School-based prevention for adolescent Internet addiction: prevention is the key. A systematic literature review

Adolescents’ media use represents a normative need for information, communication, recreation and functionality, yet problematic Internet use has increased. Given the arguably alarming prevalence rates worldwide and the increasingly problematic use of gaming and social media, the need for an integration of prevention efforts appears to be timely. The aim of this systematic literature review is (i) to identify school-based prevention programmes or protocols for Internet Addiction targeting adolescents within the school context and to examine the programmes’ effectiveness, and (ii) to highlight strengths, limitations, and best practices to inform the design of new initiatives, by capitalizing on these studies’ recommendations. The findings of the reviewed studies to date presented mixed outcomes and are in need of further empirical evidence. The current review identified the following needs to be addressed in future designs to: (i) define the clinical status of Internet Addiction more precisely, (ii) use more current psychometrically robust assessment tools for the measurement of effectiveness (based on the most recent empirical developments), (iii) reconsider the main outcome of Internet time reduction as it appears to be problematic, (iv) build methodologically sound evidence-based prevention programmes, (v) focus on skill enhancement and the use of protective and harm-reducing factors, and (vi) include IA as one of the risk behaviours in multi-risk behaviour interventions. These appear to be crucial factors in addressing future research designs and the formulation of new prevention initiatives. Validated findings could then inform promising strategies for IA and gaming prevention in public policy and education

Flatness-Aware Minimization for Domain Generalization

Domain generalization (DG) seeks to learn robust models that generalize well under unknown distribution shifts. As a critical aspect of DG, optimizer selection has not been explored in depth. Currently, most DG methods follow the widely used benchmark, DomainBed, and utilize Adam as the default optimizer for all datasets. However, we reveal that Adam is not necessarily the optimal choice for the majority of current DG methods and datasets. Based on the perspective of loss landscape flatness, we propose a novel approach, Flatness-Aware Minimization for Domain Generalization (FAD), which can efficiently optimize both zeroth-order and first-order flatness simultaneously for DG. We provide theoretical analyses of the FAD's out-of-distribution (OOD) generalization error and convergence. Our experimental results demonstrate the superiority of FAD on various DG datasets. Additionally, we confirm that FAD is capable of discovering flatter optima in comparison to other zeroth-order and first-order flatness-aware optimization methods.Comment: Accepted by ICCV202

Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network

Data-driven methodologies for evaluation and recommendation of energy efficiency measures in buildings. Applications in a big data environment

Tesi en modalitat de compendi de publicacionsIn order to reach the goal set in the Paris agreement of limiting the rise in global average temperature well below 2 ºC compared to pre-industrial levels, massive efforts to reduce global greenhouse gas emissions are required. The building sector is currently responsible for about 28% of total global CO2 emissions, meaning that there is substantial savings potential lying in the correct energy management of buildings and the implementation of renovation strategies. Digital tools and data-driven techniques are rapidly gaining momentum as approaches that are able to harness the large amount of data gathered in the building sector and provide solutions able to reduce the carbon footprint of the built environment. The objective of this doctoral thesis is to investigate the potential of data-driven techniques in different applications aimed at improving energy efficiency in buildings. More specifically, different novel approaches to verify energy savings, characterize consumption patterns, and recommend energy retrofitting strategies are described. The presented methodologies prove to be powerful tools that can produce valuable, actionable insights for energy managers and other stakeholders. Initially, a comprehensive and detailed overview is provided of different state-of-the-art methodologies to quantify energy efficiency savings and to predict the impact of retrofitting strategies in buildings. Strengths and weaknesses of the analyzed approaches are discussed, and guidance is provided to assess the best performing methodology depending on the case in analysis and data available. Among the reviewed approaches there are statistical and machine learning models, Bayesian methods, deterministic approaches, and hybrid techniques combining deterministic and data-driven models. Subsequently, a novel data-driven methodology is proposed to perform measurement and verification calculations, with the main focus on non-residential buildings and facilities. The approach is based on the extraction of frequent consumption profile patterns and on a novel technique able to evaluate the building’s weather dependence. This information is used to design a model that can accurately estimate achieved energy savings at daily scale. The method was tested on two use-cases, one using synthetic data generated using a building energy simulation software and one using monitoring data from three existing buildings in Catalonia. The results obtained with the proposed methodology are compared with the ones provided by a state-of-the-art model, showing accuracy improvement and increased robustness to missing data. The second data-driven tool that developed in this research work is a Bayesian linear regression methodology to calculate hourly energy baseline predictions in non-residential buildings and characterize their consumption patterns. The approach was tested on 1578 non-residential buildings that are part of a large building energy consumption open dataset. The results show that the Bayesian methodology is able to provide accurate baseline estimations with an explainable and intuitive model. Special focus is also given to uncertainty estimations, which are inherently provided by Bayesian techniques and have great importance in risk assessments for energy efficiency projects. Finally, a concept methodology that can be used to recommend and prioritize energy efficiency projects in buildings and facilities is presented. This data-driven approach is based on the comparison of groups of similar buildings and on an algorithm that can map savings obtained with energy renovation strategies to the characteristics of the buildings where they were implemented. Recommendation for implementation of such a methodology in big data building energy management platforms is provided.Para alcanzar el objetivo fijado en el acuerdo de París de limitar el aumento de la temperatura media mundial muy por debajo de los 2 °C con respecto a los niveles preindustriales, es necesario realizar esfuerzos masivos para reducir las emisiones mundiales de gases de efecto invernadero. El sector de la edificación es actualmente responsable de alrededor del 28% de las emisiones totales de CO2 a nivel mundial, lo que significa que existe un potencial de ahorro sustancial en la correcta gestión energética de los edificios y en la aplicación de estrategias de renovación. Las herramientas digitales y las técnicas basadas en datos están ganando rápidamente impulso como enfoques capaces de aprovechar la gran cantidad de datos recopilados en el sector de la edificación y proporcionar soluciones capaces de reducir la huella de carbono del entorno construido. El objetivo de esta tesis doctoral es investigar el potencial de las técnicas basadas en datos en diferentes aplicaciones destinadas a mejorar la eficiencia energética de los edificios. Más concretamente, se describen diferentes enfoques novedosos para verificar el ahorro de energía, caracterizar los patrones de consumo y recomendar estrategias de rehabilitación energética. Las metodologías presentadas demuestran ser poderosas herramientas que pueden producir valiosos conocimientos para los gestores energéticos y otras partes interesadas. En primer lugar, se ofrece una visión general y detallada de las distintas metodologías más avanzadas para cuantificar el ahorro de energía y predecir el impacto de las estrategias de rehabilitación en los edificios. Se discuten los puntos fuertes y débiles de los enfoques analizados y se ofrecen orientaciones para evaluar la metodología más eficaz en función del caso en análisis y de los datos disponibles. Entre los enfoques revisados hay modelos estadísticos y de aprendizaje automático, métodos Bayesianos, enfoques deterministas y técnicas híbridas que combinan modelos deterministas y basados en datos. Posteriormente, se propone una novedosa metodología basada en datos para realizar cálculos de medición y verificación, centrada principalmente en edificios e instalaciones no residenciales. El enfoque se basa en la extracción de patrones de perfiles de consumo frecuentes y en una técnica innovadora capaz de evaluar la dependencia climática del edificio. Esta información se utiliza para diseñar un modelo que puede estimar con precisión el ahorro energético conseguido a escala diaria. El método se ha probado en dos casos de uso, uno con datos sintéticos generados mediante un software de simulación energética de edificios, y otro con datos de monitorización de tres edificios existentes en Cataluña. Los resultados obtenidos con la metodología propuesta se comparan con los proporcionados por un modelo de última generación, mostrando una mejora de la precisión y una mayor robustez ante la falta de datos. La segunda herramienta basada en datos que se desarrolló en este trabajo de investigación es una metodología de regresión lineal Bayesiana para calcular las predicciones de línea base de energía horaria en edificios no residenciales y para caracterizar sus patrones de consumo. El enfoque se probó en 1578 edificios no residenciales que forman parte de un gran conjunto de datos abiertos de consumo energético de edificios. Los resultados muestran que la metodología Bayesiana es capaz de proporcionar estimaciones precisas de la línea de base con un modelo explicable e intuitivo. También se presta especial atención a las estimaciones de incertidumbre, que son inherentes a las técnicas bayesianas y que tienen gran importancia en las evaluaciones de riesgo de los proyectos de eficiencia energética. Por último, se presenta una metodología conceptual que puede utilizarse para recomendar y priorizar proyectos de eficiencia energética en edificios e instalaciones. Este enfoque basado en datos se basa en la comparación de grupos de edificios similares y en un algoritmo que puede asociar los ahorros obtenidos con las estrategias de renovación energética a las características de los edificios en los que se aplicaron. Se recomiendan las aplicaciones de esta metodología en plataformas de gestión energética de edificios de big data.Postprint (published version

Triploid Atlantic salmon and triploid Atlantic salmon × brown trout hybrids have better freshwater and early seawater growth than diploid counterparts

The use of reproductively sterile triploid salmonids would enhance the environmental sustainability of the aquaculture industry by preventing genetic exchange between escapees and wild conspecifics. To this end, we assessed smoltification and early seawater performance (241 days) following a yearling production cycle (i.e. spring smolts) in diploid and triploid female Atlantic salmon (Salmo salar) × male brown trout (Salmo trutta) hybrids compared to purebred diploid and triploid salmon. During freshwater rearing (n = 180/group), hybrids demonstrated a degree of bimodality in body size, significantly (p < 0.05) more so in diploid than triploid hybrids (11 and 37% in the lower mode, respectively) that was not seen in purebred salmon of either ploidy. This resulted in diploid hybrids being 66% smaller on average at sea transfer, whereas no hybridisation effect was seen in triploids, and both triploid groups were significantly heavier (16–43%) than diploid salmon. Irrespective of ploidy, lower mode hybrids grew poorly and showed low survival in seawater, suggesting they had failed to undergo smoltification. However, the upper mode diploid hybrids showed a similar Na+/K+-ATPase (NKA) enzyme activity surge during the spring as in diploid and triploid salmon, despite a higher ratio of the freshwater to seawater mRNA abundance of the NKA subunits (nkaα1a and nkaα1b) and a reduced plasma cortisol surge. At the end of the experimental period, both hybrids weighed significantly less than their salmon counterparts although the hybrid effect was again greater in diploids (71% smaller) than triploids (6% smaller). In addition, both triploid groups were on average heavier (15–22%) than diploid salmon. As such, both triploid Atlantic salmon and triploid hybrids can show enhanced growth performance from juveniles up to post-smolts compared to diploid salmon in an aquaculture setting.publishedVersio

Application of Asynchronous Transfer Mode (Atm) technology to Picture Archiving and Communication Systems (Pacs): A survey

Broadband Integrated Services Digital Network (R-ISDN) provides a range of narrowband and broad-band services for voice, video, and multimedia. Asynchronous Transfer Mode (ATM) has been selected by the standards bodies as the transfer mode for implementing B-ISDN; The ability to digitize images has lead to the prospect of reducing the physical space requirements, material costs, and manual labor of traditional film handling tasks in hospitals. The system which handles the acquisition, storage, and transmission of medical images is called a Picture Archiving and Communication System (PACS). The transmission system will directly impact the speed of image transfer. Today the most common transmission means used by acquisition and display station products is Ethernet. However, when considering network media, it is important to consider what the long term needs will be. Although ATM is a new standard, it is showing signs of becoming the next logical step to meet the needs of high speed networks; This thesis is a survey on ATM, and PACS. All the concepts involved in developing a PACS are presented in an orderly manner. It presents the recent developments in ATM, its applicability to PACS and the issues to be resolved for realising an ATM-based complete PACS. This work will be useful in providing the latest information, for any future research on ATM-based networks, and PACS

Making Bread From Broken Eggs: A Basic Recipe for Conflict Resolution Using Earned Sovereignty

Questions of state sovereignty are the cause of many conflicts today. The theory of earned sovereignty is an evolving concept. A review of recent practice in southern Sudan, Bougainville, and Aceh shows that the core elements of earned sovereignty offer a three-part roadmap for conflict resolution beginning with shared sovereignty, continuing through institution building, and ending at a determination of final status. Other parts of the theory called, “optional elements,” are tools stakeholders in a conflict situation may use in order to move from one core element to another until a final status solution is obtained. Though the optional elements of phased sovereignty, conditional sovereignty, and constrained sovereignty are parts of earned sovereignty they need not always be used. In-depth analysis of the peace agreements in southern Sudan, Bougainville, and Aceh show that, while the core elements are implemented throughout, the optional elements are used to varying degrees and in some instances not at all

Bacteria Hunt: Evaluating multi-paradigm BCI interaction

The multimodal, multi-paradigm brain-computer interfacing (BCI) game Bacteria Hunt was used to evaluate two aspects of BCI interaction in a gaming context. One goal was to examine the effect of feedback on the ability of the user to manipulate his mental state of relaxation. This was done by having one condition in which the subject played the game with real feedback, and another with sham feedback. The feedback did not seem to affect the game experience (such as sense of control and tension) or the objective indicators of relaxation, alpha activity and heart rate. The results are discussed with regard to clinical neurofeedback studies. The second goal was to look into possible interactions between the two BCI paradigms used in the game: steady-state visually-evoked potentials (SSVEP) as an indicator of concentration, and alpha activity as a measure of relaxation. SSVEP stimulation activates the cortex and can thus block the alpha rhythm. Despite this effect, subjects were able to keep their alpha power up, in compliance with the instructed relaxation task. In addition to the main goals, a new SSVEP detection algorithm was developed and evaluated

CASPR: Judiciously Using the Cloud for Wide-Area Packet Recovery

We revisit a classic networking problem -- how to recover from lost packets in the best-effort Internet. We propose CASPR, a system that judiciously leverages the cloud to recover from lost or delayed packets. CASPR supplements and protects best-effort connections by sending a small number of coded packets along the highly reliable but expensive cloud paths. When receivers detect packet loss, they recover packets with the help of the nearby data center, not the sender, thus providing quick and reliable packet recovery for latency-sensitive applications. Using a prototype implementation and its deployment on the public cloud and the PlanetLab testbed, we quantify the benefits of CASPR in providing fast, cost effective packet recovery. Using controlled experiments, we also explore how these benefits translate into improvements up and down the network stack