, highlighting the importance of addressing the challenges posed by climate change. By understanding the geopolitical dynamics and identifying key areas of concern, policymakers can develop informed strategies to promote stability and sustainable development in the region.

Machine learning-based product quality classification of the enterprise producing aluminum flat coil

A fundamental research challenge in organizations is determining product quality by constructing prediction models considering the parameters that affect product quality. By solving this problem, shipment planning can be organized quickly based on product quality status, the line can be stopped when necessary, or when it is expected that the required product quality will not be achieved, such products can be planned in the following productions production plan. When such forecasts are not made, the product quality can be recognized much later in the production process through quality control operations. Re-planning and rework expenditures are incurred as a result of this condition. This paper proposes a data-mining-based methodological framework for predicting the quality of flat coils produced by an aluminum flat coil casting company. In the study, the quality of aluminum flat coil was classified considering the data such as OperatorSidePrint, RollEntranceWaterTemperature, UpRollDiameter, DownRollDiameter, UpSpreyVelocity, DownRollExitWaterTemperature, SteelPlateLineVelocity, SteelPlateOperatorSideBending, DownSpreyBulk that affect the product quality by the use of K-means, CLARANS, BIRCH, Ward's Hierarchical Agglomerative Clustering, Clustering, and Logistic Regression, KNN, Artificial Neural Network, CART (Decision Tree), Random Forest (RF), Gradient Boosting Machines, Feed-Forward Neural Network, Naive Bayes, XGBoost and RF, Gradient Boosting RF and AdaBoost-LSTM ensemble models, and Simple RNN classification techniques. Following the application of algorithms to 23 different semi-finished products, RNN provided the best results in 35% of the semi-finished products, and AdaBoost-LSTM provided the best results in 18% of the semi-finished products.

Selection and Yield Formation Characteristics of Dry Direct Seeding Rice in Northeast China.

Dry direct seeding rice (DSR) is an emerging production system because of increasing labor and water scarcity in rice cultivation. The limited availability of rice cultivars suitable for dry direct seeding hampers the widespread adoption of this cultivation method in Northeast China. This study aimed to investigate grain production and plant characteristics associated with dry direct seeding rice. We conducted a field experiment on 79 japonica rice cultivars in Shenyang City, Liaoning Province, Northeast China, in 2020 and 2021. This study found that the grain yield of the tested rice cultivars ranged from 5.75-11.00 t ha-1, with a growth duration lasting between 144-161 days across the cultivars. These cultivars were then categorized into high yielding (HY), medium yielding (MY), and low yielding (LY) based on daily yield by using Ward's hierarchical clustering method. The higher grain yield for HY compared to MY and LY was attributed to more spikelets per unit area. The HY alleviated the conflict between higher panicle density and larger panicle size by improving the seedling emergence rate and productive stem rate. It also significantly increased shoot biomass at maturity. The HY reduced the period between seeding and beginning of heading (BBCH 51) and the proportion of dry matter partitioned to the leaf at the heading stage. However, it also increased the accumulation of dry matter in the grain and the proportion of dry matter partitioned to the grain at maturity. Furthermore, the HY markedly increased the harvest index and grain-leaf ratio, which are beneficial to coordinate the source-sink relationship. A quadratic function predicted that 98 days is the optimum growth duration before heading (BBCH 51) for achieving maximum yield. In conclusion, for dry direct seeding rice, it is appropriate to select high-yielding japonica inbred rice cultivars with shorter growth duration before heading (about 93-102 day), higher panicle number (about 450-500 × 104 ha-1), more spikelet number per panicle (about 110-130), higher seedling emergence rate (about 70-75%), higher productive stem rate (about 60-70%), and greater harvest index (about 50-55%).

Assessment of magnesium deficiency in greenhouse tomato crops grown on calcareous soil

AbstractMagnesium (Mg) deficiency in greenhouse vegetable plants affects crop yields and quality. Assessing the degree of Mg deficiency and influencing factors is necessary for evidence‐based Mg supplementation. In this study, an investigation was conducted at 71 tomato cultivation greenhouses with calcareous soil, located in the main greenhouse vegetable production area of China. Plant and soil Mg contents were analysed, as well as nitrogen (N), phosphorus (P), potassium (K) and other nutrients. The tomatoes showed Mg deficiency, with none of the farms supplementing this element. The critical leaf Mg concentration of the tomatoes was 3.40 mg kg−1. Using Ward's hierarchical cluster analysis, three clusters of Mg leaf content were obtained, which were 1.07–3.00 mg kg−1 (SD cluster), 2.56–5.64 mg kg−1 (MD cluster) and 5.12–6.88 mg kg−1 (ND cluster). Tomato yields in the SD and MD clusters were 31.4% and 16.5% lower than the ND cluster, respectively. Both soil exchangeable Mg content and soil pH in the SD cluster were the lowest, while total NPK, available NPK, K/Mg and the Ca/Mg ratio were the highest. A principal component analysis (PCA) showed that Mg leaf content was negatively correlated with the exchangeable K/Mg ratio, and positively correlated with soil exchangeable Mg content, soil pH and tomato yield. Mg deficiency in the tomatoes was attributed to the high accumulation of available K in the calcareous soil and the fact that the SD cluster with greatest Mg deficiency also had the lowest soil total Mg concentration. Considering the accumulation of NPK and the causes of Mg deficiency, foliar Mg spraying is suggested to mitigate Mg deficiency in calcareous greenhouse tomato soil.

Key performance indicators used by dairy consultants during the evaluation of reproductive performance during routine visits.

Dairy farms need thorough and efficient reproduction control. Consultants specialized in reproduction use key performance indicators (KPI) to monitor the reproductive performance of farms and must be able to decipher between the approach in a first visit and routine visits. A total of 49 consultants specialized in dairy reproduction from 21 countries responded to an online survey conducted to determine the most suitable parameters during routine visits every 2 to 4 weeks. The survey was comprised of 190 questions, 178 of them rated from 0 (irrelevant) to 10 (maximum importance) points. The questions were divided into five sections: (1) consultant and farm model, (2) general data of the farm, (3) cow reproduction, (4) postpartum and metabolic disease, and (5) heifer reproduction. The median, interquartile range, minimum and maximum values, and 95% confidence interval were determined for each question. Afterward, a multivariate analysis, using between-group linkage via Ward's hierarchical clustering was conducted to generate clusters of consultants according to their response pattern. Finally, a chi-square test was conducted to assess the association between years of experience of the consultant and farm size within the clusters generated in each section of the questionnaire. The majority of the consultants considered 34 parameters to be highly important (rated 8-10) to analyze during routine visits. The consultants used several KPI (in variable quantitative range) to evaluate any of the presented sections and considered that all the five sections are critical to control. They are aware of using KPI that reflect heat detection, fertility, and farming efficiency as well as KPI that can provide information on reproductive efficiency in the near future for cows, such as postpartum and metabolic diseases. However, parameters that are relatively old and ineffective, in terms of reproductive performance control, are still highly regarded by the majority of consultants in a routine-visit scenario. Farm size and years of experience of the consultant did not influence the type or number of parameters chosen as KPI during routine visits. The parameters rated with the highest importance (rate 10) that could be considered for an easy, fast, and universal use in routine visits to assess the reproductive status were: First service CR (%), Overall pregnancy rate (%) for cows, and age at first calving (d) for heifers.

What do hospital-based health professionals need and expect from an mHealth app to support the first 1000 days of life? Results from a cross sectional study.

Several attempts have been made to introduce mHealth solutions to support maternal, newborn, and child health (MNCH). However, most of the available apps do not meet the needs of end-users, underscoring the urgency of involving them in the co-design of telehealth interventions. With this in mind, we investigated the needs and expectations of hospital-based health professionals (i.e., secondary users) providing care to pregnant women and new mothers with their babies for a hypothetical mHealth app to support the first 1000 days of life. From November 2021 to March 2022, we surveyed health professionals using a questionnaire that explored the perceived importance of specific content, functionalities, and technical features of the proposed app. We also collected sociodemographic information from secondary users. We performed descriptive analysis and then used Ward hierarchical clustering method to classify respondents according to their response patterns. We recorded the needs and expectations of 145 hospital-based health professionals from obstetrics/gynecology, nursery/neonatology, and pediatrics. We found general agreement with the proposed content of the app, particularly general information about health during pregnancy (92%) and potential risky infections during pregnancy (91%). Three clusters emerged from the analysis, with the high and medium demanding clusters rating the importance of app content and technical features as very high and high, respectively, while low demanding cluster expressing more skepticism, especially about some of the proposed functionalities of the app. Assessing the needs and expectations of end-users is an essential process for developing tailored and effective mHealth solutions. This study has shown that hospital-based health professionals generally recognize the value of the proposed app, suggesting their propensity to integrate such a telehealth solution into mainstream clinical practice.

Best Practice Data Sharing Guidelines for Wind Turbine Fault Detection Model Evaluation

In this paper, a set of best practice data sharing guidelines for wind turbine fault detection model evaluation is developed, which can help practitioners overcome the main challenges of digitalisation. Digitalisation is one of the key drivers for reducing costs and risks over the whole wind energy project life cycle. One of the largest challenges in successfully implementing digitalisation is the lack of data sharing and collaboration between organisations in the sector. In order to overcome this challenge, a new collaboration framework called WeDoWind was developed in recent work. The main innovation of this framework is the way it creates tangible incentives to motivate and empower different types of people from all over the world to share data and knowledge in practice. In this present paper, the challenges related to comparing and evaluating different SCADA-data-based wind turbine fault detection models are investigated by carrying out a new case study, the “WinJi Gearbox Fault Detection Challenge”, based on the WeDoWind framework. A total of six new solutions were submitted to the challenge, and a comparison and evaluation of the results show that, in general, some of the approaches (Particle Swarm Optimisation algorithm for constructing health indicators, performance monitoring using Deep Neural Networks, Combined Ward Hierarchical Clustering and Novelty Detection with Local Outlier Factor and Time-to-failure prediction using Random Forest Regression) appear to exhibit high potential to reach the goals of the Challenge. However, there are a number of concrete things that would have to have been done by the Challenge providers and the Challenge moderators in order to ensure success. This includes enabling access to more details of the different failure types, access to multiple data sets from more wind turbines experiencing gearbox failure, provision of a model or rule relating fault detection times or a remaining useful lifetime to the estimated costs for repairs, replacements and inspections, provision of a clear strategy for training and test periods in advance, as well as provision of a pre-defined template or requirements for the results. These learning outcomes are used directly to define a set of best practice data sharing guidelines for wind turbine fault detection model evaluation. The guidelines can be used by researchers in the sector in order to improve model evaluation and data sharing in the future.

Excessive α-β Oscillations Mark Enlarged Motor Sign Severity and Parkinson's Disease Duration.

β Oscillations in the subthalamic nucleus (STN) have been proven to contribute to Parkinson's disease (PD), but the exact borders of β subbands vary substantially across the studies, and information regarding heterogeneity of β rhythmic activity is still limited. Recently, α oscillations in the basal ganglia have also become the focus of PD research. The aim was to study rhythmic oscillations in the STN in PD patients to identify different subbands with stable oscillatory peaks within a broad α-β range and to establish their associations with motor symptoms. Local field potentials inside the STN were recorded during deep brain stimulation (DBS) surgeries. After calculating power spectra and extracting an aperiodic component, oscillatory peaks in the 8- to 35-Hz range with amplitude exceeding 90th percentile were clustered into three bands. Peak parameters were estimated for two lower subbands. Clinical features were compared in patients with and without oscillation peaks in the lowest α-β subband. We isolated α-β (8-15 Hz), β (15-25 Hz), and β-γ (25-35 Hz) subbands within the 8- to 35-Hz spectral range using oscillatory parameters and Ward's hierarchical clustering. Additional α-β oscillatory peaks were found in about half of patients with β peaks; they were located more ventrally compared to β. We have found a significant increase in disease duration, bradykinesia, and rigidity scores in the group with additional α-β peaks. Increased α-β oscillations may emerge as additional phenomena complementing β oscillations; they may mark disease progression in PD and affect DBS stimulation setup. © 2023 International Parkinson and Movement Disorder Society.

Characterization of biophysical contexts leading to severe wildfires in Portugal and their environmental controls

Characterizing the fire regime in regions prone to extreme wildfire behavior is essential for providing comprehensive insights on potential ecosystem response to fire disturbance in the context of global change. We aimed to disentangle the linkage between contemporary damage-related attributes of wildfires as shaped by the environmental controls of fire behavior across mainland Portugal. We selected large wildfires (≥100 ha, n = 292) that occurred during the 2015–2018 period, covering the full spectrum of large fire-size variation. Ward's hierarchical clustering on principal components was used to identify homogeneous wildfire contexts at landscape scale on the basis of fire size, proportion of high fire severity, and fire severity variability, and their bottom-up (pre-fire fuel type fraction, topography) and top-down (fire weather) controls. Piecewise Structural Equation Modeling was used to disentangle the direct and indirect relationships between fire characteristics and fire behavior drivers. Cluster analysis evidenced severe and large wildfires in the central region of Portugal displaying consistent fire severity patterns. Thus, we found a positive relationship between fire size and proportion of high fire severity, which was mediated by distinct fire behavior drivers involving direct and indirect pathways. A high fraction of conifer forest within wildfire perimeters and extreme fire weather were primarily responsible for those interactions. In the context of global change, our results suggest that pre-fire fuel management should be targeted at expanding the fire weather settings in which fire control is feasible and promote less flammable and more resilient forest types.

Exploring the Needs and Expectations of Expectant and New Parents for an mHealth Application to Support the First 1000 Days of Life: Steps toward a Co-Design Approach.

To improve maternal and child health, it is essential to adhere to health-promoting and preventive measures. However, reliable information as well as effective tools are not easy to identify in this field. Our cross-sectional study investigated the needs and expectations of expectant and new mothers and fathers as potential primary users of a hypothetical application supporting the first 1000 days of life. Between May and August 2022, we recruited expectant and new parents by administering an 83-item 5-point Likert scale questionnaire related to the content, functionalities, and technical features of the hypothetical app. We stratified responses using sociodemographic characteristics and then performed ward hierarchical clustering. The 94 women and 69 men involved in our study generally agreed with the proposed content, but expressed low interest in certain app functionalities or features, including those related to the interaction mechanism and interactivity. Women were generally more demanding than men. Our findings, resulting from the engagement of end-users, may be useful for designers and technology providers to implement mHealth solutions that, in addition to conveying reliable information, are tailored to the needs and preferences of end-users in the first 1000 days of life.

Ward Hierarchical Clustering: Indonesia's Green Economy Implementation Performance based on Environmental Quality towards Sustainable Development Goals

Environmental issues have recently emerged as an essential topic of discussion on a global scale. Actions to protect and preserve the environment should be addressed, which causes several problems such as water and air pollution, soil degradation, forest destruction, and diversion of agricultural land. Economic goals are given higher priority than other goals, such as environmental and community goals, in national development strategies and programs. The "green economy" concept is a solution to the sustainable concept to overcome the abovechallenges. Thus, this paper attempts to build a sustainable economic model based on research from various scientific sources. The indicators presented in this paper can help advise regional policymakers to continue progressing and developing according tothe Sustainable Development Goals (SDGs) pillars. This study uses the Ward Hierarchical Clustering method. The first cluster is the provinces of Aceh, North Sumatra, and West Sumatra, which also interpret that the performance of implementing a green economy in related areas based on environmental quality is very good. The second cluster is East Java, South Sumatra, and South Sulawesi, which indicates that the performance of the implementation of a green economy in related areas based on environmental quality is quite good. Then the third cluster is West Java, which interprets that the performance of implementing a green economy in related areas based on environmental quality still needs attention from the local government. It is known that 97.06% of Indonesia's territory has implemented a green economy in related areas based on very good environmental quality.

Can vegetation be discretely classified in species‐poor environments? Testing plant community concepts for vegetation monitoring on sub‐Antarctic Marion Island

The updating and rethinking of vegetation classifications is important for ecosystem monitoring in a rapidly changing world, where the distribution of vegetation is changing. The general assumption that discrete and persistent plant communities exist that can be monitored efficiently, is rarely tested before undertaking a classification. Marion Island (MI) is comprised of species-poor vegetation undergoing rapid environmental change. It presents a unique opportunity to test the ability to discretely classify species-poor vegetation with recently developed objective classification techniques and relate it to previous classifications. We classified vascular species data of 476 plots sampled across MI, using Ward hierarchical clustering, divisive analysis clustering, non-hierarchical kmeans and partitioning around medoids. Internal cluster validation was performed using silhouette widths, Dunn index, connectivity of clusters and gap statistic. Indicator species analyses were also conducted on the best performing clustering methods. We evaluated the outputs against previously classified units. Ward clustering performed the best, with the highest average silhouette width and Dunn index, as well as the lowest connectivity. The number of clusters differed amongst the clustering methods, but most validation measures, including for Ward clustering, indicated that two and three clusters are the best fit for the data. However, all classification methods produced weakly separated, highly connected clusters with low compactness and low fidelity and specificity to clusters. There was no particularly robust and effective classification outcome that could group plots into previously suggested vegetation units based on species composition alone. The relatively recent age (c. 450,000 yearsB.P.), glaciation history (last glacial maximum 34,500 yearsB.P.) and isolation of the sub-Antarctic islands may have hindered the development of strong vascular plant species assemblages with discrete boundaries. Discrete classification at the community-level using species composition may not be suitable in such species-poor environments. Species-level, rather than community-level, monitoring may thus be more appropriate in species-poor environments, aligning with continuum theory rather than community theory.

Hereditary analysis and genotype × environment interaction effects on growth and yield components of Bambara groundnut (Vigna subterranea (L.) Verdc.) over multi-environments

This investigation was carried out to explore G × E interaction for yield and its associated attributes in 30 Bambara groundnut genotypes across four environments in tropical Malaysia. Such evaluations are essential when the breeding program's objective is to choose genotypes with broad adaption and yield potential. Studies of trait relationships, variance components, mean performance, and genetic linkage are needed by breeders when designing, evaluating, and developing selection criteria for improving desired characteristics in breeding programs. The evaluation of breeding lines of Bambara groundnut for high yield across a wide range of environments is important for long-term production and food security. Each site's experiment employed a randomized complete block design with three replicates. Data on vegetative and yield component attributes were recorded. The analysis of variance revealed that there were highly significant (p ≤ 0.01) differences among the 30 genotypes for all variables evaluated. A highly significant and positive correlation was identified between yield per hectare and dry seed weight (0.940), hundred seed weight (0.844), fresh pod weight (0.832), and total pod weight (0.750); the estimated correlation between dry weight of pods and seed yield was 1.0. The environment was more important than genotype and G × E in determining yield and yield components.A total of 49% variation is covered by PC1 (33.9%) and PC2 (15.1%) and the genotypes formed five distinct clusters based on Ward hierarchical clustering (WHC) method. The genotypes S5G1, S5G3, S5G5, S5G6, S5G8, S5G7, S5G2, S5G4, S5G10, S5G13, S5G11, and S5G14 of clusters I, II, and III were closest to the ideal genotype with superior yield across the environments. The PCA variable loadings revealed that an index based on dry pod weight, hundred seed weight, number of total pods and fresh pod weight could be used as a selection criteria to improve seed yield of Bambara groundnut.

Key Performance Indicators Used by Dairy Consultants During the Evaluation of Reproductive Performance in a First Visit

Reproduction plays a fundamental role in the profitability of dairy farms. Consultants use key performance indicators (KPI) to monitor the reproductive performance of the farms. They must decipher between the most suitable ones to face two different scenarios that may need the analysis of different parameters: (1) approach in a first visit, and (2) routine visits. Forty-nine consultants specialized in dairy reproduction from 21 countries responded to an online survey conducted to determine the most suitable parameters in a first visit approach. The survey was comprised of 190 questions, 178 of them rated from 0 (irrelevant) to 10 (maximum importance) points. The questions were divided into 5 sections: (1) consultant and farm model, (2) general data of the farm, (3) cow reproduction, (4) postpartum and metabolic disease, and (5) heifer reproduction. The median, interquartile range, minimum and maximum values, and 95% confidence interval (CI) were determined for each question. Afterward, a multivariate analysis, using between-group linkage via Ward's hierarchical clustering was conducted to generate clusters of consultants according to their response pattern. Lastly, a Chi-square test was conducted to assess the association between the years of experience of the consultant and farm size within the clusters generated in each section of the questionnaire. Most of the consultants considered 27 parameters to be highly important to analyze during the first visit. Consultants use several KPIs (in variable quantitative range) to evaluate any of the presented sections. Moreover, consultants preferred parameters focused on heat detection, fertility, and pregnancy achievement regarding the production cycle of a dairy cow. Consultants also showed high interest in obtaining a general overview of milk production, farming efficiency, and the heifer rearing process; but the farm size and the years of experience of the consultant influenced the type and number of parameters chosen as KPI. The parameters rated with the highest importance (rate 10) that could be considered for an easy, fast, and universal first visit to assess the reproductive status were: first service conception rate, overall pregnancy rate, and 21d pregnancy rate for cows, and age at first calving for heifers.

Molecular Characterization of Salmonella Typhimurium and E. coli O157:H7 Isolated from Ready-to-eat Chicken Meat

Background: Virulence genes are important in the pathogenesis of bacteria and in the mechanism of bacterial pathogenicity.
 Objective: The study aimed at molecular detection of virulence genes such as sdiA, fimH, InvA and fliC and determination of similarity and links among the isolates.
 Method: A total of 67 isolates including 4 controls were tested by Polymerase Chain Reaction (PCR) with 4 primer pairs including invA, fliC, sdiA and fimH to avoid bias. Full sequences of the 16S-Sequencing gene of both strains were carried out with 29 Salmonella and 30 E. coli isolates that were positive for amplification at 1500bp.ward hierarchical clustering model and agglomeration procedure was used. Clustered grouping and relational affinity test were conducted and depicted by the dendrogram. Molecular identification and interpretation were done using BLASTn Protocol.
 Result: None of the isolates were positive to the invA or fliC gene fragments. One isolate from each of Salmonella Typhimurium and E. coli O157:H7 was positive to sdiA and fimH respectively. Three E. coli isolates were positive with an amplification of 500bp which is specific for fimH genes. One of the isolates E459 showed amplification of fimH gene with multidrug resistance to 5 drugs namely Ceftazidime, Cefuroxime, Augmentin, Nitrofurantoin and Ampicillin. Four Salmonella isolates had an amplification of 274bp specific for sdiA gene. Two variants of E. coli O157:H7 (unit g1 and unit g2) were identified. A mutant strain Salmonella Typhimurium LTS (STMD1) causing human gastroenteritis was identified also, Salmonella Typhimurium DT104 isolated exhibited multiple resistant genes (ACSSuT, SGI1) against several antibiotics. These are of public health significance.
 Conclusion: This study has indicated presence of sdiA and fimH genes in Salmonella typhimurium and E. coli 0157:H7 respectively isolated from ready-to-eat chicken meats from public eateries. It also indicated association of sdiA and fimH genes with multi-drugs resistance.

Welfare regimes as enablers of just energy transitions: Revisiting and testing the hypothesis of synergy for Europe

Welfare States are considered key tools to provide just transitions. The hypothesis of synergy states that Social-democratic regimes are in a better position to accomplish them. While synergy has been widely theorised, its empirical verification has remained less studied and provided contradictory results. The weaknesses detected in the state-of-the-art, such as the misalignment of empirical testing and the theoretical drivers of synergy, as well as inconsistencies in the selection of variables and biases imposed by classifications in discrete timings, lead us to define an improved methodological framework. We apply the continuous observation of Ward's hierarchical clustering in squared Euclidean distances under Thorndike's criterium to twenty-three European countries between 2008 and 2016 and reject synergy after detecting that Social-democratic regimes display the best social conditions but the worst environmental performances and that society and the environment are not linked. This outcome motivates us to propose a discussion with a focus on the sustainability of economic growth and the opportunities for sustainable welfare scholarship to settle it.

Identifying species complexes based on spatial and temporal clustering from joint dynamic species distribution models

AbstractData-limited species are often grouped into a species complex to simplify management. Commonalities between species that may indicate if species can be adequately managed as a complex include the following: shared habitat utilization (e.g., overlapping fine-scale spatial distribution), synchrony in abundance trends, consistent fishing pressure or gear susceptibility, or life history parameters resulting in similar productivity. Using non-target rockfish species in the Gulf of Alaska as a case study, we estimate spatial and temporal similarities among species to develop species complexes using the vector autoregressive spatio-temporal (VAST) model, which is a joint dynamic species distribution model. Species groupings are identified using Ward's hierarchical cluster analysis based on spatial and temporal species correlations. We then compare the spatial and temporal groupings with cluster analysis groupings that use exploitation and life history characteristics data. Based on the results, we conclude that there are some rockfish species that consistently group together, but the arrangement and number of clusters differ slightly depending on the data used. Developing species complexes for fisheries management requires a variety of analytical approaches including species distribution models and cluster analyses, and these can be applied across the full extent of available data sources.