This paper presents a robust system for real-time object detection and counting in ecological video streams. It is based on the YOLOv8 architecture integrated within a multi-threaded video processing architecture. The system reduces latency and improves throughput by parallelizing object detection and preprocessing tasks. This leads to outperforming traditional single-threaded implementations in continuous video analysis. The system also incorporates dynamic thresholding methods, fine-tuning, and data augmentation to enhance object detection reliability in dynamic natural environments. These mechanisms improve robustness to changing lighting, occlusions, and background complexity, common challenges in outdoor footage. The system is thoroughly evaluated through performance comparisons between multi-threaded and single-threaded implementations, environmental stress tests, and an ablation study. Results demonstrate improved consistency in object detection and counting in dynamic environments, along with significant gains in processing speed. Designed for deployment on lightweight and low-power devices, the system is suitable for remote or resource-constrained settings. While designed for biodiversity monitoring, the approach is applicable to other domains requiring efficient, real-time video analysis in unstructured environments.

Abstract Under many scenarios where resources may be scarce or a good Quality of Service is a requirement, appropriately sizing components and devices is one of the main challenges. New scenarios, such as IoT, mobile cloud computing, mobile edge computing or fog computing, have emerged recently. The ability to design, model and simulate those infrastructures is critical to dimension them correctly. Queuing theory models provide a good approach to understanding how a given architecture would behave for a given set of parameters, thus helping to detect possible bottlenecks and performance issues in advance. This work presents a fog-computing modelling framework based on queuing theory. The proposed framework was used to simulate a given scenario allowing the possibility of adjusting the system by means of user-defined parameters. The results show that the proposed model is a good tool for designing optimal fog architectures regarding QoS requirements. It can also be used to fine-tune the designs to detect possible bottlenecks or improve the performance parameters of the overall environment.

BACKGROUND: Previous works have shown that risk factors for some kinds of cancer depend on people's lifestyle (e.g. rural or urban residence). This article looks into this, seeking relationships between cancer, age group, gender and population in the region of Lleida (Catalonia, Spain) using Multiple Correspondence Analysis (MCA). METHODS: The dataset analysed was made up of 3408 cancer episodes between 2012 and 2014, extracted from the Population-based Cancer Registry (PCR) for Lleida province. The cancers studied were colon and rectal (1059 cases), lung (551 cases), urinary bladder (446 cases), prostate (609 cases) and breast (743 cases). The MCA technique was applied and used to search relationships among the main qualitative features. The basic statistics were the percentage explaining (variance), the inertia and the contribution of each qualitative variable. RESULTS: General outcomes showed a low and moderate contribution of living in rural areas to colorectal and male prostate cancer. Males in urban areas were slightly and heavily affected by lung and urinary bladder cancer respectively. The analysis of each cancer provided additional information. Colorectal cancer greatly affected males aged <60, urban residents aged 70-79, and rural females aged ≥ 80. The impact of lung cancer was high among urban females <60, moderate among males aged 70-79 and high among rural females aged ≥ 80. The results for urinary bladder cancer results were similar to those for lung cancer. Prostate cancer affected both the <60 and ≥ 80 age groups significantly in rural areas. Breast cancer hit the 70-79 group significantly and, somewhat less so, rural females aged ≥ 80. CONCLUSIONS: MCA was a significant help for detecting the contributions of qualitative variables and the associations between them. MCA has proven to be an effective technique for analyzing the incidence of cancer. The outcomes obtained help to corroborate suspected trends, as well as detecting and stimulating new hypotheses about the risk factors associated with a specific area and cancer. These findings will be helpful for encouraging new studies and prevention campaigns to highlight observed singularities.

SUMMARY: EasyModel is a new user-friendly web application that contains ready-for-simulation versions of the BioModels Database, and allows for the intuitive creation of new models. Its main target audience is the experimental biologist and students of bioinformatics or systems biology without programming skills. Expert users can also benefit from it by implementing basic models quickly and downloading the code for further tailoring. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at https://easymodel.udl.cat. Implementation is described in its own section.

The initial diagnosis of rare diseases is difficult because they are infrequent and doctors do not often see or recognize their symptoms. Developing tools to assist in this diagnosis would provide a way to facilitate medical practice in this area. The broader goal of this project is to develop such a tool, which we name rare disease discovery (http://disease-discovery.udl.cat). This tool is designed to identify rare diseases on the basis of a patient's symptoms. To create it, several software entities were designed and integrated. First, a database of symptoms associated with every human rare disease known was designed and implemented. This database was derived from information available from Orphanet. Orphanet provides gold-standard data regarding rare diseases in the world. Second, a user-friendly website was also designed, implemented, and connected to the database. This website connects the users, the database, and the third software entity, a disease prediction engine. Overall, we create an accurate, efficient, and user-friendly diagnosis tool that can be quickly learnt and handled by medical doctors for the prediction of rare diseases.

MOTIVATION: Chemical named entity recognition is used to automatically identify mentions to chemical compounds in text and is the basis for more elaborate information extraction. However, only a small number of applications are freely available to identify such mentions. Particularly challenging and useful is the identification of International Union of Pure and Applied Chemistry (IUPAC) chemical compounds, which due to the complex morphology of IUPAC names requires more advanced techniques than that of brand names. RESULTS: We present CheNER, a tool for automated identification of systematic IUPAC chemical mentions. We evaluated different systems using an established literature corpus to show that CheNER has a superior performance in identifying IUPAC names specifically, and that it makes better use of computational resources. AVAILABILITY AND IMPLEMENTATION: http://metres.udl.cat/index.php/9-download/4-chener, http://chener.bioinfo.cnio.es/

Server clustering is a common design principle employed by many organisations who require high availability, scalability and easier management of their infrastructure. Servers are typically clustered according to the service they provide whether it be the application(s) installed, the role of the server or server accessibility for example. In order to optimize performance, manage load and maintain availability, servers may migrate from one cluster group to another making it difficult for server monitoring tools to continuously monitor these dynamically changing groups. Server monitoring tools are usually statically configured and with any change of group membership requires manual reconfiguration, an unreasonable task to undertake on large-scale cloud infrastructures. In this paper we present the Cloudlet Control and Management System (C2MS), a system for monitoring and controlling dynamic groups of physical or virtual servers within cloud infrastructures. The C2MS extends Ganglia - an open source scalable system performance monitoring tool - by allowing system administrators to define, monitor and modify server groups without the need for server reconfiguration. In turn administrators can easily monitor group and individual server metrics on large-scale dynamic cloud infrastructures where roles of servers may change frequently. Furthermore, we complement group monitoring with a control element allowing administrator-specified actions to be performed over servers within service groups as well as introduce further customized monitoring metrics. This paper outlines the design, implementation and evaluation of the C2MS.

Biblio-MetReS is a single-thread data mining application that facilitates the reconstruction of molecular networks based on automated text mining analysis of published scientific literature. This application is very CPU-intensive, requiring High Performace Computing (HPC). Due to the amount of execution tasks, it can be quite slow. Those tasks are repetitive and consist in mining the information from large sets of scientific documents, a process where the time-cost of the application could be improved through paralellization.

This paper proposes a distributed computing architecture using the P2P paradigm. Our proposal gathers the peers into markets according to their computational resources. Each market is arranged in an N-tree and the trees are linked by a Bruijn graph. The tree topology allows efficient searching of available resources in a specific market, while Bruijn provides good scalability as search complexity does not depend on the number of markets. A Hilbert function is used to arrange markets in one ordered and mono-dimensional space. This way, the proposed architecture exploits the Bruijn and N-tree topologies together with the Hilbert function in a completely distributed, scalable and fault tolerant way. Likewise, the paper describes the main operations involved in the peer management (insertion, maintenance and output of peers) and analyses its reliability. The main parameters associated with our proposal are discussed and tuned by means of simulation. A look-up query mechanism of multiple attributes for exact and approximate queries is also introduced over this architecture. The performance of our proposal was analyzed by means of simulation in relation to the widely used Chord overlay and the Baton algorithm.

BACKGROUND: Parallel T-Coffee (PTC) was the first parallel implementation of the T-Coffee multiple sequence alignment tool. It is based on MPI and RMA mechanisms. Its purpose is to reduce the execution time of the large-scale sequence alignments. It can be run on distributed memory clusters allowing users to align data sets consisting of hundreds of proteins within a reasonable time. However, most of the potential users of this tool are not familiar with the use of grids or supercomputers. RESULTS: In this paper we show how PTC can be easily deployed and controlled on a super computer architecture using a web portal developed using Rapid. Rapid is a tool for efficiently generating standardized portlets for a wide range of applications and the approach described here is generic enough to be applied to other applications, or to deploy PTC on different HPC environments. CONCLUSIONS: The PTC portal allows users to upload a large number of sequences to be aligned by the parallel version of TC that cannot be aligned by a single machine due to memory and execution time constraints. The web portal provides a user-friendly solution.

BACKGROUND: Reconstruction of genes and/or protein networks from automated analysis of the literature is one of the current targets of text mining in biomedical research. Some user-friendly tools already perform this analysis on precompiled databases of abstracts of scientific papers. Other tools allow expert users to elaborate and analyze the full content of a corpus of scientific documents. However, to our knowledge, no user friendly tool that simultaneously analyzes the latest set of scientific documents available on line and reconstructs the set of genes referenced in those documents is available. RESULTS: This article presents such a tool, Biblio-MetReS, and compares its functioning and results to those of other user-friendly applications (iHOP, STRING) that are widely used. Under similar conditions, Biblio-MetReS creates networks that are comparable to those of other user friendly tools. Furthermore, analysis of full text documents provides more complete reconstructions than those that result from using only the abstract of the document. CONCLUSIONS: Literature-based automated network reconstruction is still far from providing complete reconstructions of molecular networks. However, its value as an auxiliary tool is high and it will increase as standards for reporting biological entities and relationships become more widely accepted and enforced. Biblio-MetReS is an application that can be downloaded from http://metres.udl.cat/. It provides an easy to use environment for researchers to reconstruct their networks of interest from an always up to date set of scientific documents.

Peer-to-Peer (P2P) computing, the harnessing of idle compute cycles through the Internet, allows computing resources from ordinary users to be shared in an open access. In this framework, the dynamic resource management offers new research challenges in the domain of distributed computing. This paper proposes a distributed computing architecture using the P2P paradigm based on creating a tree hierarchical topology of dynamic areas of computing resources (peers) in a completely distributed, scalable and fault tolerant way. Likewise, the paper describes the main operations involved in the peer management (insertion, maintenance and removal of peers) and analyzes its reliability. The main parameters associated to our proposal are discussed and tuned by means of simulation.

The computation capacity of the workstations in an open laboratory is enough to execute not only the local workload but some distributed computation. Unfortunately, the local workload introduces much uncertainty into the predictability of the system, which hinders the applicability of the job scheduling strategies. In this work, we introduce an estimation engine into our job scheduling system, termed CISNE. This prediction capacity allows us guarantee some limits to the turnaround time of parallel jobs. With this aim, three different estimation methods have been proposed and implemented in the CISNE system: a simulation tool, a historical system and an integration of both (hybrid). In this framework, we have compared our proposals to representative estimation methods in the literature. Likewise, we have analyzed these estimation methods in relation to different scheduling policies. These results reveal that the hybrid method achieves the best performance due to the fact that it combines the flexibility of a simulator to represent such a dynamic system as a non-dedicated cluster together with the accuracy given by the historical information

Coscheduling remains an open question when parallel jobs are executed in a non-dedicated Cluster system jointly with the local workload. Our efforts are directed towards maximizing parallel efficiency and minimizing impact on the local workload performance in such a systems, by using predictive coscheduling techniques. A coscheduling model for non-dedicated Cluster computing is presented in this paper. Some new coscheduling performance metrics based on this model are defined. A new predictive coscheduling algorithm for this model is also proposed. The performance of our algorithm is analyzed and its performance compared with other coscheduling algorithms in the literature by simulation.