Agglomerative Hierarchical Clustering Research Articles

Chemometric and ICP-OES analyses of Forsythia europaea Degen & Bald. and its extracts

Forsythia represents a group of plants originating mainly from China and Japan, but one species is endemic and grows in the Balkans (Balkan forsythia, Forsythia europaea Degen & Bald.). Our previous studies on polyphenols in investigated extracts of Balkan forsythia showed that this plant is a good source of polyphenols. Analysis of the various extracts of Balkan forsythia (Forsythia europaea Degen & Bald.) by the application of ICP-OES method showed that they are rich in different macro and microelements. The abundance order of macroelements is K>Ca>P>Mg>Na in all extracts. Among the transition metals iron, manganese, zinc and copper are particularly important, and the order of abundance is Zn>Fe>Cu>Mn. Heavy metals which are the most frequent contaminants of food are lead, cadmium and arsenic, and the determination of their contents is of special importance on the safe use of plant species. The determination shows that aqueous extracts contain the highest quantity of elements, which is especially important. The contents of toxic elements are significantly lower than the permitted values. Statistical methods (Principal Component Analysis (PCA) and Agglomerative Hierarchical Clustering (AHC)) are useful tools for the grouping of samples and determining relations between investigated elements. This analysis shows that when higher quantities of Cr and Ba are present, the lower quantities of V are present, and vice versa. Based on our studies on polyphenols and minerals, we can expect the anti-inflammatory effects of extracts of Balkan forsythia.

Strategy for improving and empowering MSMEs through grouping using the AHC method

The high number of migrants in the city of Yogyakarta has resulted in increased opportunities for Micro, Small and Medium Enterprises (MSMEs) in Culinary and Handicrafts. The large amount of data collected by the Cooperative Office, which reached thousands, caused inas to have difficulties in determining what training was needed by MSMEs and also difficulties in choosing which MSMEs would receive training held by the Cooperative Office. In addition, the Yogyakarta Cooperatives and UMKM Office had difficulties in selecting which UMKM needed to receive these trainings. Grouping can be used as a strategy in selecting MSMEs and determining training according to their individual needs. The purpose of this study was to group SMEs using the Agglomerative Hierarchical Clustering Single Linkage method and its application to provide recommendations for MSME groups to the Yogyakarta Cooperative and MSME Office. The results of the recommendations for the number of groups can be used in providing implementation, design, and evaluation of the development and empowerment of MSME data in the City of Yogyakarta. This study uses the Agglomerative Hierarchical Clustering Single Linkage method. The stages in this research are Load Data, Cleaning Data, Data Selection, Transformation Data, Clustering Process with AHC single linkage, Silhouette Coefficient, and Knowledge Representation. This research resulted in 2 group recommendations from a total of 1336 Culinary MSME data and 3 group recommendations from a total of 145 Handicraft MSME data. The results of the silhouette score test in the Culinary Sector are included in the strong structure category with a value of 0.79 and the Crafts Sector is included in the Medium Structure category with a value of 0.615. From the number of these groups, recommendations were obtained for improving a service in increasing MSMEs, especially those with a turnover of less than 10 million, marketing purposes within the Yogyakarta area, and not having financial assistance from the government. The high number of immigrants in the city of Yogyakarta has resulted in increased opportunities for Micro, Small and Medium Enterprises (MSMEs) in the Culinary and Crafts sector. The large number of MSMEs creates increasingly higher competitiveness. Apart from that, the large amount of data collected by the Department of Cooperatives and MSMEs, which reaches thousands, causes the Department to have difficulties in efforts to improve and empower these MSMEs. Grouping is one method that can be used as a strategy in mapping MSMEs, especially in efforts to improve and empower MSMEs through training conducted by the Department. The aim of this research is to group MSMEs using the Agglomerative Hierarchical Clustering (AHC) method in an effort to achieve strategies for improving and empowering MSMEs. The focus of this research is[a1] MSMEs in the craft sector and MSMEs in the culinary sector. The results of this research provide 2 group recommendations from a total of 1336 Culinary MSME data and 3 group recommendations from a total of 145 Craft MSME data. The silhouette score test results in the Culinary Sector are in the strong structure category with a value of 0.79 and in the Crafts Sector are in the Medium Structure category with a value of 0.615. From the number of groups in the two MSMEs, strategies were obtained to improve and empower MSMEs, especially those with a turnover of less than 10 million, marketing objectives within the Yogyakarta area, and not having capital assistance from the government. [a1]the result of the revision of the Abstract

Phytosociological research into the Illyrian oak–hornbeam forest on the limestone cliffs of Kupa River canyon (Kamanje, West-Central Croatia)

In this paper, we present 14 relevés of oak-hornbeam forest from the limestone cliffs of Kupa River canyon in ­Kamanje, West-Central Croatia. The investigated forest is part of the Natura 2000 ecological network, and it represents one of the rare remnants of climax forest in the area that is not highly influenced by anthropogenic management. Plant ­community composition was recorded using the six-degree Braun-Blanquet scale, and its relation to similar forests studied by other authors was assessed using agglomerative hierarchical clustering. Because of the abundance of limestone rocks and consequently microhabitats, the forest is very species-rich, with a total of 164 vascular species ­recorded, ranging from 42 to 80 species per relevé. The Kamanje forest belongs to the association Epimedio-Carpinetum betuli, with regard to traditional classification to the subassociation staphyletosum, and is rich in species of the Illyrian-­ -­Balkan floral geoelement. However, compared to the forests of the same association from other areas, this forest has some distinct floristic features. Among the trees, Quercus petraea is replaced by Q. robur. The herb layer is abundant with ferns, and geophytes and hemicryptophytes are much more frequent. The most distinct feature of the forest is the high abundance of ferns from the class Asplenietea trichomanis, specifically Asplenium trichomanes and Polypodium vulgare. The cluster analyses separated the Kamanje forest as a unique group in relation to all other traditional ­subassociations. The Kamanje forest could be considered as a fern-rich subass. staphyletosum. However, only after the problems of traditional classification of Epimedio-Carpinetum betuli into subassociations are resolved will the exact floristic context of the studied forest be clearer. </p>

Chemometric assessment of the chemical profile of tea seed (Camellia sinensis) with different size determined by GC and ICP/OES

This study was carried out to compare the chemical profile of tea seeds of different sizes determined by GC and ICP/OES devices with chemometric analysis. This study was carried out to determine the effects of different seed size [11.00–12.50 (SD1), 12.51–14.00 (SD2), 14.01–15.50 (SD3), 15.51–17.00 mm (SD4)] on seed internal ratio, crude oil content, fatty acid composition, mineral composition of tea seed by using different chemometric analysis methods (correlation, agglomerative hierarchical clustering, principal component analysis). In the study, fatty acid and mineral compositions were determined in GC and ICP/OES devices, respectively. Crude oil content, palmitic, stearic, oleic, linoleic, eicosenoic acids, SFA, MUFA, PUFA, Al, Fe, Mg elements were found to be significant according to different seed size. Among six different fatty acids determined, oleic acid was major component. Positive correlations were found between oleic acid and MUFA, between linoleic and α-linolenic acid, between linoleic acid and PUFA, between α-linolenic acid and PUFA, between Al and Na, between Ca and Pb, between Ca and Zn, between Co and K, between Cr and Cu, between Mg and Ni, between Mg and P. In PCA analysis, it was determined that the SD1 group was different from the other groups in terms of the stearic, linoleic, α-linolenic, eicosenoic acids, PUFA, B mineral. In AHC analysis, seed sizes were divided into two different groups according to crude oil content, fatty acid and mineral composition, seed internal ratio. Also, different chemometric analysis methods such as correlation, agglomerative hierarchical clustering, principal component analysis were useful and decisive in determining the quality characteristics of tea seeds classified according to seed size.

Research on Clustering of Material Inspection Data for Highway Construction Projects Based on Agglomerative Hierarchical Clustering

In large-scale highway construction projects, the project implementers will carry out detailed testing of various material indicators used in the project. By processing and analyzing these material testing data, we can reasonably classify large-scale highway construction projects, so that each project implementer can better manage the quality of the materials, which will greatly help all parties to strengthen the quality control of the project and improve the level of project management. This study first preprocesses the material testing data to obtain a structured data set suitable for data analysis. Then statistical features are constructed for the features in the dataset, including maximum, minimum, mean, median and standard deviation, to improve the performance and accuracy of the model. Next, the clustering hierarchical clustering method is applied for classification and the classification results are visualized in the form of dendrograms. Finally, through the comparison of performance analysis and clustering evaluation indexes, it is concluded that the classification of works into 3 or 4 categories according to material performance is in line with the actual level of engineering quality.

Biochar and vermicompost modulated Pb toxicity in summer savory (Satureja Hortensis L.) plants through inducing physiological and biochemical changes

The primary environmental factor affecting the biochemical attributes of plants is soil pollution by heavy metals. The detrimental effects of heavy metals on plants can be lessened by soil amendments. The goal of the current research was to determine the potential of biochar (BC) and vermicompost (VC) as soil amendments in reducing Pb toxicity in summer savory (Satureja HortensisL.) though physiological and biochemical modifications. Therefore, the pot experiment was conducted with Pb toxicity (control (non-Pb), 300, and 600 mg kg soil-1) and soil amendments (control, 2% BC, 10% VC, and 1% BC+5% VC) based on a completely randomized design. The results showed that Pb toxicity at 600 mg kg soil-1 led to significant decreases in shoot weight (41%), root weight (25%), leaf relative water content (20%), and chlorophyll content (39%) compared to the control. However, it resulted in increases in malondialdehyde (61%) and electrolyte leakage (49%) when BC and VC were not applied. The results showed that Pb toxicity at 600 mg kg soil-1 led to decreases in shoot weight (41%), root weight (25%), leaf relative water content (20%), and chlorophyll content (39%), but increases in malondialdehyde (61%), and electrolyte leakage (49%) compared to the control in the treatments without BC and VC application. However, BC and VC, particularly their combination were more effective in improving plant growth. The interaction of Pb at 300 mg kg-1 and combined BC and VC resulted in higher total phenolic content, total flavonoid content, essential oil (EO) content, and EO yield with 29, 62, and 39, and 35% raises compared with the control. Agglomerative hierarchical clustering revealed that Pb at 300 and 600 mg kg soil-1 differed from the control, and that combined VC and BC significantly varied from their individual values. Combining BC and VC is more effective than using them separately in alleviating Pb toxicity, as it enhances plant growth and secondary metabolite production. The results have the potential to benefit the improvement of summer savory resistance in Pb-polluted soils.

Alternative Approach to the Segmentation of Fishing Fleets

The appropriate segmentation of fishing fleets is controversially discussed in fisheries research and management and a variety of approaches has been introduced. The present approach, developed in a pilot study funded by the European Commission – Data Collection Framework (DCF), introduces a standardized multivariate approach for characterizing fisheries fleet segments by hierarchical agglomerative cluster analysis (HAC) of their catch composition. We chose data from 2021 of the Romanian fishing fleet as the basis of our analysis. Statistical analyses were performed using the program RStudio V3.6.1 by running the fleet segmentation package script. The specific indices, tests, and visual validation methods of the package were applied to determine the optimal number of clusters. The procedure was finalized by a post-hoc validation of the clustering result to identify the actual fleet segments. From the basic data, 6 fleet segments for Vessels using active and passive gears (PMP) were highlighted, representing 52 boats, where it was noted that fishing at Rapa whelk (RPW) prevailed with 91.94% of the total catches on the segment, respective 5 fleet segments for Vessels using passive gears (PG) only for vessels 12m, representing 78 vessels where the main catches or recorded at European anchovy (ANE) 25.16%, turbot (TUR) 13.08%, horse mackerel (HMM) 8.75%, thus 130 vessels from two classes of different gears in total. We detected mied fishing, especially on various assemblages of demersal and pelagic fish, as well as target fishing on demersal and pelagic fish, Rapa whelk, and mussels. For a better understanding of the approach, further research is needed.

Agglomerative Hierarchical Clustering Methodology to Restore Power System considering Reactive Power Balance and Stability Factor Analysis

Despite there are significant advancements in modern power systems, blackouts remain a potential risk, necessitating efficient restoration strategies. This paper introduces an innovative concept for power system restoration, focusing on balancing active and reactive power while ensuring voltage stability. For instance, this paper employs an agglomerative clustering technique, which partitions the power system into segments with balanced reactive power, facilitating swift restoration postblackout. Central to this methodology is the use of the line stability factor, which assesses the voltage stability of individual lines, identifying the system’s stronger and weaker sections based on voltage stability levels. This paper demonstrates the effectiveness of the proposed methodology through case study analysis, comparing voltage stability levels across agglomerative clusters and their geographical locations. The power system is divided into two stable partitions, considering the number of black-start generators, available reactive power, and voltage stability levels. This partitioning reveals that the clusters formed by the agglomerative method are inherently stable, suggesting enhanced system stability, dependability, and availability during the restoration phase following a blackout. In addition, this paper discusses the potential causes of blackouts, offering insights into their prevention, and finishes with a novel clustering methodology for power systems, considering reactive power and voltage stability. This method facilitates the parallel restoration of the system’s independent partitions, significantly reducing restoration time; it addresses critical challenges and outcomes, underscoring the methodology’s potential to revolutionize blackout recovery processes in modern power systems.

Classifying self-management clusters of patients with mild cognitive impairment associated with diabetes: A cross-sectional study.

This study aims to propose a self-management clusters classification method to determine the self-management ability of elderly patients with mild cognitive impairment (MCI) associated with diabetes mellitus (DM). MCI associated with DM is a common chronic disease in old adults. Self-management affects the disease progression of patients to a large extent. However, the comorbidity and patients' self-management ability are heterogeneous. A cross-sectional study based on cluster analysis is designed in this paper. The study included 235 participants. The diabetes self-management scale is used to evaluate the self-management ability of patients. SPSS 21.0 was used to analyse the data, including descriptive statistics, agglomerative hierarchical clustering with Ward's method before k-means clustering, k-means clustering analysis, analysis of variance and chi-square test. Three clusters of self-management styles were classified as follows: Disease neglect type, life oriented type and medical dependence type. Among all participants, the percentages of the three clusters above are 9.78%, 32.77% and 57.45%, respectively. The difference between the six dimensions of each cluster is statistically significant. This study classified three groups of self-management styles, and each group has its own self-management characteristics. The characteristics of the three clusters may help to provide personalized self-management strategies and delay the disease progression of MCI associated with DM patients. Typological methods can be used to discover the characteristics of patient clusters and provide personalized care to improve the efficiency of patient self-management to delay the progress of the disease. In our study, we invited patients and members of the public to participate in the research survey and conducted data collection.

A Simple Unsupervised Knowledge-Free Domain Adaptation for Speaker Recognition

Despite the great success of speaker recognition models based on deep neural networks, deploying a pre-trained model in real-world scenarios often leads to significant performance degradation due to the domain mismatch between training and testing conditions. Various adaptation methods have been developed to address this issue by modifying either the front-end embedding network or the back-end scoring model. However, existing methods typically rely on knowledge of the network, scoring model, or even the source data. In this study, we introduce a knowledge-free adaptation approach that only necessitates the unlabeled target data. Our core concept is based on the assumption that domain mismatch primarily stems from distributional distortion in the embedding space, such as shifting, rotation, and scaling while maintaining inter-speaker discrimination for data from unknown domains. Building on this assumption, we propose clustering LDA (C-LDA), a full-rank linear discriminant analysis (LDA) based on agglomerative hierarchical clustering (AHC) to compensate for this distortion. This approach does not need any human labels and does not rely on any knowledge of the model in the source domain, making it suitable for real-world applications. Theoretical analysis indicates that with cosine scoring, C-LDA is capable of eliminating distributional distortion related to global shift and within-speaker covariance rotation and scaling. Surprisingly, our experiments demonstrated that this simple approach can outperform more complex methods that require full or partial knowledge, including front-end approaches such as fine-tuning and distribution alignment, and back-end approaches such as unsupervised probabilistic linear discriminant analysis (PLDA) adaptation. Additional experiments demonstrated that C-LDA is insensitive to hyperparameters and works well in both multi-domain and single-domain adaptation scenarios.

Evaluating total nitrogen and phosphorous concentrations in a watershed impacted by diverse anthropic activities in a developing country.

This research aims to identify critical contamination points by nutrients, their possible origin (point and nonpoint sources), their spatial distribution, and possible attenuation by natural and anthropogenic processes. The study area is the Velhas River Basin, located in the Southeast Region of Brazil (17.0°-20.5° S; 43.5°-45.0°W). A historical series of water quality monitoring, land cover map, demographic and agricultural censuses, sewage treatment diagnostics, and local hydrographic networks were used to achieve the objectives. In addition, the regions were divided into incremental areas, enabling individualized analyses of each sub-basin. Descriptive statistics, seasonality, categorized data tests, agglomerative hierarchical cluster analysis, and principal component analysis were used. There was a significant contribution of nutrients in the most important urban agglomeration of the basin, resulting in peak concentrations measured at that place. Although the values were reduced by the mouth (650 km), the percentage of legislation violations remained high. The effects of punctual contamination were intensified by the low percentage of treated sewage in the basin, the absence of adequate treatment technologies to remove nutrients, and the disorderly urbanization. Furthermore, it was estimated that the nutrient load from animal husbandry is approximately 75% of the load from domestic effluents due to the high number of cattle in the basin and the low percentage of forests.

Spatial distribution and health risk assessment of toxic metal(oid)s in soils of coal mining areas of the Salt Range, Punjab, Pakistan

Long-term coal mining operations have been linked to increases in HM concentrations in subterranean soils, which degrades soil quality. The clusters of coal mines and mine tailings considerably increase metal(loid)s concentrations in the surrounding soils and air of the Eastern (ESR), Central (CSR), and Trans-Indus Salt Range (TSR). The present study was conducted in this scenario to ascertain the occurrence and dispersion of toxic metals from mine tailings to different environmental components and their pollution load in soils and possible risk. The geochemical characterization of the soil, coal waste, and dust particulate matter revealed that waste samples are highly enriched in As, Cd, Pb, Fe, and Zn. In terms of soil quality standards, geo-accumulation index (Igeo), enrichment factor (EF), and contamination factor (CF), Fe, Mn, and Zn are extremely contaminated. However, As, Cd, Co, and Cr show moderate contamination. These harmful soil contaminants, primarily originate from coal-associated tailings. Further, the principal component analysis, multiple linear regression (PCA-MLR), and agglomerative hierarchical clustering (AHC) signify that mineralization and weathering of sulfide and carbonate minerals within the coal wastes and lithological units of the present study area cause the release of environmentally sensitive trace elements, which upon dispersion, leaching, and atmospheric deposition enters the air and soils of the present study area. We propose that higher HI index of Co (4), Fe (35), Mn (11), and Zn (13) values can be a significant human health concern.

Investigation of Phytochemical, Antioxidant and Antidiabetic Potentials of Scabiosa L. (Caprifoliaceae) Species with Chemometric Methods.

In this research, the total phenolic and flavonoid amounts, phenolic compositions, in vitro antioxidant, antibacterial and antidiabetic properties of the methanol extracts obtained from Scabiosa L. (Caprifoliaceae) species distributed in the flora of Türkiye were investigated using chemometric methods. For this purpose, principal component (PCA) and agglomerative hierarchical clustering analysis were performed as chemometric methods. Chlorogenic acid, quinic acid and cyranoside were determined in the extracts. According to chemometric analysis, S. columbaria subsp. ochroleuca var. ochroleuca and S. triniifolia species were found to be valuable in terms of methanol extract yields, total phenolic and flavonoid contents, antioxidant and antidiabetic activities while S. columbaria subsp. ochroleuca var. webbiana species were found to be valuable in terms of phenolic composition. The methanol extracts of Scabiosa species showed high antioxidant activity, with high phenolic and flavonoid contents. Among the tested 13 bacteria, Scabiosa extracts showed only low activity against Klebsiella pneumoniae, Streptococcus pneumoniae and Pseudomonas aeruginosa. The extracts showed high α-amylase and α-glucosidase inhibitory activity. The results show that Scabiosa methanol extracts may be a source of alternative antioxidants that may be beneficial in slowing or preventing the progression of various oxidative stress-related diseases.

Profiles of theory of mind impairments and personality in clinical and community samples: integrating the alternative DSM-5 model for personality disorders.

Deficits in theory of mind (ToM)-the ability to infer the mental states of others-have been linked to antagonistic traits in community samples. ToM deficits have also been identified in people with personality disorders (PD), although with conflicting evidence, partly due to the use of categorical diagnoses. The DSM-5 Alternative Model for Personality Disorders (AMPD) provides an opportunity for a more precise understanding of the interplay between ToM abilities and personality pathology. Therefore, the study aims to determine whether and how individuals with diverse ToM profiles differ regarding personality impairment (AMPD Criterion A) and pathological facets (AMPD Criterion B). Adults with PD (n = 39) and from the community (n = 42) completed tests assessing ToM skills and self-reported questionnaires assessing AMPD Criteria A and B. Hierarchical agglomerative and TwoStep cluster analyses were consecutively computed using scores and subscores from ToM tests as clustering variables. Multivariate analyses of variance were subsequently performed to compare the clusters on both AMPD Criteria. Five clinically and conceptually meaningful clusters were found. The most notable differences across clusters were observed for Intimacy and Empathy dysfunctions (Criterion A), as well as for the Deceitfulness, Callousness, and Hostility facets from the Antagonism domain and the Restricted affectivity facet from the Detachment domain (Criterion B). The results support the association between antagonistic personality facets and ToM deficits. However, clusters showing impairments in ToM abilities did not necessarily exhibit high levels of personality dysfunction or pathological facets, emphasizing that both constructs are not isomorphic. Nevertheless, specific profiles can help refine existing interventions to make them more sensitive and specific to the nature of ToM dysfunctions while considering personality functioning and facets.

Evaluation of master’s degree programmes in project and construction management: an agglomerative hierarchical clustering study

This study examines master's degree programmes in project and construction management in Turkey and the TRNC until 2022. It covers 16 master's degree programmes and represents their current status. The study groups courses to evaluate and develop the contents of the master's degree programmes. It creates course knowledge areas through course grouping and provides the numerical data necessary for the clustering process. The study clusters similar and related master's degree programmes using the AHC method and proposes new programme names for the classes obtained with AHC. This paper intends to serve as a guide for research on active master's degree programmes in project and construction management, to update and/or develop such programmes, and to evaluate the past and present situation for future studies.

Antécédents d’adversité et types d’homicide : une étude menée en Unité pour Malades Difficiles

In this article, we explore the links between adverse childhood experiences (ACEs) and the commission of certain types of homicide(s): infanticide, filicide, uxoricide, matricide, patricide, parricide, fratricide, aviolicide, double homicide, mass killings, serial murders, and so on. The patient files of 423 male perpetrators of homicide(s) hospitalized in a French secure forensic psychiatric unit between 1963 and 2020 were examined. A thematic collection grid was specially designed to reconstruct the traumatogenic trajectories of the subjects concerned, and to thereby determine the presence or absence of different forms of ACE (physical, emotional, and sexual violence, physical and emotional neglect, exposure to domestic abuse and conjugal violence, etc.). After calculating prevalence data, multiple correspondence analyses (MCA) were performed, in order to identify patterns of association linking specific ACEs to the types of homicide investigated. Projection of the results within a multidimensional space will enable us to visualize the main associations that may be established for clinical and preventive purposes. Agglomerative hierarchical clustering (AHC) is then used to group individuals into distinct latent classes, based on the similarities and dissimilarities of the homicidal criminals studied. The profiles outlined are then discussed, as are the particularly high rates of ACEs found – especially in comparison with data obtained from other studies (carried out in general, prison or forensic populations). Finally, we suggest a few avenues for future research.

Trust Aware Nero Fuzzy Based Agglomerative Hierarchical Clustering with Secure Whale Optimization Routing for Enhancing Energy Efficiency in WSN

Wireless sensor networks (WSNs) comprise a network of dispersed, carefully positioned sensor nodes in their deployment environment to monitor and collect data on natural phenomena. These sensor nodes collaborate to transmit data via multi-hop communication, ultimately reaching a central base station for processing. However, WSNs face significant challenges due to the resource-constrained nature of these devices and the harsh, open environments in which they operate. Addressing energy optimization and ensuring secure communication are primary concerns in the successful operation of WSNs. This paper introduces anovelTrust aware Neuro Fuzzy Clustering head selection (TNFCH) and agglomerative hierarchical clustering approach (AHC) with Secure Whale Optimization (SWO) Algorithm Routing to enhance energy-efficient transmission in WSNs. Our proposed protocol (TNFCH-AHWO) efficiently organizes nodes by utilizing neural network and Fuzzy logic then securely transfers the data into the communication network. We employ a Trust calculation algorithm in our system to ensure Trust and data integrity, facilitating efficient lightweight operations such as key generation, encryption, decryption, and verification. This ensures hop-to-hop authentication among the nodes in WSNs. To assess the performance of our proposed protocol, we conducted simulations using the NS3 simulator. The findings of the simulation show that the suggested protocol greatly enhances various performance metrics, including energy consumption analysis, throughput, network delay, network lifetime, and packet delivery ratio when compared to existing protocols.

ORCA: Outage Root Cause Analysis in DER-Rich Power Distribution System Using Data Fusion, Hierarchical Clustering and FP-Growth Rule Mining

Power outages have been an inevitable part of power delivery systems. In case of large scale outages with conflicting information in the distribution network with distributed energy resources (DER), recognition of the root causes becomes increasingly complex. Root cause detection is very important for routing the crew and for faster restoration. Number of studies have been conducted on analysis of the outages, planning predictive actions, and improving the restoration process for minimizing the load outages using various statistical and data processing. A comprehensive methodology with the ability to find root causes in quick and efficient manner is still missing. This work proposes an Outage Root Cause Analysis (ORCA) tool, with various internal modules. The proposed ORCA utilizes the available data from diverse set of sensors (Distribution Phasor Measurement Units, metering and relay) within the distribution system (DS) to: a) aggregate using data fusion algorithm: an Ensemble Extended Kalman Filter (EEKF) approach, b) classify events at broader level (fault, breaker operation or cyber-attack with/without load outage) using hierarchical agglomerative clustering in online and offline manners, and c) narrow down the outage root cause to the possible nuanced identified causes (vegetation, animal, weather, wildfire, protection, equipment failure, planned) using the available historical data and the Frequent Pattern-Growth data mining approach. Simulation results demonstrate the superiority of the proposed approach compared to the other existing key approaches using two different test systems and multiple outage with different locations case scenarios.

Claw lesion status in Brazilian commercial sow herds from 2013 to 2023.

Claw lesions significantly contribute to lameness, greatly affecting sow welfare. This study investigated different factors that would impact the severity of claw lesions in the sows of Brazilian commercial herds. A total of 129 herds (n = 12,364 sows) were included in the study. Herds were in the Midwest, Southeast, or South regions of Brazil. Inventory sizes were stratified into 250-810 sows, 811-1,300 sows, 1,301-3,000 sows, and 3,001-10,000 sows. Herds belonged to Cooperative (Coop), Integrator, or Independent structures. The herd management was conducted either maintaining breeds from stock on-site (internal), or through purchase of commercially available genetics (external). Herds adopted either individual crates or group housing during gestation. Within each farm, one randomly selected group of sows was scored by the same evaluator (two independent experts evaluated a total of 129 herds) from 0 (none) to 3 (severe) for heel overgrowth and erosion (HOE), heel-sole crack (HSC), separation along the white line (WL), horizontal (CHW) and vertical (CVW) wall cracks, and overgrown toes (T), or dewclaws (DC) in the hind legs after parturition. The study assessed differences and similarities between herds using Principal Component Analysis (PCA) and Hierarchical Agglomerative Clustering (HAC) analysis. The effects of factors (i.e., production structure, management, housing during gestation, and region) were assessed using the partial least squares method (PLS). Heel overgrowth and erosion had the highest prevalence, followed by WL and CHW, while the lowest scores were observed for T, DC, and CVW. Herds were grouped in three clusters (i.e., C1, C2, and C3). Heel overgrowth and erosion, HSC, WL, CHW, CVW, and T were decreased by 17, 25, 11, 25, 21, and 17%, respectively, in C3 compared to C1 and 2 combined. Independent structure increased the L-Index in all three clusters. Furthermore, individual housing increased the L-Index regardless of the cluster. The results suggest that shifting toward larger, more technologically advanced herds could potentially benefit claw health. Additionally, adopting group gestation housing appears to mitigate the adverse effects on claw health, although further validation is necessary, as Brazil has only recently transitioned from individual housing practices.

HAC: Hierarchical Agglomerative Clustering With Linear Programming for Wireless Sensor Networks

HAC: Hierarchical Agglomerative Clustering With Linear Programming for Wireless Sensor Networks