Miracle-dz/UniFAR-FTUs huggingface dataset:
Doing institutions. A dialectic reading of institutions and social practices and its relevance for development geography

Summary : Twelve years after the discussions on development and institutions at the meeting of the Geographischer Arbeitskreis Entwicklungstheorien ( GAE 2000) in Zürich, this paper seeks to put institutions back on the research agenda in development geography. The authors explore recent trends in institutional theory and propose a dialectic understanding of “doing institutions” that positions social actors and their everyday practices at the center of institutional analysis. Institutions are the socially (re-)produced rules that enable, constrain and give meaning to the social practices of actors. Actors make institutions; at the same time institutions make actors do certain things. Based on this foundation, three central aspects of the institutional debate are elaborated: Institutional plurality, the legitimacy of institutions, and the role of the state. Two case studies from recent research in South Asia illustrate aspects of the conceptual considerations. The article ends with an out-look on the implications of “doing institutions” for future research in development geography. dem soziale Akteure und ihre alltäglichen Praktiken im Zentrum der Analyse stehen. Menschen machen Institutionen und gleichzeitig strukturieren Institutionen menschliches Handeln. Wir definieren Institutionen daher als sozial (re)produzierte Regeln, welche die alltäglichen Praktiken von Akteuren ermöglichen, beschränken und ihnen eine Bedeutung zuweisen. Drei zentrale Aspekte der Instituti-onendebatte werden aufgeworfen: die Pluralität von institutionellen Regelungen, die Legitimität von Institutionen sowie die Rolle des Staates. Das entwickelte Verständnis von Institutionen und sozialer Praxis wird anhand von zwei Fallstudien aus Südasien illustriert. Der Beitrag schließt mit einem Ausblick auf mögliche zukünftige Forschungsfelder der Geographischen Entwicklungsforschung.
O R I G I N a L I N V E S T I G a T I O N Open Access

Genetic analysis of haptoglobin polymorphisms with cardiovascular disease and type 2 diabetes in the diabetes heart study Abstract Background: Haptoglobin (HP) is an acute phase protein that binds to freely circulating hemoglobin. HP exists as two distinct forms, HP1 and HP2. The longer HP2 form has been associated with cardiovascular (CVD) events and mortality in individuals with type 2 diabetes (T2DM). Methods: This study examined the association of HP genotypes with subclinical CVD, T2DM risk, and associated risk factors in a T2DM-enriched sample. Haptoglobin genotypes were determined in 1208 European Americans (EA) from 473 Diabetes Heart Study (DHS) families via PCR. Three promoter SNPs (rs5467, rs5470, and rs5471) were also genotyped.
Bulk-power Grid Support: Distributed OPF for Voltage and Frequency Regulation

As the electric power distribution system evolves towards a distributed architecture, the task of providing ancillary services to bulk-power systems through grid-edge assets becomes increasingly crucial. In this paper, we present a novel, scalable, distributed optimization-based method formulated to deliver such ancillary services, including voltage and frequency regulation, by coordinating reactive and active power outputs from grid-edge devices. The proposed method has been extensively validated through comparisons with equivalent solvers, demonstrating its effectiveness and feasibility.
Classification of Retinal Occlusion in Fundus Images

Convolutional Neural Networks (CNNs), a type of Artificial Neural Network is used for the detection of eye diseases in the field of ophthalmology. Eye diseases are a significant issue globally, especially in developing countries, but the use of pattern recognition techniques, such as CNNs, is helping to address the problem. The proposed algorithm is a novel intelligent pattern classification system that uses a CNN and will be tested using the K-Fold Cross Validation method.The performance of the classifier will be confirmed by high numerical metrics such as Accuracy, Recall, Specificity, Precision, and F1 score. Retinal occlusion is a disease that can lead to loss of vision. They are classified into 3 types: Central Retinal Vein Occlusion, Branch Retinal Vein Occlusion and Retinal Artery Occlusion. Detection of these diseases in the early stages can help the patient from permanent loss of vision. In early stages of retinal occlusion, detection of disease is very difficult, and, in some cases, there may be combined retinal vascular occlusion that make it difficult to classify. Detection and classification of this diseases during early stages may help the patient from cardio-vascular diseases. The use of automated diagnosis for retinal occlusion can streamline clinical procedures and optimize treatment plans. Unfortunately, patients with retinal conditions may experience severe vision loss due to limited awareness of these diseases and inadequate medical resources. The project can detect and classify retinal occlusion diseases. If a combination of retinal occlusion occurs, then the severity cannot be known in the early stages. This project checks for combination and predict the severity of the disease.
A Stochastic Earthquake Ground‐Motion Prediction Model for the United Kingdom

Low‐seismicity regions such as the United Kingdom (UK) pose a challenge for seismic hazard analysis in view of the limited amount of locally recorded data available. In particular, ground‐motion prediction is faced with the problem that most of the instrumental observations available have been recorded at large distances from small earthquakes. Direct extrapolation of the results of regression on these data to the range of magnitudes and distances relevant for the seismic hazard analysis of engineered structures generally leads to unsatisfactory predictions. The present study presents a new ground‐motion prediction equation (GMPE) for the UK in terms of peak ground acceleration (PGA), peak ground velocity (PGV), and 5% damped pseudospectral acceleration (PSA), based on the results of numerical simulations using a stochastic point‐source model calibrated with parameters derived from local weak‐motion data. The predictions from this model are compared with those of previous GMPEs based on UK data, other GMPEs derived for stable continental regions (SCRs), as well as recent GMPEs developed for the wider European area.
High-level expression and purification of a Mollusca 1 endoglucanase from Ampullaria crossean in Pichia . pastoris

15 EG27I is an endogenous glucanase belonging to glyco side hydrolase family (GHF) 45 16 from the Mollusca, Ampullaria crossean. In this study, the EG27I mature peptide gene 17 fused to HFBII secretion signal of Trichoderma reesei was expressed under GAP 18 promoter of Pichia pastoris in SMD1163 strain. Bioactive EG27I with a molecular 19 weight of 27 kDa was successfully expressed and sec reted into the culture medium. 20 When high cell density fermentation of the recombin ant Pichia pastoris was 21 performed by a fed-batch strategy for totally 132 h in a 7.5 L fermenter, the OD 600 and 22 the hydrolytic activity reached final values of 333 and 1.28 U/mL, respectively. The 23 recombinant protein concentration was 47.7 mg/L of fermentation supernatant. EG27I 24 was purified from the fermentation supernatant by u ltrafiltration, cation exchange and 25 hydrophobic interaction, consecutively. The specifi c activity of the recombinant 26 EG27I was 26.8 U/mg. The optimal pH and temperature fo enzyme were pH 5 and 27 50 °C, respectively. The half-life of enzyme activi ty at 100 °C was able to reach 40 28 min. N-terminal amino acid sequence analysis of pur ified recombinant protein 29 confirmed that the amino-terminal sequence was cons istent with natural structure. The 30 high quantity and purity of the EG27I pave the way for future structural and 31 functional studies. 32
Rock phosphate solubilisation by strains of Penicillium spp. isolated from farm and forest soils of three agro ecological zones of Cameroon

Rock Phosphate (RP) is theoretically the cheapest fertilizer and most abundant, but its direct application in the soils is not always agronomically effective due to its low phosphorus (P) availability. An environmentally friendly and economically alternative to traditional processing of these deposits is the use of Phosphate Solubilising Microorganisms (PSM). Three Penicillium strains were screened for their aptitude in solubilising sparingly soluble phosphates as well as phosphate rock on plates and in broth supplemented with the different phosphate types. The three strains show the ability in solubilising at least two from the sparingly soluble phosphates types, with the solubilisation index (SI) varying from 1.44 to 4.74. All the strains were able to show halo zone on plates supplemented with Malian and Algerian rock phosphates with solubilisation index ranging from 1.2 to 1.8. No halo zone was detected on plates with Moroccan rock phosphate as sole phosphate source. However, significant amount of phosphorus were mobilised by all strains in broth with the different rock phosphates, including the Moroccan rock phosphate. The amount of solved P varied from 257.46 mgP.L-1 (Penicillium sp. 43) to 326.65 mgP.L-1 (Penicillium sp. 109) for Malian rock phosphate, from 152.61 mgP.L-1 (Penicillium sp. 43) to 337.56 mgP.L-1 (Penicillium sp. 27) for Algerian rock phosphate, and from 283.79 mgP.L-1 (Penicillium sp. 43) to 347.16 mgP.L-1 (Penicillium sp. 109) for Moroccan rock phosphate. The rock phosphate solubilisation was associated with pH media drop and both parameters were strongly correlated.
Unified-Affine-Projection-Like Subband-Adaptive Filtering Algorithm for Channel Estimation Under Impulsive Noise Environments

The unified-affine-projection-like subbands adaptive filter (UAPLSAF) algorithm is proposed, which makes use of the structure of a multiband adaptive filter and affine projection algorithm, and is robust against for reducing the affects on the outliuers consed by these unexpected impulsive-noises (INs). By changing the suitable parameters in the cost function, we can get different excellent affine projection (AP)-like algorithms. All of these algorithms have outstanding performance under INs. Besides, the created UAPLSAF presents better behaviors if we use convergence rate as an criterion and compare it with the former subband adaptive-filters like IMSAF and APSSAF with a worse noise environments like INs. Simulations have verified the effectiveness of the UAPLSAF algorithm.
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In this paper, the output voltage requirements of the piezoelectric ceramic stack actuator drive circuit are briefly described. A high-voltage charging and discharging circuit is proposed as the piezoelectric ceramic stack actuator drive circuit. Two drive circuit schemes are listed and compared. A practical scheme is selected to build experimental bench and simulation model to verify the analysis. The experimental and simulation results both show that the output voltage amplitude can reach 1kV, the charging time is less than 5ms, and the discharging time is less than 10ms, which satisfies the drive circuit output requirements. Keywords—piezoelectric ceramic stack, drive circuit, actuator, active vibration control,
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This paper investigates reconfigurable physical unclonable function (PUF) design by exploiting the polarization switching variation and stochasticity in ferroelectric field-effecttransistors (FeFETs). The proposed PUFs include 1transistor/cell (1T/C) and 2T/C designs. The denser 1T/C PUF splits random ‘0’ and ‘1’ states using a tactically pre-defined reference. The 2T/C PUF needs no dedicated references and obtains unbiased random states by differentiating two FeFETs under a proposed sensing error cancellation scheme. Experimental measurements have shown the uniform randomness, uniqueness, repeatability and reconfigurability of the response. Further simulations using an experimentally calibrated multi-domain FeFET model show high energy efficiency and robustness on design parameters. Keywords—Physical unclonable function, FeFET, hardware security, stochasticity, reconfigurable PUF, offset cancellation.
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In traditional congestionaware techniques, congestion is measured at a router level and delivered to other routers, either local or nonlocal.In this work, we present an efficient fully adaptive fault-tolerant routing algorithm for 3D Network-on-Chip (3D NoC). The presented approach is discussed in both two-dimensional (2D) and three-dimensional (3D) mesh networks. To collect and propagate the congestion information of different clusters, a distributed approach is presented. Instead of rerouting packets around the fault regions when fault occurs, our proposed algorithm applies a fault detection scheme which can get the fault information one hop away in advance, and it combines the fault information when doing the path computation. This algorithm can deal with multi faults in the 3D NoC architecture. Simulation results show that our proposed routing algorithm can achieve lower latency, energy consumption and higher packet arrival rate compared with other traditional routing algorithms in various network applications.
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Nowadays, environmental pollution, global warming and energy crisis pose a great threat to human development. To deal with these challenges, the concept of global energy interconnection (GEI) is proposed, which aims building an extensively interconnected power grid in the world. The economic dispatch (ED) problem is an essential topic of GEI, in which the country information privacy should be kept. Moreover, along with the time periods increasing, the problem dimension will be larger and cause more calculation burden. Thus, a bi-level distributed algorithm for ED problem of GEI is proposed in this paper. The first level realizes an areadistributed algorithm, which holds the information privacy; the second level gives a time-parallel algorithm, which accelerates the calculation speed. Numerical simulation verifies the effectiveness of proposed algorithm.
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The integration of energy storage technologies with renewable energy systems can significantly reduce the operating costs for microgrids (MG) in future electricity networks. This paper presents a novel energy management system (EMS) which can minimize the daily operating cost of a MG and maximize the self-consumption of the RES by determining the best setting for a central battery energy storage system (BESS) based on a defined cost function. This EMS has a two-layer structure. In the upper layer, a Convex Optimization Technique is used to solve the optimization problem and to determine the reference values for the power that should be drawn by the MG from the main grid using a 15 minute sample time. The reference values are then fed to a lower control layer, which uses a 1 minute sample time, to determine the settings for the BESS which then ensures that the MG accurately follows these references. This lower control layer uses a Rolling Horizon Predictive Controller and Model Predictive Controllers to achieve its target. Experimental studies using a laboratory-based MG are implemented to demonstrate the capability of the proposed EMS. © 20xx xxxxxxxx. Hosting by Elsevier B.V. All rights reserved.
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In this paper, an equivalent dipole moment method and full wave simulation are proposed to predict radiated spurious emissions (RSE). The proposed method is utilized in design of a practical electronic device. Based on near field patterns, the radiation physics of 2.4GHz Wi-Fi transmitter harmonics in the device is identified to be a magnetic dipole moment. Reduction of loop size is proved to be an effective method to reduce RSE of 2.4 GHz. In addition, RSE of 5 GHz Wi-Fi transmitter is simulated using a full wave solver and methods to reduce RSE are discussed. Measured RSE results are also shown to validate proposed prediction and mitigation methods. Keywords—RSE, emission, dipole moment, near field to far field
Update on Polar ozone: Past, present, and future

As stated in the previous Assessments, ozone-depleting substance (ODS) levels reached a maximum in the polar regions around the beginning of this century and have been slowly decreasing since then, consistent with the expectations based on compliance with the Montreal Protocol and its Amendments and adjustments. Considering the current elevated levels od ODSs, and their slow rate of decrease, changes in the size and depth of the Antarctic ozone hole and in the magnitude of the Arctic ozone depletion since 2000 have been mainly controlled by variatins in temperature and dynamical processes
Comment on the application of the Szilagyi–Jozsa advection–aridity model for estimating actual terrestrial evapotranspiration in "Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis" by McMahon et al. (2013)

In the paper by McMahon et al. (2013, supplemen- tary sections S8 and S19, worked example 8), the Szilagyi- Jozsa advection-aridity model (Szilagyi, 2007; Szilagyi and Jozsa, 2008) was not applied in the worked example as in- tended by author J. Szilagyi. This commentary seeks to clar- ify the issue and provide the correct procedure.
WarpDiffusion: Efficient Diffusion Model for High-Fidelity Virtual Try-on

Image-based Virtual Try-On (VITON) aims to transfer an in-shop garment image onto a target person. While existing methods focus on warping the garment to fit the body pose, they often overlook the synthesis quality around the garment-skin boundary and realistic effects like wrinkles and shadows on the warped garments. These limitations greatly reduce the realism of the generated results and hinder the practical application of VITON techniques. Leveraging the notable success of diffusion-based models in cross-modal image synthesis, some recent diffusion-based methods have ventured to tackle this issue. However, they tend to either consume a significant amount of training resources or struggle to achieve realistic try-on effects and retain garment details. For efficient and high-fidelity VITON, we propose WarpDiffusion, which bridges the warping-based and diffusion-based paradigms via a novel informative and local garment feature attention mechanism. Specifically, WarpDiffusion incorporates local texture attention to reduce resource consumption and uses a novel auto-mask module that effectively retains only the critical areas of the warped garment while disregarding unrealistic or erroneous portions. Notably, WarpDiffusion can be integrated as a plug-and-play component into existing VITON methodologies, elevating their synthesis quality. Extensive experiments on high-resolution VITON benchmarks and an in-the-wild test set demonstrate the superiority of WarpDiffusion, surpassing state-of-the-art methods both qualitatively and quantitatively.
Journal of Economic Behavior & Organization

We introduce a new method for measuring the decision to lie in experiments. In the game, the decision to lie increases own payment independent of the counterpart’s decision, but potentially at a cost for the counterpart. We identify at the individual level the decision to lie, and measure how individuals react to different incentives to lie. Furthermore we investigate how lying behavior changes over time. Our method allows us to classify people into types, including those who never lie, those who always lie, and those who react to incentives to lie. We suggest this method as a useful instrument for examining factors that inﬂuence the decision to lie.
Journal of Economic Behavior & Organization Social Image Concerns and Prosocial Behavior: Field Evidence from a Nonlinear Incentive Scheme

Using longitudinal data on the entire population of blood donors in an Italian town, we examine how donors respond to a nonlinear award scheme that rewards them with symbolic prizes (medals) when they reach certain donation quotas. Our results indicate that donors significantly increase the frequency of their donations immediately before reaching the thresholds for which the rewards are given, but only if the prizes are publicly announced in the local newspaper and awarded in a public ceremony. The results are robust to several specifications, sample definitions, and controls for observable and unobservable heterogeneity. Our findings indicate that social image concerns are a primary motivator of prosocial behavior and that symbolic prizes are most effective as motivators when they are awarded publicly. We discuss the implications of our findings for policies aimed at incentivizing prosocial behavior.
Journal of Economic Behavior & Organization to Deceive or Not to Deceive: the Effect of Deception on Behavior in Future Laboratory Experiments

JEL classification: A12 C81 C90 Keywords: Laboratory experimental methods Experimental economics Deception Psychology and economics Laboratory selection effects a b s t r a c t Experimental economists believe (and enforce the idea) that researchers should not employ deception in the design of experiments. This rule exists in order to protect a public good: the ability of other researchers to conduct experiments and to have participants trust their instructions to be an accurate representation of the game being played. Yet other social sciences, particularly psychology, do not maintain such a rule. We examine whether such a public goods problem exists by purposefully deceiving some participants in one study, informing them of this fact, and then examining whether the deceived participants behave differently in a subsequent study. We find significant differences in the selection of individuals who return to play after being deceived as well as (to a lesser extent) the behavior in the subsequent games, thus providing qualified support for the proscription of deception. We discuss policy implications for the maintenance of separate participant pools.
Advancing Music Genre Identification Through Deep Learning Techniques

Music plays a huge role in human's lives, inspiring a wide range of feelings including enthusiasm and nostalgia. 97 million songs have been recorded globally; an incredible figure that is only increasing exponentially as more people use music applications. Many people love particular musical genres, such as classical, hip-hop, or disco, and want for an easy way to arrange their music in accordance with their preferences. This gave rise to the idea of “music genre classification”. In the current environment, where there are many music recordings available both online and offline, proper genre categorization has become essential. Several machine learning methods may be used to categorize the different types of music. This model's emphasis is on using deep learning methods to identify the musical genre. The Mel-frequency cepstral coefficients (MFCC), a technique that works exceptionally well for music files, are used to extract key elements. The proposed model is excellent at categorizing musical subgenres and provides default settings as well as interactive options depending on user preferences. The simple web interface built using Flask allows users to submit audio files and view the anticipated genre. The system also has the capability of recommending new genres to the user.