Miracle-dz/UniFAR-FTUs huggingface dataset:
On wage policies and unemployment

In economies where the price of labour is determined outside of competitive markets the question arises as to whether the observed evolution of wages is likely to contribute to a decline in unemployment. I develop and discuss a benchmark, the neutral wage policy, to which the actual evolution of wages can be compared. Here, neutrality refers to the unemployment rate and not to the level of employment. If the actual wage growth falls short of this benchmark then the evolution of wages is said to have contributed to a decline in the unemployment rate. This benchmark is based on fairly general assumptions on the aggregate production technology, incorporates changes in the supply of labour, and accounts for changes in the competitive environment of firms. Finally, I discuss the relation to the benchmark of the German Council of Economic Experts (1). In many economies the price of labour is determined outside of competitive markets. Negotiations between unions and employers' associations are a case in point. In these economies the question arises as to how an assessment of the results of such negotiations should be made. The present paper develops a measuring rod against which the observed evolution of real wages can be assessed in view of its implications for the evolution of the level of employment and the unemployment rate. There are at least two related observations as to why assessments of the kind presented here matter. First, in order to legitimate their stances both negotiating parties pretend to act in the public interest. Workers' purchasing power and the cost-of-labor argument put forward by unions and employers do not only benefit their respective interest groups. They are also meant to be in the public interest since they raise the level of employment. The difficult challenge is then to ask who is right. Second, the institutional framework for wage negotiations affects the bargaining power of both parties and has, therefore, an important impact on the resulting wage. If an appropriate benchmark indicated that the negotiated wage failed to be in the public interest then the political system is likely to intervene and to change this framework. For instance, if wages were considered to be too high it is likely that changes in the institutional framework would strengthen the employers' position. I refer to the benchmark developed here as the neutral wage policy, i.e., if the actual evolution of the real wage coincides with the evolution prescribed by this benchmark, then the unemployment rate remains constant; if it falls short of it, the unemployment rate declines. This concept incorporates the following features of the aggregate demand
Development of Methods for Top-Down Methane Emission Measurements of Oil and Gas Facilities in an Offshore Environment Using a Miniature Methane Spectrometer and Long-Endurance UAS

With atmospheric methane concentrations rising, spurring increased social concern, there is a renewed focus in the oil and gas industry on methane emission monitoring and control. In 2019, a methane emission survey at a bp asset west of Shetland was conducted using a closed-cavity methane spectrometer mounted onboard a long-endurance fixed-wing unmanned aerial vehicle (UAV). This flight represents the first methane emissions survey of an offshore facility with a miniature methane spectrometer onboard a UAV with subsequent flights performed. The campaign entailed gathering high-density methane concentration data in a cylindrical flight pattern that circumnavigated the facility in close proximity. A small laser spectrometer was modified from an open-cavity system to a closed-cavity onboard the aircraft and yielded in-flight detection limits (3s) of 1065ppb methane above background for the 2019/2020 sensor version and 150ppb for the 2021 sensor versions. Through simulation, the sensors minimum detection limits in mass flow rate were determined to be 50 kg/h for the 2019/2020 campaign and 2.5kg/h for the 2021 campaigns; translating to an obtainable measurement for 23% and 82% of assets reporting higher than 1 kg/h according to the 2019 EEMS dataset, respectively. To operationalize the approach, a simulation tool for flight planning was developed utilizing a gaussian plume model and a scaled coefficient of variation to invoke expected methane concentration fluctuations at short time intervals. The simulation is additionally used for creation of synthetic datasets to test and validate algorithm development. Two methods were developed to calculate offshore facility level emission rates from the geolocated methane concentration data acquired during the emission surveys. Furthermore, a gaussian plume simulator was developed to predict plume behavior and aid in error analysis. These methods are under evaluation, but all allow for the rapid processing (<24h) of results upon landing the aircraft. Additional flights were conducted in 2020 and 2021 with bp and several UK North Sea Operators through Net Zero Technology Centre (NZTC) funded project, resulting in a total of 18 methane emission survey flights to 11 offshore assets between 2019 and 2021. The 2019 flight, and subsequent 2020/21 flights, demonstrated the potential of the technology to derive facility level emission rates to verify industry emission performance and data.
Climate Feedback From Wetland Emissions of Methane May Necessitate Greater Anthropogenic Reductions

14.7 parts-per-billion (ppb)—the largest annual increase since atmospheric records began in 1983 (https:// research.noaa.gov/article/ArtMID/587/ArticleID/2742/Despite-pandemic-shutdowns-carbon-dioxide-and-methane-surged-in-2020). This is concerning because CH4 is the second most important long-lived greenhouse after CO2 and has a global warming potential 28 times that of CO2 per unit mass on a 100-year time scale (Myhre et al., 2013). Moreover, pathways to limit global warming to 1.5°C, or even 2.0°C, require reductions in non-CO2 emissions and, in particular those of CH4, by 35% with respect to 2010 levels by 2050 (Forster et al., 2018). An important consideration though in how much the anthropogenic emissions will need to be reduced, is how strong the climate feedback will be in natural emissions, especially those from wetlands, which are the most important natural source and the most uncertain component of the CH4 budget (Saunois et al., 2020; Stocker et al., 2013; Zhang et al., 2017). Observational constraints are crucial to better understanding the climate sensitivity of wetland emissions. Ma et al. provide an innovative way to use atmospheric observations to explore how well land surface models capture wetland CH4 emissions and their sensitivity to temperature, precipitation, and soil carbon availability globally.
Building Footprint Generation Through Convolutional Neural Networks With Attraction Field Representation

Building footprint generation is a vital task in a wide range of applications, including, to name a few, land use management, urban planning and monitoring, and geographical database updating. Most existing approaches addressing this problem fall back on convolutional neural networks (CNNs) to learn semantic masks of buildings. However, one limitation of their results is blurred building boundaries. To address this, we propose to learn attraction field representation for building boundaries, which is capable of providing an enhanced representation power. Our method comprises two elemental modules: an Img2AFM module and an AFM2Mask module. More specifically, the former aims at learning an attraction field representation conditioned on an input image, which is capable of enhancing building boundaries and suppressing the background. The latter module predicts segmentation masks of buildings using the learned attraction field map. The proposed method is evaluated on three datasets with different spatial resolutions: the ISPRS dataset, the INRIA dataset, and the Planet dataset. From experimental results, we find that the proposed framework can well preserve geometric shapes and sharp boundaries of buildings, which brings significant improvements over other competitors. The trained model and code are available at https://github.com/lqycrystal/AFM_building.
A Novel Machine Learning Model to Predict the Photo-Degradation Performance of Different Photocatalysts on a Variety of Water Contaminants

This paper describes an innovative machine learning (ML) model to predict the performance of different metal oxide photocatalysts on a wide range of contaminants. The molecular structures of metal oxide photocatalysts are encoded with a crystal graph convolution neural network (CGCNN). The structure of organic compounds is encoded via digital molecular fingerprints (MF). The encoded features of the photocatalysts and contaminants are input to an artificial neural network (ANN), named as CGCNN-MF-ANN model. The CGCNN-MF-ANN model has achieved a very good prediction of the photocatalytic degradation rate constants by different photocatalysts over a wide range of organic contaminants. The effects of the data training strategy on the ML model performance are compared. The effects of different factors on photocatalytic degradation performance are further evaluated by feature importance analyses. Examples are illustrated on the use of this novel ML model for optimal photocatalyst selection and for assessing other types of photocatalysts for different environmental applications.
Containing COVID‐19 risk in the UAE: Mass quarantine, mental health, and implications for crisis management

Abstract The COVID‐19 pandemic is the first global “NASECH disaster,” owing to its natural hazard (NH) origin and unprecedented subsequent repercussions for global society (S), economy (EC), and health (H). Emergency health control measures required the implementation of compulsory mass quarantine (CMQ) or so‐called periods of “lockdown. ” Yet, CMQ is an instrument with iatrogenic consequences, associated with a rise in societal levels of depression, anxiety, and posttraumatic stress. With a view to informing future crisis management, the study investigated sociodemographic factors associated with mental wellbeing during the March–April 2020 lockdown in the United Arab Emirates. Respondents (n = 1585) completed self‐report measures of depression (PHQ8) and generalized anxiety (GAD7). Rates of symptomatology were notably higher than those observed in similar UAE‐based studies before the pandemic. Younger age, urban‐dwelling, female‐gender, and a history of mental health problems were significant factors linked to elevated levels of depression and anxiety. Findings emphasize (1) the crucial need for psychological intervention after disasters and (2) the importance of strengthening the nexus at the intersection of public health and disaster risk reduction (DRR). Implications are that future pandemic containment would benefit from adopting new Health–DRR paradigms and ensuring these are effectively translated into disaster policy.
IMPLEMENTASI METODE SMARTER UNTUK SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN LAHAN KELAPA SAWIT PADA PT EKA DURA INDONESIA

Aplikasi ini merupakan sistem pendukung keputusan untuk penentuan lokasi lahan perkebunan kelapa sawit dengan menggunakan metode Simple Multi-Attribute Rating Technique Exploiting Ranks (SMARTER ) dan menggunakan Rank Order Centroid (ROC) untuk perhitungan, dengan studi kasus yang digunakan adalah PT. Eka Dura Indonesia. sesuai dengan hasil penelitian yang dilakukan diperoleh 5 (lima) kriteria yang dijadikan penilaian dalam analisa kelayakan pemilihan lokasi pekebunan yaitu luas area, tekstur tanah, curah hujan, lereng, dan topografi. Hasil akhir dari perhitungan SMARTER ini beruparank nilai dari total keseluruhan data yang sudah di rangkingkan sehingga nilai tertinggi merupakan rekomendasi terbaik untuk dibangun lahan perkebunan kelapa sawit. Output penelitian ini berupa sebuah aplikasi Pemilihan Lahan Kelapa sawit yang dapat digunakan dengan mudah dan dapat diimplementasikan untuk mendukung dalam merekomendasikan sebuah pemilihan berdasarkan analisa yang telah dilakukan. Dengan adanya SPK ini diharapkan dapat membantu pihak PT.Eka Dura Indonesia dalam mengambil keputusan secara terkomputerisasi dalam pemilihan lokasi pembangunan lahan kelapa sawit selanjutnya.
QUARANTINE VLOGS: DIGITAL AFFECTIVE LABOR AND SELF-GOVERNANCE DURING
 COVID-19

This research explores how did influencers incorporate the Covid-19 pandemic into their regular content production on YouTube by specifically examining the recent genre of "quarantine vlog," which emerged in concurrence with global lockdowns. I adopt a grounded theory approach to analyze the YouTube transcriptions of purposefully selected 9 quarantine vlogs filmed by women influencers during the early months of the pandemic, along with 250 user comments. My analysis shows that quarantine vlogs are significantly different than ordinary vlogs. I draw on existing research on influencer cultures to explain this dissimilarity as a tension between influencers' struggle to form an intimacy with the viewers - which can have a soothing effect in a moment of a crisis - and the use of vlogs as a neoliberal device in order to preserve their aspirational image. I demonstrate that quarantine vlogs reveal that influencers are no longer able to perform an aspirational ideal in their videos without first engaging with the mental stress, anxiety, confusion, and loneliness brought by COVID-19 or apologizing for their relative privilege and demonstrating sympathy towards their followers who are in hardship. At the same time, to preserve their aspirational persona, they reframe the pandemic moment as an opportunity for productivity and self-growth. As a result, influencers carry out substantial affective labor and engage in a delicate self-governance to preserve their relevance and online visibility during a global moment of crisis.
FORMULATION AND EVALUATION OF TRANSDERMAL PATCHES OF PANTOPRAZOLE SODIUM

Objective: The present study was an attempt to develop an alternative dosage form for the existing conventional oral, parenteral proton pump inhibitor (PPI) as transdermal patches for treating peptic ulcers. Methods: Transdermal patches of PPI were prepared using HPMC E5 with PVP K 30 and HPMC E5 with Eudragit L100 polymers in different ratios by a solvent evaporation method. All the formulated patches were subjected to various evaluation parameters such as thickness, folding endurance, weight uniformity, content uniformity, swelling index, percentage moisture content, moisture uptake, surface pH and in vitro release studies. Results: All patches exhibited satisfactory characteristics regarding integrity, flexibility, dispersion of drug, and other quality control parameters. In the in vitro release studies of transdermal patches, formulation F1 showed the prolonged release of drug (98.99 %) for 24 h, which indicates the maximum availability of the drug, and the in vitro skin permeability studies also showed that 96.26 % of drug Pantoprazole sodium permeated through the rat abdominal skin in 24 h. The kinetic studies were carried out and it was found that all the formulations follow zero-order and the release mechanism of drugs was found to be diffusion rate-limited, Non-Fickian mechanism which was confirmed by Korsmeyer–Peppas model. Conclusion: This suggests the transdermal application of Pantoprazole sodium holds the promised controlled release of the drug for an extended period of time.
Non-Adherence to Pain Medication Increases Risk of Postoperative Frozen Shoulder

Background: Postoperative frozen shoulder (FS) or adhesive capsulitis is a relatively frequent complication (5-20%), even after simple arthroscopic shoulder surgeries. The pathophysiology is still unclear, but psychological factors may play a pivotal role. From clinical experience, we hypothesized that patients, who are reluctant to take medications, particularly “pain-killers,” have an increased incidence of postoperative FS. Methods: We identified twenty patients who underwent limited arthroscopic operations of the shoulder and developed postoperative FS. Twenty patients with matching type of surgery, age, and gender served as control group (n = 20). All patients were at least one year postoperative and asymptomatic at the time of examination. Demographic data, the patient's adherence to self-medication (including self-medicating scale, SMS), development the Quality of life (QoL), and depression scale (PHQ-4-questionnaire) were assessed. Results: Patients with FS had a 2-fold longer rehabilitation and 3-fold longer work inability compared to the patients without FS (P < 0.009 and P < 0.003, respectively). Subjective shoulder value SSV (P = 0.075) and post-operative improvement of QoL (P = 0.292) did not differ among the groups. There was a trend—but not significant—toward less coherence to self-medication in the FS-group (26.50 vs. 29.50; P = 0.094). Patients with postoperative FS significantly more often stated not to have “taken pain-killers as prescribed” (P = 0.003). Conclusions: Patients reporting unwillingness to take the prescribed pain medications had a significantly higher incidence of postoperative FS. It remains unclear whether the increased risk of developing FS is due to reduced postoperative analgesia or a critical attitude toward taking medication. However, patients who are reluctant to take painkillers should strongly be encouraged to take medications as prescribed.
Palladium-Functionalized Graphene for Hydrogen Sensing Performance: Theoretical Studies

The adsorption characteristics of H2 molecules on the surface of Pd-doped and Pd-decorated graphene (G) have been investigated using density functional theory (DFT) calculations to explore the sensing capabilities of Pd-doped/decorated graphene. In this analysis, electrostatic potential, atomic charge distribution, 2D and 3D electron density contouring, and electron localization function projection, were investigated. Studies have demonstrated the sensing potential of both Pd-doped and Pd-decorated graphene to H2 molecules and have found that the gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO), i.e., the HOMO-LUMO gap (HLG), decreases to 0.488 eV and 0.477eV for Pd-doped and Pd-decorated graphene, respectively. When H2 is adsorbed on these structures, electrical conductivity increases for both conditions. Furthermore, chemical activity and electrical conductivity are higher for Pd-decorated G than Pd-doped G, whereas the charge transfer of Pd-doped graphene is far better than that of Pd-decorated graphene. Also, studies have shown that the adsorption energy of Pd-doped graphene (−4.3 eV) is lower than that of Pd-decorated graphene (−0.44 eV); a finding attributable to the fact that the recovery time for Pd-decorated graphene is lower compared to Pd-doped graphene. Therefore, the present analysis confirms that Pd-decorated graphene has a better H2 gas sensing platform than Pd-doped graphene and, as such, may assist the development of nanosensors in the future.
Unveiling children’s perceptions of World Heritage Sites: a visual and qualitative approach

ABSTRACT This paper investigates World Heritage (WH) perception from the perspective of children, both as physical representations in space and as experiences and understandings of place. The Little Artists Exhibition serves as a case study. This exhibition was a global call initiated by UNESCO inviting children between 6 and 12 years old to draw a WH site that matters to them. More than 500 children participated in the online exhibition and posted their artwork on Instagram. By proposing a mixed visual and qualitative text analysis method for the assessment of children’s drawings and narratives, this paper captures both children’s perceived image and values of WH. Results show that children express a holistic perception of heritage combining built, natural, and intangible assets. Correspondingly, findings from the qualitative text analysis highlight: (1) children’s historic and aesthetic appreciation of WH; (2) children’s emotional and experiential association with built heritage and natural landscape; (3) children’s inspirational and imaginative stories; and (4) heritage as a social construct in the minds of children. We discuss our findings by reflecting on their conceptual contributions as they speak to broader debates on WH and associated values.
The use of sugammadex in an infant with prolonged neuromuscular blockade - A case report -

Background Residual neuromuscular blockade (RNMB) is a frequent event after general anesthesia, which can lead to serious complications, such as upper airway obstruction. Sugammadex is useful in reversing RNMB. However, its use in infants has not yet been approved by the Food and Drug Administration. Therefore, anesthesiologists can be hesitant use it, even in situations where no other choice is available. Case A two-month-old baby presented to the hospital for umbilical polypectomy. At the end of the surgery, neostigmine was administered. Even after waiting for 30 min and injecting an additional dose of neostigmine, neuromuscular blockade was not adequately reversed. Eventually, sugammadex was administered, and spontaneous breathing returned. Conclusions If there were no particular causes of delayed return to spontaneous breathing in infants, RNMB should be considered and reversal with sugammadex would be useful.
$G_{\delta \sigma }$
 GAMES AND INDUCTION ON REALS

Abstract It is shown that the determinacy of $G_{\delta \sigma }$ games of length $\omega ^2$ is equivalent to the existence of a transitive model of ${\mathsf {KP}} + {\mathsf {AD}} + \Pi _1\textrm {-MI}_{\mathbb {R}}$ containing $\mathbb {R}$ . Here, $\Pi _1\textrm {-MI}_{\mathbb {R}}$ is the axiom asserting that every monotone $\Pi _1$ operator on the real numbers has an inductive fixpoint.
VDES R‐Mode: Vulnerability analysis and mitigation concepts

VDES R‐Mode aims at providing a contingency maritime positioning and navigation system when the operation of Global Navigation Satellite Systems (GNSS) is disrupted. However, VDES R‐Mode, similarly to GNSS, can itself also be subject to different types of attacks, such as jamming or spoofing. In this paper, we evaluate the vulnerabilities of VDES R‐Mode and discuss the effectiveness and cost of different types of countermeasures. The outcome of this cost‐benefit analysis is a recommendation to introduce authentication for the navigation messages of R‐Mode using the Timed Efficient Stream Loss‐Tolerant Authentication (TESLA) protocol.
In search of legitimacy: conflicting logics and identities of management consultants in public administration

ABSTRACT Management consultants are often hidden and therefore understudied actors in public administration. Building on institutional and social identity theory, we investigate consultants’ identity work in search of legitimacy as knowledge providers in the public sector. We substantiate our theoretical reasoning with 43 semi-structured interviews with consultants and clients and extract four social identities of consultants, only one of which complies with what the literature commonly considers a management consultant in the public sector. Our study extracts new patterns of identity work and shows how they help actors in need of legitimacy to cope with the institutional complexity of public administration.