Remembrance activities can support the Culture of Care(CoC)in Laboratory Animal Science(LAS)not only by promoting a culture of respect,gratitude and thankfulness for animal life but also by helping the emotional proce...Remembrance activities can support the Culture of Care(CoC)in Laboratory Animal Science(LAS)not only by promoting a culture of respect,gratitude and thankfulness for animal life but also by helping the emotional processing and healing of lab animal researchers and animal facility staff.Even though remembrance activities are practiced in many parts of the world,we did not come across any reported cases in Sri Lanka before 2022.Therefore,here,we report on the various remembrance activities and practices observed within our local scientific community.展开更多
Cancer continues to pose a formidable challenge in global health,with conventional treatments such as chemotherapy and radiotherapy often resulting in severe toxicities that significantly degrade patients’quality of ...Cancer continues to pose a formidable challenge in global health,with conventional treatments such as chemotherapy and radiotherapy often resulting in severe toxicities that significantly degrade patients’quality of life and restrict therapeutic outcomes.Addressing this pressing issue,this review presents a thorough and systematic analysis of innovative and emerging strategies designed to minimize the toxicity induced by treatment,while maintaining or even enhancing antitumor efficacy.The focus is on six promising therapeutic approaches:combination therapies utilizing natural bioactive products,molecularly targeted therapies,immunotherapies,nanotechnology-mediated drug delivery systems,adjunct traditional Chinese medicine interventions,and low-dose spatiotemporally concerted regimens.Each approach employs unique mechanisms—such as enhanced targeting precision,immune system activation,tumor microenvironment reprogramming,and multi-component synergistic effects—to mitigate damage to normal tissues and reduce systemic adverse reactions.Despite promising preclinical and clinical advancements,several challenges persist,including drug resistance,high economic costs,a lack of reliable predictive biomarkers,and complexities in clinical translation and regulatory approval.Looking ahead,the incorporation of artificial intelligence,multi-omics profiling,and novel biomimetic nanotechnologies offers unprecedented opportunities for developing highly personalized,low-toxicity treatment frameworks.This review highlights a fundamental shift in oncology towards precision medicine that balances efficacy with safety,demonstrating the transformative potential of these strategies in shaping the future of cancer therapy and enhancing patient care globally.展开更多
African drylands occupied 19.6 million km~2(46%of the total global area)and 525 million people.Soil erosion models are useful for assessing the impact of soil erosion in the dryland areas.This review provides an asses...African drylands occupied 19.6 million km~2(46%of the total global area)and 525 million people.Soil erosion models are useful for assessing the impact of soil erosion in the dryland areas.This review provides an assessment of soil erosion/deposition models and soil conservation practices,which are supportive for mitigating the impact of soil erosion and maintaining soil health and soil functional services for food security in African drylands.The theories of soil erosion models and soil conservation practices provide advanced ways to understand the detailed impact of soil erosion and management solutions.The paper reviews a set of useful soil erosion models and traditional conservation practices,which can control soil erosion and enhance dryland farming systems in Africa.Soil erosion models are classified into three categories:empirical,conceptual,and physical.Soil conservation practices include reduced tillage,advanced cover crops,mechanical structures(barriers made of stones/gravel/vegetation),advanced mechanical roller-crimper technique,mixed cropping,intercropping,crop rotation systems,terracing techniques,and land modification techniques.These conservation practices are effective in controlling soil erosion,reducing soil damage,improving soil health and quality,enhancing soil fertility,and ensuring food security.The existing assessment suggests that understanding the theories of soil erosion models and soil conservation practices is a first step towards addressing soil erosion problems in African drylands.展开更多
Debate over the benefits and harms of icing acute muscle injuries remains unresolved.Some contend that ice is ineffective or even harmful,while others promote cryotherapy as a universal remedy.Centrists,often academic...Debate over the benefits and harms of icing acute muscle injuries remains unresolved.Some contend that ice is ineffective or even harmful,while others promote cryotherapy as a universal remedy.Centrists,often academics,call for more high-quality randomized controlled trials(RCTs)to resolve the issue.This viewpoint reframes the debate around 3 key points:first,although ice produces analgesia,evidence for sustained pain relief,beyond the immediate post-treatment period.展开更多
Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal ...Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal alignment,temporal consistency,and robust handling of noisy or incomplete inputs across multiple modalities.We propose Multi Agent-Chain of Thought(CoT),a novel multi-agent chain-of-thought reasoning framework where specialized agents for text,vision,and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms.Our architecture incorporates self-reflection modules,conflict resolution protocols,and dynamic rationale alignment to enhance consistency,factual accuracy,and user engagement.The framework employs a hierarchical attention mechanism with cross-modal fusion and implements adaptive reasoning depth based on dialogue complexity.Comprehensive evaluations on Situated Interactive Multi-Modal Conversations(SIMMC)2.0,VisDial v1.0,and newly introduced challenging scenarios demonstrate statistically significant improvements in grounding accuracy(p<0.01),chain-of-thought interpretability,and robustness to adversarial inputs compared to state-of-the-art monolithic transformer baselines and existing multi-agent approaches.展开更多
A key pathological feature of Parkinson’s disease(PD)is that lysosomes are overwhelmed with cellular materials that need to be degraded and cleared.While the build-up of protein is characteristic of neurodegenerative...A key pathological feature of Parkinson’s disease(PD)is that lysosomes are overwhelmed with cellular materials that need to be degraded and cleared.While the build-up of protein is characteristic of neurodegenerative diseases such as PD and Alzheimer’s disease(AD)and is thought to reflect lysosome dysfunction,lipid accumulation may also contribute to and be indicative of severe lysosomal dysfunction.Much is known about the detrimental effects of glucosylceramide accumulation in PD lysosomes.展开更多
We study the split common solution problem with multiple output sets for monotone operator equations in Hilbert spaces.To solve this problem,we propose two new parallel algorithms.We establish a weak convergence theor...We study the split common solution problem with multiple output sets for monotone operator equations in Hilbert spaces.To solve this problem,we propose two new parallel algorithms.We establish a weak convergence theorem for the first and a strong convergence theorem for the second.展开更多
Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted featur...Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems.In this study,we propose a hybrid model,BertGCN,that integrates BERT-based contextual embedding with Graph Convolutional Networks(GCNs)to identify anomalies in raw system logs,thereby eliminating the need for log parsing.TheBERT module captures semantic representations of log messages,while the GCN models the structural relationships among log entries through a text-based graph.This combination enables BertGCN to capture both the contextual and semantic characteristics of log data.BertGCN showed excellent performance on the HDFS and BGL datasets,demonstrating its effectiveness and resilience in detecting anomalies.Compared to multiple baselines,our proposed BertGCN showed improved precision,recall,and F1 scores.展开更多
It is known that correlation does not imply causality.Some relationships identified in the analysis of data are coincidental or unknown,and some are produced by real-world causality of the situation,which is problemat...It is known that correlation does not imply causality.Some relationships identified in the analysis of data are coincidental or unknown,and some are produced by real-world causality of the situation,which is problematic,since there is a need to differentiate between these two scenarios.Until recently,the proper−semantic−causality of the relationship could have been determined only by human experts from the area of expertise of the studied data.This has changed with the advance of large language models,which are often utilized as surrogates for such human experts,making the process automated and readily available to all data analysts.This motivates the main objective of this work,which is to introduce the design and implementation of a large language model-based semantic causality evaluator based on correlation analysis,together with its visual analysis model called Causal heatmap.After the implementation itself,the model is evaluated from the point of view of the quality of the visual model,from the point of view of the quality of causal evaluation based on large language models,and from the point of view of comparative analysis,while the results reached in the study highlight the usability of large language models in the task and the potential of the proposed approach in the analysis of unknown datasets.The results of the experimental evaluation demonstrate the usefulness of the Causal heatmap method,supported by the evident highlighting of interesting relationships,while suppressing irrelevant ones.展开更多
The primary role of the gastrointestinal tract in broiler chickens is nutrient assimilation,with transporter proteins facilitating the uptake of amino acids,peptides,monosaccharides,fatty acids,and minerals across the...The primary role of the gastrointestinal tract in broiler chickens is nutrient assimilation,with transporter proteins facilitating the uptake of amino acids,peptides,monosaccharides,fatty acids,and minerals across the intestinal epithelium.Among these nutrient transporters,members of the solute carrier family are particularly important,and gene expression analyses targeting these transporters have provided informative insights into how birds adapt to diverse dietary,environmental,and physiological challenges to maintain nutrient homeostasis.These transporters are expressed either at the brush border membrane,where they facilitate the absorption of nutrients from the gut lumen into enterocytes,or at the basolateral membrane,where they mediate the transfer of nutrients from the enterocytes into the bloodstream.The expression of these transporters is influenced by a range of factors,including bird age,sex,intestinal segment,dietary substrate availability and source,as well as external stressors such as heat stress and pathogen exposure.While upregulation of transporter genes often suggests an enhanced capacity for nutrient uptake,it does not always correlate with improved growth performance,due to compensatory physiological responses and fluctuations in nutrient bioavailability.Understanding the regulation and functional dynamics of nutrient transporters presents valuable opportunities to develop targeted dietary and management strategies aimed at optimizing nutrient utilization and improving bird performance.This review summarizes current knowledge on the classification,function,and regulation of key nutrient transporters in broilers,highlights factors influencing their expression,and explores their implications for nutrition and production efficiency.展开更多
The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issu...The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issue is the formalisation of extracting meaning from information.Meaning emerges through a three-stage interpretative process,where the spectrum of possible interpretations is collapsed into a singular outcome by a particular context.However,this approach currently lacks practical grounding.In this research,we developed a model based on contexts,which applies interpretation principles to the visual information to address this gap.The field of computer vision and object recognition has progressed essentially with artificial neural networks,but these models struggle with geometrically transformed images,such as those that are rotated or shifted,limiting their robustness in real-world applications.Various approaches have been proposed to address this problem.Some of them(Hu moments,spatial transformers,capsule networks,attention and memory mechanisms)share a conceptual connection with the contextual model(CM)discussed in this study.This paper investigates whether CM principles are applicable for interpreting rotated images from the MNIST and Fashion MNIST datasets.The model was implemented in the Rust programming language.It consists of a contextual module and a convolutional neural network(CNN).The CMwas trained on the rotated Mono Icons dataset,which is significantly different from the testing datasets.The CNN module was trained on the original MNIST and Fashion MNIST datasets for interpretation recognition.As a result,the CM was able to recognise the original datasets but encountered rotated images only during testing.The findings show that the model effectively interpreted transformed images by considering them in all available contexts and restoring their original form.This provides a practical foundation for further development of the contextual hypothesis and its relation to theAGI domain.展开更多
The aim of this research was to synthesize a new totally bio wood adhesive entailing the use of oxidized starch(OST),urea,and oxidized lignin(OL).For this reason,non-modified(L)and oxidized lignin(OL)at different cont...The aim of this research was to synthesize a new totally bio wood adhesive entailing the use of oxidized starch(OST),urea,and oxidized lignin(OL).For this reason,non-modified(L)and oxidized lignin(OL)at different contents(20%,30%,and 40%)were used to prepare the starch-urea-lignin(SUL)and starch-urea-oxidized lignin(SUOL)resin.Sodium persulfate(SPS)as oxidizer was employed to oxidize both starch and lignin.Urea was just used as a low cost and effective crosslinker in the resin composition.The properties of the synthesized resins and the plywood panels bonded with themweremeasured according to relevant standards.The viscosity and gel time of the SUOL resins containing oxidized lignin are respectively higher and faster than for non-modified lignin(SUL).The lignin phenolic hydroxyl groups(-OH)proportion was markedly increased by oxidation as shown by Fourier Transform Infrared(FTIR)spectrometry.The molecular mass and the polydispersity of the lignin did also decrease by its oxidization pretreatment.DSC analysis showed a decrease of the glass transition temperature of the lignin(Tg)due to its oxidation.The thermal analysis of the oxidized lignin SUOL resin also showed that it had a lower peak temperature than the SUL equivalent non-modified lignin resin.The plywood panels bonded with oxidized lignin gave acceptable bending modulus,bending strength,peak temperature by thermal analysis and dry shear strength as well as a better plywood dimensional stability when used in the SUOL formulation.The synthesized SUOL adhesive is a lignin-derived,totally bio,no-aldehyde added,inexpensive resin applicable to bond plywood.展开更多
Accurately recognizing driver distraction is critical for preventing traffic accidents,yet current detection models face two persistent challenges.First,distractions are often fine-grained,involving subtle cues such a...Accurately recognizing driver distraction is critical for preventing traffic accidents,yet current detection models face two persistent challenges.First,distractions are often fine-grained,involving subtle cues such as brief eye closures or partial yawns,which are easily missed by conventional detectors.Second,in real-world scenarios,drivers frequently exhibit overlapping behaviors,such as simultaneously holding a cup,closing their eyes,and yawning,leading tomultiple detection boxes and degradedmodel performance.Existing approaches fail to robustly address these complexities,resulting in limited reliability in safety critical applications.To overcome these pain points,we propose YOLO-Drive,a novel framework that enhances YOLO-based driver monitoring with EfficientViM and Polarized Spectral–Spatial Attention(PSSA)modules.Efficient ViMprovides lightweight yet powerful global–local feature extraction,enabling accurate recognition of subtle driver states.PSSA further amplifies discriminative features across spatial and spectral domains,ensuring robust separation of concurrent distraction cues.By explicitly modeling fine-grained and overlapping behaviors,our approach delivers significant improvements in both precision and robustness.Extensive experiments on benchmark driver distraction datasets demonstrate that YOLO-Drive consistently out-performs stateof-the-art models,achieving higher detection accuracy while maintaining real-time efficiency.These results validate YOLO-Drive as a practical and reliable solution for advanced driver monitoring systems,addressing long-standing challenges of subtle cue recognition and multi-cue distraction detection.展开更多
Non-O1/non-O139 Vibrio(V.)cholerae(NOVC)has emerged as a potential pathogen in patients with compromised health conditions[1].We report the whole genome sequencing(WGS)of a rare NOVC sepsis isolate(GenBank Accession:G...Non-O1/non-O139 Vibrio(V.)cholerae(NOVC)has emerged as a potential pathogen in patients with compromised health conditions[1].We report the whole genome sequencing(WGS)of a rare NOVC sepsis isolate(GenBank Accession:GCF_051906115.1)from an 89-year-old male admitted to the Intensive Care Unit(ICU)with septic shock(lactate 6.61 mmol/L)digestive illness.展开更多
The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integra...The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integrates transformer-based models(RoBERTa)and large language models(LLMs)(GPT-OSS 120B,LLaMA3.370B,and Qwen332B)to enhance smishing detection performance significantly.To mitigate class imbalance,we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques.Our system employs a duallayer voting mechanism:weighted majority voting among LLMs and a final ensemble vote to classify messages as ham,spam,or smishing.Experimental results show an average accuracy improvement from 96%to 98.5%compared to the best standalone transformer,and from 93%to 98.5%when compared to LLMs across datasets.Furthermore,we present a real-time,user-friendly application to operationalize our detection model for practical use.PhishNet demonstrates superior scalability,usability,and detection accuracy,filling critical gaps in current smishing detection methodologies.展开更多
Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.Howev...Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.展开更多
The Moroccan populations of Alnus glutinosa(L.)Gaertn.(Betulaceae)They are located at the southern limit of the species'distribution and are represented by tetraploid cytotypes.Assessing phenotypic variability in ...The Moroccan populations of Alnus glutinosa(L.)Gaertn.(Betulaceae)They are located at the southern limit of the species'distribution and are represented by tetraploid cytotypes.Assessing phenotypic variability in reproductive traits is crucial for understanding the persistence,evolution,and range dynamics of plant populations.However,no previous studies have analyzed the relative importance of variability in explaining inter-or intra-population differences in reproductive traits.To address this gap,we investigated phenotypic variation in reproductive organs by examining 10 traits in 3.600 male catkins,3.600 female catkins,and seeds from 12 populations across the Moroccan Rif Mountains.Our results highlighted the significance of inter-population variability.However,we found that the contribution of within-tree variation to total phenotypic variability was greater than that of both inter-and intra-population variation.Principal component analysis(PCA)revealed a phenotypic gradient among populations,primarily driven by female catkin size,though this gradient was not associated with geographic conditions.This finding was further supported by Mantel test results,which showed no correlation between phenotypic variability and population conditions.These findings have important implications for the genetic improvement,conservation,and resource management of Alnus glutinosa in the future.展开更多
This paper presents an automated imaging-to-CAD reconstruction system that combines telecentric vision and deep learning for high-accuracy digital reconstruction of printed circuit boards(PCBs).The framework integrate...This paper presents an automated imaging-to-CAD reconstruction system that combines telecentric vision and deep learning for high-accuracy digital reconstruction of printed circuit boards(PCBs).The framework integrates a telecentric camera with a Cartesian scanning platform to capture distortion-free,high-resolution PCB images,which are stitched into a single orthographic composite.A YOLO-based detection model,trained on a dataset of 270 PCB images across 23 component classes with data augmentation,identifies and localizes electronic components with a mean average precision of 0.932.Detected components are automatically matched to corresponding 3D CAD models from a part library and assembled within a Fusion 360 environment,producing a 3D digital replica.Experimental results show a similarity score of 0.894 and dimensional deviations below 2%,outperforming both SensoPart image measurement and manual vernier methods.The proposed approach bridges optical metrology and CAD automation,providing a scalable solution for AI-assisted reverse engineering,digital archiving,and intelligent manufacturing.展开更多
Soil bacteria are integral to ecosystem functioning,significantly contributing to nutrients cycling and organic matter decomposition,and enhancing soil structure.This research considered the composition and dynamics o...Soil bacteria are integral to ecosystem functioning,significantly contributing to nutrients cycling and organic matter decomposition,and enhancing soil structure.This research considered the composition and dynamics of soil bacterial communities under different vegetation types(native Quercus brantii Lindl.and Amygdalus scoparia Spach,and non-native Pinus eldarica Medw.and Cupressus arizonica Greene.)in Zagros mountain area of Iran.This study involved a comparative analysis of soil culturable heterotrophic bacterial communities in spring(wet season)and summer(dry season)to clarify the effects of seasonal changes and vegetation on the dynamics of soil microorganisms.Soil samples were randomly collected under the canopies of various tree species and a control area,yielding a total of 48 composite samples analyzed for bacterial composition.Results indicated that 11 Gram-negative(e.g.,Citrobacter freundii,Enterobacter cloacae,Escherichia coli,Klebsiella oxytoca,Klebsiella pneumoniae,etc.)and 2 Gram-positive(Staphylococcus epidermidis and Staphylococcus aureus)bacteria were identified,showing significant seasonal variation.Specifically,53.85%of bacterial species were common to both seasons,with notable shifts in community composition observed between spring and summer,highlighting a higher abundance of Gram-negative species in spring.Bacterial community structure was significantly influenced by vegetation type,with various tree species shaping distinct microbial assemblages.Moreover,Pearson's correlations revealed that soil properties,particularly pH,phosphorus,and moisture content,were critical drivers of bacterial diversity and abundance.Our findings underscore the dynamic nature of soil bacterial communities in response to seasonal and vegetation changes,emphasizing the importance of repeated temporal sampling for accurate assessments of microbial diversity.Understanding these microbial dynamics is essential for improving soil management strategies and enhancing ecosystem resilience,particularly in arid and semi-arid areas where environmental fluctuations play a pivotal role.This research not only confirms our hypotheses but also enhances our understanding of soil biogeochemical processes and informs future vegetation management practices.展开更多
There is increasing interest in developing reduced-crude protein(CP)diets for broiler chickens because their commercial adoption would generate a diverse range of advantages that would enhance the sustainability of th...There is increasing interest in developing reduced-crude protein(CP)diets for broiler chickens because their commercial adoption would generate a diverse range of advantages that would enhance the sustainability of the chickenmeat industry.However,the development of reduced-CP broiler diets is proving to be not straightforward,particularly when dietary CP reductions exceed 30 g/kg.The capacity of broilers to accommodate dietary CP reductions when offered maize-based diets is superior to their counterparts offered wheat-based diets.Numerous factors could be contributing to this difference but have yet to be identified with certainty.Maize-based,reduced-CP diets characteristically support better weight gains and efficiencies of feed conversion than wheat-based diets,but this better growth performance is associated with increased fat deposition,monitored as heavier relative abdominal fat-pad weights.This is an intriguing dichotomy.Insulin is a powerful anabolic hormone in mammalian species capable of promoting fat deposition,protein accretion and growth,but the importance of insulin in avian species is usually dismissed.This is because broiler chickens are considered both hyperglycaemic and resistant to insulin.However,the likelihood is that young broiler chickens are more sensitive to insulin than is generally recognised and the anabolic properties of insulin may be contributing to the diverse responses observed between maize and wheat in the context of reduced-CP diets.Dietary CP reductions may trigger increased plasma ammonia concentrations and metabolic acidosis,but both factors can influence insulin secretion and insulin resistance.Maize has slower rates of starch digestion and glucose absorption than wheat and it has been suggested that this generates a more sustained insulin release resulting in increased weight gains and fat deposition.If so,this could be driving the differences generated by the feed grain selected as the basis of reduced-CP diets.The intention of this review is to explore this proposition because if the causal factors of the differences between maize and wheat can be identified the development and acceptance of reduced-CP broiler diets should be accelerated.展开更多
文摘Remembrance activities can support the Culture of Care(CoC)in Laboratory Animal Science(LAS)not only by promoting a culture of respect,gratitude and thankfulness for animal life but also by helping the emotional processing and healing of lab animal researchers and animal facility staff.Even though remembrance activities are practiced in many parts of the world,we did not come across any reported cases in Sri Lanka before 2022.Therefore,here,we report on the various remembrance activities and practices observed within our local scientific community.
文摘Cancer continues to pose a formidable challenge in global health,with conventional treatments such as chemotherapy and radiotherapy often resulting in severe toxicities that significantly degrade patients’quality of life and restrict therapeutic outcomes.Addressing this pressing issue,this review presents a thorough and systematic analysis of innovative and emerging strategies designed to minimize the toxicity induced by treatment,while maintaining or even enhancing antitumor efficacy.The focus is on six promising therapeutic approaches:combination therapies utilizing natural bioactive products,molecularly targeted therapies,immunotherapies,nanotechnology-mediated drug delivery systems,adjunct traditional Chinese medicine interventions,and low-dose spatiotemporally concerted regimens.Each approach employs unique mechanisms—such as enhanced targeting precision,immune system activation,tumor microenvironment reprogramming,and multi-component synergistic effects—to mitigate damage to normal tissues and reduce systemic adverse reactions.Despite promising preclinical and clinical advancements,several challenges persist,including drug resistance,high economic costs,a lack of reliable predictive biomarkers,and complexities in clinical translation and regulatory approval.Looking ahead,the incorporation of artificial intelligence,multi-omics profiling,and novel biomimetic nanotechnologies offers unprecedented opportunities for developing highly personalized,low-toxicity treatment frameworks.This review highlights a fundamental shift in oncology towards precision medicine that balances efficacy with safety,demonstrating the transformative potential of these strategies in shaping the future of cancer therapy and enhancing patient care globally.
基金part of the project on soil and water management approved and supported by the Department of Agronomy,Nasarawa State University,Keffi(NSUK),Nigeria。
文摘African drylands occupied 19.6 million km~2(46%of the total global area)and 525 million people.Soil erosion models are useful for assessing the impact of soil erosion in the dryland areas.This review provides an assessment of soil erosion/deposition models and soil conservation practices,which are supportive for mitigating the impact of soil erosion and maintaining soil health and soil functional services for food security in African drylands.The theories of soil erosion models and soil conservation practices provide advanced ways to understand the detailed impact of soil erosion and management solutions.The paper reviews a set of useful soil erosion models and traditional conservation practices,which can control soil erosion and enhance dryland farming systems in Africa.Soil erosion models are classified into three categories:empirical,conceptual,and physical.Soil conservation practices include reduced tillage,advanced cover crops,mechanical structures(barriers made of stones/gravel/vegetation),advanced mechanical roller-crimper technique,mixed cropping,intercropping,crop rotation systems,terracing techniques,and land modification techniques.These conservation practices are effective in controlling soil erosion,reducing soil damage,improving soil health and quality,enhancing soil fertility,and ensuring food security.The existing assessment suggests that understanding the theories of soil erosion models and soil conservation practices is a first step towards addressing soil erosion problems in African drylands.
文摘Debate over the benefits and harms of icing acute muscle injuries remains unresolved.Some contend that ice is ineffective or even harmful,while others promote cryotherapy as a universal remedy.Centrists,often academics,call for more high-quality randomized controlled trials(RCTs)to resolve the issue.This viewpoint reframes the debate around 3 key points:first,although ice produces analgesia,evidence for sustained pain relief,beyond the immediate post-treatment period.
文摘Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal alignment,temporal consistency,and robust handling of noisy or incomplete inputs across multiple modalities.We propose Multi Agent-Chain of Thought(CoT),a novel multi-agent chain-of-thought reasoning framework where specialized agents for text,vision,and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms.Our architecture incorporates self-reflection modules,conflict resolution protocols,and dynamic rationale alignment to enhance consistency,factual accuracy,and user engagement.The framework employs a hierarchical attention mechanism with cross-modal fusion and implements adaptive reasoning depth based on dialogue complexity.Comprehensive evaluations on Situated Interactive Multi-Modal Conversations(SIMMC)2.0,VisDial v1.0,and newly introduced challenging scenarios demonstrate statistically significant improvements in grounding accuracy(p<0.01),chain-of-thought interpretability,and robustness to adversarial inputs compared to state-of-the-art monolithic transformer baselines and existing multi-agent approaches.
文摘A key pathological feature of Parkinson’s disease(PD)is that lysosomes are overwhelmed with cellular materials that need to be degraded and cleared.While the build-up of protein is characteristic of neurodegenerative diseases such as PD and Alzheimer’s disease(AD)and is thought to reflect lysosome dysfunction,lipid accumulation may also contribute to and be indicative of severe lysosomal dysfunction.Much is known about the detrimental effects of glucosylceramide accumulation in PD lysosomes.
基金supported by the Science and Technology Fund of TNU-Thai Nguyen University of Science.
文摘We study the split common solution problem with multiple output sets for monotone operator equations in Hilbert spaces.To solve this problem,we propose two new parallel algorithms.We establish a weak convergence theorem for the first and a strong convergence theorem for the second.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant no.(GPIP:1074-612-2024).
文摘Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems.In this study,we propose a hybrid model,BertGCN,that integrates BERT-based contextual embedding with Graph Convolutional Networks(GCNs)to identify anomalies in raw system logs,thereby eliminating the need for log parsing.TheBERT module captures semantic representations of log messages,while the GCN models the structural relationships among log entries through a text-based graph.This combination enables BertGCN to capture both the contextual and semantic characteristics of log data.BertGCN showed excellent performance on the HDFS and BGL datasets,demonstrating its effectiveness and resilience in detecting anomalies.Compared to multiple baselines,our proposed BertGCN showed improved precision,recall,and F1 scores.
基金supported by University Grant Agency of Matej Bel University in Banská Bystrica project number UGA-14-PDS-2025.
文摘It is known that correlation does not imply causality.Some relationships identified in the analysis of data are coincidental or unknown,and some are produced by real-world causality of the situation,which is problematic,since there is a need to differentiate between these two scenarios.Until recently,the proper−semantic−causality of the relationship could have been determined only by human experts from the area of expertise of the studied data.This has changed with the advance of large language models,which are often utilized as surrogates for such human experts,making the process automated and readily available to all data analysts.This motivates the main objective of this work,which is to introduce the design and implementation of a large language model-based semantic causality evaluator based on correlation analysis,together with its visual analysis model called Causal heatmap.After the implementation itself,the model is evaluated from the point of view of the quality of the visual model,from the point of view of the quality of causal evaluation based on large language models,and from the point of view of comparative analysis,while the results reached in the study highlight the usability of large language models in the task and the potential of the proposed approach in the analysis of unknown datasets.The results of the experimental evaluation demonstrate the usefulness of the Causal heatmap method,supported by the evident highlighting of interesting relationships,while suppressing irrelevant ones.
文摘The primary role of the gastrointestinal tract in broiler chickens is nutrient assimilation,with transporter proteins facilitating the uptake of amino acids,peptides,monosaccharides,fatty acids,and minerals across the intestinal epithelium.Among these nutrient transporters,members of the solute carrier family are particularly important,and gene expression analyses targeting these transporters have provided informative insights into how birds adapt to diverse dietary,environmental,and physiological challenges to maintain nutrient homeostasis.These transporters are expressed either at the brush border membrane,where they facilitate the absorption of nutrients from the gut lumen into enterocytes,or at the basolateral membrane,where they mediate the transfer of nutrients from the enterocytes into the bloodstream.The expression of these transporters is influenced by a range of factors,including bird age,sex,intestinal segment,dietary substrate availability and source,as well as external stressors such as heat stress and pathogen exposure.While upregulation of transporter genes often suggests an enhanced capacity for nutrient uptake,it does not always correlate with improved growth performance,due to compensatory physiological responses and fluctuations in nutrient bioavailability.Understanding the regulation and functional dynamics of nutrient transporters presents valuable opportunities to develop targeted dietary and management strategies aimed at optimizing nutrient utilization and improving bird performance.This review summarizes current knowledge on the classification,function,and regulation of key nutrient transporters in broilers,highlights factors influencing their expression,and explores their implications for nutrition and production efficiency.
文摘The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issue is the formalisation of extracting meaning from information.Meaning emerges through a three-stage interpretative process,where the spectrum of possible interpretations is collapsed into a singular outcome by a particular context.However,this approach currently lacks practical grounding.In this research,we developed a model based on contexts,which applies interpretation principles to the visual information to address this gap.The field of computer vision and object recognition has progressed essentially with artificial neural networks,but these models struggle with geometrically transformed images,such as those that are rotated or shifted,limiting their robustness in real-world applications.Various approaches have been proposed to address this problem.Some of them(Hu moments,spatial transformers,capsule networks,attention and memory mechanisms)share a conceptual connection with the contextual model(CM)discussed in this study.This paper investigates whether CM principles are applicable for interpreting rotated images from the MNIST and Fashion MNIST datasets.The model was implemented in the Rust programming language.It consists of a contextual module and a convolutional neural network(CNN).The CMwas trained on the rotated Mono Icons dataset,which is significantly different from the testing datasets.The CNN module was trained on the original MNIST and Fashion MNIST datasets for interpretation recognition.As a result,the CM was able to recognise the original datasets but encountered rotated images only during testing.The findings show that the model effectively interpreted transformed images by considering them in all available contexts and restoring their original form.This provides a practical foundation for further development of the contextual hypothesis and its relation to theAGI domain.
基金funded by Semnan University,research grant No.226/1403/T140211.
文摘The aim of this research was to synthesize a new totally bio wood adhesive entailing the use of oxidized starch(OST),urea,and oxidized lignin(OL).For this reason,non-modified(L)and oxidized lignin(OL)at different contents(20%,30%,and 40%)were used to prepare the starch-urea-lignin(SUL)and starch-urea-oxidized lignin(SUOL)resin.Sodium persulfate(SPS)as oxidizer was employed to oxidize both starch and lignin.Urea was just used as a low cost and effective crosslinker in the resin composition.The properties of the synthesized resins and the plywood panels bonded with themweremeasured according to relevant standards.The viscosity and gel time of the SUOL resins containing oxidized lignin are respectively higher and faster than for non-modified lignin(SUL).The lignin phenolic hydroxyl groups(-OH)proportion was markedly increased by oxidation as shown by Fourier Transform Infrared(FTIR)spectrometry.The molecular mass and the polydispersity of the lignin did also decrease by its oxidization pretreatment.DSC analysis showed a decrease of the glass transition temperature of the lignin(Tg)due to its oxidation.The thermal analysis of the oxidized lignin SUOL resin also showed that it had a lower peak temperature than the SUL equivalent non-modified lignin resin.The plywood panels bonded with oxidized lignin gave acceptable bending modulus,bending strength,peak temperature by thermal analysis and dry shear strength as well as a better plywood dimensional stability when used in the SUOL formulation.The synthesized SUOL adhesive is a lignin-derived,totally bio,no-aldehyde added,inexpensive resin applicable to bond plywood.
基金funded by the Guangzhou Development Zone Science and Technology Project(2023GH02)the University of Macao(MYRG2022-00271-FST)research grants by the Science and Technology Development Fund of Macao(0032/2022/A)and(0019/2025/RIB1).
文摘Accurately recognizing driver distraction is critical for preventing traffic accidents,yet current detection models face two persistent challenges.First,distractions are often fine-grained,involving subtle cues such as brief eye closures or partial yawns,which are easily missed by conventional detectors.Second,in real-world scenarios,drivers frequently exhibit overlapping behaviors,such as simultaneously holding a cup,closing their eyes,and yawning,leading tomultiple detection boxes and degradedmodel performance.Existing approaches fail to robustly address these complexities,resulting in limited reliability in safety critical applications.To overcome these pain points,we propose YOLO-Drive,a novel framework that enhances YOLO-based driver monitoring with EfficientViM and Polarized Spectral–Spatial Attention(PSSA)modules.Efficient ViMprovides lightweight yet powerful global–local feature extraction,enabling accurate recognition of subtle driver states.PSSA further amplifies discriminative features across spatial and spectral domains,ensuring robust separation of concurrent distraction cues.By explicitly modeling fine-grained and overlapping behaviors,our approach delivers significant improvements in both precision and robustness.Extensive experiments on benchmark driver distraction datasets demonstrate that YOLO-Drive consistently out-performs stateof-the-art models,achieving higher detection accuracy while maintaining real-time efficiency.These results validate YOLO-Drive as a practical and reliable solution for advanced driver monitoring systems,addressing long-standing challenges of subtle cue recognition and multi-cue distraction detection.
文摘Non-O1/non-O139 Vibrio(V.)cholerae(NOVC)has emerged as a potential pathogen in patients with compromised health conditions[1].We report the whole genome sequencing(WGS)of a rare NOVC sepsis isolate(GenBank Accession:GCF_051906115.1)from an 89-year-old male admitted to the Intensive Care Unit(ICU)with septic shock(lactate 6.61 mmol/L)digestive illness.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under Grant No.(GPIP:1074-612-2024).
文摘The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integrates transformer-based models(RoBERTa)and large language models(LLMs)(GPT-OSS 120B,LLaMA3.370B,and Qwen332B)to enhance smishing detection performance significantly.To mitigate class imbalance,we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques.Our system employs a duallayer voting mechanism:weighted majority voting among LLMs and a final ensemble vote to classify messages as ham,spam,or smishing.Experimental results show an average accuracy improvement from 96%to 98.5%compared to the best standalone transformer,and from 93%to 98.5%when compared to LLMs across datasets.Furthermore,we present a real-time,user-friendly application to operationalize our detection model for practical use.PhishNet demonstrates superior scalability,usability,and detection accuracy,filling critical gaps in current smishing detection methodologies.
文摘Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.
文摘The Moroccan populations of Alnus glutinosa(L.)Gaertn.(Betulaceae)They are located at the southern limit of the species'distribution and are represented by tetraploid cytotypes.Assessing phenotypic variability in reproductive traits is crucial for understanding the persistence,evolution,and range dynamics of plant populations.However,no previous studies have analyzed the relative importance of variability in explaining inter-or intra-population differences in reproductive traits.To address this gap,we investigated phenotypic variation in reproductive organs by examining 10 traits in 3.600 male catkins,3.600 female catkins,and seeds from 12 populations across the Moroccan Rif Mountains.Our results highlighted the significance of inter-population variability.However,we found that the contribution of within-tree variation to total phenotypic variability was greater than that of both inter-and intra-population variation.Principal component analysis(PCA)revealed a phenotypic gradient among populations,primarily driven by female catkin size,though this gradient was not associated with geographic conditions.This finding was further supported by Mantel test results,which showed no correlation between phenotypic variability and population conditions.These findings have important implications for the genetic improvement,conservation,and resource management of Alnus glutinosa in the future.
基金funded by the Ratchadaphiseksomphot Endowment Fund,Chulalongkorn University grant number:RSF-AnH-69-06-21-01.
文摘This paper presents an automated imaging-to-CAD reconstruction system that combines telecentric vision and deep learning for high-accuracy digital reconstruction of printed circuit boards(PCBs).The framework integrates a telecentric camera with a Cartesian scanning platform to capture distortion-free,high-resolution PCB images,which are stitched into a single orthographic composite.A YOLO-based detection model,trained on a dataset of 270 PCB images across 23 component classes with data augmentation,identifies and localizes electronic components with a mean average precision of 0.932.Detected components are automatically matched to corresponding 3D CAD models from a part library and assembled within a Fusion 360 environment,producing a 3D digital replica.Experimental results show a similarity score of 0.894 and dimensional deviations below 2%,outperforming both SensoPart image measurement and manual vernier methods.The proposed approach bridges optical metrology and CAD automation,providing a scalable solution for AI-assisted reverse engineering,digital archiving,and intelligent manufacturing.
文摘Soil bacteria are integral to ecosystem functioning,significantly contributing to nutrients cycling and organic matter decomposition,and enhancing soil structure.This research considered the composition and dynamics of soil bacterial communities under different vegetation types(native Quercus brantii Lindl.and Amygdalus scoparia Spach,and non-native Pinus eldarica Medw.and Cupressus arizonica Greene.)in Zagros mountain area of Iran.This study involved a comparative analysis of soil culturable heterotrophic bacterial communities in spring(wet season)and summer(dry season)to clarify the effects of seasonal changes and vegetation on the dynamics of soil microorganisms.Soil samples were randomly collected under the canopies of various tree species and a control area,yielding a total of 48 composite samples analyzed for bacterial composition.Results indicated that 11 Gram-negative(e.g.,Citrobacter freundii,Enterobacter cloacae,Escherichia coli,Klebsiella oxytoca,Klebsiella pneumoniae,etc.)and 2 Gram-positive(Staphylococcus epidermidis and Staphylococcus aureus)bacteria were identified,showing significant seasonal variation.Specifically,53.85%of bacterial species were common to both seasons,with notable shifts in community composition observed between spring and summer,highlighting a higher abundance of Gram-negative species in spring.Bacterial community structure was significantly influenced by vegetation type,with various tree species shaping distinct microbial assemblages.Moreover,Pearson's correlations revealed that soil properties,particularly pH,phosphorus,and moisture content,were critical drivers of bacterial diversity and abundance.Our findings underscore the dynamic nature of soil bacterial communities in response to seasonal and vegetation changes,emphasizing the importance of repeated temporal sampling for accurate assessments of microbial diversity.Understanding these microbial dynamics is essential for improving soil management strategies and enhancing ecosystem resilience,particularly in arid and semi-arid areas where environmental fluctuations play a pivotal role.This research not only confirms our hypotheses but also enhances our understanding of soil biogeochemical processes and informs future vegetation management practices.
基金Australian Research Council Linkage project(LP220100292)for partially supporting Mengzhu Wang’s PhD scholarship。
文摘There is increasing interest in developing reduced-crude protein(CP)diets for broiler chickens because their commercial adoption would generate a diverse range of advantages that would enhance the sustainability of the chickenmeat industry.However,the development of reduced-CP broiler diets is proving to be not straightforward,particularly when dietary CP reductions exceed 30 g/kg.The capacity of broilers to accommodate dietary CP reductions when offered maize-based diets is superior to their counterparts offered wheat-based diets.Numerous factors could be contributing to this difference but have yet to be identified with certainty.Maize-based,reduced-CP diets characteristically support better weight gains and efficiencies of feed conversion than wheat-based diets,but this better growth performance is associated with increased fat deposition,monitored as heavier relative abdominal fat-pad weights.This is an intriguing dichotomy.Insulin is a powerful anabolic hormone in mammalian species capable of promoting fat deposition,protein accretion and growth,but the importance of insulin in avian species is usually dismissed.This is because broiler chickens are considered both hyperglycaemic and resistant to insulin.However,the likelihood is that young broiler chickens are more sensitive to insulin than is generally recognised and the anabolic properties of insulin may be contributing to the diverse responses observed between maize and wheat in the context of reduced-CP diets.Dietary CP reductions may trigger increased plasma ammonia concentrations and metabolic acidosis,but both factors can influence insulin secretion and insulin resistance.Maize has slower rates of starch digestion and glucose absorption than wheat and it has been suggested that this generates a more sustained insulin release resulting in increased weight gains and fat deposition.If so,this could be driving the differences generated by the feed grain selected as the basis of reduced-CP diets.The intention of this review is to explore this proposition because if the causal factors of the differences between maize and wheat can be identified the development and acceptance of reduced-CP broiler diets should be accelerated.
