Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Inherent Distributional Robustness of Stochastic and Nominal Model Predictive Control

We define a notion of distributional robustness, via the Wasserstein metric, for closed-loop systems subject to errors in the disturbance distribution used to construct the controller. We then establish sufficient conditions for stochastic model predictive control (SMPC) to satisfy this definition of distributional robustness and establish a similar notion of distributional robustness for economic applications of SMPC. These results address incorrectly or unmodeled disturbances, demonstrate the efficacy of scenario optimization as a means to approximate and solve the SMPC problem, and unify the descriptions of robustness for stochastic and nominal model predictive control. This definition of distributional robustness for closed-loop systems is general and can be applied to other stochastic optimal control algorithms and, potentially, the developing field of distributionally robust control.
Numerical simulation of turbulent exchange flow in aquatic canopies

ABSTRACT In the present study, lock-exchange flows through rigid emergent vegetation are investigated numerically. The volume-averaged Reynolds-averaged Navier–Stokes equations and the transport equations of the renormalization group k-ε turbulence model, which include additional terms due to vegetation, are solved numerically. The drag coefficient CD is estimated as a function of the local volume-averaged velocity, which varies in space and time. The dynamics of gravity currents in regions covered by vegetation (fully and partly) is examined. The solid volume fraction of vegetation ϕ ranges from 0.03 to 0.35. The numerical model is validated with available experimental data for sparse vegetation. The characteristics of gravity currents in dense vegetation, where there is no sufficient information, are investigated. Ιt is found that the toe velocity in the open area is influenced by the adjacent vegetation for ϕ > 0.104. The currents become drag dominated from the beginning of the exchange flow for ϕ > 0.141.
Exploring the utilisation and management of Sclerocarya birrea (A. Rich.) Hochst (Marula tree) in the Beitbridge district of southern Zimbabwe

Abstract The marula tree can play a vital role to enhance the livelihoods within marginalised communities and aid forest management efforts. This research aimed to explore the utilisation and management of the marula tree (Sclerocarya birrea) in ward 3 of Beitbridge district, Southern Zimbabwe. A representative sample of 300 randomly selected households from five villages of ward 3 were interviewed telephonically and through the WhatsApp social media platform using a semi-structured questionnaire. The study observed that 95% of the sampled households harvested and utilised marula fruits to produce several products including jam, marula wine, nuts for consumption, and soda. In addition, marula makes a significant contribution to family income at (35%) when in season compared to other livelihood options such as livestock rearing and cropping. An average of 443 kg of marula fruits were harvested per season per village and there were significant differences (p < 0.05) in quantities harvested across villages. Households involved in marula fruit trading increased by 27% in 20 years due t\o an improvement in the marula tree value chain in the district. Therefore, there is a distinct possibility for overutilisation of the marula tree products, which necessitates the need for the development of effective marula forest management plans.
Evaluation of Two Configurations of Hydroxyapatite and Beta-Tricalcium Phosphate in Sinus Grafts with Simultaneous Implant Installation: An Experimental Study in Rabbits

Background: This study aimed to evaluate peri-implant bone formation in rabbits after sinus grafting mediated by hydroxyapatite and beta-tricalcium phosphate (HA + β-TCP) in granule or paste configurations, concomitant with immediate implant installation. Material & methods: Thirty-four rabbit maxillary sinuses were grafted with HA + β-TCP, half of which were applied in a granule and half in a paste composition. Implant placement was performed simultaneously. At 7 and 40 days postoperatively, the animals were euthanized, and samples were prepared for tomographic, microtomographic, histological, histometric (hematoxylin and eosin staining, HE), and immunohistochemical (labeling of transcription factor Runx-2 [RUNX2], vascular endothelial growth factor [VEGF], osteocalcin [OCN], and tartrate-resistant acid phosphatase [TRAP]) analysis. Implant removal torque was also measured. Results: On tomography, maintenance of sinus membrane integrity was observed in both the groups. Higher values of morphometric parameters evaluated by micro-CT were found in the “paste group” after seven days. At 40 days, there were no significant differences between the groups in most of the microtomographic parameters evaluated. In histological sections stained with HE, a higher percentage of newly formed bone was observed in the “granule group” after 40 days. Similar positive immunolabeling was observed for both RUNX2 and OCN in both the experimental groups. TRAP immunolabeling was similar in both groups as well. VEGF labeling increased in the “granule group”, indicating a higher osteoconductive potential in this biomaterial. Similar removal torque values were observed in both groups. Thus, the two HA + β-TCP configurations showed similar healing patterns of simultaneously installed implants adjacent to sinus floor elevation. However, significantly higher bone values were observed for the “granule configuration”. Conclusions: The HA + β-TCP granules and paste presentations showed favorable long-term healing results, with bone formation in similar quantities and quality adjacent to the implants.
Attitude formation control of multiple vehicles subject to communication constraints

This paper presents a constrained dynamical systems based attitude synchronization and tracking for a formation of rigid bodies. The generalized constrained dynamics equations of motion are developed utilizing constraint potential energy functions that enforce communication constraints. The constraint potential energy is synthesized based on a graph theoretic formulation of the vehicle-vehicle communication. Additional constraint stabilization is achieved via a modified Baumgarte stabilization method. The performance of these constrained dynamics based formations is evaluated for bounded control authority.
Reverse Power Flow Due to Solar Photovoltaic in the Low Voltage Network

Distributed generation has enhanced power production in recent times. Due to their benefits, Ghana is interested in grid-tied solar photovoltaic (PV) systems. Despite the benefits, solar PV integration studies in Ghana have not advanced. This study examines reverse power flow (RPF) due to solar PV in Low Voltage (LV) network branches. The methodology uses a modified IEEE European test network and an Electricity Company of Ghana (ECG) LV network. ETAP software is used to simulate the two solar PV integrated LV networks, and the obtained data is used to formulate correlation models of solar PV penetration and key network parameters in Excel. Model results estimate the RPF critical values for the modified IEEE European test network and the ECG LV network as 7.36 kW and 7.44 kW, respectively. The RPF values are obtained at maximum penetration depths of 62.6% and 69.8% respectively. At maximum penetration levels, predicted line loadings are 6.42% and 7.28% respectively. Further analysis reveals branch-transformer RPF margins of 26.8% and 23.1% in the modified IEEE European test network and the ECG LV network respectively. The results are essential for establishing pre-determined settings to safeguard LV network branches and transformers from overload due to RPF.
Synergy, Additivity and Antagonism between Esculetin and Six Commonly Used Chemotherapeutics in Various Malignant Melanoma Cell Lines—An Isobolographic Analysis

(1) Malignant melanomas are dangerous skin cancers, and the treatment of melanomas with various cytostatic drugs often causes side effects and after their prolonged use resistance to these drugs appears. The aim of this study was to evaluate the anticancer effects of esculetin (a simple coumarin) and to assess pharmacodynamic interactions between esculetin and six commonly used cytostatic drugs (cisplatin, epirubicin, docetaxel, paclitaxel, mitoxantrone and vemurafenib) using an isobolographic analysis. (2) The experiments were carried out on four human malignant melanoma cell lines (FM55P, A375, FM55M2 and SK-MEL28). The effects of esculetin on viability, cell proliferation and cytotoxicity were verified in the range of concentrations of 2–200 μM. (3) Esculetin inhibited, in a dose-dependent manner, malignant melanoma cell viability and proliferation. The IC50 for esculetin ranged from 18.20 ± 2.93 to 120.64 ± 30.39 μM depending on the melanoma cell lines used. The combinations of esculetin with epirubicin and vemurafenib showed antagonistic interactions, the combinations of esculetin with cisplatin, docetaxel and paclitaxel showed additive interactions. For the combinations of esculetin with mitoxantrone, the isobolographic analysis displayed synergy. (4) In the treatment of malignant melanoma, esculetin should not be combined with epirubicin or vemurafenib, due to the reduction of their anticancer effects, while the synergistic interactions (esculetin + mitoxantrone) deserve a preclinical recommendation as a beneficial combination during anticancer therapy.
Synergistic Effects of Tragacanth and Anti-ethylene Treatments on Postharvest Quality Maintenance of Mango (Mangifera indica L.)

Mango (Mangifera indica L.) is one of the most popular tropical fruits grown in Egypt and several other countries, making it a potential export commodity. Excessive deterioration after harvest requires various treatments to maintain fruit quality. We evaluated the treatments effects of melatonin (MT) as an anti-ethylene agent and tragacanth gum (TRG) as an edible coating individually and together (MT–TRG) before storing mangoes at 12 °C for 32 days under 85–90% relative humidity. Compared with control, all treatments were significantly effective in preserving fruit quality. Fruits treated with MT–TRG showed significantly lower decay values, respiration rates, ethylene production, and weight loss than untreated fruits. MT–TRG treatment significantly enhanced fruit quality, thereby maintaining fruit appearance, flesh color, firmness, total soluble solids and phenolic contents, and pectin methyl esterase, polyphenol oxidase, and peroxidase activities during the storage period. We propose 200 µM MT + 1% TRG as a safe postharvest treatment to reduce the deterioration of mangoes and maintain fruit quality.
SDG-L: A Semiparametric Deep Gaussian Process based Framework for Battery Capacity Prediction

Lithium-ion batteries are becoming increasingly omnipresent in energy supply. However, the durability of energy storage using lithium-ion batteries is threatened by their dropping capacity with the growing number of charging/discharging cycles. An accurate capacity prediction is the key to ensure system efficiency and reliability, where the exploitation of battery state information in each cycle has been largely undervalued. In this paper, we propose a semiparametric deep Gaussian process regression framework named SDG-L to give predictions based on the modeling of time series battery state data. By introducing an LSTM feature extractor, the SDG-L is specially designed to better utilize the auxiliary profiling information during charging/discharging process. In experimental studies based on NASA dataset, our proposed method obtains an average test MSE error of 1.2‰. We also show that SDG-L achieves better performance compared to existing works and validate the framework using ablation studies.
Positive-Pair Redundancy Reduction Regularisation for Speech-Based Asthma Diagnosis Prediction

Asthma affects an estimated 334 million people worldwide, causing over 461 000 deaths. Exacerbations or asthma attacks can be predicted with new sensor technologies. We explore how recordings of human voice, and machine learning can provide better diagnostics for pulmonary diseases like asthma, as well as tools for helping patients better manage it. Past studies have focused on data collection processes that either mimic traditional auscultation, or make multi-sensor measurements, where the application of specialised recording hardware is required, possibly by expert personnel. This is costly and places limits on the size of the studies (e.g., number of study participants, and recording devices). In this paper, we consider another avenue, that of modelling self-recorded voice samples made using regular smartphones, along with self-reported clinical diagnosis annotations; specifically of asthma. We propose the usage of self-supervised learning that aims to reduce within-class representation redundancy among heterogeneous samples as an auxiliary task to promote robust, bias-free learning. The application of our method achieves an absolute increase of 1.80% in area under the Precision-Recall curve, compared to not using it, and a total of 3.54% compared to our baseline.
The nonlinear relationships between built environment features and urban street vitality: A data-driven exploration

The relationship between the built environment and urban street vitality, as a key issue of contemporary urban design, has been discussed over decades. However, most existing studies relying on linear regression models do not reveal the complicated impacts of built environment features and often neglect their threshold effects. As a response, this study applies the gradient boosting decision tree (GBDT) model with a large amount of new urban data to explore the in-depth understanding of urban street vitality. Based on the street samples from 12 Chinese cities, a series of morphological, functional, and human-scale features were analyzed together with socioeconomic indicators as control variables. The street vitality is measured by street activity intensity computed from billions of location-based service records. The results show that the nonlinear model brings an overall improvement in resolution. Specifically, compared with the functional and human-scale features, the morphological characteristics, especially the street intersection density, average block size, and building density, are dominant contributors to street vitality. It is also worth noting that most built-up environment features obtain the threshold effects on street vitality, which means there is a turning point where the effect of features changes. The interaction between built environment characteristic variables is common and can be divided into two typical types. Insights achieved in this study help to indicate an effective interval of built environment characteristics on vitality, which was missed in previous studies, and thus contribute to more precise urban design practices. Moreover, by clarifying the interaction influence mechanism, this study emphasizes the need for the planner to exploit synergies between variables through optimal combinations while avoiding their antagonistic effects.
Influence of WO3-Based Antireflection Coatings on Current Density in Silicon Heterojunction Solar Cells

Antireflection coatings (ARCs) with an indium thin oxide (ITO) layer on silicon heterojunction solar cells (SHJ) have garnered significant attention, which is due to their potential for increasing current density (Jsc) and enhancing reliability. We propose an additional tungsten trioxide (WO3) layer on the ITO/Si structure in this paper in order to raise the Jsc and demonstrate the influence on the SHJ solar cell. First, we simulate the Jsc characteristics for the proposed WO3/ITO/Si structure in order to analyze Jsc depending on the thickness of WO3 using an OPAL 2 simulator. As a result, the OPAL 2 simulation shows an increase in Jsc of 0.65 mA/cm2 after the 19 nm WO3 deposition on ITO with a doping concentration of 6.1 × 1020/cm2. We then fabricate the proposed samples and observe an improved efficiency of 0.5% with an increased Jsc of 0.75 mA/cm2 when using a 20 nm thick WO3 layer on the SHJ solar cell. The results indicate that the WO3 layer can be a candidate to improve the efficiency of SHJ solar cells with a low fabrication cost.
DeepCOVID-Fuse: A Multi-Modality Deep Learning Model Fusing Chest X-rays and Clinical Variables to Predict COVID-19 Risk Levels

The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, highlighting the need for accurate and timely risk prediction models that can prioritize patient care and allocate resources effectively. This study presents DeepCOVID-Fuse, a deep learning fusion model that predicts risk levels in patients with confirmed COVID-19 by combining chest radiographs (CXRs) and clinical variables. The study collected initial CXRs, clinical variables, and outcomes (i.e., mortality, intubation, hospital length of stay, Intensive care units (ICU) admission) from February to April 2020, with risk levels determined by the outcomes. The fusion model was trained on 1657 patients (Age: 58.30 ± 17.74; Female: 807) and validated on 428 patients (56.41 ± 17.03; 190) from the local healthcare system and tested on 439 patients (56.51 ± 17.78; 205) from a different holdout hospital. The performance of well-trained fusion models on full or partial modalities was compared using DeLong and McNemar tests. Results show that DeepCOVID-Fuse significantly (p < 0.05) outperformed models trained only on CXRs or clinical variables, with an accuracy of 0.658 and an area under the receiver operating characteristic curve (AUC) of 0.842. The fusion model achieves good outcome predictions even when only one of the modalities is used in testing, demonstrating its ability to learn better feature representations across different modalities during training.
Spiritual Needs of Older Adults Living with Dementia: An Integrative Review

Older adults living with dementia experience progressive decline, prompting reliance on others for spiritual care and support. Despite a growing interest in studying persons living with dementia (PLwDs), empirical evidence on the spiritual needs of PLwDs has not been synthesized. Using the Whittemore and Knafl method, this integrative review examined the literature from 2000 to 2022 on the spiritual care needs of PLwDs. We sought to identify characteristics of the spiritual needs of PLwDs and ways to address them. The ATLA Religion, CINAHL, PsycINFO, PubMed, and Socindex databases were used to search the literature, and 12 peer-reviewed articles met the inclusion criteria. Spiritual care needs varied across studies. Overall, findings support the importance of identifying PLwDs’ religious and spiritual backgrounds to inform person-centered care. Spiritual needs were identified as verbal and non-verbal expressions related to past meaning and religious and spiritual background and were not consistently addressed in care. Providers reported observing spiritual distress in the mild stage prompting the need for spiritual care. There is a great need for dementia-specific spiritual assessment tools and spiritual care interventions to support spiritual well-being in dementia care. Spiritual care involves facilitating religious rituals and providing spiritual group therapy and religious and spiritual activities.
Complementary Learning System Based Intrinsic Reward in Reinforcement Learning

Deep reinforcement learning has achieved encouraging performance in many realms. However, one of its primary challenges is the sparsity of extrinsic rewards, which is still far from solved. Complementary learning system theory suggests that effective human learning relies on two complementary learning systems utilizing short-term and long-term memories. Inspired by the fact that humans evaluate curiosity by comparing current observations with historical information, we propose a novel intrinsic reward, namely CLS-IR, which aims to address the problems caused by sparse extrinsic rewards. Specifically, we train a self-supervised predictive model with short-term and long-term memories via exponential moving averages. We employ the information gain between the two memories as the intrinsic reward, which does not incur additional training costs but leads to better exploration. To investigate the effectiveness of CLS-IR, we conduct extensive experimental evaluations; the results demonstrate that CLS-IR can achieve state-of-the-art performance on Atari games and DeepMind Control Suite.
Coupled Overset Unstructured Discontinuous Galerkin Method for Launch Environment Acoustics Prediction

A novel approach for the accurate prediction of launch environment acoustic physics is presented. Launch vehicles experience extreme acoustic loads during liftoff, driven by the interaction of rocket plumes and plume-generated acoustic waves with ground structures. In this work, a well-established hybrid Reynolds-averaged Navier–Stokes/large-eddy simulation unstructured mesh solver is used to efficiently model the complex turbulent plume physics, and a high-order accurate discontinuous Galerkin solver is used to accurately propagate acoustic waves across large distances throughout the launch environment. The two solvers operate on separate overlapping meshes, and an innovative overset coupling approach is used to transmit the plume-generated acoustics to the far field in a one-way manner in which the turbulent plume prediction is unaffected by the outer acoustic propagation physics. The framework upon which the solvers are developed is described along with details outlining the overset domain connectivity.
(H,Ω)DUALITY THEORY FOR NONLINEAR PROGRAMMING

By use of the (H,Ω) conjugate function theory,the (H,Ω) duality theory isdeveloped for the following nonlinear programming:min f(x)s.t. g(x)≧0,h(x)=0,z∈X(? )R~nwhere f∶R~n→R,g∶R~n→R~m,h∶R~n→R~P.Almost all results to date on dualityof nonlinear programming can be considered as special cases of this theory.
The Association Between College Students` Participation Behavior and Social Media Use

This paper examines college students` participation behaviors and the use of social media tools. The descriptive survey model was applied to investigate university students` participation behaviors. The data was collected through a “Participation Questionnaire” from 284 college students in Kazakhstan. The result showed that students who spend less time on social media are positively related to participatory citizenship. Moreover, students with fewer social media accounts are more likely to engage in political, volunteer, and social events than students with fewer social media accounts.