Miracle-dz/UniFAR-FTUs huggingface dataset:
Creative Industries: Contracts between Art and Commerce

Preface Introduction: Economic Properties of Creative Activities Part I: Supplying Simple Creative Goods 1. Artists as Apprentices 2. Artists, Dealers, and Deals 3. Artist and Gatekeeper: Trade Books, Popular Records, and Classical Music 4. Artists, Starving and Well-Fed Part II: Supplying Complex Creative Goods 5. The Hollywood Studios Disintegrate 6. Contracts for Creative Products: Films and Plays 7. Guilds, Unions, and Faulty Contracts 8. The Nurture of Ten-Ton Turkeys 9. Creative Products Go to Market: Books and Records 10. Creative Products Go to Market: Films Part III: Demand for Creative Goods 11. Buffs, Buzz, and Educated Tastes 12. Consumers, Critics, and Certifiers 13. Innovation, Fads, and Fashions Part IV: Cost Conundrums 14. Covering High Fixed Costs 15. Donor-Supported Nonprofit Organizations in the Performing Arts 16. Cost Disease and Its Analgesics Part V: The Test of Time 17. Durable Creative Goods: Rents Pursued through Time and Space 18. Payola 19. Organizing to Collect Rents: Music Copyrights 20. Entertainment Conglomerates and the Quest for Rents 21. Filtering and Storing Durable Creative Goods: Visual Arts 22. New versus Old Art: Boulez Meets Beethoven Epilogue Notes Index
IMG2HEIGHT: height estimation from single remote sensing image using a deep convolutional encoder-decoder network

ABSTRACT Height estimation from single remote sensing image is a challenging inherently ambiguous and technically ill-posed problem that we address in this study by resorting to deep learning approach. A spatial enhanced and multi-scale aggregated encoder-decoder network, SM-EDNet, is proposed, which takes a single image as input and produces an estimated height map as output. First, residual network (ResNet) is applied to extract low-level and deep features to cope with the heterogeneous characteristics of remote sensing scenes. Then, the multi-scale context information is aggregated through DenseASPP (Dense Atrous Spatial Pyramid Pooling) by extracting features from multiple dilated convolution layers. The skip connection is constructed by using the structure preserving model, DULR, to aggregate ResNet low-level features and multi-scale high-level features. The deformable convolution module is constructed to enhance the sensitivity to differences in geometric shapes of ground objects. For model training, three-layer deep supervision mechanism is designed to counteract the adverse effects of unstable gradients changes. Experimental results on three benchmark datasets, including ISPRS Vaihingen, ISPRS Potsdam, and DFC2018, show that the proposed method achieves the most outstanding performance compared with the state-of-the-art networks. The source codes are available at: https://github.com/xjh0929/2HEIGHT.
Using Video Calls to Reduce Risky Behaviors and Enhance Instruction Understanding of Patients in Acute Care Hospitals During the COVID-19 Pandemic

Backgrounds During the COVID-19 pandemic, visitor restrictions in healthcare settings adversely affected patients. Video calls have emerged as an essential digital alternative that can decrease patients’ anxiety and improve satisfaction. This study investigated whether family-initiated video calls could mitigate delirium symptoms and risky behaviors and enhance patients’ comprehension of instructions. Methods This observational study used medical chart data and the Diem Payment System from a single acute care hospital in Fukuoka, Japan. The study involved patients hospitalized between May 2020 and August 2021 who used video chat systems. Patients or their relatives used video calls through Skype. The frequency of video chat use served as the primary exposure. Changes in the patients' risky behaviors and instruction comprehension upon discharge were the primary outcomes. Results A total of 532 patients were included in the study, with an average age of over 70 years. After implementing the inverse probability of treatment weighting adjustment, an improved balance across age, sex, BMI categories, and other variables was observed. The effects of video calls on risky behaviors and instruction comprehension varied. Patients with three or more video calls showed distinct effects compared with those with fewer calls. When hospitalization was limited to three weeks, video calls noticeably influenced risky behaviors (p=0.022, 95% CI:1.08-2.63), but not instruction comprehension (p=0.226, 95% CI:0.43-1.22). Conclusions The use of video calls as a visitation method in acute care hospitals during a pandemic suggests that video calls reduce risky behaviors in patients with a three-week stay. This alternative to physical visitations contributes positively to patient safety and supports ongoing efforts to prevent the spread of COVID-19.
Validity of method de Haan for wind speed and set-up

Failure of coastal protection due to flooding is mainly caused by a combination of high water level and waves. If one considers these as independent variables, the probability of failure can be calculated easily, but this would be an incorrect assumption. These two variables are de-pendent, so a method suitable for multivariate statistics is to be applied, if one does not want to over- of underestimate the probability of failure. 'Method de Haan' is a method that is suit-able for extrapolation of dependent variables. In general this method shifts a cloud of observa-tions in exponential space into the area of failure or other relevant area. Because this method does not have a physical basis it is important that the relation between the variables that are used, holds in cases of extreme events if one wants to determine events with a very low fre-quency of occurrence. In this study the relation between (skewed) set-up and wind speed is examined. Most impor-tant reason to examine these variables is that at RIKZ (National Institute for Coastal and Ma-rine Management) these variables are used to determine the hydraulic variables in case of extreme events. A relation between water level and wave height is used to determine the cor-responding wave heights. Because the highest water levels are of importance skewed set-up is used as a variable instead of water level. By using this variable a more homogeneous data set is generated and the high-est water level can be constructed afterwards. Skewed set-up is the surge effect determined by the highest water level around during a tidal cycle, where this moment does not necessarily coincides with the moment the astronomical tide occurs. Data of four locations are used, covering a period of 23 years of simultaneous data. In order to use homogeneous data selections based on wind speed and set-up are carried out. Three dif-ferent thresholds are imposed for these selection procedures. The extrapolation path with method de Haan using these data sets is compared with a reference situation and results of hydrodynamic model runs where manipulated storm fields are used as input. In 'WAQUA', over 80 model runs are carried out, where a wind and atmospheric pressure field of the 1953 storm, is manipulated. The intensities, storm duration and phase of the storm relative to the astronomical tide are varied. Based on model runs with WAQUA the relation between skewed set-up and aver-age/maximum wind speed at a certain location appears to hold under extreme circumstances. However, only one wind drag relation (Charnock) is used in the model runs. The relation be-tween skewed set-up and average/maximum wind speed with method de Haan (extrapolation path) is physically more plausible if the asymptotic dependence is good, judged by the asymp-totic plots. The extrapolation path also is better if data are selected based on thresholds re-garding set-up. These paths lie closer to the reference situation, although strongly varying with different thresholds that were used.
Modelling the Environmental Implications of Car Ownership and Energy Consumption in the UK: Evidence from NARDL Model

We investigated the asymmetric effects of energy consumption, car ownership and tourism activities on CO 2 emissions in the UK. Empirical results from the Non-Linear Autoregressive Distributed Lag (NARDL) model reveal that in the UK, only car ownership has asymmetric effects on emissions with a magnitude of -1.428% (positive) and 10.108% (negative) shocks that highlight the impact of car ownership on emission level in UK while rising energy consumption and GDP have symmetric positive impacts on emissions, and tourism has a negative impact on emissions. Furthermore, on causality analysis, we found a unidirectional causality runs from GDP per capita to car ownership, and that car ownership and tourism both causes energy consumption in a one-way relationship. Apart from encouraging environmentally friendly energy sources to reduce carbon emission in the UK, the
A Relay-Assisted Communication Scheme for Collaborative On-Device CNN Execution Considering Hybrid Parallelism

Deep learning (DL) has gained increasing prominence in latency-critical artificial intelligence (AI) applications. Due to the intensive computational requirements of these applications, cloud-centric approaches have been attempted to address this issue, but they result in intolerable latency, network congestion, and privacy concerns. An alternative concept called edge intelligence, which combines AI and edge computing, has been proposed to perform DL execution at the edge in multiple resource-constrained devices (RCDs) collaboratively. This paper proposes a relay-assisted, distributed, and collaborative on-device convolutional neural network (CNN) execution scheme for latency-critical applications. The system employs hybrid parallelism, combining both data and model parallelism, to optimize collaborative CNN execution on RCDs. A relay-assisted communication technique is used to reduce the input data size per RCD and avoid excessive point-to-point communication between the data owner RCD and collaborative RCDs. The proposed approach reduces communication overhead using two strategies: layer block formation and optimal filter assignment. These strategies are applied to multiple collaborative RCDs, considering their different computing capabilities and network conditions. Finally, a convex optimization problem is formulated to minimize the overall energy consumption by jointly optimizing the workload of each RCD in each layer and communication and computation parameters.
The Impact of Paper Versus Digital Map Technology on Students' Spatial Thinking Skill Acquisition

Abstract This study investigates whether spatial learning outcomes differ with respect to different instructional media. Spatial thinking skills were tested pre- and postlesson implementation via the spatial thinking ability test as eighth grade students participated in either paper-based or digital map-based instruction. Students' attitudes toward geography, past travel experience, and demographic variables such as gender, were also examined to search for differences in spatial thinking skill development. Results of the study reveal that both paper and digital media aid in developing and improving spatial thinking skill acquisition among students.
Multiplicity and Path Dependency in the Modeling of Historical Routes: A Case Study of the Wonju Basin in the Central Region of the Korean Peninsula

This article introduces how an interdisciplinary approach to modeling historical routes enriches route formation discourses. It combines the current least cost path-based modeling with a novel method developed by the authors, which employs hillslope position-based movement suitability as the primary cost factor to supplement slope gradient. Multiple routes can be modeled by using differently weighted combinations of the two cost factors, but for this study, three different routes were modeled for the case region, the Wonju Basin of the central Korean Peninsula, using archaeological site data roughly categorized into six time periods. By modeling the three different routes for each period, six time slices of movement were produced, and overlapping route sections were identified by superimposing the time slices. The results highlighted the multiplicity of routes, often overlooked when only the most optimal single route is modeled. Diachronic modeling also illustrated how route formation over time was influenced by self-enforcing processes, thereby illuminating path dependency. Guided by complex systems theory, this article provides insights into how the emergence of routes can be approached as both a physical and human spatial phenomenon.
Photonics of Some Monomethine Cyanine Dyes in Solutions and in Complexes with Biomolecules

In search of new probes for biomolecules, the spectral fluorescent study of four monomethine cyanine dyes (MCD), both unsymmetrical and symmetrical, has been carried out in different organic solvents, in aqueous buffer solutions, and in the presence of DNA and HSA. The complexation of MCD with biomacromolecules leads to a steep growth of the fluorescence intensity. Complexes of MCD with dsDNA and HSA of various types were modeled in silico by molecular docking. Experiments on thermal dissociation of dsDNA in the presence of MCD showed the formation of intercalative complexes of MCD with DNA. Quenching of intrinsic fluorescence of HSA by MCD occurred with rate constants much higher than the diffusion limit, that is, in dye–HSA complexes. Effective constants of MCD complexation with the biomacromolecules were estimated. MCD 1 has the best characteristics as a possible fluorescent probe for dsDNA and can serve as a sensitive and selective probe for dsDNA in the presence of HSA. Photochemical properties of MCD complexed with DNA have been also studied. An increase in the quantum yield of the triplet states of MCD in complexes with DNA has been found, which may be important for using these dyes as potential candidates in photodynamic therapy.
DYNAMICS OF SANDWICH CURVED BEAMS WITH VISCOELASTIC CORE DESCRIBED BY FRACTIONAL DERIVATIVE OPERATORS

This paper presents a ﬁnite element formulation for transient dynamic analysis of sandwich curved beams with embedded viscoelastic material whose constitutive behavior is modeled by means of fractional derivative operators. The sandwich conﬁguration is composed of a band as a viscoelastic core bonded to elastic metallic strips. The viscoelastic model used to describe the behavior of the core is a four-parameter fractional derivative model. The Gr¨unwald deﬁnition of the fractional operator is used to implement the viscoelastic model into a ﬁnite element formulation. Then, discretized motion equations are solved with a direct time integration scheme based on the Newmark method. A useful aspect of the procedure is that only the anelastic displacements history is kept. This allows an important save of computational resources associated with the non-locality of the operators for fractional derivatives. Numerical studies are presented in order to validate the curved beam model with other approaches (frames of straight beams) as well as to analyze the inﬂuence of different parameters in the
Auranofin Modulates Thioredoxin Reductase/Nrf2 Signaling in Peripheral Immune Cells and the CNS in a Mouse Model of Relapsing–Remitting EAE

Multiple sclerosis (MS) is one of the most prevalent chronic inflammatory autoimmune diseases. It causes the demyelination of neurons and the subsequent degeneration of the central nervous system (CNS). The infiltration of leukocytes of both myeloid and lymphoid origins from the systemic circulation into the CNS triggers autoimmune reactions through the release of multiple mediators. These mediators include oxidants, pro-inflammatory cytokines, and chemokines which ultimately cause the characteristic plaques observed in MS. Thioredoxin reductase (TrxR) and nuclear factor erythroid 2-related factor 2 (Nrf2) signaling plays a crucial role in the regulation of inflammation by modulating the transcription of antioxidants and the suppression of inflammatory cytokines. The gold compound auranofin (AFN) is known to activate Nrf2 through the inhibition of TrxR; however, the effects of this compound have not been explored in a mouse model of relapsing–remitting MS (RRMS). Therefore, this study explored the influence of AFN on clinical features, TrxR/Nrf2 signaling [heme oxygenase 1 (HO-1), superoxide dismutase 1 (SOD-1)] and oxidative/inflammatory mediators [IL-6, IL-17A, inducible nitric oxide synthase (iNOS), myeloperoxidase (MPO), nitrotyrosine] in peripheral immune cells and the CNS of mice with the RR type of EAE. Our results showed an increase in TrxR activity and a decrease in Nrf2 signaling in SJL/J mice with RR-EAE. The treatment with AFN caused the amelioration of the clinical features of RR-EAE through the elevation of Nrf2 signaling and the subsequent upregulation of the levels of antioxidants as well as the downregulation of oxidative/pro-inflammatory mediators in peripheral immune cells and the CNS. These data suggest that AFN may be beneficial in the treatment of RRMS.
Hip fracture; an interruption that has consequences four months after a hip fracture. A qualitative study

Background: Effects following a hip fracture often lead to functional disabilities and increased dependence on others. Although persons sustaining a hip fracture constitute a heterogeneous group in Swedish health care they tend to be treated as a homogenous one. Aim: The aim of this study was to reveal how previously healthy people, aged 65 years and older, described how they had adapted to daily life four months after a hip fracture. Method: The follow-up interviews were performed by the first author four months after the hip fracture. Data were analysed using conventional inductive content analysis. Findings: The results from the interviews highlights that sustaining a hip fracture —even four months later— was seen by the participants as an interruption leading to lasting consequences for everyday life. The recovery process during this period was complex and consisted of both physical and psychological strain. Some were resigned, some strived in order to regain independence and some handled the situation by means of self-confidence and self-efficacy. Conclusion: Previous healthy and independently-living participants described, in different ways that the hip fracture was an interruption that still affected everyday life. The absence of psychological support may be one of the reasons for dependency after four months.
. (2016). A High Input Impedance Low Noise Integrated Front-End Amplifier for Neural Monitoring. IEEE Transactions on Biomedical Circuits and Systems, 10(6), 1079-1086.

—Within neural monitoring systems, the front-end amplifier forms the critical element for signal detection and pre-processing, which determines not only the fidelity of the biosignal, but also impacts power consumption and detector size. In this paper, a novel combined feedback loop-controlled approach is proposed to compensate for input leakage currents generated by low noise amplifiers when in integrated circuit form alongside signal leakage into the input bias network. This loop topology ensures the FEA maintains a high input impedance across all manufacturing and operational variations. Measured results from a prototype manufactured on the AMS 0. 35µm CMOS technology is provided. This FEA consumes 3.1 µW in 0.042 mm 2 , achieves input impedance of 42 GΩ, and 18.2 𝒏𝒗/√𝑯𝒛 input-referred noise.
Acute Toxicity and Anti-Inflammatory Activity of the Aqueous and Alcohol Extracts of Philippine Endemic Pandanus spp.

The leaf extracts of three endemic species of Pandanus namely, P. luzonensis , P. panayensis , and P. simplex were investigated for their safety profile and anti-inflammatory property. The Approximate Lethal Dose (ALD) was established through acute oral toxicity test. Carrageenan-induced rat paw edema assay assessed the anti-inflammatory property. Each treatment group received assigned dose of extract (250, 500, 1000 mg/kg BW), 1% Tween 80 and diclofenac sodium (100 mg/kg BW) for the negative and positive control groups, respectively. The ethanol leaf extract P. panayensis at 500 mg/kg BW showed sustained inflammatory inhibition for 6 hours of observation with comparable inhibitory activity with diclofenac sodium (ave p=0.970). Histopathology of the inflamed paw tissues showed mild inflammation. IC 50 against COX-1 and 2 are 610.69 ug/mL and >1000 ug/mL respectively against <10 ug/mL of Indomethacin as standard. These results suggest that all Pandanus leaf extracts have an ALD greater than 2000 mg/kg BW while P. payanensis extracts may serve as potential sources of anti-inflammatory agents. This activity may be contributed by flavonoids, steroids, terpenoids and fatty acid which were identified by Liquid Chromatography-Mass Spectrometry.
Robust Four-Channel Teleoperation Through Hybrid Damping-Stiffness Adjustment

This paper presents three strategies that adjust the coordination damping and stiffness of four-channel teleoperators to maintain the teleoperation stable regardless of time-varying delays in the transmission of operator and environment forces between the master and slave robots. A first strategy employs hybrid control terms that depend on position errors and local velocities. Thus, the hybrid terms simultaneously and dynamically regulate the master–slave coupling and the local damping injections. They increase the robustness of the system to perturbations caused by delayed transmission of operator and environment forces, but are singular at zero velocities. A second strategy injects additional damping around zero velocities, according to the master–slave position error. The additional damping makes the hybrid term nonsingular and eliminates chattering at zero velocities. However, it cannot synchronize the master and slave robots in the presence of large position error. This problem is addressed by a third strategy, which reduces the order of the position error in the hybrid term to guarantee the dominance of the Proportional term in coordination. Then, the two robots can be synchronized from arbitrarily large position errors. Lyapunov stability analysis and hardware-in-the-loop experimental results verify and compare these three proposed hybrid approaches.
Pyramid Transformer Driven Multibranch Fusion for Polyp Segmentation in Colonoscopic Video Images

Colonoscopic polyp segmentation is essential and valuable to early diagnosis and treatment of colorectal cancer. It remains challenging to accurately extract these polyps due to their small sizes, irregular shapes, image artifacts, and illumination variations. This work proposes a new encoder-decoder architecture called pyramid transformer driven multibranch fusion to precisely segment different types of colorectal polyps during colonoscopy. Specifically, our architecture employs a simple, convolution-free pyramid transformer as its encoder that is a flexible and powerful feature extractor. Next, a multibranch fusion decoder is employed to reserve the detailed appearance information and fuse semantic global cues, which can deal with blurred polyp edges caused by nonuniform illumination and the shaky colonoscope. Additionally, a hybrid spatial-frequency loss function is introduced for accurate training. We evaluate our proposed architecture on colonoscopic polyp images with four types of polyps with different pathological features, with the experimental results showing that our architecture significantly outperforms other deep learning models. Particularly, our method improves the average dice similarity and intersection over union to 90.7% and 0.848, respectively.