Browse Articles
Discover research articles across all indexed journals
A reliable unmanned aerial vehicle multi-ship tracking method
As the global economy expands, waterway transportation has become increasingly crucial to the logistics sector. This growth presents both significant challenges and opportunities for enhancing the accuracy of ship detection and tracking through the application of artificial intelligence. This article introduces a multi-object tracking system designed for unmanned aerial vehicles (UAVs), utilizing the YOLOv7 and Deep SORT algorithms for detection and tracking, respectively. To mitigate the impact of limited ship data on model training, transfer learning techniques are employed to enhance the YOLOv7 model’s performance. Additionally, the integration of the SimAM attention mechanism within the YOLOv7 detection model improves feature representation by emphasizing salient features and suppressing irrelevant information, thereby boosting detection capabilities. The inclusion of the partial convolution (PConv) module further enhances the detection of irregularly shaped or partially occluded targets. This module minimizes the influence of invalid regions during feature extraction, resulting in more accurate and stable features. The implementation of PConv not only improves detection accuracy and speed but also reduces the model’s parameters and computational demands, making it more suitable for deployment on computationally constrained UAV platforms. Furthermore, to address issues of false negatives during clustering in the Deep SORT algorithm, the IOU metric is replaced with the DIOU metric at the matching stage. This adjustment enhances the matching of unlinked tracks with detected objects, reducing missed detections and improving the accuracy of target tracking. Compared to the original YOLOv7+Deep SORT model, which achieved an MOTA of 58.4% and an MOTP of 78.9%, the enhanced system achieves a MOTA of 65.3% and a MOTP of 81.9%. This represents an increase of 6.9% in MOTA and 3.0% in MOTP. After extensive evaluation and analysis, the system has demonstrated robust performance in ship monitoring scenarios, offering valuable insights and serving as a critical reference for ship surveillance tasks.
Sex differences in the association between composite dietary antioxidant index and hyperlipidemia: Insights from NHANES
Background Previous studies have shown that both the composite dietary antioxidant index (CDAI) and sex are strongly associated with a variety of cardiovascular diseases, but sex differences between CDAI and hyperlipidemia are unknown. Objective This study utilized data from the National Health and Nutrition Examination Survey (NHANES) to investigate the sex differences between CDAI and hyperlipidemia. Method We calculated the CDAI of the six dietary antioxidants using data from NHANES, explored the relationship between CDAI and the prevalence of hyperlipidemia using multivariate logistic regression analysis, and analyzed for potential nonlinear associations using restricted cubic spline. Finally, the association between CDAI and hyperlipidemia was further explored using multivariate logistic regression in different genders. Results The study included a total of 34,754 participants with a mean age of 47.04 years, of whom 49.37% were man. In a fully adjusted multivariable binary logistic regression model, CDAI was negatively associated with the prevalence of hyperlipidemia (OR = 0.99, 95% CI:0.98–0.99). In addition, participants in the highest quartile had a lower risk of hyperlipidaemia compared with the lowest quartile of CDAI (OR = 0.83, 95%CI: 0.76–0.92). We also found a non-linear relationship (non-linear P = 0.003, Inflection point = -0.179). Finally, we found that the association between CDAI and the prevalence of hyperlipidemia was significantly stronger in the female population than in the male population (P for interaction <0.05). Conclusion Our study highlights the L-shaped association between CDAI and the prevalence of hyperlipidemia in the general adult population. In addition, this association was more significant in the female population than in the male population.
Baseline characteristics and 2-year functional outcome data of patients undergoing an arthroscopic rotator cuff repair in Switzerland, results of the ARCR_Pred study
The ARCR_Pred study was initiated to document and predict the safety and effectiveness of arthroscopic rotator cuff repair (ARCR) in a representative Swiss patient cohort. In the present manuscript, we aimed to describe the overall and baseline characteristics of the study, report on functional outcome data and explore case-mix adjustment and differences between public and private hospitals. Between June 2020 and November 2021, primary ARCR patients were prospectively enrolled in a multicenter cohort across 18 Swiss and one German orthopedic center. Baseline characteristics, including sociodemographic and diagnostic variables, were reported. Clinical scores and patient-reported outcome measures were assessed up to 24-month follow-up. After screening 2350 individuals, 973 patients with ARCR were included. Follow-up rates reached 99%, 95%, 89% and 88% at 6 weeks, 6, 12, and 24 months, respectively. While the proportion of massive tears was higher in the study population (44% vs. 20%, Std. Diff. = 0.56), there were no other major differences in key characteristics between enrolled and non-enrolled patients or in patients lost to follow-up. Functional scores improved over time, with positive changes rates ranging from 83% to 92% at 6-month, reaching 91% to 97% at 12- and 24-month follow-up. In linear mixed models, used to estimate the associations between baseline factors, hospital type and standardized 0–100 scores, marginal effects for time ranged from 20 to 30, 28 to 39 and 34 to 41 points at the 6-, 12- and 24-month follow-up, respectively. Except at the 12-month follow-up, where marginal effects for the interaction terms ranged from -5 to -4 points in the standardized scores, there were no consistent outcome differences between public and private hospitals. Increasing number of years of education was consistently associated with better scores, greater feelings of depression and anxiety, smoking and ASA group III-IV were consistently associated with worse scores. Tear severity showed a consistent negative association solely for the Constant-Score. The ARCR_Pred study shows high potential for generalizability to the population of patients undergoing an ARCR in Switzerland. Further analyses are needed to establish relevant clinimetrics for the Swiss population and to compare outcomes for surgical techniques, surgeon experiences profiles and post-operative management.
Validating quality standards in Palestinian emergency departments: An e-Delphi survey approach
To validate Palestine’s previously derived emergency department quality standards (EDQS) using an e-Delphi survey. A two-round e-Delphi survey validated the EDQS, developed in an earlier study through a literature review and consensus-building among Palestinian emergency medicine and healthcare quality experts. The study purposively sampled 53 emergency department and healthcare quality experts with over 5 years of experience. A Likert scale was used to rate the standards on readability, clarity, and comprehensiveness in the initial round to reach consensus on the EDQS, with detailed feedback. An expanded expert group refined the shortlisted standards in the next phase. Lime Survey collected data anonymously. A set of 100 EDQS was validated through a two-round e-Delphi survey. In the initial round, 103 standards were presented, and consensus was achieved, resulting in a refined list of 100 standards. Among these, 39 standards fell under the clinical pathway domain, and 61 under the administrative pathway domain. In the second round, the validity of these standards was affirmed, with 96.4% consensus for clinical standards and 97.3% for administrative standards. Additionally, seven subdomains of EDQS were associated with the clinical pathway domain: triage, treatment, transportation, medication safety, patient flow, and medical diagnostic services, and nine subdomains were linked to the administration pathway domain: documentation, information management systems, access-location, design, leadership, management, workforce staffing, training, equipment, supplies, capacity-resuscitation rooms, resources for a safe working environment, performance indicators, and patient safety-infection prevention and control programs. The study validated context emergency department quality standards in Palestine, with over 97% consensus indicating a commitment to quality care. Experts suggest further research on implementation feasibility. Validated standards can aid healthcare leaders in resource allocation, staff training, and enhancing patient care, potentially leading to significant improvements in emergency healthcare in Palestine.
Hydrological prediction in ungauged basins based on spatiotemporal characteristics
Hydrological prediction in ungauged basins often relies on the parameter transplant method, which incurs high labor costs due to its dependence on expert input. To address these issues, we propose a novel hydrological prediction model named STH-Trans, which leverages multiple spatiotemporal views to enhance its predictive capabilities. Firstly, we utilize existing geographic and topographic indicators to identify and select watersheds that exhibit similarities. Subsequently, we establish an initial regression model using the TrAdaBoost algorithm based on the hydrologic data from the selected watershed stations. Finally, we refine the initial model by incorporating multiple spatiotemporal views, employing semi-supervised learning to create the STH-Trans model. The results of our experiments underscore the efficiency of the STH-Trans model in predicting runoff for ungauged basins. This innovation leads to a substantial increase in model accuracy ranging from 7.9% to 30% compared to various conventional methods. The model not only offers data support for water resource management, flood mitigation, and disaster relief efforts, but also provides decision support for hydrologists.
First report of a major management target species, chironomid Paratanytarsus grimmii (Diptera: Chironomidae) larvae, in drinking water treatment plants (DWTPs) in South Korea
Ensuring the supply of safe and high-quality drinking water can be compromised by the presence of chironomid larvae in drinking water treatment plants (DWTPs), which may contaminate municipal water systems through freshwater resources. Chironomids are dominant species known for their resilience to a broad range of extreme aquatic environments. This study aimed to identify the morphological characteristics and obtain genetic information of the chironomid Paratanytarsus grimmii found in the water intake source and freshwater resource of DWTPs in Korea, highlighting the potential possibility of a parthenogenetic chironomid outbreak within DWTP networks. The distribution of chironomid larvae at the water intake source site (DY) of the Danyang DWTP and the freshwater resource (ND) of the Nakdong River was investigated. A total of 180 chironomid individuals, encompassing three subfamilies and six species from six 6 genera were identified at the DY site, with Procladius nigriventris being the dominant species. At the ND site, fifty chironomid individuals, encompassing two subfamilies and six species from six genera, were identified, with Cricotopus sylvestris being the dominant species. The morphological characteristics of the head capsule, mentum, mandible, and antennae of six P. grimmii larvae collected from the DY and ND sites were characterized. DNA barcoding and phylogenetic analysis revealed distinct mitochondrial diversities between the P. grimmii larvae from DY and those from ND. These results provide crucial information for the morphological identification and DNA barcoding of the key management target chironomid P. grimmii larvae, which can be used to detect the occurrence of this chironomid species in DWTPs.
Protocol for extracting flow hydrograph shape metrics for use in time-series flood hydrology analysis
The shape characteristics of flow hydrographs hold essential information for understanding, monitoring and assessing changes in flow and flood hydrology at reach and catchment scales. However, the analysis of individual hydrographs is time consuming, making the analysis of hundreds or thousands of them unachievable. A method or protocol is needed to ensure that the datasets being generated, and the metrics produced, have been consistently derived and validated. In this lab protocol, we present workflows in Python for extracting flow hydrographs with any available temporal resolution from any Open Access or publicly available gauging station records. The workflow identifies morphologically-defined flow and flood types (i.e. in-channel fresh, high flow and overbank flood) and uses them to classify hydrographs. It then calculates several at-a-station and upstream-to-downstream hydrograph shape metrics including kurtosis, skewness, peak hydrograph stage, peak arrival time, rate-of-rise, peak-to-peak travel time, flood wave celerity, flood peak attenuation, and flood wave attenuation index. Some metrics require GIS-derived data, such as catchment area and upstream-to-downstream channel distance between gauges. The output dataset provides quantified hydrograph shape metrics which can be used to track changes in flow and flood hydrographs over time, or to characterise the flow and flood hydrology of catchments and regions. The workflows are flexible enough to allow for additional hydrograph shape indicators to be added or swapped out, or to use a different hydrograph classification method that suits local conditions. The protocol could be considered a change detection tool to identify where changes in hydrology are occurring and where to target more sophisticated modelling exercises to explain the changes detected. We demonstrate the workflow using 117 Open Access gauging station records that are available for coastal rivers of New South Wales (NSW), Australia.
The impact of de-familization on green innovation: Evidence from SRDI family firms in China
Green innovation is essential for sustainable development, especially in China’s Specialized-Refined-Differentiated-Innovative (SRDI) enterprises. Family-owned SRDI firms, in particular, have attracted attention due to their de-familization strategies and their influence on green innovation. Our study analyzes panel data from 2016 to 2021 for listed SRDI family firms to investigate how de-familization in management rights and ownership impacts green innovation. Using socio-emotional wealth (SEW) theory and a fixed-effects model, we find that de-familization significantly negatively affects green innovation, with corporate governance serving as a mediating factor. Digital transformation moderates these negative effects, while market concentration exacerbates them. These impacts are more pronounced in firms before being designated as "Little Giants," those receiving higher government subsidies, those located in eastern regions, or those not classified as major polluters. This research provides actionable insights for SRDI family firms to strategically manage de-familization, optimize resource allocation, implement customized governance strategies, and promote sustainable growth.
Measuring women’s empowerment in aquaculture in northwestern Bangladesh using a project level women’s empowerment in fisheries index (pro-WEFI)
Gender equality and women’s empowerment have been increasingly emphasised in food production systems, including fisheries and aquaculture. Accurate assessment and understanding of the state, progress and changes in women’s empowerment in the sub-sectors is required. We applied the project level Women’s Empowerment in Fisheries and Aquaculture Index (pro-WEFI), which is based on the project-level women’s empowerment in agriculture index (pro-WEAI) to standardize the measurement of women’s agency and empowerment in fisheries and aquaculture. Drawing on a survey conducted in north-western Bangladesh, we examined quantitative pro-WEFI data collected from 217 households engaged in aquaculture. Only 33% of the women and 48% of the men in the sample achieved empowerment in aquaculture, attaining scores of 0.75 and above. The mean disempowerment score (1-3DE) revealed that both women and men failed to achieve adequacy on average in nearly 28% of the indicators. Nearly 40% of the dual adult households did not attain gender parity with women achieving lower adequacy scores than men from the same household. Women’s disempowerment was primarily driven by lack of autonomy in their use of income (18.5%), inability to visit important locations (17.4%), and inadequate access to and decision making on financial services (13.4%). Our findings emphasize the significance of conducting comprehensive assessments of women’s empowerment in aquaculture initiatives and its various domains and indicators inform the development of targeted and effective interventions. By identifying domains where gender inequality is most pronounced, projects can better design interventions to create targeted impacts in critical areas.
Enhancing semantical text understanding with fine-tuned large language models: A case study on Quora Question Pair duplicate identification
Semantical text understanding holds significant importance in natural language processing (NLP). Numerous datasets, such as Quora Question Pairs (QQP), have been devised for this purpose. In our previous study, we developed a Siamese Convolutional Neural Network (S-CNN) that achieved an F1 score of 82.02% (95% C.I.: 81.83%-82.20%). Given the growing attention toward large language models (LLMs) like ChatGPT, we aimed to explore their effectiveness in text similarity tasks. In this research, we leveraged 5 pretrained LLMs, conducted various fine-tuning approaches (prompt engineering, n-shot learning, and supervised learning using the low-rank adaptation [LoRA]), and compared their performance using F1 score. To ensure a fair comparison, we followed our previous study’s design and dataset by employing a 10-fold cross-validation for supervised model training and evaluation. Additionally, we conducted a secondary study by introducing a recent larger LLM with 70B parameters and comparing it with the 7B model using the GLUE benchmark, and both models were finetuned with the corpus. The fine-tuned LLaMA model with 7B parameters (qLLaMA_LoRA-7B) using 100,000 QQP corpus yielded the best results, achieving an F1 score of 84.9% (95% C.I.: 84.13%-85.67%), which outperformed the Alpaca_LoRA-65B (finetuned based on LLaMA-65B) (F1: 64.98% [64.72%-65.25%]; P<0.01) and had a 3% improvement compared to our previously published best model, S-CNN. The finetuned LLaMA3.1-70B (qLLaMA3.1_LoRA-70B) with 70B parameters (F1: 74.4%) outperformed the qLLaMA_LoRA-7B (F1: 71.9%) using the GLUE benchmark. The study demonstrated an effective LLM finetuning framework, which highlights the importance of finetuning LLMs for improved performance. Our task-specific supervised finetuning demonstrated improved LLM performance compared to larger pretrained models with or without n-shot learning; moreover, finetuning a larger LLM further improved performance compared to finetuning a smaller LLM. Our LLM-based finetuning framework may potentially improve various document similarity tasks, such as matching resumes with job descriptions, recommending subject-matter experts, or identifying potential reviewers for grant proposals or manuscript submissions.
Impact of capacity building through learning, training, and coaching on agricultural innovation
Successful innovation requires employees to have intellectual and technical capacity. This study explored the effects of capacity building through educational learning, organizational training, and coaching on agricultural innovation. A sample of 142 operational-level agriculture scientists working within a public sector agricultural research organisation in Zimbabwe. Six key informants were also consulted. Descriptive statistics, factor analysis, principal component analysis (PCA), correlation analysis, and binary logistic regression analysis were employed. Results showed significant positive relationships between the variables stating that operational scientists receive adequate academic study opportunities from the research organisation and the one stating that they have relevant academic qualifications to help drive agriculture innovation (r = 0.421***). This reflects that once capacitated through educational learning, the employees gain the confidence and ability to innovate, which could be attributed to improved intelligence quotient (IQ). A significant positive relationship was also observed between the variables, stating that research employees required further training and that improving training would significantly improve innovation (r = 0.47***). Lastly, the variables, stating that direct supervisors offer adequate guidance to stimulate innovation positively correlated to the one stating that coaching has helped improve the operational staff’s innovativeness (P = 0.493***). This implies that efforts being put in by supervisors significantly contribute to innovation. Budgetary constraints were the leading challenge mentioned by 90.1% of the sample. Notably, there is a critical need to improve physical training workshops, exposure visits, and short courses to enhance innovativeness, as revealed by more than 70% of the respondents. The study also suggests cost-effective strategies to enhance capacity building and consequently stimulate innovation.
FRESH extrusion 3D printing of type-1 collagen hydrogels photocrosslinked using ruthenium
The extrusion bioprinting of collagen material has many applications relevant to tissue engineering and regenerative medicine. Freeform Reversible Embedding of Suspended Hydrogels (FRESH) technology is capable of 3D printing collagen material with the specifications and details needed for precise tissue guidance, a crucial requirement for effective tissue repair. While FRESH has shown repeated success and reliability for extrusion printing, the mechanical properties of completed collagen prints can be improved further by post-print crosslinking methodologies. Photoinitiator-based crosslinking methods are simple and have proven effective in strengthening protein-based materials. The ruthenium and sodium persulfate photoinitiator system (Ru(bpy)3/SPS) has been suggested as an effective crosslinking method for collagen materials. Herein, we describe the procedure our group has developed to combine extrusion-based 3D printing of type-1 collagen using FRESH technology with Ru(bpy)3/SPS photoinitiated crosslinking methods to improve the strength and stability of 3D printed collagen structures. Mechanical testing and cell biocompatibility assessments were performed to investigate the impact of Ru(bpy)3/SPS photoinitiated crosslinking and highlight the potential limitations of this method. These results demonstrate a significant improvement in the compressive strength of type-1 collagen samples as the Ru(bpy)3/SPS concentration increases. Additionally, type-1 collagen samples crosslinked with up to 1/10 mM Ru(bpy)3/SPS support PC12 cell viability over a period of 7 days. The primary limitations that were observed and described in detail in this protocol are: the FRESH slurry preparation, printing environment, extrusion printer hardware, and quality of the ruthenium reagent.
Synthesising enablers and barriers to hepatocellular carcinoma surveillance—A systematic review of qualitative findings
Background & aims This systematic literature review of qualitative findings aims to identify the perceived barriers and enablers for hepatocellular carcinoma (HCC) surveillance from patient and clinician perspectives. Methods A systematic search of databases using key term combinations with the following inclusion criteria: 1) qualitative and quantitative (survey) studies exploring barriers and enablers of HCC surveillance, and 2) qualitative and quantitative (survey) studies exploring barriers and enablers of enagagement in clinical care for patients with cirrhosis and/or viral hepatitis. Results The search returned 445 citations: 371 did not meet the study criteria and were excluded. 74 studies proceeded to full-text review, leading to 21 included studies (15 studies from searching with a further six studies from citation review) progressing to data extraction by two independent reviewers. Results from studies exploring patients’ perspectives reinforce that barriers are experienced by patients across different health settings, cultures, and regions. Logistical barriers including costs and transportation, and knowledge/awareness barriers were commonly identified. Studies that included clinician perspectives highlighted the need for healthcare provider education and system-level interventions to optimize HCC surveillance uptake in clinical practice. Conclusion These findings highlight the critical need for interventions that enable engagement in HCC surveillance in health services.
Antibiotic consumption and utilization at a large tertiary care level hospital in Uganda: A point prevalence survey
Introduction Effective antimicrobial stewardship programs require data on antimicrobial consumption (AMC) and utilization (AMU) to guide interventions. However, such data is often scarce in low-resource settings. We describe the consumption and utilization of antibiotics at a large tertiary-level hospital in Uganda. Methods In this cross-sectional study at Kiruddu National Referral Hospital, we analyzed medicine delivery records for the period July 2021 to June 2022, accessed on 12/08/2022, to extract AMC data expressed as defined daily doses (DDDs) per 1000 inhabitants per day (DID). We used the WHO point prevalence survey (PPS) to analyze AMU data with a systematic sampling of outpatient department (OPD) for a period between June and August 2022 and selected all inpatient department (IPD) patients admitted before 8:00 AM on 27/11/2022. AMU data was analyzed as the proportion of individual antibiotic prescriptions, indications for prescriptions, and compliance with the national treatment guidelines. Both AMC and AMU data were categorized by the WHO AWaRe (access, watch, and reserve) criteria. Results In the year 2021–2022, a total of 6.05 DID of antibiotics were consumed comprising 3.61 DID (59.6%) access, 2.44 DID (40.3%) watch, and 0.003 (0.1%) reserve antibiotics. The most consumed antibiotics comprised penicillin (1.61 DID, 26.7%), cephalosporins (1.51, 25%), and imidazole (1.10 DID, 18.1%). A total of 119/211 (56%) patients in the OPD and 99/172 (57.5%) patients in the IPD were prescribed antibiotics. Of the 158 OPD antibiotic prescriptions, 73 (46.2%) were access, 72 (45.6%) were watch, 0 (0%) were reserve, and 13 (8.2%) were unclassified antibiotics. Of the 162 IPD antibiotic prescriptions, 62 (38.3%) were access, 88 (54.3%) were watch, 01 (0.6%) was reserve, and 11 (6.8%) were unclassified antibiotics. Indications for antibiotic prescriptions in the OPD comprised respiratory tract infections (53, 38.1%), urinary tract infections (34, 26.6%), gastrointestinal infections (20, 14.4%), sepsis (17, 12.2%), and medical prophylaxis (12, 8.7%). The indications for antibiotic prescriptions in the IPD comprised sepsis (28.2%), respiratory tract infections (18.3%), burn wounds (14.1%), and gastrointestinal infections (14.1%). Conclusion Prescription of watch antibiotics in both OPD and IPD hospital settings was high. Establishment of robust antimicrobial stewardship measures could help improve the rational prescription of antibiotics.
Measuring social, economic, policy, and health system determinants of maternal health and survival: An urgent global priority
In 2015, the World Health Organization (WHO) released global targets and strategies for reducing maternal mortality in the Sustainable Development Goal (SDG) period developed through broad stakeholder consultations. The targets and strategies identified in the “Strategies toward Ending Preventable Maternal Mortality (EPMM)” report are grounded in a systemic and human rights approach to maternal health and aim to address the broad spectrum of key social, political, economic, and health system determinants of maternal health and survival, as exemplified by 11 Key Themes. These upstream determinants of maternal survival are not well represented in maternal health measurement efforts, which tend to focus on service delivery. Thus, work was undertaken to develop a core set of maternal health indicators that could drive progress toward achieving the recommendations laid out in the EPMM Strategies that identified a menu of 25 indicators and 7 standard stratifiers related to the legal and policy environment, accountability mechanisms, inequities in access and quality, and empowerment of women, girls, families, and communities. Measurement experts have called for more research to ensure that indicators for monitoring maternal health, including its upstream determinants, are valid, which is critical if such measures are to be effective for driving and tracking progress toward ending preventable maternal deaths. This paper describes nine research reports emanating from seven discrete research studies to validate ten indicators in India, Ghana and Argentina that are compiled in a PLOS Collection with the aim of illustrating the breadth of the validation work, extracting some unifying themes and common findings, and discussing the implications for policy and practice they suggest.
Anatomical variations in Eurasian wolf (Canis lupus lupus) (Carnivora: Canidae) of the salivary glands: A histological and histochemical investigation
The study involved a gross anatomical description of the parotid gland, mandibular gland, monostomatic sublingual gland, polystomatic sublingual gland, and zygomatic gland in 12 adult Eurasian wolves (Canis lupus lupus) (wild free-ranging individuals and their zoo counterparts), including their morphometry and microscopic evaluation using hematoxylin & eosin, mucicarmine, azan trichrome, PAS, AB pH 1.0, AB pH 2.5; AB pH 2.5/PAS, and HDI stainings. Topographically, the salivary glands were located similarly to those of other terrestrial Carnivora. The parotid gland in all wolves had a shape similar to a trapezoid (four angles). The parotid duct opened onto the fourth upper premolar (P4). The parotid gland was a branched alveolar complex that produced serous secretion. In captive specimens, the mandibular gland was a complex branched tubular gland producing mucous secretion, while in free-ranging wolves it was a branched tubuloalveolar gland producing mucoserous secretion. The monostomatic sublingual gland in free-ranging wolves was a complex branched tubuloalveolar gland that produced seromucous secretion, while in captive wolves, it revealed a mucoserous secretion character. The polystomatic sublingual gland consisted of several independent packets (from 6–7 to 7–8) and was a complex branched tubuloalveolar gland with seromucous secretion. The zygomatic duct opened onto the last upper molar tooth (M3), and this gland was a complex branched tubular gland producing mucous secretion. The anatomical and histological similarities between the salivary glands of the oral cavity in captive and free-ranging wolves, compared to other terrestrial carnivores, provide valuable insights for veterinary treatments and understanding pathological conditions. These findings emphasize the need for further research on diverse populations of wolves and related species within the Canidae family to better understand the influence of diet on salivary gland morphology.
A hybrid unsupervised machine learning model with spectral clustering and semi-supervised support vector machine for credit risk assessment
In credit risk assessment, unsupervised classification techniques can be introduced to reduce human resource expenses and expedite decision-making. Despite the efficacy of unsupervised learning methods in handling unlabeled datasets, their performance remains limited owing to challenges such as imbalanced data, local optima, and parameter adjustment complexities. Thus, this paper introduces a novel hybrid unsupervised classification method, named the two-stage hybrid system with spectral clustering and semi-supervised support vector machine (TSC-SVM), which effectively addresses the unsupervised imbalance problem in credit risk assessment by targeting global optimal solutions. Furthermore, a multi-view combined unsupervised method is designed to thoroughly mine data and enhance the robustness of label predictions. This method mitigates discrepancies in prediction outcomes from three distinct perspectives. The effectiveness, efficiency, and robustness of the proposed TSC-SVM model are demonstrated through various real-world applications. The proposed algorithm is anticipated to expand the customer base for financial institutions while reducing economic losses.
Synergistic effects of fly ash and graphene oxide composites at high temperatures and prediction using ANN and RSM approach
Characterization of indigenous chicken phenotypes in Liban Jawi District, Ethiopia: A qualitative and quantitative analysis
Indigenous chickens play a crucial role in the livelihoods of smallholder farmers in rural Ethiopia. This study aimed to phenotypically characterize indigenous chickens in the Liban Jawi district, focusing on measurements of phenotypic characteristics. The multi-stage sampling method selected 192 households with at least two mature indigenous chickens from 2,166 households, resulting in the sampling of 224 chickens (138 females and 86 males) for phenotypic characterization. Qualitative trait analysis revealed that male chickens exhibited a plain head shape (54.7%), single combs (39.5%), red feather plumage (26.7%), yellow shanks (54.7%), red earlobes (51.6%), and white skin (68.6%). Female chickens showed single combs (75.4%), red earlobes (51.4%), yellow shanks (50.1%), and brown mottled feathers (23.9%), with red (23.1%) and black (9.4%) feathers observed. Quantitatively, cocks had an average body weight of 1.46±0.02 kg, while hens weighed 1.21±0.01 kg. Cocks exhibited larger body dimensions, with significant measurements in body length (38.05±0.26 cm) and wingspan (38.56±0.13 cm). For hens, the average body length was 31.55±0.33 cm. Shank length emerged as a moderate predictor of body weight for both sexes (r = 0.45 for hens and r = 0.44 for cocks), indicating it should be combined with other factors for accurate assessments. Cluster and multivariate analysis revealed distinct phenotypic groupings among the indigenous chickens, highlighting significant variations in both qualitative and quantitative traits. This suggests the potential for improvement through selective breeding at the community level, influenced by unique environmental conditions and practices. These findings provide valuable insights into the phenotypic characterization of indigenous chickens, serving as a foundation for future breeding programs and conservation efforts aimed at enhancing productivity and preserving genetic diversity. Further molecular-level characterization is recommended to validate the current phenotypic results.