Development of a Microcontroller-Based Recurrent Neural Network Predictive System for Lower Limb Exoskeletons

Practical deployments of exoskeletons can often be limited by cost, limiting access to their usage by those that would benefit from them. Minimising cost whilst not harming effectiveness is therefore desirable for exoskeleton development. For Control Systems governing assistive and rehabilitative exoskeletons that react to the wearer’s movements, there will inevitably be some delay between when their wearer intends to move and when the exoskeleton can assist with this movement. This can lead to situations where a user may be limited by their own assistive exoskeleton, reducing their ability to move freely. A potential solution to this is to provide a proactive method of control, where the most likely path of the wearer’s movement is predicted ahead of the wearer making the motion themselves. This can be used to give the user assistance immediately as they are walking, as well as potentially pre-emptively adjust their gait if they suffer from predictable gait deficiencies. The purpose of this paper is to investigate the Data Collection, Implementation, and Effectiveness of an LSTM Recurrent Neural Network dynamically predicting future movement based off of prior movement. These methods were developed to use off the shelf, Low-Cost Microcontrollers as to minimise their Financial, Weight, and Power Impact on an overall Low-Cost exoskeleton design, as well as to evaluate how effective such an implementation would be when compared to running such a Neural Network on a more powerful processor. The created model was capable of achieving similar accuracies to far more powerful models on High-Powered Laptops

Examination of runs of homozygosity distribution patterns and relevant candidate genes of potential economic interest in Russian goat breeds using whole-genome sequencing

Background/Objectives: Whole-genome sequencing (WGS) data provide valuable information about the genetic architecture of local livestock but have not yet been applied to Russian native goats, in particular, the Orenburg and Karachay breeds. A preliminary search for selection signatures based on single nucleotide polymorphism (SNP) genotype data in these breeds was not informative. Therefore, in this study, we aimed to address runs of homozygosity (ROHs) patterns and find the respective signatures of selection overlapping candidate genes in Orenburg and Karachay goats using the WGS approach. Methods: Paired-end libraries (150 bp reads) were constructed for each animal. Next-generation sequencing was performed using a NovaSeq 6000 sequencer (Illumina, Inc., San Diego, CA, USA), with ~20X genome coverage. ROHs were identified in sliding windows, and ROH segments shared by at least 50% of the samples were considered as ROH islands. Results: ROH islands were identified on chromosomes CHI3, CHI5, CHI7, CHI12, CHI13, and CHI15 in Karachay goats; and CHI3, CHI11, CHI12, CHI15, and CHI16 in Orenburg goats. Shared ROH islands were found on CHI12 (containing the PARP4 and MPHOSPH8 candidate genes) and on CHI15 (harboring STIM1 and RRM1). The Karachay breed had greater ROH length and higher ROH number compared to the Orenburg breed (134.13 Mb and 695 vs. 78.43 Mb and 438, respectively). The genomic inbreeding coefficient (FROH) varied from 0.032 in the Orenburg breed to 0.054 in the Karachay breed. Candidate genes associated with reproduction, milk production, immunity-related traits, embryogenesis, growth, and development were identified in ROH islands in the studied breeds. Conclusions: Here, we present the first attempt of elucidating the ROH landscape and signatures of selection in Russian local goat breeds using WGS analysis. Our findings will pave the way for further insights into the genetic mechanisms underlying adaption and economically important traits in native goats

PAMS: The Perseus Arm Molecular Survey – I. Survey description and first results

The external environments surrounding molecular clouds vary widely across galaxies such as the Milky Way, and statistical samples of clouds are required to understand them. We present the Perseus Arm Molecular Survey (PAMS), a James Clerk Maxwell Telescope (JCMT) survey combining new and archival data of molecular-cloud complexes in the outer Perseus spiral arm in 12CO, 13CO, and C18O (J = 3–2). With a survey area of ∼8 deg2, PAMS covers well-known complexes such as W3, W5, and NGC 7538 with two fields at ≈ 110◦ and ≈ 135◦. PAMS has an effective resolution of 17 arcsec, and rms sensitivity of Tmb = 0.7–1.0 K in 0.3 km s−1 channels. Here we present a first look at the data, and compare the PAMS regions in the Outer Galaxy with Inner Galaxy regionsfrom the CO Heterodyne Inner Milky Way Plane Survey (CHIMPS). By comparing the various CO data with maps of H2 column density from Herschel, we calculate representative values for the CO-to-H2 column-density X-factors, which are X12CO (3−2) = 4.0 × 1020 and X13CO (3−2) = 4.0 × 1021 cm−2 (K km s−1) −1 with a factor of 1.5 uncertainty. We find that the emission profiles, size–linewidth, and mass–radius relationships of 13CO-traced structures are similar between the Inner and Outer Galaxy. Although PAMS sources are slightly more massive than their Inner Galaxy counterparts for a given size scale, the discrepancy can be accounted for by the Galactic gradient in gas-to-dust mass ratio, uncertainties in the X-factors, and selection biases. We have made the PAMS data publicly available, complementing other CO surveys targeting different regions of the Galaxy in different isotopologues and transitions

Harnessing transient CAAC-stabilized mesitylborylenes for chalcogen activation

Newly synthesized adducts of CAAC-bound mesitylborylene with carbon monoxide (CO) and trimethylphosphine (PMe3) are established as efficient precursors for the in situ generation of the dicoordinate borylene [(CAAC)BMes] (CAAC = cyclic(alkyl)(amino)carbene), as demonstrated by their ability to activate elemental chalcogens. Upon thermal or photolytic activation, these precursors readily react with sulfur and selenium, yielding boron chalcogenides characterized by terminal boron–chalcogen double bonds. In contrast, the reaction with tellurium leads to the formation of an unusual diradical ditelluride species with a Te–Te bond. Quantum chemical calculations of its electronic structure indicate an open-shell singlet ground state characterized by significant diradical character. Further investigations into the redox behavior of these boron chalcogenides reveal intriguing transformations, including the redox-induced formation and cleavage of E–E bonds

Realistic object reconstruction under different depths through light field imaging for virtual reality

Virtual reality (VR) immerses users in digital environments and is used in various applications. VR content is created using either computer-generated or conventional imaging. However, conventional imaging captures only 2D spatial information, which limits the realism of VR content. Advanced technologies like light field (LF) imaging can overcome this limitation by capturing both 2D spatial and 2D angular information in 4D LF images. This paper proposes a depth reconstruction model through LF imaging to aid in creating realistic VR content. Comprehensive calibrations are performed, including adjustments for camera parameters, depth calibration, and field of view (FOV) estimation. Aberration corrections, like distortion and vignetting effect correction, are conducted to enhance the quality of the reconstruction. To achieve realistic scene reconstruction, experiments were conducted by setting up a scenario with multiple objects positioned at three different depths. Quality assessments were carried out to evaluate the reconstruction quality across these varying depths. The results demonstrate that depth reconstruction quality improves with the proposed method. It also indicates that the model reduces LF image size and processing time. The depth images reconstructed by the proposed model have the potential to generate realistic VR content and can also facilitate the integration of refocusing capabilities within VR environments

Does learning more about others impact liking them? Replication and extension Registered Report of Norton et al.’s (2007) lure of ambiguity (Registered report)

Norton et al., 2007, demonstrated a counterintuitive phenomenon that knowing other people better and/or having more information about them is associated with decreased liking. They summarized it as ambiguity leads to liking, whereas familiarity can breed contempt. In a Registered Report with a US Prolific undergraduate student sample (N = 801), we directly replicated Studies 1a, 1b and 2 and conceptually replicated Studies 3 and 4 from Norton et al., 2007. Extending their research, we also proposed that curiosity provides an alternative path to liking, hypothesizing that curiosity mediates the relationship between knowledge and liking. Overall, we found weak support for the original findings. Consistent with the original article, participants believed they would like someone who they knew more about (original: h = 0.52–0.70; replication: h = 0.55–0.75) and that knowledge positively predicts liking (original: h = 0.21–0.45; replication: h = 0.57–0.76). However, we found no indication of the number of traits known influencing liking (original: r = −0.43 to −0.005; replication: r = −0.05 to 0.06) or perceived similarity to the target (d = 0.00), for a mediating effect of perceived similarity, for a dissimilarity cascade effect, or for changes in liking or perceived similarity as a factor of learning more about the target. In our extensions, we found support for a positive relationship between curiosity and liking (r = 0.62–0.70), but not for knowledge and curiosity (r = −0.06 to 0.05). Overall, our findings suggest that learning more about others may not influence perceptions of liking, similarity or curiosity towards them. Materials, data and code are available on https://osf.io/j6tqr/. This Registered Report has been officially endorsed by Peer Community in Registered Reports: https://doi.org/10.24072/pci.rr.100947

Pricing VXX options with observable volatility dynamics from high-frequency VIX index

This paper develops a discrete-time joint analytical framework for pricing volatility index (VIX) and VXX options consistently. We show that our framework is more flexible than continuous-time VXX models as it allows the information contained in the high-frequency VIX index to be incorporated for the joint pricing of VIX and VXX options, and the joint pricing formula is derived. Our empirical analysis shows that the model that utilizes the realized variance (RV) computed from the high-frequency VIX index data significantly outperforms the model that does not rely on the VIX RV in the joint pricing both in-sample and out-of-sample, reinforcing the beliefs that high-frequency data are informative about the derivatives pricing

Modern triple resonance protein NMR backbone assignment, using AlphaFold and unlabelling to drive chemical shifts assignment in proteins

NMR is a powerful technique to study the structure, dynamics and interactions of proteins. However, to obtain atomic resolution data, NMR signals must first be correlated with specific chemical groups – a problem called assignment. The software AlphaFold has shown to be a great advancement in modern-day science. Until now, structural analysis of proteins had been bottlenecked by months/ years’ worth of slower techniques that were traditionally used to determine a protein structure In this project we have designed and applied a semi-automated assignment program called SNAPS (Simple NMR Assignment using Predicted Shifts). This allows the user to go from a set of NMR spectra of a protein with a known 3D AlphaFold or X-ray crystallography structure to a fully assigned chemical shifts of the backbone resonances. In addition, unlabelling experimental data can be incorporated into the use of the program to generate more reliable assignment data by helping the program along in the mapping of the amino acids for assignment by providing places in the assignment where the amino acid it is known. The program was largely written by Dr Alex Heyam from the university of Leeds but testing scripts and well as a NEF importer was written to ensure the program was user-friendly and ensured rigid, fool-proof datasets being imported for backbone assignment. The program also underwent large-scale testing on roughly 150 proteins with known 3D crystal structures as well as 15N unlabelleing data being implemented to improve assignment. AlphaFold structures could also be implemented for use in the program also. Assignment was as good as 86-88% dependent upon parameters, with the unlabelling being slightly more effective with assignment (for PDB crystal structures)

Absence of elevation‐dependent warming in Antarctica inferred from blue ice paleoclimate records

Reconstructing the past Antarctic climate commonly involves deep drilling of ice cores. However, the ∼1% of the Antarctic ice sheet surface covered with blue ice also provides unique, yet largely unexploited paleoclimatic opportunities. Here, we analyze 444 ice samples collected in blue ice surfaces located around the Sør Rondane Mountains. Isotope measurements (δ18O) on these samples enable us to estimate surface paleotemperatures for both the current interglacial period and the Last Glacial Maximum. Combining these paleotemperatures with the spatially varying source elevation of the sampled blue ice provides new insights on the (lack of) lapse rate evolution (i.e., changes in the elevation‐temperature relationship) outside the 40°N–40°S latitudinal band. This result contrasts with low‐latitude areas that have experienced elevation‐dependent warming (EDW) during this period. Our results hint at a future (lack of) EDW in Antarctica, thereby highlighting the potential of blue ice area paleoclimatic archives to better predict future climatic changes

Advancing gender equality in executive leadership: The role of cultural norms and organizational practices in sustainable development—A case study of Taiwan and Guatemala

The persistent gender gap in executive leadership remains a challenge to sustainable development. Despite evidence linking diverse leadership to enhanced organizational performance and economic growth, women still face barriers to leadership roles. This study examines cultural norms, organizational policies, and workplace practices sustaining gender inequality in executive positions in Taiwan and Guatemala. Using a quantitative research design, data were collected from a cross-sectional survey of 250 women executives in private organizations. The findings highlight cultural norms and organizational policies as key factors perpetuating the gender gap. Traditional gender roles and male-dominated networks act as barriers, while inclusive practices and leadership development programs promote equality. Organizational culture also mediates the relationship between inclusivity and leadership opportunities, emphasizing the role of empathy-driven policies. This research aligns with the 2030 Agenda for Sustainable Development, particularly SDG 5 (Gender Equality) and SDG 10 (Reduced Inequalities), underscoring the need for gender-equal leadership to foster innovation and sustainable growth