Miracle-dz/UniFAR-FTUs huggingface dataset:
Unemployment and Job Search Behavior among People with Disabilities during the First Year of the COVID-19 Pandemic in Germany

Not much is known about how the COVID-19 pandemic affected the labor market experiences of people with disabilities. Since they constitute a generally disadvantaged group in the labor market, it is important to scrutinize whether their position has worsened during these difficult times and how they reacted with regard to their job search behavior. We therefore used data for the year 2020 from a large German panel (Panel Arbeitsmarkt und Soziale Sicherung, PASS), in order to scrutinize the prevalence of unemployment among people with disabilities (N = 739) during the first year of the COVID-19 pandemic. The factors that affected their unemployment status were also analyzed. The study found that people with legally recognized disabilities were more often unemployed than non-disabled people, even when controlling for possible confounding factors such as age, gender, or education. This effect was significant for severe disabilities and marginally significant for minor disabilities. Additionally, the type of disability affected the probability of being unemployed, with cardiovascular diseases, mental illnesses, and musculoskeletal disorders carrying a higher risk. In terms of job-seeking behavior, unemployed people with disabilities reported using some job search methods more frequently than their non-disabled counterparts. However, the intensity of the job search did not differ significantly between the two groups. Further differences were found when analyzing the reasons for abstinence from searching for a job, with unemployed people with disabilities primarily citing health-related factors (with a frequency of over 90%). In summary, health played a pivotal role in determining disabled people’s labor market experiences during the COVID-19 pandemic.
Assessment of Manufacturing Process Selection Utilizing the TOPSIS Method

: Bancassurance. A bank and an insurance provider will enter into a "bancassurance" agreement so that the insurance provider can market to the bank's clients. The insurance provider gains from higher sales and a broader customer base without having to hire more salespeople. The marketing of insurance policies through banks is known as bancassurance. Cooperation between banks and insurance firms allows the bank to market its customers the insurance products of the associated insurance company. The Reserve Bank of India, which oversees the banking system, recognized that it was important for banks to diversify their business models and gave them permission to enter the insurance industry. In order for the insurance provider to market to the bank's customers, the bank another insurance provider will enter into a "bancassurance" agreement. More sales and a larger customer base benefit the insurance provider without the need to add more salesmen. Bancassurance refers to the sale of insurance products through banks. Banks and insurance companies can promote each other's insurance products to customers through a partnership between the two parties. Since it was crucial for banks to diversify their business models, the Reserve Bank of India, which regulates the banking system, approved their entry into the insurance sector. GRA (Gray Relational Analysis) Method, Branches, Employees, Private loans, Deposits, Customers, Life insurance premiums Alternatives Deutsche Bank, Kommerz bank, Krediet bank, Volksbanken bank. Branches, Employees, Private loans, Deposits, Customers, and Life insurance premiums. Deutsche Bank, Kommerz bank, Krediet bank, Volksbanken bank. Private loans got the first rank whereas Deposits has the lowest rank.
Are we doing the same? A worldwide analysis of business commitment to the SDGs

The COVID-19 pandemic has set back progress toward the 2030 Agenda. This raises concerns about the ability to achieve the Sustainable Development Goals (SDGs) as outlined in this global action plan. The responsibility to act to protect the planet, eradicate poverty and improve the current and future situations of people around the world requires the commitment of all actors, especially the public and private sectors. The objective of this paper is twofold. First, it aims to demonstrate the level of business commitment to the 2030 Agenda at a global level in a post-COVID period (2020–2021). Second, it aims to analyse whether this commitment, in general, and for each of the SDGs, is determined by the institutional context in which companies operate, as associated with the regulatory frameworks and cultural values of the different continents and countries (coercive and normative isomorphism). Based on a sample of the world's leading listed companies, i.e. 8,201 observations for the period of 2020–2021, the multivariate statistical technique HJ-biplot was applied to analyse whether business commitment to the 2030 Agenda is determined by the institutional context of the companies. The results show significant differences in the level of business commitment to sustainable development across regions and countries, as well as in the prioritisation of the SDGs. Countries in Asia (the Philippines) and Europe (Spain and Portugal) stand out as leaders, with the highest levels of SDG commitment. The USA and Qatar show the lowest engagement in business contribution to the SDGs. SDG 8, SDG 13 and SDG 12 appear as the top three priorities for companies in all countries. The results show that, in countries characterised by collectivism and feminism and with strong legal systems (civil law countries), companies are under greater pressure to adopt sustainable practices, which enables countries to improve their commitment to the SDGs.
Mechanisms of Resistance to Antibody-Drug Conjugates

The treatment of cancer patients has dramatically changed over the past decades with the advent of monoclonal antibodies, immune-checkpoint inhibitors, bispecific antibodies, and innovative T-cell therapy. Antibody-drug conjugates (ADCs) have also revolutionized the treatment of cancer. Several ADCs have already been approved in hematology and clinical oncology, such as trastuzumab emtansine (T-DM1), trastuzumab deruxtecan (T-DXd), and sacituzumab govitecan (SG) for the treatment of metastatic breast cancer, and enfortumab vedotin (EV) for the treatment of urothelial carcinoma. The efficacy of ADCs is limited by the emergence of resistance due to different mechanisms, such as antigen-related resistance, failure of internalization, impaired lysosomal function, and other mechanisms. In this review, we summarize the clinical data that contributed to the approval of T-DM1, T-DXd, SG, and EV. We also discuss the different mechanisms of resistance to ADCs, as well as the ways to overcome this resistance, such as bispecific ADCs and the combination of ADCs with immune-checkpoint inhibitors or tyrosine-kinase inhibitors.
High Throughput Acceleration of Scabbard Key Exchange and Key Encapsulation Mechanism Using Tensor Core on GPU for IoT Applications

High throughput key encapsulations and decapsulations are needed by Internet of Things (IoT) applications in order to simultaneously process a multitude of small data in secure communication. In this article, we present two novel techniques for accelerating the implementation of polynomial convolution on a graphics processing unit (GPU), utilizing advanced Tensor cores, which benefit the performance of key encapsulations. First, a polynomial restructuring technique is proposed to allow several polynomials with distinct public keys to be processed in a single communication cycle. This is an improvement compared to the previous work by Lee et al. Next, we observe that polynomial convolution in some key encapsulation mechanisms contains reduction patterns that are not friendly to parallel implementation. We propose separating the multiplication and reduction processes so they can be parallelized independently. To verify the effectiveness of our proposed techniques, we applied it to two key-encapsulation mechanisms from the Scabbard post-quantum key-encapsulation mechanism suite and evaluate their performance. Experimental results show that polynomial convolution using Tensor cores is <inline-formula> <tex-math notation="LaTeX">$1.05\times $ </tex-math></inline-formula> faster (for the Florete scheme) and <inline-formula> <tex-math notation="LaTeX">$3.6\times $ </tex-math></inline-formula> faster (for the Sable scheme) than using compute unified device architecture core-based multiplication with conventional cores on a GPU. The Tensor cores-based encapsulations and decapsulations are faster than a reference implementation on a CPU supporting AVX2 by more than <inline-formula> <tex-math notation="LaTeX">$5.6\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$6.4\times $ </tex-math></inline-formula>, respectively, for the Florete scheme and <inline-formula> <tex-math notation="LaTeX">$8.3\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$13.3\times $ </tex-math></inline-formula> faster, respectively, for the Sable scheme. This shows that the proposed techniques can achieve significantly higher throughput for key exchange and encapsulation mechanisms, which are important for securing IoT applications.
Prediction of Land Surface Temperature Considering Future Land Use Change Effects under Climate Change Scenarios in Nanjing City, China

Land use and land cover (LULC) changes resulting from rapid urbanization are the foremost causes of increases in land surface temperature (LST) in urban areas. Exploring the impact of LULC changes on the spatiotemporal patterns of LST under future climate change scenarios is critical for sustainable urban development. This study aimed to project the LST of Nanjing for 2025 and 2030 under different climate change scenarios using simulated LULC and land coverage indicators. Thermal infrared data from Landsat images were used to derive spatiotemporal patterns of LST in Nanjing from 1990 to 2020. The patch-generating land use simulation (PLUS) model was applied to simulate the LULC of Nanjing for 2025 and 2030 using historical LULC data and spatial driving factors. We simulated the corresponding land coverage indicators using simulated LULC data. We then generated LSTs for 2025 and 2030 under different climate change scenarios by applying regression relationships between LST and land coverage indicators. The results show that the LST of Nanjing has been increasing since 1990, with the mean LST increased from 23.44 °C in 1990 to 25.40 °C in 2020, and the mean LST estimated to reach 26.73 °C in 2030 (SSP585 scenario, integrated scenario of SSP5 and RCP5.8). There were significant differences in the LST under different climate scenarios, with increases in LST gradually decreasing under the SSP126 scenario (integrated scenario of SSP1 and RCP2.6). LST growth was similar to the historical trend under the SSP245 scenario (integrated scenario of SSP2 and RCP4.5), and an extreme increase in LST was observed under the SSP585 scenario. Our results suggest that the increase in impervious surface area is the main reason for the LST increase and urban heat island (UHI) effect. Overall, we proposed a method to project future LST considering land use change effects and provide reasonable LST scenarios for Nanjing, which may be useful for mitigating the UHI effect.
Analytical Modeling of Electromagnetic Scattering and HRRP Characteristics from the 3D Sea Surface with a Plunging Breaker

Electromagnetic (EM) scattering of sea surface may exhibit sea-spikespikes when there exist breaking waves. As sea-spikes are usually mistaken for targets, the investigation of EM scattering characteristics and high-range resolution profiles (HRRPs) of three-dimensional (3D) sea surfaces with a plunging breaker is meaningful for target detection and recognition. To describe the basic features of a plunging breaker, this paper developed a feasible and wind-related plunging breaker model. Here, profiles of a plunging breaker in its life cycle are modeled according to the wind speed and time factor, and the small-scale roughness is considered. Then, the sea surface and plunging breaker are combined to obtain the composite model. Additionally, a hybrid algorithm based on the Capillary Wave Modification Facet Scattering Model (CWMFSM) and ray tracing technique is developed to calculate the EM scattering of 3D sea surface with a plunging breaker. Simulation results show that the sea-spike phenomenon is more likely to occur for the upwind and large incident angles. The amplitude of the backscattering electric field from the plunging breaker is much stronger than that of the sea surface. Furthermore, the HRRPs of 3D sea surface with a plunging breaker and target are computed. Sharp peaks from the plunging breaker that exhibit obvious target-like features are observed.
False Dilemmas of Political Leadership: A Polemic

The article seeks to show the presence of false dilemmas (FDs) in political leadership discussions. In some areas of the political leadership debate, arguments are made from two opposing positions. When issues are presented from two dissenting viewpoints, essential alternatives are removed from the discussion. The paper utilizes narrative literature by relying on secondary data for the analysis and argues that some dichotomies in political leadership discussion, such as cause-effect, nature-nurture, and politics-administration could be false. The paper concludes that there is a need for further debate to address these FDs to advance the political leadership discussion.
Reinforcement Learning Environment for Cyber-Resilient Power Distribution System

Recently, numerous data-driven approaches to control an electric grid using machine learning techniques have been investigated. Reinforcement learning (RL)-based techniques provide a credible alternative to conventional, optimization-based solvers especially when there is uncertainty in the environment, such as renewable generation or cyber system performance. Efficiently training an agent, however, requires numerous interactions with an environment to learn the best policies. There are numerous RL environments for power systems, and, similarly, there are environments for communication systems. Most cyber system simulators are based in a UNIX environment, while the power system simulators are based in the Windows operating system. Hence the generation of a cyber-physical, mixed-domain RL environment has been challenging. Existing co-simulation methods are efficient, but are resource and time intensive to generate large-scale data sets for training RL agents. Hence, this work focuses on the development and validation of a mixed-domain RL environment using OpenDSS for the power system and leveraging a discrete event simulator Python package, SimPy for the cyber system, which is operating system agnostic. Further, we present the results of co-simulation and training RL agents for a cyber-physical network reconfiguration and Volt-Var control problem in a power distribution feeder.
Combined H∞ and anti‐disturbance control for semi‐Markovian jump systems via a nonlinear disturbance observer

This paper investigates a combined H∞$$ {H}_{\infty } $$ and anti‐disturbance control problem for a class of semi‐Markovian jump nonlinear systems with constant time delay, and both modeled and unmodeled disturbances. In particular, the modeled disturbance is thought to be produced by a nonlinear exogenous system and is estimated by introducing a mode‐dependent nonlinear disturbance observer. The desired controller for the addressed system is then proposed, which consists of two components: (i) state feedback, which ensures the system's stochastic stability by attenuating the unmodeled disturbance; and (ii) disturbance estimate, which compensates for the modeled disturbance effect. Following that, a novel mode‐dependent asymmetric Lyapunov‐Krasovskii functional is used to derive the sufficient conditions for the existence of the proposed controller and disturbance observer. The developed theoretical results are supported by three numerical examples that demonstrate the utility of the proposed design method.
First record of the dinoflagellate Tripos rotundatus (Jørgensen) Gómez in the Adriatic Sea

This report presents the first record of Tripos rotundatus (Jørgensen) Gómez in the Adriatic Sea. The species was found in the net sample in the layer of 50 – 100 m depth, on July 2021 at the coastal southern Adriatic Sea. The species T. rotundatus was probably previously mistaken as morphologically similar species T. digitatus (Schütt) Gómez.
Modes of Operation and Forcing in Oil Spill Modeling: State-of-Art, Deficiencies and Challenges

Oil spills may have devastating effects on marine ecosystems, public health, the economy, and coastal communities. As a consequence, scientific literature contains various up-to-date, advanced oil spill predictive models, capable of simulating the trajectory and evolution of an oil slick generated by the accidental release from ships, hydrocarbon production, or other activities. To predict in near real time oil spill transport and fate with increased reliability, these models are usually coupled operationally to synoptic meteorological, hydrodynamic, and wave models. The present study reviews the available different met-ocean forcings that have been used in oil-spill modeling, simulating hypothetical or real oil spill scenarios, worldwide. Seven state-of-the-art oil-spill models are critically examined in terms of the met-ocean data used as forcing inputs in the simulation of twenty-three case studies. The results illustrate that most oil spill models are coupled to different resolution, forecasting meteorological and hydrodynamic models, posing, however, limited consideration in the forecasted wave field (expressed as the significant wave height, the wave period, and the Stokes drift) that may affect oil transport, especially at the coastal areas. Moreover, the majority of oil spill models lack any linkage to the background biogeochemical conditions; hence, limited consideration is given to processes such as oil biodegradation, photo-oxidation, and sedimentation. Future advancements in oil-spill modeling should be directed towards the full operational coupling with high-resolution atmospheric, hydrodynamic, wave, and biogeochemical models, improving our understanding of the relative impact of each physical and oil weathering process.
Analysis of Cyber Security Aspects of Data Transmission in Large-Scale Networks Based on the LoRaWAN Protocol Intended for Monitoring Critical Infrastructure Sensors

Cyber security is nowadays synonymous with the reliability of elements connected to the internet. Better control of factories, security systems or even individual sensors is possible through the use of Internet of Things technology. The security of the aforementioned structures and the data they transmit has been a major concern in the development of IoT solutions for wireless data transmission. If we add to this prospect of low-cost end devices, we can seriously consider implementing such solutions in critical infrastructure areas. This article aims to assess the state of the art and experience and identify the main risks and directions for further development in order to improve the cyber security situation of LoRaWAN-based networks. LoRaWAN meets the three key requirements of IoT applications (low cost, large-scale deployability, high energy efficiency) through an open standard and the construction of autonomous networks without third-party infrastructure. However, many research issues remain to be solved/improved such as resource allocation, link coordination, transmission reliability, performance and, above all, security. Thus, we have defined a research gap in the area of LoRaWAN security. The contribution of this work is to structure the knowledge in the field of LoRaWAN security, based on previous publications and our own experience, in order to identify challenges and their potential solutions. This will help move LoRaWAN security research to the next stage.
On multiple covering of a circle with random arcs

On a circle of unit circumference arcs of length a are placed at random. Let N α be equal to the necessary number of arcs to cover at least the length 1 − p, 0 ≦ p < 1, of the circumference at least m (≧1) times. In the present paper limit distributions of Nα are derived when α → 0. Some results for spacings are also obtained.
Ground-motion model for Housner’s spectrum intensity based on a novel hybrid-scenario approach

We use a novel hybrid-scenario approach to develop a ground-motion model (GMM) for Housner’s spectrum intensity (SI) using estimates of pseudo-spectral acceleration (PSA) from the authors existing GMMs based on a scenario approach proposed in the literature and predictor variables from the same database used to develop the PSA GMMs. These estimates of SI are used together with the predictor variables to develop a hybrid-scenario GMM using mixed-effects regression analysis. Because the GMM is based on predicted values of PSA, the aleatory variability from the regression is not indicative of the actual variability of observed values of SI. Instead, a hybrid-scenario model for magnitude-dependent between-event, within-event, and total aleatory standard deviations is derived from the PSA GMMs using the scenario approach. The predicted values of SI and its standard deviations from the hybrid-scenario model are found to be relatively consistent with the residuals between these predictions and estimates of SI from the database (i.e. the observations). However, the values of SI predicted from a purely empirical GMM developed directly from the observations are different than those from the hybrid-scenario model by up to a few tens of percent. These differences are the result of an insufficient number of observations resulting from bandwidth limitations of the database that lead to a bias in the empirical results. The near-source standard deviations from the hybrid-scenario GMM are found to be generally consistent with both those from the empirical model and those from the residuals between the hybrid-scenario model and the observations. However, the far-source standard deviations of the hybrid-scenario model are smaller than those from these other methods.
Magnetic properties of La1−xSrxMnO3 nanoparticles prepared in a molten salt

Single phase La1−xSrxMnO3 nanoparticles (x = 0.18 − 0.37) are prepared by a novel way in molten NaNO2 at 500 °C. Their rhombohedral perovskite structure is confirmed by XRD patterns and exact chemical composition is determined by XRF (X-ray fluorescence) spectroscopy. The mean size of the as-prepared crystallites is of about 50 nm, the grains are rather separated and do not form sintering bridges that were typical for products synthesized by sol-gel routes followed by annealing. On the other hand, the synthesized nanoparticles exhibit rather low magnetization and decreased Curie temperature. Their neutron diffraction study indicates a complex magnetic ordering together with anomalously high atomic displacements factors that decreased considerably after annealing the samples at elevated temperature.
The Flow at the Core-Mantle Boundary

Since the first time geomagneticists tried to derive the expression of the fluid flow at the top of the core, various assumptions have been made in order to reduce first, the non-uniqueness and second, the number of free parameters of the problem. We show that a geostrophic flow with a plane reflexion symmetry about the equatorial plane and a simple symmetry about the Earth's centre for the non-zonal component accounts for the 1980 secular variation (S.V.) although the number of parameters of the flow is reduced by a factor eight (from the number necessary when no assumption is made); the relative misfit between the predicted and the observed S.V. is of the same order as those existing between various 1980 S.V. models. The so-obtained flow exhibits a rather simple configuration. We suggest a means of downward continuing the flow into the body of the core which leads to some integral constraints on the density heterogeneity in the core.