4,186 research outputs found

    A hybrid RBF neural network based model for day-ahead prediction of photovoltaic plant power output

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    Renewable energy resources like solar power contribute greatly to decreasing emissions of carbon dioxide and substituting generators fueled by fossil fuels. Due to the unpredictable and intermittent nature of solar power production as a result of solar radiance and other weather conditions, it is very difficult to integrate solar power into conventional power systems operation economically in a reliable manner, which would emphasize demand for accurate prediction techniques. The study proposes and applies a revised radial basis function neural network (RBFNN) scheme to predict the short-term power output of photovoltaic plant in a day-ahead prediction manner. In the proposed method, the linear as well as non-linear variables in the RBFNN scheme are efficiently trained using the whale optimization algorithm to speed the convergence of prediction results. A nonlinear benchmark function has also been used to validate the suggested scheme, which was also used in predicting the power output of solar energy for a well-designed experiment. A comparison study case generating different outcomes shows that the suggested approach could provide a higher level of prediction precision than other methods in similar scenarios, which suggests the proposed method can be used as a more suitable tool to deal such solar energy forecasting issues

    Research on renewable energy power demand forecasting method based on IWOA-SA-BILSTM modeling

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    This paper introduces a novel coupling method to enhance the precision of short- and medium-term renewable energy power load demand forecasting. Firstly, the Tent chaotic mapping incorporates the standard WOA and modifies its internal convergence factor to a nonlinear convergence mode, resulting in an improved IWOA. It is used for the weight optimization part of BILSTM. Then, the SA is introduced to optimize the learning rate, the number of nodes in hidden layers 1 and 2, and the number of iterations of BILSTM, constructing an IWOA-SA-BILSTM prediction model. Finally, through case analysis, the prediction model proposed in this paper has the highest improvement of 76.7%, 74.5%, and 45.9% in terms of Mean Absolute Error, Root Mean Square Error, and R2, respectively, compared to other optimal benchmark models, proving the effectiveness of the model

    Fuzzy Norm-Explicit Product Quantization for Recommender Systems

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    As the data resources grow, providing recommendations that best meet the demands has become a vital requirement in business and life to overcome the information overload problem. However, building a system suggesting relevant recommendations has always been a point of debate. One of the most cost-efficient techniques in terms of producing relevant recommendations at a low complexity is Product Quantization (PQ). PQ approaches have continued developing in recent years. This system’s crucial challenge is improving product quantization performance in terms of recall measures without compromising its complexity. This makes the algorithm suitable for problems that require a greater number of potentially relevant items without disregarding others, at high-speed and low-cost to keep up with traffic. This is the case of online shops where the recommendations for the purpose are important, although customers can be susceptible to scoping other products. A recent approach has been exploiting the notion of norm sub-vectors encoded in product quantizers. This research proposes a fuzzy approach to perform norm-based product quantization. Type-2 Fuzzy sets (T2FSs) define the codebook allowing sub-vectors (T2FSs) to be associated with more than one element of the codebook, and next, its norm calculus is resolved by means of integration. Our method finesses the recall measure up, making the algorithm suitable for problems that require querying at most possible potential relevant items without disregarding others. The proposed approach is tested with three public recommender benchmark datasets and compared against seven PQ approaches for Maximum Inner-Product Search (MIPS). The proposed method outperforms all PQ approaches such as NEQ, PQ, and RQ up to +6%, +5%, and +8% by achieving a recall of 94%, 69%, 59% in Netflix, Audio, Cifar60k datasets, respectively. More and over, computing time and complexity nearly equals the most computationally efficient existing PQ method in the state-of-the-art

    Impact of the Completed South Carolina Post Critical Incident Seminar on the Well-Being of the Law Enforcement Participants

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    The purpose of this grounded theory study was to discover the impact of the completed South Carolina Post Critical Incident Seminar (SC PCIS) on the participants’ well-being, and the impact on the peer team members. Critical stress events or traumatic loss of life did have a significant impact on the law enforcement officer and those that were associated with that officer. The theory that guides this was study was the general strain theory; it allowed for the relationship between the duration, severity, and certainty of the stressors that had a negative influence on the well-being of those that attend the SC PCIS process. Research showed that unaddressed stressors lead to rapid and drastic effects on the psychological and physiological aspects of the law enforcement officer. These stressors could quickly and rapidly overwhelm traditional coping skills leading to a decline in overall well-being and quality of life. The ability to identify a program that addresses the efforts to improve well-being and create long-lasting benefits was vital for all parties that interact with the law enforcement officer. The qualitative study did involve semi-structured interviews with peer team members that interacted with all participants on multiple occasions at the SC PCIS seminar. The grounded theory was used in the data analysis strategies. The completed study showed that there was a positive impact on the well-being of the participants that completed the program. The impact of the SC PCIS on the peer team members was positive as well

    Discrete functional inequalities on lattice graphs

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    In this thesis, we study problems at the interface of analysis and discrete mathematics. We discuss analogues of well known Hardy-type inequalities and Rearrangement inequalities on the lattice graphs Z^d, with a particular focus on behaviour of sharp constants and optimizers. In the first half of the thesis, we analyse Hardy inequalities on Z^d, first for d=1 and then for d >= 3. We prove a sharp weighted Hardy inequality on integers with power weights of the form n^\alpha. This is done via two different methods, namely 'super-solution' and 'Fourier method'. We also use Fourier method to prove a weighted Hardy type inequality for higher order operators. After discussing the one dimensional case, we study the Hardy inequality in higher dimensions (d >= 3). In particular, we compute the asymptotic behaviour of the sharp constant in the discrete Hardy inequality, as d \rightarrow \infty. This is done by converting the inequality into a continuous Hardy-type inequality on a torus for functions having zero average. These continuous inequalities are new and interesting in themselves. In the second half, we focus our attention on analogues of Rearrangement inequalities on lattice graphs. We begin by analysing the situation in dimension one. We define various notions of rearrangements and prove the corresponding Polya-Szego inequality. These inequalities are also applied to prove some weighted Hardy inequalities on integers. Finally, we study Rearrangement inequalities (Polya-Szego) on general graphs, with a particular focus on lattice graphs Z^d, for d >=2. We develop a framework to study these inequalities, using which we derive concrete results in dimension two. In particular, these results develop connections between Polya-Szego inequality and various isoperimetric inequalities on graphs.Open Acces

    Encode and Permute that Database! Single-Server Private Information Retrieval with Constant Online Time, Communication, and Client-Side Storage

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    Private Information Retrieval (PIR) facilitates the retrieval of database entries by a client from a remote server without revealing which specific entry is being queried. The preprocessing model has emerged as a significant technique for constructing efficient PIR systems, allowing parties to execute a one-time, query-independent offline phase, and then a fast online retrieval phase. In particular, Corrigan-Gibbs and Kogan (EUROCRYPT 2020) presented a new framework for constructing PIR with sublinear online time. Nevertheless, their protocol is deemed impractical in the single-server setting due to the heavy use of Fully Homomorphic Encryption (FHE). More recently, two state-of-the-art (SOTA) single-server PIR protocols (Zhou et al., S&P 2024 and Mughees-Ren, ePrint 2023) have eliminated FHE, at the price of linear offline communication. However, the client-side storage is still relatively large (O~(n)\tilde{O}(\sqrt{n})), which poses challenges to practical deployment, especially when the client has limited computation and storage capabilities. To address such limitation, we propose a novel PIR protocol Pai, which only requires constant online time, communication, and client-side storage. The price we pay is only a 11 - 5×5\times increase in offline communication, which would be acceptable since it is a one-time cost.Building upon our Pai, we also present a Symmetric KPIR (KSPIR) PaiKSPIR and a Chargeable KSPIR (CKSPIR) PaiCKSPIR. These two variants of PIR offer enhanced functionalities while maintaining computational complexities similar to the original Pai. In addition to providing rigorous theoretical proofs of correctness and privacy for Pai, we have undertaken comprehensive protocol implementations and conducted extensive experiments to validate their high efficiency. Our empirical findings demonstrate that our protocols achieve notably higher online efficiency than SOTA protocols, e.g., Pai exhibits 8.88.8 - 91.8×91.8\times better online communication cost and 2.52.5 - 8.8×8.8\times better online time. Given the superior online time and storage, our protocol is well-suited for practical deployment

    Financial Contagion and Financial Lockdowns

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    Extreme financial shocks often elicit extraordinary policy interventions that preclude financial activity on a large scale, for example as the 1933 U.S. “bank holiday.” We study these interventions using a random matching framework where the financial contagion process is explicit and the diffusion of the initial shock can be analytically characterized. The study suggests that there is scope for forced closures of individual firms or even economy-wide financial lockdowns only when firms are financially vulnerable and policy institutions are not well-functioning. Here, ordinary policy alone cannot prevent or sufficiently mitigate contagion, while complementing it with a lockdown or individual closures can do so, and improve social welfare if the initial shock is severe but not widespread

    A NON-LINEAR OPTIMISATION APPROACH TO IN-PIT HAUL ROAD DESIGN

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    Truck haul costs, as one of the predominant operational costs for mining and quarrying operations, are known to be heavily dependent on the design parameters of haul roads. Furthermore, in-pit haul road design parameters determine the pit limits and therefore, the potential feasibility of the mining operation. Thus, when in search of an optimal solution in terms of in-pit haul roads, one should primarily consider the location of the in-pit haul road, its design features and the deriving operational costs regarding extraction and haul costs. A suitable objective function in this case may be the undiscounted profit for the ultimate pit design. However, for each considered scenario, truck and excavator operational costs can be calculated using simulation techniques for better accuracy. Furthermore, finding an optimal solution requires the execution of a reliable and efficient algorithm, depending on the shape of the objective function. Hence, a non-linear optimisation approach was proposed in this paper for solving the in-pit haul road optimisation problem, based on a simulation of the materials allocation, which was used for calculating the objective function. Design parameters were assumed to be predetermined, while the only variable used for finding an optimal solution was the location of the in-pit haul road inside the pit contour. In addition, two 1-D algorithms were compared for finding the optimal solution (Search with accelerated step size and 1-D Simplex method). Furthermore, two regression models are proposed (Multiple Linear Regression – MLR and Non-Linear Regression - NLR), which could identify the more feasible region for the in-pit haul road location and reduce the number of iterations required for convergence

    Mathematical Problems in Rock Mechanics and Rock Engineering

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    With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering

    Digital emancipators or oppressors : Evidence from Mobile Personal Finance Applications

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    This study seeks to investigate the impact of mobile personal finance applications on young adults' perceived financial well-being and unanticipated side effects, specifically emancipation and oppression. The study first examines the impact of these applications on financial well-being, examining the motivations and goals of the users behind using these applications. Second, it explores the unexpected side effects on an individual's behaviour and experience, looking at how the pursuit of financial well-being affects the user's freedom in other areas of life. The research is part of the DigiConsumers project, which aims to improve young people's digital financial skills. The research was carried out in two phases. The first phase used the method of empathy-based stories (MEBS) for data collection. This degree represents the second phase, redefining the research questions and analyzing the new theoretical framework. The study investigates using mobile financial applications by users in personal financial management. This reveals that the use of the applications is focused on a few purposes, such as consumption, borrowing and financial monitoring and budgeting. The demographic composition of the participants affected the services used. The research shows that applications concerning today's economy are more important to their users than applications concerning the future economy. Although some areas require improvement, users achieved their goals, and remarkably improved financial well-being with budgeting and planning tools. These apps were found to be both emancipating and oppressive. Users experienced emancipation by simplifying and speeding up tasks such as budgeting, saving and investing. Easy payment transactions also increased the emancipation of some users. However, some faced oppression due to impulsive buying, confusion about the value of digital money, and the over-facilitation of money transfers. The study also found indirect effects of PFM tools on personal and social connections. Personally, the tools speeded up actions, and offered freedom from being tied to time and place, but correspondingly caused stress related to security, privacy and user errors. Social applications, e.g. ai alleviated stress regaTämä tutkimus pyrkii selvittämään mobiilien henkilökohtaisten taloussovellusten vaikutusta nuorten aikuisten koettuun taloudelliseen hyvinvointiin ja odottamattomiin sivuvaikutuksiin, erityisesti emansipaatioon ja sortoon. Tutkimus tarkastelee ensin näiden sovellusten vaiku-tusta taloudelliseen hyvinvointiin, tutkien käyttäjien motivaatioita ja tavoitteita näiden sovel-lusten käytön taustalla. Toiseksi se tutkii odottamattomia sivuvaikutuksia yksilön käyttäytymi-seen ja kokemukseen, tarkastellen miten taloudellisen hyvinvoinnin tavoittelu vaikuttaa käyttäjän vapauteen muilla elämänalueilla. Tutkimus on osa DigiConsumers-projektia, joka pyrkii parantamaan nuorten digitaalisia taloustaitoja. Tutkimus toteutettiin kahdessa vaihees-sa. Ensimmäinen vaihe käytti eläytymismenetelmäksi kutsuttua menetelmää (MEBS) tiedon-keruuseen. Tämä gradu edustaa toista vaihetta, uudelleen määrittäen tutkimuskysymykset ja analysoi uutta teoreettista viitekehystä. Tutkimus tutkii käyttäjien mobiilien taloussovellus-ten hyödyntämistä henkilökohtaisessa taloudenhallinnassa. Tämä paljastaa sovellusten käy-tön keskittyvän muutamiin käyttötarkoituksiin kuten kulutukseen, lainaamiseen ja talouden seurantaan sekä budjetointiin. Osallistujien demografinen koostumus oletettavasti vaikutti käytettyihin palveluihin. Tutkimus osoittaa, että nykyhetken taloutta koskevat sovellukset ovat käyttäjilleen tärkeämpiä kuin tulevaisuuden taloutta koskevat sovellukset. Vaikka jotkin alueet vaativat parannuksia, käyttäjät saavuttivat tavoitteensa, erityisesti parantuneen ta-loudellisen hyvinvoinnin budjetointi- ja suunnittelutyökalujen avulla. Näiden sovellusten todettiin sekä emansipoivan että sortavan. Käyttäjät kokivat emansipaation yksinkertaista-malla ja nopeuttamalla tehtäviä, kuten budjetointia, säästämistä ja sijoittamista. Myös hel-pottunut maksaminen lisäsi emansipaatiota osalla käyttäjiä. Sen sijaan osa kohtasi sortoa impulsiivisen ostamisen, digitaalisen rahan arvon sekaannuksen ja rahansiirtojen liiallisen helpottumisen vuoksi. Tutkimuksessa havaittiin myös PFM-työkalujen epäsuorat vaikutukset henkilökohtaisiin ja sosiaalisiin yhteyksiin. Henkilökohtaisesti työkalut nopeuttivat toimia, tarjosivat vapautusta aika- ja paikkasidonnaisuudesta, mutta aiheuttivat vastaavasti stressiä turvallisuuteen, yksityisyyteen ja käyttäjävirheisiin liittyen. Sosiaalisesti applikaatiot mm. ai-heuttivat stressiä sosiaalisen median esittämän kuluttamisen suhteen, mutta paransivat myös viestintää ja jaettuja kokemuksia. Sovellukset voisivat hyödyntää tekoälyteknologiaa ymmärtääkseen paremmin käyttäjiään ja kokonaisuuksia paremmin sekä neuvoakseen käyt-täjiä tehokkaasti. Tämä ratkaisu voisi lisätä emansipaatiota eri konteksteissa ja edistää talou-dellisia tavoitteita eri tasoilla. On olennaista tutkia, miten teknologiaan pohjautuvaa tavoittei-den optimointia voidaan kehittää eettisesti ja läpinäkyvästi kaikille sidosryhmille
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