Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Care Management for Older Adults with Illnesses as Family Members’ Responsibility : The need for Social Workers’ Support Services in Nigeria

Introduction: Aging oftentimes comes with health-related issues. Usually, family members are obliged to provide care management for older adults. They must perform this task despite their limited knowledge of their patients’ health conditions. Hence, securing care management for older adults with illnesses becomes a dilemma for consideration. Aims: Older adults require support in managing health conditions. This study hopes to promote the following: First, provide understanding of how family members perceive caregiving. Second, provide insight into family members’ management of health care challenges. Third, explore the need for geriatric support services in providing care for older adults. Methods: The study employed a qualitative research method. Focus Group Discussion was utilized to elicit data from 24 participants admitted to four health care facilities at Enugu-North, Nigeria. Results: Findings indicated that participants perceived care management for the older adults as a filial responsibility that should be provided till their demise. However, the majority experienced difficulties to this effect, other major concerns found include age and the older adult’s attitude, financial challenges, the changing contemporary society, and caregiving stress due to the care recipient’s health status. While many of the participants indicated a preference for traditional medicine as an alternative to rapidly increasing health service charges owing to lagging geriatric support; others were less reliant on the governmental welfare support services. Conclusions: With the government implementing aging and health care policies, Non-Governmental Organizations (NGOs) and Civil Society Organizations (CSOs) in collaboration with geriatric social workers can help develop care-aid programs for older adults’ healthy wellbeing.
CURIE‐POINT WRITING OF MAGNETIC HOLOGRAMS ON MnBi

Reversible phase holograms have been made on thin films of MnBi by Curie‐point writing. Magnetic holograms are formed when the interference pattern, created by conventional holographic techniques, heats the film above its Curie temperature, causing a magnetic domain pattern that corresponds to the interference pattern. The magnetic hologram can be nondestructively read out, without the use of polarizers or analyzers, by means of either the Faraday or Kerr magneto‐optic effect. The hologram can be erased by application of a magnetic field.
Origin and removal of stacking faults in Ge islands nucleated on Si within nanoscale openings in SiO2

We have previously reported that Ge films formed after nucleation of Ge islands within nanometer size openings in SiO2 and their subsequent coalescence over the SiO2 template exhibit threading dislocation densities below 106 cm−2. However, these films contain a density of twin/stacking fault defects on the order of 5 × 1010 cm−2 that emanate primarily from the Ge-SiO2 interface. Most of these faults self-terminate within 200 nm of the interface; however, a total of 5 × 107 cm−2 propagate to the Ge surface. These defects are found to be detrimental to the morphology and minority carrier lifetime in III-V films integrated onto the Ge-on-Si virtual substrates. We have found that annealing the Ge islands during the initial stage of coalescence eliminates stacking faults, but further Ge growth leads to a film containing a threading dislocation density of 5 × 107 cm−2. To explain the origin of the twin/stacking fault defects in the Ge films and their removal after annealing Ge islands, we have studied the Ge is.
Low resistivity p-ZnO films fabricated by sol-gel spin coating

N-doped and In-N-codoped ZnO films were fabricated on quartz glass substrate by sol-gel spin coating. Their p-type conductivities were characterized by the Hall measurements, revealing low resistivities of the order of 10−1Ωcm. Thin-film junctions comprising an undoped ZnO layer and a N-doped ZnO layer displayed the typical rectifying characteristics, suggesting formation of p-n homojunctions at the interfaces.
Quinoxalinones as A Novel Inhibitor Scaffold for EGFR (L858R/T790M/C797S) Tyrosine Kinase: Molecular Docking, Biological Evaluations, and Computational Insights

Combating acquired drug resistance of EGFR tyrosine kinase (TK) is a great challenge and an urgent necessity in the management of non-small cell lung cancers. The advanced EGFR (L858R/T790M/C797S) triple mutation has been recently reported, and there have been no specific drugs approved for this strain. Therefore, our research aimed to search for effective agents that could impede the function of EGFR (L858R/T790M/C797S) TK by the integration of in silico and in vitro approaches. Our in-house quinoxalinone-containing compounds were screened through molecular docking and their biological activity was then verified by enzyme- and cell-based assay. We found that the four quinoxalinone-containing compounds including CPD4, CPD15, CPD16, and CPD21 were promising to be novel EGFR (L858R/T790M/C797S) TK inhibitors. The IC50 values measured by the enzyme-based assay were 3.04 ± 1.24 nM; 6.50 ± 3.02 nM,10.50 ± 1.10 nM; and 3.81 ± 1.80 nM, respectively, which are at a similar level to a reference drug; osimertinib (8.93 ± 3.01 nM). Besides that, they displayed cytotoxic effects on a lung cancer cell line (H1975) with IC50 values in the range of 3.47 to 79.43 μM. In this proposed study, we found that all screened compounds could interact with M793 at the hinge regions and two mutated residues including M790 and S797; which may be the main reason supporting the inhibitory activity in vitro. The structural dynamics revealed that the screened compounds have sufficient non-native contacts with surrounding amino acids and could be well-buried in the binding site’s cleft. In addition, all predicted physicochemical parameters were favorable to be drug-like based on Lipinski’s rule of five, and no extreme violation of toxicity features was found. Altogether, this study proposes a novel EGFR (L858R/T790M/C797S) TK inhibitor scaffold and provides a detailed understanding of compounds’ recognition and susceptibility at the molecular level.
A novel method based on discrete multiple wavelet transform to multispectral image fusion

Image fusion refers to the techniques that integrate complementary information from multiple image sensor data such that the new images are more suitable for the purpose of human visual perception and the compute-processing tasks. In this paper, a new image fusion algorithm based on multiple wavelet, namely multiwavelet, transform to fuse multispectral images is presented. Multiwavelets are extensions from scalar wavelet, and have several unique advantages in comparison with scalar wavelets, so that multiwavelet is employed to decompose and reconstruct images in this algorithm. In this paper, the image fusion is performed at the pixel level, other types of image fusion schemes, such as feature or decision fusion, are not considered. In this fusion algorithm, a feature-based fusion rule is used to combine original subimages and to form a pyramid for the fused image. When images are merged in multiwavelet space, different frequency ranges are processed differently. It can merge information from original images adequately and improve abilities of information analysis and feature extraction. The experiment on the fusion of registered multiband SPOT multispectral Panchromatic band \XS3 band images is presented in this paper. The experiment results show that this fusion algorithm, based on multiwavelet transform, is an effective approach in image fusion area.
Interval Design for Signal Parameter Estimation From Quantized Data

We consider the problem of optimizing the quantization intervals (or thresholds) of low-resolution analog-to-digital converters (ADCs) via the minimization of a Cramér-Rao bound (CRB)-based metric. The interval design is formulated as a dynamic programming problem. A computationally efficient global algorithm, referred to as the interval design for enhanced accuracy (IDEA) algorithm, is presented to solve this optimization problem. If the realization in hardware of a quantizer with optimized intervals is difficult, it can be approximated by a design whose practical implementation is feasible. Furthermore, the optimized quantizer can also be useful in signal compression applications, in which case no approximation should be necessary. As an additional contribution, we establish the equivalence between the Lloyd-Max type of quantizer and a low signal-to-noise ratio version of our IDEA quantizer, and show that it holds true if and only if the noise is Gaussian. Furthermore, IDEA quantizers for several typical signals, for instance normally distributed signals, are provided. Finally, a number of numerical examples are presented to demonstrate that the use of IDEA quantizers can enhance the parameter estimation performance.
Evaluation of Counseling Programs at Junior High Schools in Malang Regency during the Covid-19 Pandemic: Discrepancy Model

Counselors become adaptive individuals to changes that exist, even though they occur so quickly. One of them is that the COVID-19 pandemic has had an impact on the implementation of the counseling program in schools. The implementation of the counseling program in schools is expected to be able to accommodate the needs of students to be able to solve complex problems due to the COVID-19 pandemic. This research is evaluation research with a discrepancy evaluation model approach. The purpose of this study was to determine the implementation of the counseling program in schools based on predetermined standards. The implementation of this research is an evaluation with the research population being teacher guidance and counseling at the junior high school level in Malang Regency with a focus on implementing counseling programs during the COVID-19 pandemic. The research instrument is based on performance-based professional teacher evaluation guidelines on the third aspect of the fifth criterion. The data obtained were then analyzed descriptively and percentages to determine the implementation of the counseling program during the pandemic. The results show that counselors can adapt well to changes due to the covid-19 pandemic. This condition is indicated by the counselor starting to use technology in counseling (cybercounseling) to overcome the problems that occur so that they can carry out the program on a scheduled and limited basis in accordance with the health protocol rules. The follow-up results are recommendations to counselors for the implementation of future counseling programs.
Materialism and Big-Five Personality Traits Shaping Low-Income University Students’ Compulsive Online-Buying Behavior

Despite the immense benefits of online shopping in modern societies, it has also generated some concern about addiction among consumers, particularly the unemployed youth from low-income families. Thus, by conducting a path analysis on quantitative survey data of randomly sampled 439 respondents, this study examines the influence of materialistic behavior and the Big-Five personality traits on the compulsive online-buying behavior of university students from low-income families (the B40 income group). Among the five personality traits, an increase in neuroticism scores, openness to experiences, and conscientiousness was observed to raise the level of materialism significantly. Similarly, materialism, neuroticism, and extraversion exhibited a direct positive influence on the compulsive online-buying behavior of the students. These findings reiterate that individuals with higher neuroticism tend to display less self-regulation or emotion control. In contrast, more extroverted individuals tend to be engrossed with the interactive on-screen platform, which cultivates their obsession with online shopping. Importantly, the mediation test showed that materialistic behavior significantly mediates the effects of neuroticism, openness to experience, and conscientiousness on compulsive buying. Overall, our findings projected neuroticism as dominating due to its direct and indirect influence on compulsive online-buying behavior in the path model.
3.4 Performance Monitoring for Galileo and other GNSS at the Galileo Competence Center

: Satellite navigation has become a vital part of our daily lives by ensuring navigation on land, in air and at sea, and by providing precise timing information for the energy, communications and finance sector. It is therefore essential to monitor the performance of the four main global navigation satellite systems (GNSS) Galileo, GPS, GLONASS and BeiDou. The Galileo Competence Center (GK), part of the German Aerospace Center (DLR), is dedicated to the further development of the European GNSS consisting of Galileo and EGNOS. Within the SigPerMon project, the GK monitors the reliability and quality of navigation signals with comparable metrics for all four GNSS, and detects deviations from the nominal state of navigation systems. Necessary data are sourced from a global network of GNSS receiver stations. These data are used to compute performance indicators to monitor and analyse the availability and health status of navigation signals, and the precision of positioning and timing solutions. In the future, machine learning models will be used to detect anomalies in the satellite signals. A summary of the results will be presented on a dedicated webpage, which provides both detailed analyses for authorized researchers and personnel, and interactive data visualizations for the general public.
An Efficient Algorithm for Robust Fractional QCQP and its Applications to Multiuser Beamforming With Bounded Channel Uncertainties

In this paper, we investigate a nonconvex robust fractional quadratically constrained quadratic problem (Robust Fractional QCQP), which has a wide application to the worst-cast resource allocation optimization in wireless multiuser beamforming systems. The state-of-the-art method is based on semidefinite relaxation (SDR), which possibly returns a high-rank solution and makes it difficult to recover a robustly feasible solution. To overcome this drawback, we propose an efficient algorithm for solving the robust fractional QCQP by employing the constrained concave-convex procedure and the cutting set method. When finding a robustly feasible initial point is nontrivial, we propose an effective method to deal with it. We also develop the parallel implementation of the proposed algorithm to speed up the computation aiming for real-time application scenarios. As a case study, in the second part of the paper we focus on two examples, namely: i) a novel robust nonorthogonal multiple access (NOMA) multiuser beamforming for physical layer security; and ii) the renowned robust signal-to-interference-and-noise ratio (SINR) balancing problem of cognitive radio multiuser beamforming systems. Numerical results are presented to verify the effectiveness of our proposed algorithm compared with the state-of-the-art methods.
Single trial detection of error-related potentials in brain–machine interfaces: a survey and comparison of methods

Objective. Error-related potential (ErrP) is a potential elicited in the brain when humans perceive an error. ErrPs have been researched in a variety of contexts, such as to increase the reliability of brain–computer interfaces (BCIs), increase the naturalness of human–machine interaction systems, teach systems, as well as study clinical conditions. Still, there is a significant challenge in detecting ErrP from a single trial, which may hamper its effective use. The literature presents ErrP detection accuracies quite variable across studies, which raises the question of whether this variability depends more on classification pipelines or on the quality of elicited ErrPs (mostly directly related to the underlying paradigms). Approach. With this purpose, 11 datasets have been used to compare several classification pipelines which were selected according to the studies that reported online performance above 75%. We also analyze the effects of different steps of the pipelines, such as resampling, window selection, augmentation, feature extraction, and classification. Main results. From our analysis, we have found that shrinkage-regularized linear discriminant analysis is the most robust method for classification, and for feature extraction, using Fisher criterion beamformer spatial features and overlapped window averages result in better classification performance. The overall experimental results suggest that classification accuracy is highly dependent on user tasks in BCI experiments and on signal quality (in terms of ErrP morphology, signal-to-noise ratio (SNR), and discrimination). Significance. This study contributes to the BCI research field by responding to the need for a guideline that can direct researchers in designing ErrP-based BCI tasks by accelerating the design steps.
A hybrid actuator scheme for robust position control of a flexible single‐link manipulator

This article presents a novel hybrid actuator scheme to actively and robustly control the endpoint position of a very flexible single-link manipulator. The control scheme consists of two actuators; a motor mounted at the beam hub and a piezoceramic bonded to the surface of the flexible link. The control torque of the motor, which produces a desired angular motion, is determined by employing the sliding mode control theory on the equation of motion of the rigid link having the same mass as that of the proposed flexible link. The torque is then applied to the flexible manipulator to activate the commanded motion. During the motion, the undesirable oscillation caused by the torque, based on the rigid link dynamics, is actively suppressed by applying a feedback control voltage to the piezoceramic actuator. Consequently, desired tip motion is achieved. Both regulating and tracking control responses are analyzed through experimental implementation to demonstrate high performance characteristics to be accrued from the proposed methodology. © 1996 John Wiley & Sons, Inc.
Climate change contrarian think tanks in Europe: A network analysis

Drawing from network theory and previous findings from US-based analyses, we measure the structure and interconnectedness of climate contrarian think tanks in Europe. This exploratory analysis can illustrate European organizations’ capacity to promote or disrupt political discourse. To this end, we use social network analysis to conduct actor-focused research. We identify the individuals bridging European think tanks, as well as their ties with the US climate change contrarian network. Our analysis reveals a discernible network structure for European climate change contrarian think tanks, with a profile connected to neoliberal organizations, including a few, but highly relevant links, with the US countermovement. We also find that the European think tanks’ institutional structure is very much shaped by a strong predominance of men, which aligns with previous research on masculinity and climate contrarianism.
Alfvén wave driven compressional fluctuations in shear flows

The nature of driven linear compressible fluctuations is studied when there is an Alfven wave propagating in the medium characterized by an inhomogeneous velocity field. This Brief Communication is an extension of our recent study [E. Kh. Kaghashvili et al., Phys. Plasmas 13, 112107 (2006)] in which analytical solutions were presented revealing a nature of driven compressional waves accurately in a low β-plasma. Here, this assumption is removed and analytical solutions that accurately describe compressional waves in the general case are obtained. While the solutions of the driven waves in a long run follow the WKB description, the main goal here is to study an initiation and evolution of compressible disturbances at earlier times. The purpose of this Brief Communication is to derive the formal solutions for compressional fluctuations driven by the initial Alfenic spectrum.
Three‐dimensional multiscale MHD model of cometary plasma environments

First results of a three-dimensional multiscale MHD model of the interaction of an expanding cometary atmosphere with the magnetized solar wind are presented. The model starts with a supersonic and super-Alfvenic solar wind far upstream of the comet (25 Gm upstream of the nucleus) with arbitrary interplanetary magnetic field orientation. The solar wind is continuously mass loaded with cometary ions originating from a 10-km size nucleus. The effects of photoionization, electron impact ionization, recombination, and ion-neutral frictional drag are taken into account in the model. The governing equations are solved on an adaptively refined unstructured Cartesian grid using our new multiscale upwind scalar conservation laws-type numerical technique (MUSCL). We have named this the multiscale adaptive upwind scheme for MHD (MAUS-MHD). The combination of the adaptive refinement with the MUSCL-scheme allows the entire cometary atmosphere to be modeled, while still resolving both the shock and the diamagnetic cavity of the comet. The main findings are the following: (1) Mass loading decelerates the solar wind flow upstream of the weak cometary shock wave (M ≈ 2, MA ≈ 2), which forms at a subsolar standoff distance of about 0.35 Gm. (2) A cometary plasma cavity is formed at around 3 × 103 km from the nucleus. Inside this cavity the plasma expands outward due to the frictional interaction between ions and neutrals. On the nightside this plasma cavity considerably narrows and a relatively fast and dense cometary plasma beam is ejected into the tail. (3) Inside the plasma cavity a teardrop-shaped inner shock is formed, which is terminated by a Mach disk on the nightside. Only the region inside the inner shock is the “true” diamagnetic cavity. (4) The model predicts four distinct current systems in the inner coma: the density peak current, the cavity boundary current, the inner shock current, and finally the cross-tail current. (5) The calculated plasma parameters (magnetic field, plasma density, speed, and temperature) are in very good agreement with published Giotto observations.
New Discrete Particle Swarm Algorithm for Traveling Salesman Problem

In this paper, a novel algorithm called CIPSO for short based on particle optimization algorithm(PSO) and Chaos optimization Algorithm(COA) is presented to solve traveling salesman problem(TSP). We propose some new operators to solve the difficulties of implementing PSO into solving this discrete problem based on the special fitness landscape of TSP. Meanwhile embedded with chaos theory it can enhance particles’ global searching ability so as not to converge to the local optimal solution too quickly, and the introduction of information intercourse can enhance thire local searching ability. Comparing to SA, GA, ACS and so on, this new algorithm shows its validity and satisfactory effect on several benchmark test problems.
Modeling the Uncertainty of Slope and Aspect Estimates Derived from Spatial Databases

Estimates of slope and aspect are commonly made from digital elevation models (DEMs), and are subject to the uncertainty present in such models. We show that errors in slope and aspect depend on the spatial structure of DEM errors. We propose a general-purpose model of DEM errors in which a spatially auto-regressive random field is added as a disturbance term to elevations. In addition, we propose a general procedure for propagating such errors through GIS operations. In the absence of explicit information on the spatial structure of DEM errors, we demonstrate the potential utility of a worst-case analysis. A series of simulations are used to make general observations about the nature and severity of slope and aspect errors.