Miracle-dz/UniFAR-FTUs huggingface dataset:
Error estimates for halfspace regularization of state constrained multiobjective elliptic control problems

This paper generalizes the halfspace regularization given in (Jadamba et al. in Syst Control Lett 61(6):707–713, 2012) from scalar to multiobjective optimization for an abstract linearly constrained multiobjective optimization problem. We establish quantitative error estimates for the regularization error in terms of the Hausdorff distance of the efficient set of the original problem to the regularized efficient set. We apply these results to state-constrained multiobjective optimal control problems. In particular, we consider two different situations: distributed and finite-dimensional controls, and for each one, we define two different halfspace regularization schemes given in terms of a family of triangulations of the domain. This leads to two regularized problems corresponding to finitely many pointwise and integral constraints. By applying the abstract results, we get regularization error estimates for the four control problems under general conditions.
Mass functions of five distant northern open star clusters

We analyse BVI CCD data of five northern open star clusters in the Galaxy in order to determine their mass functions. The clusters are Berkeley 81, Berkeley 99, NGC 6603, NGC 7044 and NGC 7510. They are distant (= 3 kpc) and compact (radius =2.8 arcmin) objects. Except for NGC 7510 whose age is 10 Myr, all are intermediate-age and old star clusters with ages between 0.5 and 3.2 Gyr. The observed cluster luminosity function (LF) is corrected for both data incompleteness and field star contamination. Theoretical stellar evolutionary isochrones are used to convert LFs into mass functions (MFs). The MF slope becomes flatter if a correction for data incompleteness is not applied, while it becomes steeper if a correction for field star contamination is ignored; however, both corrections increase with decreasing stellar brightness. In the mass range ~ 1-14 M, the MF slope of NGC 7510 is 1.1 ± 0.2. As the cluster is not dynamically evolved, its present-day MF can be considered as the initial MF. It is not too different from the Salpeter value (x = 1.35). In a narrow mass range ~ 0.6 - 2 M, the values of the MF slope in the four intermediate-age and old clusters range from 0.3 to 2.5 and differ significantly from each other. For Berkeley 99 and NGC 6603, the MF slopes are 1.4 ± 0.6 and 1.1 ± 0.4 respectively. They agree with the Salpeter value within the errors. However, significantly different values of MF slopes, 2.5 ± 0.2 and 0.3 ± 0.2, are found for Berkeley 81 and NGC 7044 respectively. We therefore conclude that the MF does vary among this cluster sample. The effects of mass segregation are observed in all the four intermediate-age and old clusters; this segregation is most probably due to dynamical evolution, as the ages of the clusters are much longer than the corresponding dynamical relaxation times. There is no obvious dependence of the MF slope on either Galactocentric distance or age of the well-studied Galactic open clusters. Except for some of the dynamically evolved older (age = 50 Myr) clusters, the MF slopes of the clusters are not too different from the Salpeter value.
Development of a novel body weight support system for gait rehabilitation

Abstract Modern rehabilitation processes for neurological patients have been widely assisted by robotic structures, with continuous research and improvements. The use of robotic assistance in rehabilitation is a consolidated technique for upper limb training sessions. However, human gait robotic rehabilitation still needs further research and development. Based on that, this paper deals with the development of a novel active body weight support (BWS) system integrated with a serious game for poststroke patients. This paper starts with a brief review of the state of the art of applied technologies for gait rehabilitation. Next, it presents the obtained mathematical model followed by multibody synthesis techniques and meta-heuristic optimization to the proposed device. The control of the structure is designed using proportional integral derivative (PID) controllers tuned with meta-heuristic optimization and associated with a suppression function to perform assist-as-needed actions. Then, the prototype is integrated with a serious game designed specifically for this application. Finally, a pilot study is conducted with the structure and healthy volunteers. The results obtained show that the mobility of the novel BWS is as expected and the proposed system potentially offers a novel tool for gait training.
Embryonic alcohol exposure disrupts the ubiquitin-proteasome system

Ethanol (EtOH) is a commonly encountered teratogen that can disrupt organ development and lead to fetal alcohol spectrum disorders (FASDs); many mechanisms of developmental toxicity are unknown. Here, we used transcriptomic analysis in an established zebrafish model of embryonic alcohol exposure (EAE) to identify the ubiquitin-proteasome system (UPS) as a critical target of EtOH during development. Surprisingly, EAE alters 20S, 19S, and 11S proteasome gene expression and increases ubiquitylated protein load. EtOH and its metabolite acetaldehyde decrease proteasomal peptidase activity in a cell type–specific manner. Proteasome 20S subunit β 1 (psmb1hi2939Tg) and proteasome 26S subunit, ATPase 6 (psmc6hi3593Tg), genetic KOs define the developmental impact of decreased proteasome function. Importantly, loss of psmb1 or psmc6 results in widespread developmental abnormalities resembling EAE phenotypes, including growth restriction, abnormal craniofacial structure, neurodevelopmental defects, and failed hepatopancreas maturation. Furthermore, pharmacologic inhibition of chymotrypsin-like proteasome activity potentiates the teratogenic effects of EAE on craniofacial structure, the nervous system, and the endoderm. Our studies identify the proteasome as a target of EtOH exposure and signify that UPS disruptions contribute to craniofacial, neurological, and endodermal phenotypes in FASDs.
Heart ID: Biometric Identification Using Wearable MIMO RF Heart Sensors

Biometric identification (ID) has become increasingly prevalent in the digital era. Static biometric methods, such as fingerprint and facial recognition are widely accepted, yet generally vulnerable to targeted presentation attacks. Current development has expanded to dynamic biometrics, such as gait and electrocardiogram, that enable continuous authentication and are significantly more resistant to presentation attacks. However, dynamic biometrics often involve cumbersome acquisition which restricts their widespread use. Here, we introduce Heart ID, a novel dynamic biometric system that uses near-field coherent sensing (NCS) with a multiple-in multiple-out (MIMO) radio-frequency (RF) antenna setup to non-invasively acquire detailed recordings of internal cardiac dielectric boundary motion over clothing. NCS couples localized energy to the heart to derive interpersonal structural differences, while MIMO significantly increases the biometric entropy compared to single-point observation. We performed a human study of 20 subjects as well as 2 longitudinal evaluations, and employed an unsupervised feature extraction method to explore the ID performance of this new biometric. We found an ensemble classification approach using features derived from unsupervised learning can achieve accuracy exceeding 99% at a 40-second epoch.
An Algorithm of Anisotropy and Self-adaptive Point Cloud Denoising ⋆

Since the method of isotropic filtering will result in blurring sharp features, a new approach of anisotropic filtering algorithm is proposed, direction and scale of such filter vary according to changes of sharp features’direction and scale. According to the local neighborhood of the point to be processed, the direction and scale of the filter were adaptively computed by covariance analysis. And because the results of covariance analysis are determined by the selection of neighborhood points, in order to adaptively select neighborhood points, a method about the selection of optimal neighborhood radius was also proposed which is based on projection direction. This method adopts projection direction to restrict the selection of neighboring points, thus adaptively get best neighborhood radius related with surface features. Experimental results show that: the algorithm combined with bilateral filtering not only can make point cloud smooth while retaining sharp features but also can adaptively select parameters.
A superior gene allele involved in abscisic acid signaling enhances drought tolerance and yield in chickpea

Abstract Identifying potential molecular tags for drought tolerance is essential for achieving higher crop productivity under drought stress. We employed an integrated genomics-assisted breeding and functional genomics strategy involving association mapping, fine mapping, map-based cloning, molecular haplotyping and transcript profiling in the introgression lines (ILs)- and near isogenic lines (NILs)-based association panel and mapping population of chickpea (Cicer arietinum). This combinatorial approach delineated a bHLH (basic helix–loop–helix) transcription factor, CabHLH10 (Cicer arietinum bHLH10) underlying a major QTL, along with its derived natural alleles/haplotypes governing yield traits under drought stress in chickpea. CabHLH10 binds to a cis-regulatory G-box promoter element to modulate the expression of RD22 (responsive to desiccation 22), a drought/abscisic acid (ABA)-responsive gene (via a trans-expression QTL), and two strong yield-enhancement photosynthetic efficiency (PE) genes. This, in turn, upregulates other downstream drought-responsive and ABA signaling genes, as well as yield-enhancing PE genes, thus increasing plant adaptation to drought with reduced yield penalty. We showed that a superior allele of CabHLH10 introgressed into the NILs improved root and shoot biomass and PE, thereby enhancing yield and productivity during drought without compromising agronomic performance. Furthermore, overexpression of CabHLH10 in chickpea and Arabidopsis (Arabidopsis thaliana) conferred enhanced drought tolerance by improving root and shoot agro-morphological traits. These findings facilitate translational genomics for crop improvement and the development of genetically tailored, climate-resilient, high-yielding chickpea cultivars.
The geometry of classical Regge calculus

Standard notions of Riemannian geometry are applied to the case of piecewise-flat manifolds. Particular care is taken to explain how one may define some particular vectors and tensors in an invariant way at points of a conical singularity. The geometry surrounding the equations of motion and the energy-momentum of the piecewise-flat manifold is developed in detail. The resolution theorem is presented, which states that on certain resolution hypersurfaces there is a clear connection between the energy-momentum of the piecewise-flat manifold and the Regge equations of motion. This theorem both generalises and explains a previous result of the author.
An Explainable Deep Learning Framework for Detecting and Localising Smoke and Fire Incidents: Evaluation of Grad-CAM++ and LIME

Climate change is expected to increase fire events and activity with multiple impacts on human lives. Large grids of forest and city monitoring devices can assist in incident detection, accelerating human intervention in extinguishing fires before they get out of control. Artificial Intelligence promises to automate the detection of fire-related incidents. This study enrols 53,585 fire/smoke and normal images and benchmarks seventeen state-of-the-art Convolutional Neural Networks for distinguishing between the two classes. The Xception network proves to be superior to the rest of the CNNs, obtaining very high accuracy. Grad-CAM++ and LIME algorithms improve the post hoc explainability of Xception and verify that it is learning features found in the critical locations of the image. Both methods agree on the suggested locations, strengthening the abovementioned outcome.
Efficiency of pain inhibition and facilitation of fibromyalgia patients is not different from healthy controls: Relevance of sensitivity-adjusted test stimuli

Background Pain is a dynamic phenomenon dependent on the balance of endogenous excitatory and inhibitory systems, which can be characterized by quantitative sensory testing. Many previous studies of pain modulatory capacity of patients with fibromyalgia syndrome (FM) have reported decreased pain inhibition or increased pain facilitation. This is the first study to assess pain modulation, including conditioned pain modulation (CPM) and temporal pain summation, in the same healthy control (HC) and FM participants. Methods Only sensitivity-adjusted stimuli were utilized for testing of conditioned pain modulation (CPM) and temporal pain summation in 23 FM patients and 28 HC. All subjects received sensitivity-adjusted ramp-hold (sRH) during testing of pain facilitation (temporal summation) and pain inhibition (CPM). CPM efficacy was evaluated with test stimuli applied either concurrently or after application of the conditioning stimulus. Finally, the effects of CPM on pressure pain thresholds were tested. Results FM subjects required significantly less intense test and conditioning stimuli than HC participants to achieve standardized pain ratings of 50 ± 10 numerical rating scale (NRS) (p = 0.03). Using such stimuli, FM subjects’ temporal pain summation and CPM efficacy was not significantly different from HC (all p > 0.05), suggesting similar pain facilitation and inhibition. Furthermore, the CPM efficacy of FM and HC participants was similar regardless of whether the test stimuli were applied during or after the conditioning stimulus (p > 0.05). Conclusion Similar to previous studies, FM participants demonstrated hyperalgesia to heat, cold, and mechanical stimuli. However, using only sensitivity-adjusted stimuli during CPM and temporal summation testing, FM patients demonstrated similarly effective pain inhibition and facilitation than HC, suggesting that their pain modulation is not abnormal.
Investigating Transfer Learning Performances of Deep Learning Models for Classification of GPR B-Scan Images

Recent advances in deep learning models have made them the state-of-the art method for image classification. Due to this success, they have been applied to many areas, such as satellite image processing, medical image interpretation, video processing, etc. Recently, deep learning models have been utilized for processing Ground Penetrating Radar (GPR) data as well. However, studies general focus on building new Convolutional Neural Network (CNN) models instead of utilizing baseline ones. This paper investigates the usefulness of existing baseline CNN models for classifying GPR B-scan images and aims to determine how well pre-trained models perform. To that end, a real bridge deck GPR data, DECKGPRHv1.0 dataset was used to evaluate the transfer learning performances of various CNN models. Different variants of the models in terms of varying depths and number of parameters were also considered and evaluated in a comparative manner. Although it is an older model, ResNet achieved the best results with 0.998 accuracy. The experimental results showed that there is generally a direct correlation between the simplicity of the model and its success. Overall, it is concluded that near perfect results are possible by just adapting pre-trained models to the problem without fine-tuning.
Analytical prediction of the cogging torque in radial-field permanent magnet brushless motors

A method for predicting the cogging torque in radial-field permanent magnet brushless motors, based on the analytical calculation of the airgap field distribution and the net lateral force acting on the stator teeth, is developed and validated. The technique is applicable to both internal and external rotor motor topologies, modeled in a cylindrical coordinate system. It can also be applied to variants of the basic motor topologies, such as those with shifted magnets (i.e. spaced unsymmetrically) or unevenly distributed stator slots, and thus it can enable design features aimed at reducing the level of cogging torque to be evaluated. Based on the proposed analytical model, the effects of varying the leading design parameters on the cogging torque can be investigated. Hence, an optimal combination of design parameters can be identified to enable low cogging torque motors to be designed. >
Molecular assay (Polymerase Chain Reaction based technology) for identification of cheating and adulteration of marketable Canned beef, Handmade sausage, Kofta and Hawawshi samples in Qalubia Governorate, Egypt

Keywords Adulteration of meat products attracted the interest of food safety groups, and it plays a significant part in environmental forensics. Meat products are a good source of protein since they include all essential amino acids and nutrients for humans. Adulteration in meat products is defined as the fraudulent substitution or addition of lower-cost animal or plant proteins, as well as the mislabeling of actual constituents. So, species identification of animal food origin is an important aspect of its control in several countries that are growing concerned about the number of serious food crimes committed by organized criminals. A total of 48 different commercial meat product samples (Canned beef, Handmade sausages, Kofta and Hawawshi samples) Twelve of each were randomly collected from Qalubia Governorate supermarkets and analyzed by Polymerase Chain Reaction (PCR) technique. We found Canned beef were free from adulteration while Handmade sausages, Kofta and Hawawshi samples were adulterated by chicken meat (31.2%), canine (4.2%), and equine (8.3%) meat. The present results concluded that PCR assay is the unique test for detecting adulteration of some meat products. Moreover, canned beef strictly undergoes inspection and food control while Handmade meat products used low–cast and undeclared meat because they don’t undergo strict food control.
d-Wave superconductivity in the strong-coupling Kondo lattice model

The Kondo lattice model is studied in the limit of an exchange interaction large compared to the hopping of conduction electrons: it is shown that in this limit an effective attractive interaction between nearest-neighbour electrons with antiparallel spins arises in second-order perturbation expansion, and this effective interaction leads to d-like superconductivity in the simple cubic lattice. Moreover, an enhancement of the effective mass at the Fermi level can be obtained when the number of conduction electrons per site is close to one.
Proposal of fully complex-valued neural networks

A novel neural network that processes input vectors and attractors fully in complex space is proposed. Real and imaginary data are treated consistently with an equivalent significance. This network can be applied for ill-posed problems concerning realistic physical objects, e.g., brain current estimations using highly sensitive magnetometers and sonic field reconstructions. A kind of local minima existing in conventional neural networks can be extinguished in this system because the proposed neural network deals with the data in a doubled dimension. Conventional systems using only real values do so in a degenerate space. The dynamics of the fully complex-valued neural networks are presented and the features are analyzed. <<ETX>>
Alfvén eigenmodes in the RFX-mod reversed-field pinch plasma

High-frequency magnetic activity has been detected at the edge region of the RFX-mod reversed-field pinch (RFP) device. The analysis, performed by means of an insertable probe measuring the magnetic field fluctuations, shows coherent modes clearly depending on the Alfvén velocity, at a frequency in the range 0.1–1.5 MHz. Two main distinct modes are observed, characterized by low (|n| ⩽ 1) toroidal periodicity, with the features of discrete Alfvén eigenmodes. A splitting of the mode frequency is detected, a sign of the relevance of non-linearity in the physical process under study. The experimental observations could suggest an interpretation of the modes in terms of global Alfvén eigenmodes. Some hints about the possible drive mechanisms are also given.
Relationship between Outward FDI and Home Country Exports: An Empirical Study of Bangladesh

The present paper investigates the association between outward FDI, exports and growth using data from 1990-2021. The novel estimation procedure of ARDL is used in the study to examine the long and short-run dynamics. Further, the causal relationship between outward FDI, exports and growth is also examined. The results imply that exports, OFDI, and economic growth in Bangladesh have a long-term cointegrating relationship. Additionally, FDI from abroad helps to increase the nation's exports. The study's conclusions are supported by the negative coefficient of the error-correcting term. The study provides useful insights for the policymakers of the country.
Maternal mRNA deadenylation and allocation via Rbm14 condensates facilitate vertebrate blastula development

Early embryonic development depends on proper utilization and clearance of maternal transcriptomes. How these processes are spatiotemporally regulated remains unclear. Here we show that nuclear RNA‐binding protein Rbm14 and maternal mRNAs co‐phase separate into cytoplasmic condensates to facilitate vertebrate blastula‐to‐gastrula development. In zebrafish, Rbm14 condensates were highly abundant in blastomeres and markedly reduced after prominent activation of zygotic transcription. They concentrated at spindle poles by associating with centrosomal γ‐tubulin puncta and displayed mainly asymmetric divisions with a global symmetry across embryonic midline in 8‐ and 16‐cell embryos. Their formation was dose‐dependently stimulated by m6A, but repressed by m5C modification of the maternal mRNA. Furthermore, deadenylase Parn co‐phase separated with these condensates, and this was required for deadenylation of the mRNAs in early blastomeres. Depletion of Rbm14 impaired embryonic cell differentiations and full activations of the zygotic genome in both zebrafish and mouse and resulted in developmental arrest at the blastula stage. Our results suggest that cytoplasmic Rbm14 condensate formation regulates early embryogenesis by facilitating deadenylation, protection, and mitotic allocation of m6A‐modified maternal mRNAs, and by releasing the poly(A)‐less transcripts upon regulated disassembly to allow their re‐polyadenylation and translation or clearance.
Leader-member exchange and creative idea validation: The role of helping and bullying

The purpose of this paper is to explore the role of leader-member exchange (LMX) for selection-focused creativity. Thus, we seek to understand when subordinates develop high- quality relationships with their supervisors, do they also get their creative ideas validated by co-workers or not? The proposed model was tested using data obtained at two points in time from three data sources (co-workers, subordinates, and supervisors) working at a software solution provider operating in Pakistan. The obtained data were then analysed for random coefficient models with Mplus. It was found that having only quality LMX will not guarantee the validation of co-workers’ ideas. The focal employee needs to signal to others that they also care about their co-workers and have quality LMX. Subordinates who maintain quality relationships with their supervisors also need to consider what they signal to their co-workers by their behaviours and actions at work. Co-workers are more likely to provide support for creativity to those who are more supportive at work. We extend novel benefits associated with high-quality LMX, that is, the validation of co-workers’ ideas. Additionally, by focusing on social relationships for selection-focused creativity, we also extended creativity literature.