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Introducción. Nuevos horizontes en la investigación bandística. En I. M.ª Ayala-Herrera y V. Sánchez-López (eds.), Vientos en el horizonte: Prácticas, discursos y proyección de las bandas de música en el mundo ibérico (pp. 5-11).
No full textLas bandas de música constituyen un elemento singular e ineludible del paisaje sonoro de pueblos y ciudades, profundamente arraigado en la memoria individual y colectiva. Esta antología de estudios se inscribe en la reciente línea historiográfica que reivindica el fenómeno bandístico desde la musicología iberoamericana. Con un total de veinte capítulos firmados por especialistas españoles e internacionales, la obra se organiza en seis secciones temáticas que proponen itinerarios de lectura tan variados como sugerentes: I) Entre el Antiguo y el Nuevo Régimen; II) Guerra y propaganda; III) Educación, filantropía y redención; IV) Movilidad, redes y proyección social; V) Territorios de ultramar; y VI) Perspectivas analíticas. La confluencia de fuentes inéditas y enfoques metodológicos diversos hace de las bandas un cruce fecundo entre música y sociedad, entre el texto y el contexto. Vientos en el horizonte supone una aportación significativa en el ámbito de los estudios bandísticos, ampliando los horizontes de la investigación musical y cultural
Eco-Innovation adoption in the olive oil sector: A sustainability analysis
Get PDFThe growing concern among consumers for more environmentally friendly products is forcing companies to align their strat-egies accordingly. In recent years, eco-innovation has emerged as a key business strategy to address these new expectations.Considering the significance of the olive oil sector in Spain, this study seeks to evaluate the extent to which eco-innovationshave been adopted in the primary processing segment of the olive oil sector. To this end, a CAWI survey with CATI follow-upwas conducted among olive oil mills in the autonomous region of Andalusia. The final sample consisted of 164 cases, drawnfrom a population of 800. The findings indicate that the level of eco-innovation in the olive oil industry is low. Furthermore, theresults enabled the categorization of olive oil mills into four groups based on their environmental actions: (1) environmentallyresponsible mills, (2) eco-innovative mills, (3) passive mills, and (4) footprint mills. These results have implications for academia,businesses, and institutions alike.This work was supported by Consejería de Economía y Conocimiento, de la Junta de Andalucía-FEDER, 1264899-FEDER-UJA
The relationship between tourism, economic growth and environmental sustainability: empirical evidence from major tourist destinations
Get PDFThe positive impact of tourism on the economy of countries and their increasing commitment to this practice has led to numerous studies in the literature, from the emergence of the TLGH at the beginning of the century to the present day. However, destinations are no strangers to the negative externalities generated by tourism. These negative externalities respond to a harmful impact on the environment, which, together with the increasing concern of governments for the environment, has also aroused the interest of the research field. Therefore, this study, using a technique that is not widely used, focuses on the analysis of tourism, economic growth and the environment in those countries that have been part of the top 10 tourism countries during the period 1995-2023. The analysis reveals a variety of causality results. Finally, policy recommendations for improvement are made on the basis of these results
Léxico textil en dotes dieciochescas destinadas a doncellas pobres y huérfanas
Get PDFEste trabajo se ha desarrollado gracias al proyecto de innovación docente «Historia del léxico español: documentación, metodología, estudio y divulgación», concedido dentro del Plan de Innovación y Mejora Docente PIMED-UJA 2019-2023 (convocatoria PIMED-UJA 2021).La presente investigación toma como objeto de estudio un documento hallado en el Archivo Municipal de Córdoba (legajo SF/C 00002-008), fechado en 1771. Se trata del «Expediente sobre repartimiento de dotes a doncellas pobres y huérfanas que faciliten su matrimonio, con motivo del parto de la Princesa de Asturias». Partimos, pues, de un acontecimiento histórico concreto: el nacimiento de Carlos Clemente, el primogénito de los príncipes de Asturias, el 19 de septiembre de 1771, hecho que dio origen a la Real y Distinguida Orden Española de Carlos III. En concreto, nos centramos en el análisis de léxico contenido en las tres dotes financiadas y, particularmente, nos interesan las voces adscritas al ámbito textil, que suponen un 71,6% del corpus (por ejemplo, bretaña, florete, gante, morlés, tripe, entre otros). Nuestra finalidad es doble: en general, dar a conocer fondos archivísticos inéditos y, en particular, listar el léxico documentado y contrastarlo con fuentes lexicográficas de la época. En definitiva, pretendemos que este trabajo contribuya a un mejor conocimiento del léxico español, concretamente de la parcela técnica que nos ocupa, la textil
Speed Sensorless Control for a Six-Phase Induction Machine based on a Sliding Mode Observer
Get PDFThis paper presents the application of a sliding mode observer for speed sensorless control of a six-phase induction machine. The use of nonlinear sliding mode techniques yields acceptable performance for both low- and high-speed motor operations over a wide speed range. The effectiveness and accuracy of the developed sensorless scheme are verified by experimental results, which demonstrate the system’s performance under various operating conditions. These results demonstrate the advantages of the proposal as a valid alternative to the conventional method, which uses a mechanical speed sensor for multiphase machines. Additionally, the sensorless approach can also serve as a redundant backup in the event of mechanical sensor failure, thereby increasing the reliability of the overall drive system
Collection of Intrusion Detection Systems (IDS) Datasets "Preprocessed"
No full textThis collection IDS-5-FCV-datasets (5-fold stratified cross-validation applied to several IDS-related datasets) contains the most relevant standardized datasets for IDS studies. The repository contains pre-processed, normalized datasets that can be used in further studies. The collection is only one in the literature and original datasets are described below:
KDD Cup 99
This is the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-99 The Fifth International Conference on Knowledge Discovery and Data Mining. The competition task was to build a network intrusion detector, a predictive model capable of distinguishing between malicious connections and benign normal connections. This database contains a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment. The corrected version of this dataset includes over 300.000 examples in total and four malicious classes: DoS, Probe, URL and U2R, as well as benign connections. Each example contains 42 attributes that mostly refer to connection information, such as error rates, number of consecutive attempts, protocols and several flags, among others.
The original dataset can be found here: https://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
UNB ISCX 2012
The UNB ISCX 2012 dataset was generated following a systematic approach to generate and provide information about attacks and benign connections, creating a dataset that is modifiable, extensible and reproducible. This dataset is based around profiles which contain detailed descriptions of intrusions and abstract models for protocols, applications or other network entities, generating real traffic for several commonplace web protocols such as HTTP, SMTP and SSH among others. This information is further complemented by multi-stage attack scenarios to generate malicious traffic, completing the dataset. The result is a dataset that contains much more information than any of the previously mentioned ones, including a complete capture of all network traffic and data, including all interactions within and between LANs as we all as the full packet load in PCAP formats, providing a vast amount of information for researchers to use. The tabular data in itself contains only 21 attributes that mostly refer to information about the source and destination, as well as packet sizes and payloads.
The original dataset can be found here: https://www.unb.ca/cic/datasets/ids.html
UNSW-NB15
This dataset was created by the IXIA PerfectStorm tool in the Cyber Range Lab of UNSW Canberra in Australia, aiming to generate a hybrid of real modern-day internet activities combined with synthetic attack behaviors. This dataset includes a total of 9 types of attack: fuzzers, analysis, backdoor, DoS, exploit, generic, reconnaissance, shellcode and worms. Furthermore, each connection includes 49 attributes generated using tools such as Argus and Bro-IDS. In total, there are roughly 2,540,044 examples. These attributes include information about both the source and destination of packet loads, statistical information on their size and contents, information about interpacket arrival time and number of connections that share the same service and address, among others.
The original dataset can be found here: https://research.unsw.edu.au/projects/unsw-nb15-dataset
NSL-KDD
This dataset was created to fix some of the problems detected in the KDD Cup 99 dataset. As such, it follows the same structure, labels and format as this dataset. The main issues it solves are the following:
Redundant records were removed, thus preventing bias towards frequent records.
The number of selected records from each group is inversely proportional to the percentage of records in the original dataset, allowing for a wider range and a more accurate evaluation of different learning techniques.
As a consequence of previous changes, the amount of data that the dataset presents is reasonable, allowing for the use of the complete set instead of a portion of it.
The original dataset can be found here: https://www.unb.ca/cic/datasets/nsl.html
WSN DS
This dataset includes information about data traffic in wireless sensor networks (WSN), which consist of a large number of autonomous sensor nodes distributed in areas of interest to gather data and transmit it to a central node in which it can be processed. Due to their nature, they are highly susceptible to attacks: an attacker can easily be injected into a WSN, and since they are permanently online sending and receiving data, they can be easily shut down using a DoS or DDoS attack. Due to this, IDS research can be applied to WSN easily, allowing to alert sensor nodes in case they are attacked, though it is usually a harder task due to the limited hardware resources that sensor nodes make use of. Regardless of the requirements of sensor nodes, the data they gather and the attacks that they may suffer can provide valuable information when detecting certain attacks. In order to properly represent this data, the WSN DS is created, modelling four types of DoS attacks: Blackhole, Grayhole, Flooding and Scheduling, including 23 features, including the node that was attacked, a timestamp, several flags, and specific information about the messages that are sent and received to and from other nodes.
The original dataset can be found here: https://www.kaggle.com/datasets/bassamkasasbeh1/wsnds
CIC-IDS2017
This dataset contains connection data on attacks that are common nowadays, as well as benign information gathered by the Canadian Institute of Cybersecurity (CIC). This real-world data was obtained by using CICFlowMeter, which is able to obtain web traffic and underlying information such as data flows, origin and destination IP, connection flags and packet flows. The dataset is stored throughout several CSV files, each of which has a specific date assigned. Each date contains information on several specific attacks. Included attacks on the dataset include SSH and FTP Bruteforce, DoS, DDoS, Heartbleed, Web Attacks, infiltrations, and bots. Each example contains 78 attributes and a label that defines it as either benign or malicious, and the specific attack type in the latter case.
The original dataset can be found here: https://www.unb.ca/cic/datasets/ids-2017.html
CIC-CSE-IDS2018
Along the previous dataset, the CIC also created CIC-CSE-IDS2018 in collaboration with the Communications Security Establishment (CSE). This dataset is structured much like the previous one, presenting several attack environments that affect over 400 computers and 30 servers. Logs with traffic information for each of these environments is provided, as well as 80 attributes extracted by using CICFlowMeter. These attributes are the same as the previous dataset, though the connection protocol and timestamp of the connection have been added. Attacks included in this dataset are more limited than in the previous dataset, containing only FTP and SSH Bruteforce and several denial of service attacks.
The original dataset can be found here: https://www.unb.ca/cic/datasets/ids-2018.html
CIC-DDoS2019
The CIC created another dataset focusing on distributed denial of service (DDoS) attacks. This dataset includes malicious data for both reflection-based DDoS and exploitation-based attacks. Reflection-based DDoS allows attackers to keep their identity hidden by using third-party components that seem legitimate. Packets are sent to reflection servers that focus all traffic towards the victim’s IP in an attempt to crash their connection with response packets. These attacks can be made from the transport layer protocols such as UDP, TCP, etc. On the other hand, exploitation-based attacks work much like the previous ones, though the intent is different, as they aim to send a massive amount of packets, such as SYN or UDP, to cause the victim’s connection to be severely slowed down or crash the system by exhausting the available bandwidth. The dataset includes examples of both types of DDoS and also benign traffic. Unlike previous CIC datasets, all data is included in one single file. Used attributes are exactly those of CIC-CSE-IDS2018. Moreover, two labels are present for each example: the first one specifies the type of attack, while the second one determines the type of connection (benign or malicious).
The original dataset can be found here: https://www.unb.ca/cic/datasets/ddos-2019.html
CIC-IoT2023
The last dataset created by the CIC is CIC-IoT2023, acting as a benchmark for the detection of large-scale attacks on IoT environments. This dataset includes 33 attacks in total, which are executed on an IoT topology composed of 105 devices. These attacks are furthermore divided into seven groups: DoS, DDoS, Recon, Web-based attacks, Brute Force, Spoofing and Mirai. Much like the previous CIC datasets, all gathered data is generated by using real connections and attacks on a controlled environment, guaranteeing a high-quality dataset. It includes a total of 46 attributes related to IoT connection data, including flow durations, header lengths, several connection flags and information on several other protocol-dependent parameters.
The original dataset can be found here: https://www.unb.ca/cic/datasets/iotdataset-2023.html
DS2OS
This dataset includes connection traces extracted from the DS2OS IoT environment. It should be noted that the traces that were gathered are extracted from the application layer instead of the network layer, therefore granting different information when compared to that provided by the other presented datasets. As it is based on an IoT network, it contains information associated with different sensors, such as thermometers, light controllers and movement sensors, as well as several smart items such as washing machines and mobile phones.
The original dataset can be found here: https://www.kaggle.com/datasets/francoisxa/ds2ostraffictraces
CIDDS-001
CIDDS, or Coburg Intrusion Detection Data Sets, is a concept to create evaluation datasets for anomaly-based network intrusion detection systems that was started by researchers from Hochschule Coburg in Germany. CIDDS-001 is the first dataset that was created under such a concept, and it emulates a small business environment that is targeted by malicious actors, and it includes Denial of Service, Brute Force attacks, and Portscans that were executed within the networks. The purpose of this dataset is to be as realistic as possible, so that different workers connect to different webs or applications based on their jobs, time of connection is considered to include breaks, and so on. The dataset includes 10 attributes, as well as the class and supporting information for it, such as unique attack IDs, specific attack type, and a description of the attack that provides further information. Other attributes include source and destination IP and port, protocol, start time of the first flow, flow duration, flow size in bytes, flow packets, and flags.
The original dataset can be found here: https://www.hs-coburg.de/forschen/cidds-coburg-intrusion-detection-data-sets/
TON-IoT
Much like the UNB ISCX 2012 dataset, this dataset was created by the UNSW to provide both information and a benchmark that researchers may use to test the behavior of several algorithms and models to detect malicious connections in the context of IoT networks. The dataset contains a vast amount of supplemental information, including information on Windows and Linux systems connected through the network, which were afterwards processed to obtain the final dataset. This dataset includes 45 attributes, which include information about the source and destination, the specific protocol that was used for communication, size and duration of the packet flows and DNS, HTTP and SSL information.
The original dataset can be found here: https://research.unsw.edu.au/projects/toniot-datasets
Kaggle Network Intrusion Dataset
This dataset was generated by simulating a US Air Force military network environment, then simulating several attacks over the resulting network, acquiring the corresponding TCP/IP dump. The resulting dataset has a total of 41 attributes and it presents a binary classification problem, in which each connection is labelled as either normal or anomalous. The information in the dataset includes the duration and size of the packet flows, the protocol used in it, which service it is associated with, several flags, information on both successful and unsuccessful logins and shell access, and server information, among others.
The original dataset can be found here: https://www.kaggle.com/datasets/sampadab17/network-intrusion-detectionThis collection contains the most relevant standardised datasets for studies on intrusion detection systems (IDS).
More specifically, the repository contains datasets that have been pre-processed and normalised using 5-fold stratified cross-validation partitions so that they can be used in subsequent studies. The collection is unique in the literature and will enable real comparisons between models using this collection for all comunity
Optimal D-STATCOM operation in power distribution systems to minimize energy losses and CO2 emissions: a master–slave methodology based on metaheuristic techniques
Get PDFIn this paper, we address the problem of intelligent operation of Distribution Static Synchronous Compensators (D-STATCOMs) in power distribution systems to reduce energy losses and CO2 emissions while improving system operating conditions. In addition, we consider the entire set of constraints inherent in the operation of such networks in an environment with D-STATCOMs. To solve such a problem, we used three master–slave methodologies based on sequential programming methods. In the proposed methodologies, the master stage solves the problem of intelligent D-STATCOM operation using the continuous versions of the Monte Carlo (MC) method, the population-based genetic algorithm (PGA), and the Particle Swarm Optimizer (PSO). The slave stage, for its part, evaluates the solutions proposed by the algorithms to determine their impact on the objective functions and constraints representing the problem. This is accomplished by running an Hourly Power Flow (HPF) based on the method of successive approximations. As test scenarios, we employed the 33- and 69-node radial test systems, considering data on power demand and CO2 emissions reported for the city of Medellín in Colombia (as documented in the literature). Furthermore, a test system was adapted in this work to the demand characteristics of a feeder located in the city of Talca in Chile. This adaptation involved adjusting the conductors and voltage limits to include a test system with variations in power demand due to seasonal changes throughout the year (spring, winter, autumn, and summer). Demand curves were obtained by analyzing data reported by the local network operator, i.e., Compañía General de Electricidad. To assess the robustness and performance of the proposed optimization approach, each scenario was simulated 100 times. The evaluation metrics included average solution quality, standard deviation, and repeatability. Across all scenarios, the PGA consistently outperformed the other methods tested. Specifically, in the 33-node system, the PGA achieved a 24.646% reduction in energy losses and a 0.9109% reduction in CO2 emissions compared to the base case. In the 69-node system, reductions reached 26.0823% in energy losses and 0.9784% in CO2 emissions compared to the base case. Notably, in the case of the Talca feeder—particularly during summer, the most demanding season—the PGA yielded the most significant improvements, reducing energy losses by 33.4902% and CO2 emissions by 1.2805%. Additionally, an uncertainty analysis was conducted to validate the effectiveness and robustness of the proposed optimization methodology under realistic operating variability. A total of 100 randomized demand profiles for both active and reactive power were evaluated. The results demonstrated the scalability and consistent performance of the proposed strategy, confirming its effectiveness under diverse and practical operating conditions
Nitro-fatty acids modulate germination onset through S-nitrosothiol metabolism
Get PDFNitro-fatty acids (NO2-FAs) have emerged as key components of nitric oxide (NO) signaling in eukaryotes. We previously described how nitro-linolenic acid (NO2-Ln), the major NO2-FA detected in plants, regulates S-nitrosoglutathione (GSNO) levels in Arabidopsis (Arabidopsis thaliana). However, the underlying molecular mechanisms remain undefined. Here, we used a combination of physiological, biochemical, and molecular approaches to provide evidence that NO2-Ln modulates S-nitrosothiol (SNO) content through S-nitrosylation of S-nitrosoglutathione reductase1 (GSNOR1) and its impact on germination onset. The aer mutant (a knockout mutant of the alkenal reductase enzyme; AER) exhibits higher NO2-Ln content and lower GSNOR1 transcript levels, reflected by higher SNO content and S-nitrosylated proteins. Given its capacity to release NO, NO2-Ln mediates the S-nitrosylation of GSNOR1, demonstrating that NO2-FAs can indirectly modulate total SNO content in plants. Moreover, the ectopic application of NO2-Ln to dormant seeds enhances germination success similarly to the aer germination rate, which is mediated by the degradation of master regulator ABSCISIC ACID INSENSITIVE 5 (ABI5). Our results establish that NO2-FAs regulate plant development through NO and SNO metabolism and reveal a role of NO2-FAs in plant physiology.This research was funded by European Regional Development Fund (ERDF) grants cofinanced by the Ministry of Science, Innovation and Universities (Projects PID2022-142973NB-I00 and PID2020-117774GA-I00), the Junta de Andalucía (Group of Biochemistry and Cell Signaling in Nitric Oxide, PAIDI BIO286), funding to recruit researchers according to Actions 10 and 9 of the Research Support Plan of the University of Jaén (2019–2020, R.02/10/2020; 2020–2021, R.01/01/2022), a grant for the Recalibration of the Spanish University System (Margarita Salas 2021-2023, R.01/01/2023), and funding from the C2 call of Research Projects of the University of Salamanca (PIC2-2023-08)
Desarrollo y aplicación de metodologías basadas en espectrometría de masas avanzadas para el análisis de contaminantes orgánicos en muestras ambientales
Get PDFRESUMEN EN CASTELLANO
La contaminación ambiental por compuestos químicos representa un desafío global, afectando ecosistemas y la salud humana, siendo crucial su monitoreo. Su detección supone un reto desde el punto de vista analítico debido a que, por lo general, se encuentran a niveles traza y las matrices ambientales suelen contener un elevado número de interferentes que dificultan su detección, requiriendo métodos sensibles y selectivos. Esta Tesis Doctoral aborda el desarrollo de métodos analíticos basados en cromatografía de líquidos acoplada a espectrometría de masas (LC-MS) para la determinación de contaminantes químicos orgánicos en aguas superficiales, suelos y biota de la provincia de Jaén, España. Se han desarrollados estrategias de análisis dirigido y no dirigido para determinar la presencia de pesticidas y contaminantes emergentes en aguas y suelos, y se ha relacionado la presencia de contaminantes orgánicos en sangre de fauna silvestre con los usos del suelo.
RESUMEN EN INGLÉS
Environmental pollution by chemical compounds poses a significant global challenge, impacting ecosystems and human health, making the monitoring of these contaminants essential. Their detection is an analytical challenge because they are generally found at trace levels, and environmental matrices often contain numerous interferents that hinder their identificatificaton, requiring highly sensitive and selective methods. This Doctoral Thesis addresses the development of analytical methods based on
liquid chromatography coupled to mass spectrometry (LC-MS) for the determination of organic chemical contaminants in surface waters, soils, and biota from the province of Jaén, Spain. Targeted and non- targeted analytical strategies have been developed to determine the presence of pesticides and emerging contaminants in water and soil. Furthermore, the presence of organic pollutants in the blood of wildlife bird species has been linked to land use patterns
Effectiveness of an Online Training Program on Brief Tobacco Intervention (BTI) for Nurses: A Quasi-Experimental Study. The E-Learning BTI Project
No full textABSTRACT
Introduction: Smoking is the leading cause of preventable deaths. The training of professionals on brief tobacco interventions
(BTIs) increases the effectiveness of these interventions.
Objective: To assess the effectiveness of an online training program on BTI based on the 5As and 5Rs model in acquiring antitobacco brief advice competencies among nurses.
Method: Quasi-experimental study with a pre-test and post-test design, with a control group and without random assignment. In
the experimental group (EG), online training was provided in three sections: BTI theoretical content and methodology, clinical
scenario videos, and feedback. Each scenario assessed the 5As and 5Rs as a validated instrument (BTI-Prof(C)). The control group
(CG) only assessed the three videos of clinical scenarios. In both groups, competence was measured at the following points in
time: T0 (before the training), T1 (at the end of the training), and T2 (after 90days). The efficacy of the intervention was measured
through a two-way ANOVA, and the variation rate was calculated from T0 to T1 and from T0 to T2.
Results: 236 nurses participated (157 EG; 79 CG). The mean age was 42.9 years, and 76.7% were women. There was a significant
group*time interaction in the three cases, indicating that the online BTI training increases the competence of these professionals
in clinical scenario 1 (F=10.210; p≤0.001; η2=0.081), clinical scenario 2 (F=6.235; p=0.002; η2=0.051), and clinical scenario 3
(F=11.271; p≤0.001; η2=0.090).
Conclusion: A brief, asynchronous, and online intervention using standardized video-based cases is effective in improving
nurses' BTI competence. This type of training can be a useful option for the National Health System as part of a global and continuous strategy for nurses to perform BTI.
Clinical Relevance: An asynchronous online training program provides nurses with standardized, evidence-based tools to implement brief tobacco interventions in routine care, offering a scalable and practical solution to strengthen preventive strategies
in health systems.This work was supported by the Ministry of Health and Families of the Andalusian Regional Government. FPS 2014—Primary Care Research Projects. Reference Number: AP-0210-2019