As Ações de Eficiência Energética encontram grandes barreiras para sua implantação. Um dos motivos pode estar na falta de conhecimento do tomador de decisão que, para evitar o custo inicial mais elevado de um equipamento eficiente, opta por instalar um equipamento mais barato, mas que consequentemente consome mais energia e aumenta os dispêndios com a eletricidade. Os sistemas de gestão de energia visam demonstrar a origem das despesas relacionadas ao consumo de energia elétrica, conscientizando os usuários acerca de tais custos. Muitos usuários não enxergam a possibilidade de economia de energia e de dinheiro, ao investir em equipamentos mais eficientes. Muitos consideram as faturas de energia como despesas fixas, logo, sem exigência de acompanhamento. Fato não compatível com os dias atuais. Ao identificar o consumo desagregado de energia da instalação, os usuários poderão avaliar os impactos de suas atividades em relação ao consumo de energia, assim com seu custo nas faturas de energia. A medição direta dos equipamentos reproduz o valor mais preciso do consumo desagregado. Entretanto, para muitas instalações esta prática é inviável, pois seus circuitos são compartilhados por diversos tipos de equipamentos e os custos de aquisição, implantação e leitura dos medidores podem se tornar proibitivos. É possível obter o valor do consumo desagregado por inspeção da instalação, que consiste no levantamento das características elétricas dos equipamentos, suas respectivas potências e períodos de utilização. Esse método, no entanto não é tão preciso na análise do consumo desagregado, pois envolve uma série de estimativas acerca da utilização dos equipamentos, que nem sempre são acertadas. Visando contornar estas situações, as técnicas de monitoramento não intrusivo de carga passaram a buscar na curva de carga as assinaturas elétricas dos equipamentos, para identificar seus períodos de funcionamento e assim obter o consumo desagregado. / The energy efficiency programs face huge difficulties to be deployed. The reason may be the lack of knowledge about total costs in acquires less efficient devices, which is cheaper, though the increases in energy bills eliminate this initial economy. Thereby, the Energy Management Systems aims to demonstrate the relation between the user\"s behavior and the electric power consumption. Many managers consider the electric bill as a fixed cost, without require tracking its origin. This means waste of energy and money. Analyzing the facility by sectors may improve the understanding about the costs in electricity and the knowledge about the disaggregated energy consumption, though is not always an easy issue to be obtained. Monitoring each equipment provides the exactly amount of energy is used in that system. However the costs of acquirement, implementation and monitoring these meters may become prohibitively. This way, the researches about nonintrusive load monitoring aim to demonstrate where the energy is being used and how it can be minimized.

As Ações de Eficiência Energética encontram grandes barreiras para sua implantação. Um dos motivos pode estar na falta de conhecimento do tomador de decisão que, para evitar o custo inicial mais elevado de um equipamento eficiente, opta por instalar um equipamento mais barato, mas que consequentemente consome mais energia e aumenta os dispêndios com a eletricidade. Os sistemas de gestão de energia visam demonstrar a origem das despesas relacionadas ao consumo de energia elétrica, conscientizando os usuários acerca de tais custos. Muitos usuários não enxergam a possibilidade de economia de energia e de dinheiro, ao investir em equipamentos mais eficientes. Muitos consideram as faturas de energia como despesas fixas, logo, sem exigência de acompanhamento. Fato não compatível com os dias atuais. Ao identificar o consumo desagregado de energia da instalação, os usuários poderão avaliar os impactos de suas atividades em relação ao consumo de energia, assim com seu custo nas faturas de energia. A medição direta dos equipamentos reproduz o valor mais preciso do consumo desagregado. Entretanto, para muitas instalações esta prática é inviável, pois seus circuitos são compartilhados por diversos tipos de equipamentos e os custos de aquisição, implantação e leitura dos medidores podem se tornar proibitivos. É possível obter o valor do consumo desagregado por inspeção da instalação, que consiste no levantamento das características elétricas dos equipamentos, suas respectivas potências e períodos de utilização. Esse método, no entanto não é tão preciso na análise do consumo desagregado, pois envolve uma série de estimativas acerca da utilização dos equipamentos, que nem sempre são acertadas. Visando contornar estas situações, as técnicas de monitoramento não intrusivo de carga passaram a buscar na curva de carga as assinaturas elétricas dos equipamentos, para identificar seus períodos de funcionamento e assim obter o consumo desagregado. / The energy efficiency programs face huge difficulties to be deployed. The reason may be the lack of knowledge about total costs in acquires less efficient devices, which is cheaper, though the increases in energy bills eliminate this initial economy. Thereby, the Energy Management Systems aims to demonstrate the relation between the user\"s behavior and the electric power consumption. Many managers consider the electric bill as a fixed cost, without require tracking its origin. This means waste of energy and money. Analyzing the facility by sectors may improve the understanding about the costs in electricity and the knowledge about the disaggregated energy consumption, though is not always an easy issue to be obtained. Monitoring each equipment provides the exactly amount of energy is used in that system. However the costs of acquirement, implementation and monitoring these meters may become prohibitively. This way, the researches about nonintrusive load monitoring aim to demonstrate where the energy is being used and how it can be minimized.

Cette thèse présente une méthode pour désagréger les appareils électriques dans le profil des bâtiments résidentiels de charge. Au cours des dernières années, la surveillance de l’énergie a obtenu beaucoup de popularité dans un environnement privé et industriel. Avec des algorithmes de la désagrégation, les données mesurées à partir de soi-disant compteurs intelligents peuvent être utilisés pour fournir de plus amples informations de la consommation d’énergie. Une méthode pour recevoir ces données est appelé non-intrusifs charge identification. La majeure partie de la thèse peut être divisée en trois parties. Dans un premier temps, un système de mesure propre a été développé et vérifié. Avec ce système, les ensembles de données réelles peuvent être générés pour le développement et la vérification des algorithmes de désagrégation. La deuxième partie décrit le développement d’un détecteur de flanc. Différentes méthodes sont présentées et évaluées, avec lequel les temps de commutation des appareils peuvent être détectés dans le profil de la charge. La dernière partie décrit un procédé de classification. Différents critères sont utilisés pour la classification. Le classificateur reconnaît et étiquette les appareils individuels de la courbe de charge. Pour les classifications différentes structures de réseaux de neurones artificiels sont comparés. / This thesis presents a method to disaggregate electrical appliances in the load profile of residential buildings. In recent years, energy monitoring has obtained significantly popularity in private and industrial environment. With algorithms of the disaggregation, the measured data from so-called smart meters can be used to provide more information of the energy usage. One method to receive these data is called non-intrusive appliance load monitoring.The main part of the thesis can be divided into three parts. At first, an own measurement system was developed and verified. With that system, real data sets can be generated for the development and verification of the disaggregation algorithms. The second part describes the development of an event detector. Different methods are presented and evaluated, with which the switching times of the appliances can be detected in the load profile. The last part describes a classification method. Different features are used for the classification. The classifier recognizes and labels the individual appliances in the load profile. For the classification different structures of artificial neural network (ANN) are compared.

<p dir="ltr">Since the beginning of the HIV/AIDS epidemic, 85.6 million people worldwide have become infected with HIV; more than half of whom have died from AIDS-related complications.[1] Sustained viral suppression below the clinically relevant threshold (1000 copies per mL) with highly active antiretroviral therapy (HAART) has proven effective at managing and prolonging the life expectancy of people living with HIV (PLHIV). However, in 2022, 11.3 million PLHIV had still not achieved viral suppression and may become susceptible to both HIV transmission and a variety of opportunistic infections. Of particular importance is the complex issue of patient non-compliance in global HIV management due to social, economic, behavioral, and healthcare access barriers, potentially disconnecting many PLHIV from the HIV care continuum. Therefore, to boost patient engagement in clinical care and to improve overall patient outcomes, new approaches to viral load monitoring practices need to be developed to increase access, particularly in regions of high HIV prevalence.</p><p dir="ltr">Nucleic acid amplification tests (NAATs) have emerged as potent tools for monitoring viral load, with reverse transcription quantitative polymerase chain reaction (RT-qPCR) being recognized as the benchmark due to its sensitivity and ability for real-time quantification enabled by fluorescence signal emission. Nevertheless, RT-qPCR is burdened by drawbacks including extended processing times, high operational costs, and the requirement for specialized laboratory facilities. In this study, we propose a novel method for HIV-1 viral load monitoring by integrating reverse-transcriptase loop-mediated isothermal amplification (RT-LAMP) with real-time particle diffusometry (PD). This approach allows for the continuous monitoring of changes in the diffusion of 400 nm fluorescent particles during RT-LAMP amplification, targeting the <i>p24</i> gene region of HIV-1 RNA. This enables the real-time detection of amplification curves, achieving a detection sensitivity in water samples as low as 25 virus particles per μL within a short duration of 30 minutes. Additionally, to address challenges related to amplification inhibition in complex human specimens, we developed a power-free sample processing system specifically designed for extracting HIV-1 RNA from both whole blood and plasma.Top of FormBottom of FormThis system modifies a commercially available spin-column protocol by integrating a syringe device and handheld bulb dryer, thus eliminating the requirement for a centrifuge. The adaptation allows for the completion of the entire extraction procedure, encompassing viral lysis, RNA capture, washing, and elution of purified HIV-1 RNA, within a timeframe of less than 16 minutes. Subsequent analyses, including RT-LAMP and RT-qPCR, demonstrate a limit of detection of 100 copies per μL and an average RNA recovery of 32% (for blood) and 70% (for plasma) in the elution fraction. Further investigations emphasize the significant presence of purified RNA in the spin column volume (termed as dead volume), and the cumulative recovered RNA copies align with those obtained using the gold standard centrifugation extraction method. Ultimately, we incorporated the real-time quantitative PD-RT-LAMP assay onto a field-compatible handheld portable platform suitable for field use, featuring built-in quality control measures. This platform enables sample-to-answer viral load testing near the point of care (POC). Subsequently, we undertook essential preparatory steps, such as reagent drying to obviate the need for cold storage, initial device calibration, and hands-on training of laboratory personnel regarding device operation, to validate device performance within a cohort of individuals living with HIV (PLHIV). These innovations facilitate quick and comprehensive viral load determination, offering promise for enhanced HIV management and patient care</p>

Le domaine lié à ce travail est appelé « désagrégation d’énergie », où la principale préoccupation est de décomposer, ou désagréger, la consommation globale d’énergie électrique (par exemple, la consommation de tout un ménage) en une consommation détaillée donnée comme information de consommation par usage (par exemple, par appareil). Cette dernière permet d’avoir un retour sur la consommation pour les consommateurs ainsi que pour les fournisseurs et est utile pour permettre des économies d’énergie. Dans ce domaine de désagrégation d’énergie, il existe trois grandes questions auxquelles il faut répondre : qui consomme ? quand ? et combien ? Les recherches menées dans cette thèse se concentrent sur l’identification des appareils électriques, c’est-à-dire la réponse à la première question, en considérant particulièrement des appareils ménagers. À cet effet, nous utilisons le courant transitoire de mise en marche que nous modélisons en utilisant un nouveau modèle que nous avons proposé. De plus, nous utilisons les paramètres estimés de ce dernier pour la tâche d’identification. / The related field to this work is called “energy disaggregation" where the main concern is to break down, or disaggregate, the global electrical energy consumption (e.g. wholehouse consumption) into a detailed consumption given as end-use (e.g. appliance-level) consumption information. This latter gives consumption feedback to consumers and electricity providers and is helpful for energy savings. Three main questions have to be answered in the energy disaggregation field : who is consuming ? when ? and how much ? The research conducted in this thesis focuses on electrical appliances identification, i.e. the who question, considering particularly home appliances. For this purpose, we use the turn-on transient current signal which we model using a new model we proposed and use its estimated model parameters for the identification task.

Submitted by Kelren Mota (kelrenlima@ufpa.br) on 2018-06-25T18:48:12Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertacao_RedeNeuralConvolucional.pdf: 2088560 bytes, checksum: 6328f6f59bc552055a366b1e4a32793d (MD5) / Approved for entry into archive by Kelren Mota (kelrenlima@ufpa.br) on 2018-06-25T18:48:32Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertacao_RedeNeuralConvolucional.pdf: 2088560 bytes, checksum: 6328f6f59bc552055a366b1e4a32793d (MD5) / Made available in DSpace on 2018-06-25T18:48:32Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertacao_RedeNeuralConvolucional.pdf: 2088560 bytes, checksum: 6328f6f59bc552055a366b1e4a32793d (MD5) Previous issue date: 2018-04-03 / Este trabalho apresenta a proposta de uma nova metodologia para identificação de equipamentos residenciais em sistemas de Monitoramento Não-Intrusivo de cargas. O sistema é baseado em uma Rede Neural Convolucional para classificação dos equipamentos, que utilizam, diretamente como entradas para o sistema, os dados do sinal transitório de potência de 7 equipamentos obtidos no momento em que estes são ligados em uma residência. A metodologia foi desenvolvida usando dados de um banco de dados público (REED) que apresenta dados coletados a uma baixa frequência (1 Hz). Os resultados obtidos na base de dados de testes apresentam acurácia superior a 90%, indicando que o sistema proposto é capaz de realizar a tarefa de identificação, além disso os resultados apresentados são considerados satisfatórios quando comparados com os resultados já apresentados na literatura para o problema em questão. / This research presents the proposal of a new methodology for the identification of residential equipment in non-intrusive load monitoring systems. The system is based on a Convolutional Neural Network to classify residential equipment, which uses directly as inputs to the system, the transient power signal data of 7 equipment obtained at the moment they are connected in a residence. The methodology was developed using data from a public database (REED) that presents data collected at a low frequency (1 Hz). The results obtained in the test database show an accuracy of more than 90%, indicating that the proposed system is capable of performing the task of identification. In addition, the results presented are considered satisfactory when compared with the results already presented in the literature for the problem in question.

Comme les gens vieillissent, ils sont souvent confrontés à un certain degré de diminution des capacités cognitives ou de la force physique. Isolement de la vie sociale, mauvaise qualité de la vie, et risque accru de blessures en sont les principales conséquences. Ambient Assisted Living (AAL) est une vision de la façon dont les gens vivent leur vie dans leur propre maison, à mesure qu'ils vieillissent : handicaps ou limitations sont compensées par la technologie, là où le personnel de prestation de soins est rare ou des proches ne sont pas en mesure d'aider. Les personnes concernées sont assistés par la technologie. Le terme "ambiante" en AAL exprime, ce que cette technologie doit être, au- delà de l’assistance. Elle doit être intégrée dans l’environnement de manière à ce qu'elle ne soit pas reconnue en tant que tel. L'interaction avec les résidents doit être intuitive et naturelle. L'équipement technique doit être discret ct bien intégré. Les domaines d'application ciblés dans cette thèse sont le suivi de l’activité et la recherche de profils d'activités dans des appartements ou des petites maisons. L'acquisition d’informations concernant l’activité des résidents est vitale pour le succès de toute la technologie d’assistance. Dans de nombreux domaines de la vie quotidienne, ceci est déjà de la routine. L’état de l’art en matière de technologie de détection comprend des caméras, des barrières lumineuses, des capteurs RFID, la radiolocalisation de signal en utilisant des transpondeurs et des planchers sensibles à la pression. En raison de leurs principes de fonctionnement, ils ont malheureusement un impact important sur les environnements domestiques et de vie. Par conséquent, cette thèse est consacrée à la recherche de technologies d’acquisition d’informations de l’activité non-intrusive ayant un impact minimal sur la vie quotidienne. Deux technologies de base, la détection de présence passive sans dispositif et le suivi de charges de manière non-intrusive, sont prises en compte dans cette thèse. / As people grow older, they are often faced with some degree of decreasing cognitive abilities or physical strength. Isolation from social life, poor quality of life, and increased risk or injuries are the consequence. Ambient Assisted Living (AAL) is a vision for the way people live their life in their own home, as they grow older: disabilities or limitations are compensated for by technology, where care-giving personnel is scarce or relatives are unable to help. Affected people are assisted by technology. The term "Ambient" in AAL expresses, what this technology needs to be, beyond assistive. It needs to integrate into the living environment in such a way that it is not recognized as such any more. Interaction with residents needs to be intuitive and natural. Technical equipment should be unobtrusive and well integrated. The areas of application targeted in this thesis are activity monitoring and activity pattern discovery in apartments or small houses. The acquisition of information regarding the residents' activity is vital for the success of any assistive technology. In many areas of daily life, this is routine already. State-of-the-art sensing technology includes cameras, light barriers, RFID sensors, radio signal localization using transponders, and pressure sensitive Floors. Due to their operating principles, they have a big impact on home and living environments. Therefore, this thesis is dedicated to research for non-intrusive activity information acquisition technology, that has minimal impact on daily life. Two base technologies are taken into account in this thesis.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-08-06T18:59:56Z No. of bitstreams: 1 PEDRO YÓSSIS SILVA BARBOSA - DISSERTAÇÃO PPGCC 2014..pdf: 17089632 bytes, checksum: 4623777c293a51dbb1b392f37d2dd75e (MD5) / Made available in DSpace on 2018-08-06T18:59:56Z (GMT). No. of bitstreams: 1 PEDRO YÓSSIS SILVA BARBOSA - DISSERTAÇÃO PPGCC 2014..pdf: 17089632 bytes, checksum: 4623777c293a51dbb1b392f37d2dd75e (MD5) Previous issue date: 2014-02-27 / Capes / Companhias de energia começaram a substituir os medidores de energia tradicionais pelos Smart Meters, que podem transmitir valores de consumo para as companhias em curtos intervalos de tempo. Com uma insfraestrutura de Smart Meters, existem muitas motivações para as concessionárias de energia coletarem dados de consumo em alta resolução. Entretanto, isto implica em informações bastante detalhadas sobre os consumidores sendo monitoradas. Consequentemente, um problema sério precisa ser resolvido: como preservar a privacidade dos consumidores sem afetar a prestação de certos serviços pelas concessionárias? Claramente, este é um tradeoff entre privacidade e utilidade. Existem diversas abordagens para preservar a privacidade, porém muitas delas afetam a utilidade dos dados ou possuem um alto custo computacional. Neste trabalho, nós propomos e avaliamos uma abordagem computacionalmente barata que preserva a privacidade e utilidade dos dados através de adição de ruído. Para validar a privacidade, nós avaliamos possíveis ataques (tal como Monitoramento Não-Intrusivo de Carga de Eletrodomésticos - NIALM, do inglês Non-Intrusive Appliance Load Monitoring) utilizando dados reais de consumidores. Para validar a utilidade, nós avaliamos a influência da abordagem em vários benefícios que podem ser providos com o uso de Smart Meters. / Power providers have started replacing traditional electricity meters for Smart Meters, which can transmit power consumption levels to the provider within short intervals. With a Smart Metering infrastructure, there are many motivations for power providers to collect highresolution data of electricity usage from consumers. However, this implies in very detailed information about the consumers being monitored. Consequently, a serious issue needs to be addressed: how to preserve the privacy of consumers but making the provision of certain services still possible? Clearly, this is a tradeoff between privacy and utility. There are several approaches for privacy preserving, but many of them affect the data usefulness or are computationally expensive. In this work, we propose and evaluate a lightweight approach for privacy and utility based on the addition of noise. To validate the privacy, we evaluate possible attacks (such as a NIALM - Non-Intrusive Appliance Load Monitoring) using real consumers' data. To validate the utility, we analyze the influence of the approach in various benefits that can be provided through the use of Smart Meters.

Dans les maisons et bâtiments intelligents, il devient nécessaire de limiter l'intervention humaine sur le système énergétique, afin de fluctuer automatiquement l'énergie consommée par les appareils consommateurs. Pour cela, un système de mesure de la consommation électrique d'équipements est aussi nécessaire et peut être déployé de deux façons : intrusive ou non-intrusive. La première solution consiste à relever la consommation de chaque appareil, ce qui est inenvisageable à une grande échelle pour des raisons pratiques liées à l'entretien et aux coûts. Donc, la solution non-intrusive (NILM pour Non-Intrusive Load Monitoring), qui est capable d'identifier les différents appareils en se basant sur les signatures extraites d'une consommation globale, est plus prometteuse. Le problème le plus difficile des algorithmes NILM est comment discriminer les appareils qui ont la même caractéristique énergétique. Pour surmonter ce problème, dans cette thèse, nous proposons d'utiliser une information externe pour améliorer la performance des algorithmes existants. Les premières informations additionnelles proposées considèrent l'état précédent de chaque appareil comme la probabilité de transition d'état ou la distance de Hamming entre l'état courant et l'état précédent. Ces informations sont utilisées pour sélectionner l'ensemble le plus approprié des dispositifs actifs parmi toutes les combinaisons possibles. Nous résolvons ce problème de minimisation en norme l1 par un algorithme d'exploration exhaustive. Nous proposons également d'utiliser une autre information externe qui est la probabilité de fonctionnement de chaque appareil fournie par un réseau de capteurs sans fil (WSN pour Wireless Sensor Network) déployé dans le bâtiment. Ce système baptisé SmartSense, est différent de la solution intrusive car seul un sous-ensemble de tous les dispositifs est surveillé par les capteurs, ce qui rend le système moins intrusif. Trois approches sont appliquées dans le système SmartSense. La première approche applique une détection de changements de niveau sur le signal global de puissance consommé et les compare avec ceux existants pour identifier les dispositifs correspondants. La deuxième approche vise à résoudre le problème de minimisation en norme l1 avec les algorithmes heuristiques de composition Paréto-algébrique et de programmation dynamique. Les résultats de simulation montrent que la performance des algorithmes proposés augmente significativement avec la probabilité d'opération des dispositifs surveillés par le WSN. Comme il n'y a qu'un sous-ensemble de tous les appareils qui sont surveillés par les capteurs, ceux qui sont sélectionnés doivent satisfaire quelques critères tels qu'un taux d'utilisation élevé ou des confusions dans les signatures sélectionnées avec celles des autres. / In smart homes, human intervention in the energy system needs to be eliminated as much as possible and an energy management system is required to automatically fluctuate the power consumption of the electrical devices. To design such system, a load monitoring system is necessary to be deployed in two ways: intrusive or non-intrusive. The intrusive approach requires a high deployment cost and too much technical intervention in the power supply. Therefore, the Non-Intrusive Load Monitoring (NILM) approach, in which the operation of a device can be detected based on the features extracted from the aggregate power consumption, is more promising. The difficulty of any NILM algorithm is the ambiguity among the devices with the same power characteristics. To overcome this challenge, in this thesis, we propose to use an external information to improve the performance of the existing NILM algorithms. The first proposed additional features relate to the previous state of each device such as state transition probability or the Hamming distance between the current state and the previous state. They are used to select the most suitable set of operating devices among all possible combinations when solving the l1-norm minimization problem of NILM by a brute force algorithm. Besides, we also propose to use another external feature that is the operating probability of each device provided by an additional Wireless Sensor Network (WSN). Different from the intrusive load monitoring, in this so-called SmartSense system, only a subset of all devices is monitored by the sensors, which makes the system quite less intrusive. Two approaches are applied in the SmartSense system. The first approach applies an edge detector to detect the step-changes on the power signal and then compare with the existing library to identify the corresponding devices. Meanwhile, the second approach tries to solve the l1-norm minimization problem in NILM with a compositional Pareto-algebraic heuristic and dynamic programming algorithms. The simulation results show that the performance of the proposed algorithms is significantly improved with the operating probability of the monitored devices provided by the WSN. Because only part of the devices are monitored, the selected ones must satisfy some criteria including high using rate and more confusions on the selected patterns with the others.

This contribution will introduce carbon-reinforced concrete components (so-called carbon concrete composites, or C³) with sensor functionalities for innovative building envelopes. For a continuous in situ structural monitoring, these textile-reinforced concrete components are equipped with textile sensor networks consisting of resistive carbon fiber sensors (CFSs), which are integrated into the carbon fiber non-crimp fabrics of the concrete reinforcement by multiaxial warp-knitting. The in situ CFSs, consisting of 1 k or 50 k carbon fiber roving with added staple fiber/multifilament dielectric cladding, are later integral to the load-distributing elements of the concrete component, and elongations within these are easy to record with good correlation to ohmic resistance changes. Gage factors of k = 0.52–1.23 at linearity deviations of ALin=4.0–8.7% are feasible. This allows a monitoring of C³ building envelopes for structural mechanical changes caused by physical changes within the component through mechanical or thermal loads or deformation and cracks.